the roles of cognitive architecture and recall strategies in performance...
TRANSCRIPT
THE ROLES OF COGNITIVE
ARCHITECTURE AND RECALL STRATEGIES
IN PERFORMANCE OF THE IMMEDIATE
SERIAL RECALL TASK
by
Shane Thomas Mueller
A dissertation submitted in partial fulfillmentof the requirements for the degree of
Doctor of Philosophy(Psychology)
in The University of Michigan2002
Doctoral Committee:
Professor David E. Meyer, ChairProfessor David E. KierasAssociate Professor Richard L. LewisAssociate Professor Jun Zhang
c© Shane Thomas Mueller 2002All Rights Reserved
For my parents.
ii
ACKNOWLEDGEMENTS
I would like to thank the many people who have guided me my way. These begin
with people from North Dakota; my parents and sisters (and cats), who all assisted in
rearing me properly, and the admonishments of Dave McDowell, (“Figures don’t lie,
but liers figure.”) that still replay in my mind. At Drew, where I first discovered the
science of psychology, several professors (Ed Domber, Robin Timmons, Phil Jensen,
and Janet Davis) taught me about research, and Phil’s words especially convinced
me to attend graduate school in experimental psychology. Ann Arbor has been
a wonderful time, and I am grateful to the many faculty and student colleagues
who have supported and distracted me during the past six years. These include
especially my erstwhile officemates Dan and Beth. Dan provided me with more
distraction than support, but together we successfully survived the internet boom of
’99 without dropping out of graduate school and becoming millionaires. Beth has
given more help than can be enumerated, and I can only look forward to many more
years of support and distraction with her. More recently, Dominic, Matt, Josh, and
Brian have taught me a few things about programming, which has opened my eyes
and broadened my horizons greatly. Finally, the advice, consent, and work of the
members of my committee (Dave Meyer, Dave Kieras, Jun Zhang, and Rick Lewis)
is greatly appreciated, and the guidance and friendship of my committee chair has
been more than a kid from North Dakota could have hoped for.
iii
TABLE OF CONTENTS
DEDICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
LIST OF APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
CHAPTER
I. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Architecture and Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Recall Strategies in the Immediate Serial Recall Task . . . . . . . . . . . . . 41.3 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
II. EMPIRICAL RESULTS THAT REVEAL ARCHITECTURAL AND STRATE-GIC COMPONENTS OF IMMEDIATE SERIAL RECALL . . . . . . . . . 7
2.1 The effect of recall direction . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 The magnitude of the recency effect . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 Serial position functions with no recency effect . . . . . . . . . . . 92.2.2 Serial position functions with typical moderate recency effects . . . 112.2.3 Serial position functions with large recency effect . . . . . . . . . . 132.2.4 Summary of recency effects. . . . . . . . . . . . . . . . . . . . . . . 19
2.3 Insights from the analysis of different scoring techniques . . . . . . . . . . . 202.3.1 “Position” serial position function. . . . . . . . . . . . . . . . . . . 202.3.2 “Relative order” serial position function. . . . . . . . . . . . . . . . 212.3.3 “Item” serial position function. . . . . . . . . . . . . . . . . . . . . 212.3.4 Position Gradients. . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4 Insights from analyses of the timing of recall . . . . . . . . . . . . . . . . . . 252.5 Tasks other than immediate serial recall . . . . . . . . . . . . . . . . . . . . 27
2.5.1 Harris (1975): Probe Recall . . . . . . . . . . . . . . . . . . . . . . 272.5.2 The Stimulus Suffix . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.5.3 Logie et al. (1996): An investigation of reported strategies. . . . . . 312.5.4 Greene (1991): The Ranschburg Effect. . . . . . . . . . . . . . . . . 31
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
III. EXPERIMENT 1: AN INVESTIGATION OF IMMEDIATE SERIALRECALL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
iv
3.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.1.2 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.1.3 Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.1.4 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.1.5 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373.2.1 Serial Recall Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . 373.2.2 Serial position functions . . . . . . . . . . . . . . . . . . . . . . . . 373.2.3 Position gradient functions . . . . . . . . . . . . . . . . . . . . . . . 413.2.4 The types of responses made during serial recall . . . . . . . . . . . 43
3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
IV. AN EPIC MODEL OF THE ARCHITECTURAL COMPONENTS IN-VOLVED IN THE IMMEDIATE SERIAL RECALL TASK . . . . . . . . . 46
4.1 The EPIC Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.1.1 Components of the EPIC Architecture Subserving Verbal Working
Memory Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.1.2 The Cognitive Processor . . . . . . . . . . . . . . . . . . . . . . . . 494.1.3 The Production Rule Interpreter . . . . . . . . . . . . . . . . . . . 494.1.4 Working Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.1.5 Auditory Perceptual Processor . . . . . . . . . . . . . . . . . . . . 534.1.6 Vocal Motor Processor . . . . . . . . . . . . . . . . . . . . . . . . . 534.1.7 Production Rule Performance Strategy . . . . . . . . . . . . . . . . 544.1.8 The Task Environment . . . . . . . . . . . . . . . . . . . . . . . . . 544.1.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2 A Modified EPIC Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 554.2.1 Current Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 564.2.2 Modifications to EPIC’s auditory perceptual processor . . . . . . . 654.2.3 The primary auditory store . . . . . . . . . . . . . . . . . . . . . . 654.2.4 Internal Representation . . . . . . . . . . . . . . . . . . . . . . . . 664.2.5 External representations . . . . . . . . . . . . . . . . . . . . . . . . 71
4.3 Implications for models using the modified auditory perceptual processor . . 754.4 Parameter values associated with the auditory perceptual processor . . . . . 76
4.4.1 Decay Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 774.4.2 Capacity Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 784.4.3 Time parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784.4.4 Other parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
V. RECALL STRATEGIES USED FOR PERFORMING THE IMMEDI-ATE SERIAL RECALL TASK . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.1 The General Recall Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 815.2 Components of task performance . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.2.1 Elimination of recalled items . . . . . . . . . . . . . . . . . . . . . . 825.2.2 Elimination of last item in list . . . . . . . . . . . . . . . . . . . . . 835.2.3 Elimination of items with known preceding items . . . . . . . . . . 835.2.4 Fill-In . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845.2.5 Guessing from known items . . . . . . . . . . . . . . . . . . . . . . 855.2.6 Error Aversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855.2.7 Summary of Sub-Strategies . . . . . . . . . . . . . . . . . . . . . . 85
5.3 Four Strategies for Performing the Immediate Serial Recall Task . . . . . . . 865.3.1 The “Abort on Error” Strategy . . . . . . . . . . . . . . . . . . . . 90
v
5.3.2 The “Order Reconstruction” Strategy . . . . . . . . . . . . . . . . 925.3.3 The “Reconstruction with fill-in before the last item” Strategy . . 945.3.4 The “Reconstruction with fill-in before end-chain” Strategy . . . . 95
5.4 Exploration of parameter settings in proposed models . . . . . . . . . . . . . 975.4.1 The speech-tag decay distribution . . . . . . . . . . . . . . . . . . . 985.4.2 The serial order decay distribution . . . . . . . . . . . . . . . . . . 985.4.3 The final item tag decay distribution . . . . . . . . . . . . . . . . . 985.4.4 The speech object capacity distribution . . . . . . . . . . . . . . . 995.4.5 The phonological content storage decay distribution . . . . . . . . 1005.4.6 Summary of the exploration of parameter settings . . . . . . . . . . 100
5.5 Models of Experiment 1 Results . . . . . . . . . . . . . . . . . . . . . . . . . 1015.5.1 Serial position functions. . . . . . . . . . . . . . . . . . . . . . . . . 1025.5.2 Position gradient functions . . . . . . . . . . . . . . . . . . . . . . . 1055.5.3 Types of responses made by the model . . . . . . . . . . . . . . . . 1055.5.4 Response time measures . . . . . . . . . . . . . . . . . . . . . . . . 1085.5.5 Limitations of the model . . . . . . . . . . . . . . . . . . . . . . . . 1085.5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
VI. EXPERIMENT 2: AN EMPIRICAL MANIPULATION OF RECALLSTRATEGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136.1.2 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136.1.3 Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136.1.4 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1146.1.5 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1176.2.1 Articulatory Duration Measurement . . . . . . . . . . . . . . . . . 1176.2.2 Overall Memory Performance . . . . . . . . . . . . . . . . . . . . . 1176.2.3 Serial Position Functions . . . . . . . . . . . . . . . . . . . . . . . . 1196.2.4 Participant compliance with instructed guessing strategies . . . . . 1216.2.5 Discussion of Empirical Results . . . . . . . . . . . . . . . . . . . . 122
6.3 EPIC Models of Strategic Guessing Performance . . . . . . . . . . . . . . . . 1246.3.1 Task performance strategies . . . . . . . . . . . . . . . . . . . . . . 1246.3.2 Predictive modeling of performance in Experiment 2 . . . . . . . . 1266.3.3 Parameter Estimation based on current data. . . . . . . . . . . . . 129
6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
VII. GENERAL DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
7.1 New insights gained from present experiments and models . . . . . . . . . . 1357.2 Limitations of the current conclusions . . . . . . . . . . . . . . . . . . . . . . 1367.3 The value of modeling both architecture and strategy . . . . . . . . . . . . . 137
7.3.1 Cognitive architecture models . . . . . . . . . . . . . . . . . . . . . 1387.3.2 Behavioral Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 1397.3.3 Mechanistic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 1407.3.4 Homunculus Models . . . . . . . . . . . . . . . . . . . . . . . . . . 1417.3.5 Other models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1417.3.6 Benefits of modeling task performance strategy . . . . . . . . . . . 142
7.4 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1437.4.1 Other components of the immediate serial recall task that are under
strategic control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
vi
7.4.2 Other verbal working memory tasks that are modulated by strate-gic control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
vii
LIST OF FIGURES
Figure
1.1 Typical idealized serial position functions in immediate serial recall, showing pri-macy and recency effects for four different lengths of lists. This graph is not derivedfrom actual data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.1 Primacy and recency effects during both forward and backward recall (from Cowanet al., 1992). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 The “Position” serial position function from Drewnowski and Murdock (1980, Exp.1). No noticeable recency effects occur for either visual or auditory presentation.) 10
2.3 Serial position functions from Dosher and Ma (1998, “word” stimuli). Moderate-sized recency effects occur here. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 Serial position functions from Henson et al. (1996). . . . . . . . . . . . . . . . . . . 13
2.5 Serial position functions from Baddeley, 1968; Exp. VI. . . . . . . . . . . . . . . . . 14
2.6 Serial position functions from Nairne and Kelley (1999). . . . . . . . . . . . . . . . 15
2.7 Serial position functions from Experiment 2 of Penney (1985). . . . . . . . . . . . . 17
2.8 Serial position functions from Experiment 1 of Nichols and Jones (2002). . . . . . . 18
2.9 Plots of three serial position functions from Experiment 1 of Drewnowski and Mur-dock (1980). All three function are based on the same data. The “relative order”function is conditioned on correct “item” recall. . . . . . . . . . . . . . . . . . . . 20
2.10 Mean durations of initial recall latency, speech production and inter-speech pausesacross different list lengths. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.1 The effect of list length and rehearsal condition on probability of correct recall.The interval shown in the lower left corner of the graph indicates the size of thestandard error of the interaction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2 Serial position functions produced in Experiment 1. . . . . . . . . . . . . . . . . . . 39
3.3 “Position” and “item” serial position functions for participants in Experiment 1 . . 40
3.4 Recency effects in the “position” serial position functions of each participant, forthe suppression condition of Experiment 1. . . . . . . . . . . . . . . . . . . . . . . . 42
viii
3.5 Position gradient plots for lists of lengths 4 through 7, under articulatory suppres-sion. For a given panel, each connected series of points represents the distributionof presented positions for a single response position. . . . . . . . . . . . . . . . . . 42
3.6 Types of responses produced in the suppression condition of Experiment 1. . . . . 43
4.1 The EPIC (Execute Process/Interactive Control) Cognitive Architecture. . . . . . 47
4.2 An example production rule used in the EPIC architecture’s performance strategyfor the immediate serial recall task. . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3 Depiction of the different types of verbal information stored by the modified EPICprimary auditory storage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.1 Hypothetical contents of working memory during immediate serial recall after therecall signal has been received. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.2 Three simplified production rules for illustrating the basic operation of the rule setused for immediate serial recall. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.3 Flowchart depicting the “Abort on Error” Strategy. . . . . . . . . . . . . . . . . . . 91
5.4 Flowchart depicting the “Order Reconstruction” strategy. . . . . . . . . . . . . . . 93
5.5 Flowchart depicting the “Fill-In” sub-phase of the “Reconstruction with Fill-InBefore Last Item” strategy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.6 Flowchart depicting the “Fill-in” sub-phase of the “Reconstruction with Fill-inBefore End-Chain” strategy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.7 Empirical and simulated serial position functions from Experiment 1 . . . . . . . . 103
5.8 Simulated and observed position gradient functions . . . . . . . . . . . . . . . . . . 106
5.9 Simulated and observed response types. . . . . . . . . . . . . . . . . . . . . . . . . 107
5.10 Inter-word response times for the “Order Reconstruction” strategy. . . . . . . . . 109
6.1 Mean “position” (top panels) and “item” (bottom panels) serial position functionsfrom Experiment 2, averaged across word sets. . . . . . . . . . . . . . . . . . . . . . 120
6.2 Total and “unacceptable” errors in the different instructed recall conditions of Ex-periment 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.3 Mean “position” (top panels) and“item” (bottom panels) serial position functions,for data from Experiment 2 (shown in blue with solid lines and filled circles) anddata produced by the modified EPIC architecture (shown in red with dashed linesand empty circles) using three different recall strategies. . . . . . . . . . . . . . . . 128
6.4 Mean “position” (top panels) and“item” (bottom panels) serial position functions,for data from Experiment 2 (shown in blue with solid lines and filled circles) anddata produced by the modified EPIC architecture (shown in red with dashed linesand empty circles) using three different recall strategies, under parameters estima-tions made specifically for Experiment 2. . . . . . . . . . . . . . . . . . . . . . . . . 130
ix
7.1 The different ways a model can incorporate strategy. . . . . . . . . . . . . . . . . . 138
B.1 “Abort on Error” strategy for different speech-tag decay parameters. . . . . . . . . 193
B.2 “Order Reconstruction” strategy for different speech-tag decay parameters. . . . . 194
B.3 “Fill In Before Last Item” strategy for different speech-tag decay parameters. . . . 195
B.4 “‘Fill In Before End-Chain” strategy for different speech-tag decay parameters. . . 196
B.5 “Abort on Error” strategy for different order tag decay parameters. . . . . . . . . . 198
B.6 “Order Reconstruction” strategy for different order tag decay parameters. . . . . . 199
B.7 “Fill In Before Last Item” strategy for different order tag decay parameters. . . . . 200
B.8 “Fill In Before End-Chain” strategy for different order tag decay parameters. . . . 201
B.9 “Abort on Error” strategy for different end item tag decay parameters. . . . . . . . 203
B.10 “Order Reconstruction” strategy for different end item tag decay parameters. . . . 204
B.11 “Fill In Before Last Item” strategy for different end item tag decay parameters. . . 205
B.12 “Fill In Before End-Chain” strategy for different end item tag decay parameters. . 206
B.13 “Abort on Error” strategy for different speech object capacity parameters. . . . . . 208
B.14 “Order Reconstruction” strategy for different speech object capacity parameters. . 209
B.15 “Fill In Before Last Item” strategy for different speech object capacity parameters. 210
B.16 “Fill In Before End-Chain” strategy for different speech object capacity parameters.211
B.17 “Abort on Error” strategy for different phonological decay parameters. . . . . . . . 213
B.18 “Order Reconstruction” strategy for different phonological decay parameters. . . . 214
B.19 “Fill In Before Last Item” strategy for different phonological decay parameters. . . 215
B.20 “Fill In Before End-Chain” strategy for different phonological decay parameters. . 216
x
LIST OF TABLES
Table
2.1 Position gradient matrix from Henson (1998). . . . . . . . . . . . . . . . . . . . . . 23
4.1 Representative entries in the working memory database . . . . . . . . . . . . . . . 51
4.2 Effects of different assumptions about short-term memory limitations. . . . . . . . 60
4.3 Representative entries in the working memory database under the new auditoryperceptual processor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.1 Summary of parameter values used to produce simulated data in Figure 5.7. . . . . 101
5.2 Goodness-of-fit measures for the four different guessing strategies, compared toserial position functions from Experiment 1. . . . . . . . . . . . . . . . . . . . . . . 104
6.1 Word sets used in Experiment 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.2 Mean proportion of items recalled in the correct position as a function of word setand recall instruction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
xi
LIST OF APPENDICES
Appendix
A. PRODUCTION RULES USED DURING TASK PERFORMANCE . . . . . . . . . . 147
B. PERFORMANCE OF IMMEDIATE SERIAL RECALL MODELS UNDER DIF-FERENT PARAMETER SETTINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
B.1 The role of the speech-tag decay distribution . . . . . . . . . . . . . . . . . . 191B.2 The role of serial order link decay distribution . . . . . . . . . . . . . . . . . 197B.3 The role of final item decay distribution . . . . . . . . . . . . . . . . . . . . . 202B.4 The role of the capacity of the primary auditory store . . . . . . . . . . . . . 207B.5 The role of the phonological storage decay parameters . . . . . . . . . . . . . 212
xii
CHAPTER I
INTRODUCTION
In the immediate serial recall task, a participant is presented with a sequence of
words and immediately attempts to recall the sequence in its presented order. This
task has become widely used both for applied purposes (e.g., to measure intelligence
and assess cognitive function) and for basic scientific research. Consequently, nu-
merous computational, mathematical, and verbal models have been produced that
attempt to describe how people perform in the immediate serial recall task, and ex-
plain why people make the errors they do (e.g., Anderson & Mattessa, 1997; Brown
& Hulme, 1995; Burgess & Hitch, 1996; and many others).
Although these models do not all agree about underlying memory mechanisms
or about why certain errors occur, most are able to account for the “serial position
function” in the immediate serial recall task. This function (shown in Figure 1.1)
represents the probability of correctly recalling each presented item in its original
position. Three important properties can usually be observed in such functions.
First, there is an effect of list length: serial position functions of longer lists tend to
have smaller values than those of shorter lists. Second, there is the “primacy” effect:
items recalled earlier in a list tend to be recalled more accurately than items recalled
later in a list. Third, there is often a “recency” effect: the final item has a slightly
1
2
higher probability of correct recall than does the item recalled immediately before
it.
Prob
abili
ty C
orre
ct Re
call
Serial Position
Primacy Effect
Recency Effect
Figure 1.1: Typical idealized serial position functions in immediate serial recall, showing primacyand recency effects for four different lengths of lists. This graph is not derived fromactual data.
Many models of immediate serial recall can produce each of these effects suitably,
and they provide reasonable fits to the observed data. Consequently, these models
are difficult to distinguish based on their data-fitting ability alone. However, the
ability of a model to fit data should not be the only criterion for selecting between
models. The assumptions that a model makes about underlying mechanisms and
processes are also important. For example, a model that assumes an effect is caused
by structural limitations is different than one that assumes the effect arises from how
a person chooses to perform the task, even if the processes are formally identical and
thus make the same predictions in many cases. To illustrate this, some theories of
multiple task performance (e.g., Pashler, 1984) propose there is a structural bottle-
neck, whereas others (e.g., Meyer & Kieras, 1997) propose that people use strategic
bottlenecks to perform some combinations of tasks. The two theories may make
identical quantitative predictions for some data, but the theories are distinguishable
in other respects.
3
1.1 Architecture and Strategy
One way to classify a model of immediate serial recall is to identify which of its
assumptions are about the underlying structural architecture, and which assumptions
are about the performance strategies used to accomplish a task. For instance, a model
of the immediate serial recall task might assume that the primacy effect stems from
iterative cumulative rehearsal of the words as they are presented. According to this
hypothesis, the words at the beginning of the list are rehearsed more often, leading to
stronger encoding and better recall accuracy. Because rehearsal is under voluntary
control, this model assumes that the primacy effect has a strategic locus. On the
other hand, a different model of immediate serial recall might assume that sequences
of words are stored in an associative chain, and that an item can only be accessed if
its predecessor was recalled correctly. According to this hypothesis, the structure of
the short-term store causes the primacy effect, and so the effect has an architectural
locus.
Although these examples may appear to attribute the primacy effect to a single
source, this is not entirely accurate. For any task, both architectural constraints and
the employed strategy produce the observed performance. In the rehearsal example
above, if rehearsal did not lead to stronger encoding (an architectural property), the
primacy effect would not occur. Similarly, in the associative chain example above, no
primacy effect would occur without a strategy that attempted to encode and recall
sequences of words (i.e., participants would recall nothing and the serial position
functions would flat.)
The distinction between architecture and strategy is not new to the study of
immediate serial recall. Encoding strategies and rehearsal strategies have both been
4
widely investigated. For instance, “blocking” is an encoding strategy people often
use in the immediate serial recall task, and “rehearsal” is a maintenance strategy
that people use (Logie et al., 1996). Yet little work has been done investigating the
role of recall strategies, even though recall accuracy is the primary type of data used
to evaluate the immediate serial recall task. I believe that our current ignorance
about the recall strategies used during immediate serial recall is a major impediment
toward understanding verbal working memory, the memory system putatively used
for performing this task. Consequently, in this thesis I investigate the role of recall
strategy, and identify the relative contributions of both recall strategy and underlying
architecture to observed measures of performance in the immediate serial recall task.
1.2 Recall Strategies in the Immediate Serial Recall Task
I have chosen to examine recall strategy (as opposed to other potential strategic
aspects) for several reasons. First, although encoding and rehearsal strategies have
been studied in the past literature (e.g., Logie et al., 1996), little is known about the
potential effects of different recall and guessing strategies on serial recall. Second,
recall accuracy is usually measured in serial recall tasks, and so recall strategies may
be investigated more directly than other types of strategies. Third, many of the
effects on serial recall change very little or change only in magnitude under different
rehearsal instructions (e.g., rehearsal versus suppression). This suggests that these
effects are relatively insensitive to strategic performance of rehearsal. Consequently,
understanding rehearsal strategies may not be as instructive for understanding pat-
terns of errors in recall. Finally, in order to construct detailed models of rehearsal in
the immediate serial recall task, many more assumptions about strategy and archi-
tecture would be required, and additional parameters would need to be estimated,
5
leading to more complex and less parsimonious models.
To investigate the relative role of architecture and strategy, I will first evaluate a
set of previously-reported empirical studies that offer instructive evidence about the
roles of architecture and strategy in the immediate serial recall task. Then, I will
present an experiment that attempts to reproduce some of these effects. Following
this experiment, I will describe the architectural constraints embodied by a model
of the verbal working memory system, and present several different recall strategies
that produce very different serial position functions under the same architectural
constraints. Finally, I will propose several new experiments and models that may
provide further insights into the relative roles of both architecture and recall strategy
in the immediate serial recall task.
1.3 Goals
The purpose of this thesis to to investigate the role that guessing strategy plays
in the immediate serial recall task. I intend to demonstrate that such strategic
components of task performance play a large role in observed effects that are of-
ten inappropriately attributed to the underlying architecture of verbal short-term
working memory.
One consequence of understanding the role of strategies in the immediate serial
recall task is that we will obtain a better understanding of how people may interpret
instructions to perform the immediate serial recall task. This is important in that it
will allow better experiments of this type to be conducted in the future. However,
it will also help us come to a better understanding of the underlying cognitive ar-
chitecture components that are used in these tasks. This architecture will only be
visible once the potential contributions of strategy are considered.
6
This thesis will take the following course: first, in Chapter II, I will review some
of the existing literature that is relevant to my investigation of the role of strategy
and architecture. Next, in Chapter III, I will present the results from an exper-
iment that replicate some of the result in the literature. Chapter IV will discuss
the architectural components used in an EPIC computational model of immediate
serial recall. Chapter V will discuss the strategic components of the model as well
as present some initial results of applying it to the data from Experiment 1. Next,
Chapter VI will present a new experiment that attempts to investigate more directly
the role of strategy in the immediate serial recall task. Finally, Chapter VII will
discuss the results and draw conclusions about their implications.
CHAPTER II
EMPIRICAL RESULTS THAT REVEALARCHITECTURAL AND STRATEGIC COMPONENTS
OF IMMEDIATE SERIAL RECALL
The immediate serial recall task has been used to study many aspects of verbal
short-term memory. Few of these experiments have investigated the relationship
between the structure of the verbal working memory architecture and the strategic
processes used to accomplish the task. However, many of these experiments can
provide instructive evidence about the contributions of architecture and strategy in
the immediate serial recall task, even if this was not their original intent. In this
chapter, I will examine some of these results, in order to better understand both the
underlying architecture involved in immediate serial recall, and the flexible strategies
used by participants for it.
2.1 The effect of recall direction
Some experiments have manipulated recall direction in order to test hypotheses
about immediate serial recall. This manipulation (i.e., whether the participant recalls
a list by starting at the first word presented and recalling in the presented order,
or by starting at the last item presented and recalling items in reverse order) can
provide evidence about the recall phase of immediate serial recall. Figure 2.1 shows
7
8
typical serial position functions obtained with forward and backward recall, plotted
by the correct recall position (from Cowan et al., 1992).1
1 2 3 4 5
0.0
0.2
0.4
0.6
0.8
1.0
Output Serial Position
Pro
babi
lity
Cor
rect
Rec
all
Forward Short−LongForward Long−ShortBackward Short−LongBackward Long−Short
Figure 2.1: Primacy and recency effects during both forward and backward recall (from Cowanet al., 1992).
One interesting observation about these data is that the serial position functions
for forward and backward recall are nearly identical (i.e., they mirror each other
when plotted by presentation position). Based on this observation, I note two rel-
evant conclusions: First, the data suggest that people are able to deftly obey task
instructions and recall lists in either forward or backward order. This indicates that
recall strategy is quite flexible and easily controlled by the participant. Second, both
the primacy and recency effects appear to be invariant with recall direction. This
finding eliminates many potential explanations of these effects, because it suggests
they arise during recall, rather than encoding or retention.2
However, this invariance does not enable us to determine whether the locus of
these effects is strategic or architectural. There are both strategic and architectural
explanations of why these functions might be similar. An architectural explanation
1The first position of the forward serial position function involves items that were presented first and should havebeen recalled first. The first position of the backward functions involve items that were presented last but shouldhave been recalled first.
2Both primacy and recency effects occur in the direction of recall under both conditions, suggesting that theseeffects stem from processes that occur during recall. If the effects are consequences of encoding processes, one wouldexpect the effects to be similar in the order of presentation, but opposite in the direction of recall.
9
is that making responses creates interference that causes other items to be more
difficult to retrieve. Alternately, a strategic explanation is that when a participant
does not know what an item is, he or she skips it and proceeds to the next item,
leading subsequent items to be recalled one position too early. Thus, an item near
the beginning of the list may tend to be recalled better because it would be less likely
to be preceded by an item that was skipped.
Given that both strategic and architectural explanations can account for this
phenomenon, these results do not provide a clear resolution. Yet they do provide
instructive evidence about the importance of the recall phase, which may help deter-
mine what types of architectural structures and strategic processes govern immediate
serial recall.
2.2 The magnitude of the recency effect
The presence and magnitude of a recency effect in immediate serial recall can
provide evidence about the contributions of recall strategy to task performance.
Experiments on the immediate serial recall task typically produce recency effects,
but the nature of this effect remains mysterious, because its magnitude varies greatly
across experiments, and it can even disappear under some conditions. I believe that
this variability is at least partly a consequence of the instructions and procedures
used in each experiment, which may suggest that this effect is modulated by strategic
factors. In the next sub-sections, I will discuss several examples of how experimental
procedures can affect the magnitude of the recency effect.
2.2.1 Serial position functions with no recency effect
Although experiments on immediate serial recall normally produce recency ef-
fects, this finding is not universal. For example, Drewnowski and Murdock (1980)
10
found that the recency effect is not immutable, and can even disappear under some
conditions. Figure 2.2 shows that with both auditory and visual stimulus presenta-
tion, recall accuracy is a non-increasing function of serial position for nearly all list
lengths.3 In these data, no noticeable recency effects occurred.
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Auditory Presentation
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Visual Presentation
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
Figure 2.2: The “Position” serial position function from Drewnowski and Murdock (1980, Exp. 1).No noticeable recency effects occur for either visual or auditory presentation.)
The serial position functions in Figure 2.2 are fairly unique in the literature on
immediate serial recall, so it is important to understand what special circumstances
may have set this experiment apart from others. In their experiments, the cause
is clear: Drewnowski and Murdock specifically instructed participants on how to
proceed when they were unsure about what word to recall next. Participants were
told that if they could not remember a word, they should neither guess nor say
“blank”, but rather move on to the next word that they were certain of. These
instructions made it clear that items did not need to be recalled in their original
positions, and encouraged participants to drop items from the list. Consequently,
3For auditory presentation of four-item lists, and visual presentation of six-item lists, the last item is recalledslightly better than the next-to-last item. Nevertheless, these are clearly exceptions to the overall pattern.
11
items near the end of the list tended to be recalled earlier, and the recency effect
was eliminated. I will examine other aspects of this experiment in Section 2.3, where
I will show more definitively how performance in this experiment was influenced by
these instructions.
2.2.2 Serial position functions with typical moderate recency effects
Serial position functions from immediate serial recall experiments typically do pro-
duce a small recency effect. One fairly typical example is found in data from Dosher
and Ma (1998), whereas several experiments by Henson et al. (1996) demonstrate
how the magnitude of this effect can be quite variable.
Dosher and Ma (1998)
Figure 2.3 shows some of the results from an experiment by Dosher and Ma’s
(1998). The figure shows serial position functions for three types of stimuli (digits,
letters, and words) across the lists lengths four through nine. In nearly every case
where performance was not perfect, the final item in the list was recalled more
accurately than the next-to-last item.
Recency effects similar in magnitude to these are fairly typical in experiments on
immediate serial recall: the final item’s probability of correct recall is moderately
higher than the next-to-last item, and the serial position function is monotonically
decreasing up until the last item. Dosher and Ma’s (1998) experiment used an
immediate serial recall procedure where responses were entered through a computer
keyboard, and each response had to be finalized before the subsequent response was
allowed. Participants also were highly practiced in the task, which involved multiple
experimental sessions. Although these aspects of the experiment were atypical, the
results were not: they resemble recency effects commonly found in immediate serial
12
0.0
0.2
0.4
0.6
0.8
1.0
Digits
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 3 5 7 9
0.0
0.2
0.4
0.6
0.8
1.0
Letters
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l1 3 5 7 9
0.0
0.2
0.4
0.6
0.8
1.0
Words
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 3 5 7 9
Figure 2.3: Serial position functions from Dosher and Ma (1998, “word” stimuli). Moderate-sizedrecency effects occur here.
recall experiments.
Henson et al. (1996)
Some experiments have produced recency effects that range from small to large
across different replications. For example, serial position functions from three im-
mediate serial recall experiments reported by Henson et al. (1996) are shown in Fig-
ure 2.4. For some of these functions, the final item is recalled nearly as well as the
first few items, but for other functions, the recency effect is practically non-existent.
Although some of the recency effects reported by Henson et al. (1996) are larger
than those discussed earlier, the rest are similar in magnitude to those from Dosher
and Ma (1998) and are typical of other immediate serial recall experiments (e.g.,
Baddeley, 1968, Exp. V; and Cowan et al., 2000). Such results (showing variability
in the magnitude of the recency effects) may be more typical than those of Dosher
and Ma (1998) (where moderate-sized recency effects occurred consistently across
several experiments).
13
1 2 3 4 5 6
0.5
0.6
0.7
0.8
0.9
1.0
Length 6
Serial Position
Prob
abili
ty C
orre
ct
1 2 3 4 5 6 7
0.5
0.6
0.7
0.8
0.9
1.0
Length 7
Serial Position
Prob
abili
ty C
orre
ctFigure 2.4: Serial position functions from Henson et al. (1996). The left panel shows three serial
recall experiments with six-word lists, and the right panel shows results from two ex-periments with seven-word lists. All of the depicted data are derived from experimentalconditions with sets of uniformly dissimilar letter stimuli.
2.2.3 Serial position functions with large recency effect
A number of immediate serial recall experiments have produced relatively large
recency effects. I will examine several of these in detail, in the hope of understanding
why such effects occurred.
Baddeley, 1968; Exp. VI
One experiment that produced a particularly large recency effect was Baddeley’s
(1968) Experiment VI. In this experiment, similar or dissimilar six-letters sequences
were presented, and participants were instructed to recall the sequences as accurately
as possible. The serial position functions produced for each condition are shown in
Figure 2.5.
These results are quite unique for several reasons. First, the final item was recalled
better than all other items in the list. Second, the primacy effect is minimal, with
14
1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
DissimilarSimilar
Figure 2.5: Serial position functions from Baddeley, 1968; Exp. VI.
items at the beginning of the list recalled only a little better than items in the middle
of the list. And third, the recency effect extends over more than a single item.
Although atypical, these results are not surprising after examining the experi-
mental instructions and procedures: participants were tested in large groups, and
recall was performed by having participants fill in their responses on a piece of pa-
per. Consequently, the experimenter had little control over which items were actually
produced first, and it is likely that many participants wrote down the last item first,
and continued writing down items in whatever order was most convenient. Conse-
quently, such conditions encouraged performance that would produce large recency
effects. This suggests that participants are easily able to adopt recall strategies that
can take advantage of whatever freedoms are allowed during recall. Furthermore,
the adoption of such strategies might modulate the magnitude of the recency effect,
as well as the shape of the serial position function in general.
15
Nairne and Kelley, 1999
Another example of serial position functions with relatively large recency effects
comes from Nairne and Kelley (1999). Across three different experiments, they
reported six different serial position functions (shown in Figure 2.6.) In nearly all
of the cases shown, the final item was recalled much better than the immediately
previous item, and almost as well as the first item in the list.
1 2 3 4 5
0.0
0.2
0.4
0.6
0.8
1.0
Exps. 1−3 of Nairne & Kelley (1999)
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
Figure 2.6: Serial position functions from Nairne and Kelley (1999).
Several aspects of Nairne and Kelley’s design may allow us to ascertain why such
large recency effects occurred. Their recall test was an item reconstruction test,
where participants had to “recall” the five presented items using arrow keys on a
computer keyboard to move each item into its correct original position. Such a
procedure allows a great deal of flexibility in a participant’s choice of performance
strategy. It is quite likely that many participants first attempted to identify the last
item in the list, since it was the most recent and most likely to be correct. Then, the
16
rest of the list was reconstructed using other search and reconstruction processes.
These studies each produced large recency effects, and each used a manual recall
method (either via handwriting or a computer keyboard entry) where the order
of recall was not constrained. Although unconstrained order recall may lead to
large recency effects, these recall methods were also similar in that they did not
require vocalization. Interestingly, large recency effects have also been found under
conditions where non-verbal recall was used but recall order was restricted to occur
in a forward-only manner. Two examples of such research are found in the studies
of Penney (1985) and Nichols and Jones (2002) on the stimulus suffix effect4
Penney (1985)
In a study of the stimulus suffix effect, Penney (1985) conducted serial recall
experiments under conditions with and without a suffix. Stimuli were digits that were
presented auditorily, and participants performed recall by writing their responses on
a specially prepared sheet of paper, leaving blanks if necessary. Participants were
encouraged to write their answers in a forward-only manner, and monitored to ensure
compliance. The serial position functions produced under the blocked list length with
active rehearsal condition of Experiment 2 are shown in Figure 2.7.
The recency effects in Figure 2.7 are large, and extend several items back from
the end of the list. The final items were recalled with nearly 100% accuracy, even for
lists of nine digits. Apparently, if participants are allowed to skip over items, even
when recall order is restricted, large recency effects may occur.
4The stimulus suffix effect is the finding that when a list is followed by an irrelevant item that the participant istold to ignore, the recency effect is normally diminished. Aside from providing an excellent example of an immediateserial recall procedure that produced a large recency effect, this procedure is of interest in its own right because itattempts to understand the role of the final item in a list. Consequently, results from Penney (1985) and Nicholsand Jones (2002) will be revisited in a later section that looks at the special role of the final item in a list.
17
0.0
0.2
0.4
0.6
0.8
1.0
With Stimulus Suffix
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7 8 9
0.0
0.2
0.4
0.6
0.8
1.0
Without Stimulus Suffix
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7 8 9
Figure 2.7: Serial position functions from Experiment 2 of Penney (1985).
Nichols and Jones (2002)
In another study of the stimulus suffix effect, Nichols and Jones (2002) presented
data that showed large and robust recency effects for the last item in the list, across
three different experiments (see Figure 2.8). Like the earlier experiments discussed in
this section, their participants did not recall items verbally. Instead, the participants
wrote their answers on prepared answer sheets, skipping blanks and moving on to
later items if they needed to. However, as in Penney’s (1985) experiment, participants
were required to write their answers in the order of presentation, and not allowed to
backtrack to earlier items once a response was written.
Like the earlier experiments described in this section, the data in Figure 2.8 have
large recency effects, even though recall order was constrained. Apparently, non-
verbal recall methods can produce large recency effects when recall positions can be
skipped.
18
1 2 3 4 5 6 7 8
0.0
0.2
0.4
0.6
0.8
1.0
Nichols & Jones, Exp. 1
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
With IntroNo IntroSuffix OnlyNo Suffix
Figure 2.8: Serial position functions from Experiment 1 of Nichols and Jones (2002).
The experiments reviewed here that produced large recency effects have several
similarities. They each used non-verbal recall methods that allowed participants to
skip over responses, leaving some positions blank. Some of the experiments allowed
items to be reported in any order the participant chose, scoring only the final or-
ganization, but large recency effects were produced whether or not recall order was
constrained in this way. These experiments also used non-verbal recall methods,
meaning that participants did not have to speak in order to perform recall. This
may have affected performance in two ways: first, participants may have been able
to covertly rehearse some items during recall, allowing better recall accuracy for those
items; second, the lack of overt responses may have reduced interference that may
normally occur during overt recall, leading to better performance for some items.
19
2.2.4 Summary of recency effects.
It appears that the magnitude of the recency effect can change across different
experiments. It can be quite large (as in Nichols & Jones, 2002), or nearly disappear
(as in Drewnowski & Murdock, 1980). These differences might arise from many dif-
ferent sources, but clearly experiments where participants were encouraged to skip
over items by leaving a response position empty or saying “Blank” produced larger
recency effects than experiments where this wasn’t allowed or specifically discour-
aged.
However, the malleability of the effect cannot be solely a result of the participants’
goals and strategies. Apparently, there is something special about the last item in
a sequence that makes it easy to identify. If this were not true, encouraging correct
position recall and allowing blank responses should not have affected recall of the
final item differentially.
The final item in a list might be “special” in a number of ways. Because no
items are presented after it, it may undergo less interference and so be more likely
to remain at recall. Or, perhaps there is a special perceptual buffer that maintains
the identity of the last thing a person hears. Or, lists of words may be organized so
that the identity of the last item can be accessed especially quickly and easily. In
later chapters, I will investigate some of these ideas in greater detail.
The experiments reviewed in this section have examined the recency effect, one
aspect of the serial position function. However, other techniques of scoring serial
recall may be used to assess performance in this task, and they can also provide
information about the relative contributions of strategy and architecture to perfor-
mance in this task. These include alternate types of serial position functions, as well
as other scoring methods that can help determine what types of errors people make
20
during the task. These scoring methods will be discussed next, in Section 2.3.
2.3 Insights from the analysis of different scoring techniques
Variations in the administration and scoring of immediate serial recall can also
provide some insights into the relative contribution of recall strategies to the shape of
the serial position function. For example Drewnowski and Murdock (1980) examined
three types of serial position functions: what they called “position”, “relative order”
and “item” serial position functions. By examining these together, further insights
into the response patterns of immediate serial recall can be gained.
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Position
Presented Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Relative Order
Presented Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Item
Presented Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
Figure 2.9: Plots of three serial position functions from Experiment 1 of Drewnowski and Murdock(1980). All three function are based on the same data. The “relative order” function isconditioned on correct “item” recall.
2.3.1 “Position” serial position function.
This function (plotted in the left panel of Figure 2.9) is the most commonly-
reported serial position function. For this function, the probability that an item
was correctly recalled in its original serial position is calculated. This plot can be
misleading, however, because it does not indicate what type of errors were made. As
discussed in Section 2.2, some types of errors can have especially large effects on the
21
shape of the “position” function. For example, errors where an early item is omitted
will have a large impact on this function, because even if each subsequent item is
recalled in the correct order, they will all be recalled one position earlier than they
should have been.
2.3.2 “Relative order” serial position function.
This function (plotted in the center panel of Figure 2.9) addresses some of the
problems of the “position” serial position function, because it assesses the relative
accuracy of sequences of items, without regard to their output position. For this
function, a presented item is scored as correct if it meets three criteria: (1) it must
have been recalled; (2) the item recalled before it (if one exists) must have been
presented before it; and (3) the item recalled after it (if one exists) must have followed
it on the presented list. In Figure 2.9, the “relative order” serial position function is
conditioned on correct “item” recall, as discussed next. Consequently, the function’s
value indicates the proportion of items from a presented position that satisfy all three
above criteria, divided by the number of those that satisfy the first criterion.
2.3.3 “Item” serial position function.
This function (found in the right panel of Figure 2.9) ignores order entirely, and
counts a presented item as correct if it occurs anywhere in the recalled sequence.
This type of function can give a good indication about which presented items tend
not to be recalled at all.
By examining all three serial position functions produced in Drewnowski and
Murdock’s (1980) Experiment 1, a few interesting facts emerge. First, even though
no notable recency effect occurs in the “position” function, the “item” function
shows that the final word in a list does tend to be recalled more frequently than
22
previous words. This recall must be occurring in the wrong serial position, because
the “position” function does not show such a recency effect. Additionally, the “item”
functions are considerably higher than the “position” functions, indicating that items
frequently are recalled, but in the wrong position. The “relative order” function
shows that when items presented in the first or last positions are recalled, they
nearly always appear as the first and last recalled item (respectively). However, the
interior list items appear to be misordered fairly frequently.
These alternate serial position functions provide more information about the
relative contribution of strategy to immediate serial recall. First, for the data of
Drewnowski and Murdock (1980), the lack of a recency effect in the “position” serial
position function may be due to strategic processes. This is probable because the
final item tends to be recalled more frequently than its predecessors (as seen in the
“item” serial position functions), and it is nearly always recalled as the last item in
a produced sequence (as seen in the “relative order” serial position functions), yet it
is recalled in the correct position infrequently. So, when participants remember the
last item, they don’t recall anything after it, even when they do not recall the last
item in the correct position. Apparently, participants are dropping items from the
interior of the list, and so that later items move into incorrect serial positions nearer
to the beginning of the list. This may indicate that the recency effect has a large
strategic component. If the participants were encouraged to guess in a way that
filled in for words they could not remember, a recency effect might have occurred for
these data.
These serial position functions are not the only way of summarizing performance
in immediate serial recall tasks. Another way is the “position gradient”, which shows
where items were recalled with respect to their position in the original list.
23
2.3.4 Position Gradients.
The “item” and “position” serial position functions provide summaries of which
items were recalled and how frequently they were recalled in the correct position.
This information can be obtained more directly by examining the position gradients
from a set of serial recall data. A position gradient matrix is an n-by-n matrix of
data for a list of size n. Each row represents an output position, and each column
represents the probability that an item presented in that column’s position was re-
called in the row’s position. An example position gradient matrix (from Henson,
1998) is shown in Table 2.1.
Table 2.1: Position gradient matrix from Henson (1998).Input Position
Output Position 1 2 3 4 5 61 .926 .028 .022 .006 .006 .0032 .035 .845 .068 .023 .021 .0063 .005 .067 .839 .048 .030 .0074 .006 .019 .025 .768 .113 .0335 .018 .025 .026 .070 .716 .1056 .031 .009 .014 .047 .063 .781
Table 2.1 shows that the most common response for any position is the item
presented in that position. But, when an item is not recalled in its correct position,
it is most frequently recalled in a position adjacent to the correct position.
Most theories of verbal short-term memory have attributed this response pattern
to the organization of the short-term memory architecture. For example, Henson
et al. (1996) suggested that these data show order is encoded as an activation gra-
dient, and these errors reflect the fact that these gradients are noisy and so become
unreliable. Models by Burgess and Hitch (1996), Anderson and Matessa (1997), Page
and Norris (1998) and others have all attributed these empirical position gradients
to the representation of order within the architecture, although each makes unique
24
claims about how this architecture is organized. In each of these models, when a
retrieval attempt for an item is made, a nearby item is sometimes produced instead,
because these models represent order with a code such that nearby items have simi-
lar representations. Presumably, according to these models, the gradients stem from
low-level representations, and are not a consequence of guessing strategy. This is not
surprising, because few of these models even consider the potential strategic contri-
butions to any aspect of the task. Even Anderson and Matessa’s (1997) model built
with the ACT-R system uses a fairly simple model of the procedures involved during
recall.
Nevertheless, at least one theory of verbal short-term memory has suggested that
these gradients stem, at least in part, from strategic processes during recall. Shiffrin
and Cook (1978) proposed a model where order was stored as associations between
adjacent items, and these associations could decay or be subject to interference.
They hypothesized that during recall, participants used the remaining information
to determine which item was most likely to be the correct response. Their models
of these processes were able to produce position gradients that predicted human’s
performance with a great deal of accuracy.
Consequently, this position gradient matrix shown in Table 2.1 is consistent with
several different patterns of responses, and many different models of verbal working
memory. Although it is not clear whether they stem primarily from representational
coding or strategic factors, they provide important evidence about the response pat-
terns in immediate serial recall, and need to be explained by the combined represen-
tational and strategic factors that contribute to serial recall.
These and other scoring techniques can provide useful information about the rep-
resentational and strategic components of the immediate serial recall task. However,
25
another type of data can also be informative: the timing of recall. By examining fac-
tors like inter-word pauses, initial item responses, and articulatory durations, better
information about these components can be ascertained.
2.4 Insights from analyses of the timing of recall
One indication that strategically-managed processes may be occurring during a
task may be found by manipulating a factor that should affect the operations of the
strategy, and examine how the times involved with processes change as a function of
the manipulation. Cowan (1992) identified and measured three interesting durations
that occur during immediate serial recall. These include the recall latency (or the
time it takes a participant to recall their first item after a recall signal is given), the
mean word durations, and the duration of the inter-word pauses. Figure 2.10 shows
these three types of data.
An important consideration in determining what types of recall strategies might
be used is the number of operations that can go on between consecutive recalled
items. Interestingly, although the latency of recall and the duration of the spoken
words does not change appreciably across list lengths two through four, the duration
of the inter-word pauses did increase as list length increases.
Cowan (1992) suggested a strategic explanation for this effect: between consecu-
tive recalls, memory search occurs, similar to that implicated by Sternberg (1975) in
a memory-search task. Although Cowan (1992) suggested that this scanning-based
search might be a form of rehearsal, it is perhaps more likely to be more directly
related to the act of recall, and so may stem from memory search and reconstruction.
Cowan’s suggestion that these inter-word durations are related to memory search
is related to research by Cavanaugh (1972). In a meta-analysis, Cavanaugh observed
26
0
200
400
600
800
1000
Durations obtained by Cowan (1992)
Number of words
Dur
atio
n (m
sec)
2 3 4
Inter−word Pauses
Latency
Word Pronunciation Duration
Figure 2.10: Mean durations of initial recall latency, speech production and inter-speech pausesacross different list lengths.
a strong relationship (R2 = .995) between memory scanning rate and inverse memory
span (found by dividing 1 by the observed memory span) across seven different
types of stimuli. Subsequently, Puckett and Kausler (1984) confirmed this finding
within individual participants, but found evidence suggesting that scanning rate and
memory span were not as closely linked as Cavanaugh had concluded. Instead, they
found that span scores ranged across a continuum, but scanning rates fell into two
groups: one group with scanning rates around 40 ms/item: (digits, letters, and
words), and a second group with scanning rates around 80 ms/item (VCs, CCs, and
CVCs). From this research, one might conclude that processes involved in memory
scanning are also components of the more complex memory span task, but that other
processes are involved as well.
More recent results have also found differences between scanning rate and mem-
27
ory span. For example, Hulme et al. (1999) performed a study where participants’
serial recall accuracies were measured. Stimuli included four sets: words and non-
words that were either long or short. Memory search rates for these words were
measured (as in Sternberg, 1975), and the durations associated with serial recall (as
in Cowan, 1992). Results showed that inter-item pauses were longer for unfamiliar
non-words than actual (more familiar) words, but did not differ between short and
long words. However, memory search slopes were about the same for words and non-
words (around 60 ms/item). Hulme et al. (1999) concluded that these inter-word
durations are primarily a result of trace selection and trace redintegration.
Consequently, the accumulated data support the claim that memory search is an
important component of immediate serial recall, but that memory search alone can-
not account for effects on inter-word pauses, because these pauses are also affected by
factors that do not affect simple memory search tasks. This may indicate that these
inter-word pauses arise from list reconstruction and guessing strategies that take
longer for longer lists. Examples of such strategies will be discussed in Chapter V.
2.5 Tasks other than immediate serial recall
The immediate serial recall task is not the only task that can provide informative
data about the organization of short-term verbal memory. In this section, I will
discuss the results of some experiments using related memory tasks that can provide
more detailed evidence about the structure of verbal short-term memory.
2.5.1 Harris (1975): Probe Recall
An experiment conducted by Harris (1975) was a variation of the serial recall
procedure called “Probe Recall”. In this task, short sequences of three, five, or seven
letters were presented to participants, followed by a probe item from the sequence.
28
Depending on the condition, participants were told to respond by naming the letter
that came before the probe item (a backward probe), or by naming the letter that
came after the probe (a forward probe). Response times for correct items were
reported.
Several of Harris’s (1975) results are especially relevant to questions about recall
architecture and strategy. His first important result was similar to those of Cowan
(1992)—mean correct response times to probes were longer for longer lists. His sec-
ond important finding was that response times to forward probes were faster than
response times to backward probes. This has implications for how items may be
accessed from verbal working memory, and suggests a powerful architectural limita-
tion. A third interesting result was that individual participants showed very different
response time patterns across serial positions. Harris interpreted these findings as
indications that different participants used different coding and recall strategies.
2.5.2 The Stimulus Suffix
A modified version of the immediate serial recall task has been used to study the
“stimulus suffix effect”. I have examined some of these experiments in Section 2.2.3,
as a demonstration of some experimental procedures that have produced large re-
cency effects. However, the results of these experiments have sometimes been used to
make inferences about both the architectural structure of verbal short-term memory,
and the strategies used by participants to perform the task.
The stimulus suffix effect is the effect that a single irrelevant item (the “suffix”)
following list presentation (the “stimulus”) has on the accuracy of recalling items
from the list. Generally, experiments have found that a single irrelevant item can
have a large and reliable detrimental effect on participants’ ability to recall the final
item in a sequence, and sometimes several items. Such effects of a stimulus suffix
29
can be seen in Figures 2.7 and 2.8).
These studies are relevant to my current investigation because they have been
used in the past to draw conclusions about strategic and architectural mechanisms
that enable performance in the immediate serial recall task. They also may provide
evidence about whether the last item in a list may be especially reliable or easily
identified. I will first examine a study by Balota and Engle (1981), and then a study
by Penney (1985).
Balota and Engle (1981)
In one study of the stimulus suffix effect, Balota and Engle (1981) manipulated
practice and presentation rate, as well as suffix condition. They found that the when
lists were followed by an irrelevant suffix, two distinguishable effects occurred. First,
recall accuracy of the final “terminal” item was poorer in the suffix condition than
in the no-suffix condition. Second, items immediately preceding the final item (“pre-
terminal items”) were recalled more poorly in the suffix condition than the no-suffix
condition. However, the recall accuracy of these pre-terminal items was modulated by
both presentation rate and practice. They concluded that the “terminal” suffix effect
stems from interference with an echoic memory buffer, but that recall of the “pre-
terminal” items depended on strategic factors that were influenced by the availability
of the final item.
Penney (1985)
Later experimentation on the stimulus suffix effect by Penney (1985) provided
some new insights into the strategic factors that may influence performance in this
task. In her first experiment, along with the manipulating presence of a suffix, she
manipulated whether the trials within a block were all of the same length (predictable
30
length) versus if a block was composed of trials of multiple list lengths (unpredictable
length condition).
Her results showed that there was an effect of stimulus suffix on the last few items
of each list length, but only when list length was predictable. If this effect stems
from strategic recall processes (as suggested by Balota & Engle, 1981), then this
result suggests that these strategies can use information about list length in order to
perform the task better. However, this may indicate that these strategies are related
to encoding rather than recall. For example, if a participant knows the length of the
sequence before encoding begins, they may attempt to ignore items in the middle
of the list, perhaps to improve their chances of getting earlier or later items correct.
When list length is unpredictable, it would be more difficult to determine which
items should be ignored, and which ones should be more carefully attended to.
These experiments by Balota and Engle (1981) and Penney (1985) do not begin to
summarize the variety of experimental and theoretical issues that have been explored
in the literature on the stimulus suffix effect. Nicholls and Jones (2002) discussed the
past 30 years of this research in greater detail, and suggested that perceptual grouping
is the most credible explanation of the effect. This contradicts many earlier theories
that attributed the suffix effect to masking from irrelevant suffix item. Apparently,
whatever system support the temporal organization of perceptual groups allows for
easy access to the final item of the group.
One observation I will make about this area of research is that in order to test
the hypotheses about perceptual masking, experiments have frequently used non-
verbal forms of recall that may enable the use of strategies not possible with verbal
recall. For example, when responses are made by filling in the blanks on a sheet
of paper, responses can be skipped, earlier responses can be examined, and it may
31
be especially easy to fill in the final item in its correct position. In many of the
experiments on the stimulus suffix effect, a suffix reduces the recency effect to a level
typically found in experiments that do not use manual response procedures. Perhaps
the more informative “suffix effect” is that experiments testing this effect produce
much larger recency effects than most other experiments using the immediate serial
recall tasks.
2.5.3 Logie et al. (1996): An investigation of reported strategies.
A few experiments have actually investigated participants’ reported strategies ex-
plicitly. For example, Logie et al. (1996) attempted to determine what types of
encoding and maintenance strategies were used by participants during the imme-
diate serial recall task. They found that participants’ reported strategies affected
the magnitude of both the articulatory duration and the phonological dissimilarity
effects. Although they did not closely examine reports of reconstruction or guessing
strategies, their conclusions about the relative roles of coding format and strategy
are relevant here. They concluded that there are a number of cognitive mechanisms
available for use during the serial recall task (e.g., a phonological system, a visual
system, a lexical system, and a semantic system), and participants can adopt strate-
gies that use one or more of these systems to accomplish a task, with different levels
of success for different strategies. However, their investigation did not examine the
strategies involved during recall, and so their conclusions have somewhat limited
applicability to this thesis.
2.5.4 Greene (1991): The Ranschburg Effect.
A more relevant demonstration that some aspects of recall are sensitive to strategic
control is provided by Greene’s (1991) investigation of the Ranschburg effect. The
32
Ranschburg effect (e.g., Crowder & Melton, 1965) is that immediate serial recall for
an item is impaired when it has appeared previously in the same list, with at least
one intervening item.5
One explanation of this effect assumes an architectural locus: output interference.
According to this explanation, when a stimulus is produced, it is subsequently more
difficult to retrieve, leading to difficulty recalling future instances of the same item in
a list. An alternate explanation assumes the effect stems from participants’ guessing
strategies. According to this explanation, during list recall, a participant will adopt
guessing strategies when he or she is unable to recall the next word in a list. When
such guessing is performed, recently-recalled items tend not to be chosen. This
depresses the probability of correctly recalling a repeated item.
To test these hypotheses, Green (1991) conducted an experiment where sequences
of eight digits were presented to participants who were instructed to recall them in
their presented order. Half of the participants were told to guess when they were
unsure about an item, whereas the other half were instructed to not guess if they
were unsure about an item. Results revealed that the Ranschburg effect only occurred
for participants who were instructed to guess. Interestingly, the critical (repeated)
items were recalled with about the same accuracy for both groups. In contrast, the
non-repeated items tended to be recalled better for the group instructed to guess.
This indicates that guessing occurred by selecting from the not-yet-recalled digits,
enhancing recall for all items but the one that had appeared on the list in an earlier
position.
Greene (1991) viewed these strategic effects as external to the memory system,
“not reflecting memory at all”, and “more apparent than real”. Thus, he failed
5The effect reverses when repeated items are presented in adjacent positions—these pairs tend to be recalledbetter. Presumably, this is because they are encoded as a single unit, and the participant may even remember theepisodic fact that an item pair was presented in a list.
33
to acknowledge that strategy is an important aspect of the memory system, and
that the system will not work without goals and a strategy to accomplish those
goals. Nonetheless, his results reveal that people are sensitive to instructions about
guessing strategy, and that their choice of guessing strategy can affect their serial
position functions.
2.6 Summary
The findings summarized here suggest that many aspects immediate serial recall
may be governed by strategic factors, although others may be more directly influ-
enced by the structure of memory coding. For example, one likely architectural
constraint is that it is easier to access items in memory in the order in which they
were encoded; another constraint is that there may be mechanisms that allow for
special access to the last item in a list. However, other aspects of performance may
have a more strategic locus. For example, the shape of the serial position function
may be influenced by strategic factors, and the time taken between the recall of
consecutive items may as well.
These findings will prove instructive for building a computational model of the
architectural, coding format, and strategic components that support performance in
the immediate serial recall task, which will be discussed in later chapters. However,
the findings summarized here come from a wide variety of experiments, each of which
involved different participants, procedures and stimuli. Additionally, only certain
aspects of their data are available. Consequently, my Experiment 1 was designed
to reproduce some of these same results in a single data set, and to allow for the
construction of formal computational models of the immediate serial recall task.
CHAPTER III
EXPERIMENT 1: AN INVESTIGATION OFIMMEDIATE SERIAL RECALL
This experiment serves several purposes. First, it allows many of the effects
reviewed in Chapter II to be examined and replicated in a single experiment. Second,
it enables new analyses that may provide clearer information about the how people
perform the immediate serial recall task. Third, it permits experimental parameters
and procedures to be controlled in a way conducive to future computational modeling.
Finally, it serves as a test-bed for initial models of the coding format of organization
of verbal short-term memory, and the strategic guessing strategies used to perform
the immediate serial recall task.
3.1 Method
3.1.1 Participants
The participants were eight undergraduate students at the University of Michigan
with normal perceptual, cognitive, and motor abilities. They were paid for their
participation, and received a bonus for performing well.
3.1.2 Apparatus
The experiment was conducted with a Pentium-class computer using special-
purpose software. Auditory stimuli were presented via headphones, and visual stimuli
34
35
were presented on the computer’s SVGA display. Performance was monitored by an
experimenter who sat next to the participant and interacted with the computer in
order to record the participant’s responses.
3.1.3 Stimuli
All testing was done using a set of eleven one-syllable words (“cult”, “dare”,
“fate”, “guess”, “hint”, “mood”, “oath”, “plea”, “rush”, “verb”, and “zeal”).
3.1.4 Design
The participants were tested individually across two different sessions, separated
by at least one day. During the first session, participants were tested with two pro-
cedures: first, an articulatory duration measurement task, and then, an immediate
serial recall task. During the second session, only the immediate serial recall task
was administered. For the immediate serial recall task, four blocks of trials (two “Re-
hearsal” blocks and two “Suppression” blocks) were completed during each session.
The rehearsal and suppression blocks were administered in an alternating fashion,
and the blocks were administered in the opposite order during the second session.
The overall order was counterbalanced across participants. Each block consisted of
16 trials (4 trials each for list lengths 4 through 7, in a randomized order), for a
total of 128 trials per participant. On each trial, words were sampled with uniform
probability without replacement from the stimulus set. Consequently, both tasks
manipulated list length , and the immediate serial task also manipulated rehearsal
condition.
3.1.5 Procedures
To assess the mean articulatory duration per word, a procedure as in Mueller
et al. (in press) was used to measure “articulatory duration for words in memorized
36
sequences”. For this procedure, 6 word lists for each list length 3 through 6 were
created by sampling without replacement so that each word in the set occurred
approximately the same number of times throughout the block. These lists were
presented in a randomized block of trials. At the beginning of each trial, a list of
words was presented on the video screen until the participant indicated (verbally)
that he or she was ready to begin. On the participant’s signal, the experimenter hit
a computer key that began the a trial sequence. At the beginning of this sequence,
three 100-ms tones were presented at approximately 500-ms intervals. After the
third tone was presented, the words disappeared from the screen and a computer-
based timer automatically started. Then, the participant attempted to recall the list
of words twice from memory at a clear rapid pace. When the participant finished
speaking the second list, the experimenter stopped a computer timer. If any speech
or memory errors were made, the trial was repeated. Total articulation times for
each trial were recorded.
The immediate serial recall task had two rehearsal conditions: “Rehearsal” and
“Suppression”. During both conditions, 16 trials per block were presented. The
participant was presented sequences of words auditorily via computer-based head-
phones. During each block, the participant first heard a number indicating the
number of words that would occur on the subsequent list. Words were presented via
a recorded male voice at 1.5 second intervals between onsets. Similarly, 1.5 seconds
after the final onset, a recall tone was presented, indicating that the participant
should initiate recall. For each word that was recalled in the correct position, a
bonus of one point was given. If an entire list was recalled correctly, a bonus of two
points per word was awarded. On suppression blocks, participants earned 1.33 cents
per point, whereas on rehearsal blocks, they earned 1 cent per point.
37
During each “Rehearsal” block, participants were encouraged to rehearse the pre-
sented words to themselves, in order to better remember the presented lists. During
the “Suppression” blocks, the participants were instructed to repeat the numbers “1,
2, 3, 1, 2, 3” at a steady pace, from the beginning of the trial until the recall beep was
presented. The experimenter monitored the participant’s counting to ensure that it
was maintained at a constant pace.
3.2 Results
3.2.1 Serial Recall Accuracy
When overall performance was analyzed by examining the probability of recalling
an entire list correctly, results showed that performance was better in the “Rehearsal”
condition than in the “Suppression” condition (F (1, 7) = 49, p < .001), and shorter
lists were remembered more accurately than longer lists (F (3, 18) = 102, p < .001).
Additionally, the condition by list length interaction was reliable (F (3, 909) = 4.6,
p < .01), indicating that the effect of suppression was not uniform across list lengths.
The mean probabilities across list lengths and rehearsal conditions are shown in
Figure 3.1.
3.2.2 Serial position functions
Although the analysis of correct list recall is informative, more detailed conclusions
can be drawn by examining the serial position functions. The “position” and “item”
serial position functions obtained in this experiment are shown in Figure 3.2. These
functions show that for the current experiment, the primacy and recency effects in the
“position” functions appear to be caused by misordering items during recall. This can
be determined because the difference between the “item” and the “position” serial
position functions is caused almost solely by items recalled in the wrong position.
38
0.0
0.2
0.4
0.6
0.8
1.0
List Length
Prob
abili
ty C
orre
ct R
ecal
l
4 5 6 7
Suppression
Rehearsal
s.e.=0.132
Figure 3.1: The effect of list length and rehearsal condition on probability of correct recall. Theinterval shown in the lower left corner of the graph indicates the size of the standarderror of the interaction.
This difference is fairly large, and so item misordering must have been the primary
way that errors were made.
Another interesting result of this experiment is that although the “Rehearsal” con-
dition produced higher recall accuracy than did the “Suppression” condition, three
typical characteristics of the “position” serial position functions were obtained under
both conditions (left side of Figure 3.2): effects of primacy, recency, and list length
are apparent under both rehearsal conditions. The “Item” serial position functions
(right side of Figure 3.2) both produced modest primacy effects and little or no re-
cency effect across different list lengths. Notably, performance was more accurate
in the rehearsal condition for both “order” and “item” serial position functions, and
lists of length seven produced a larger recency effect when participants were encour-
aged to rehearse the list. Because of the similarity between these two conditions,
only the “Suppression” condition will be examined in greater detail. Presumably,
39
1 2 3 4 5 6 7
0.0
0.4
0.8
Order with Suppression
Serial Position
Prob
abili
ty R
ecal
l
1 2 3 4 5 6 7
0.0
0.4
0.8
Item with Suppression
Serial Position
Prob
abili
ty R
ecal
l1 2 3 4 5 6 7
0.0
0.4
0.8
Order with Rehearsal
Serial Position
Prob
abili
ty R
ecal
l
1 2 3 4 5 6 70.
00.
40.
8
Item with Rehearsal
Serial Position
Prob
abili
ty R
ecal
l
Figure 3.2: Serial position functions produced in Experiment 1.
this condition better reflects the architectural contributions of the verbal working
memory system, because the contributions of different rehearsal strategies have been
eliminated.
Although the serial position functions in Figure 3.2 look fairly smooth and regu-
lar, this is a bit deceiving. A great deal of variability existed in the performance of
different participants. Figure 3.3 shows the individual serial position functions for
participants under each list length used in the “Suppression” condition of Experi-
ment 1.
It is clear that performance differed considerably between participants. It is dif-
ficult to determine whether these differences across participants reflect variability in
strategies used to accomplish the task, or if they represent more fundamental dif-
ferences in the participants’ underlying memory structures. However, these results
40
“Position” Serial Position Functions
0.0
0.2
0.4
0.6
0.8
1.0
Participant 1
Serial Position
Prob
abili
ty C
orre
ct P
ositi
on R
ecal
l
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 2
Serial Position
Prob
abili
ty C
orre
ct P
ositi
on R
ecal
l1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Participant 3
Serial Position
Prob
abili
ty C
orre
ct P
ositi
on R
ecal
l
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 4
Serial Position
Prob
abili
ty C
orre
ct P
ositi
on R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Participant 5
Serial Position
Prob
abili
ty C
orre
ct P
ositi
on R
ecal
l
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 6
Serial Position
Prob
abili
ty C
orre
ct P
ositi
on R
ecal
l
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 7
Serial Position
Prob
abili
ty C
orre
ct P
ositi
on R
ecal
l
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 8
Serial Position
Prob
abili
ty C
orre
ct P
ositi
on R
ecal
l
1 2 3 4 5 6 7
“Item” Serial Position Functions
0.0
0.2
0.4
0.6
0.8
1.0
Participant 1
Serial Position
Prob
abili
ty C
orre
ct I
tem
Rec
all
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 2
Serial Position
Prob
abili
ty C
orre
ct I
tem
Rec
all
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 3
Serial Position
Prob
abili
ty C
orre
ct I
tem
Rec
all
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 4
Serial Position
Prob
abili
ty C
orre
ct I
tem
Rec
all
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Participant 5
Serial Position
Prob
abili
ty C
orre
ct I
tem
Rec
all
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 6
Serial Position
Prob
abili
ty C
orre
ct I
tem
Rec
all
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 7
Serial Position
Prob
abili
ty C
orre
ct I
tem
Rec
all
1 2 3 4 5 6 70.0
0.2
0.4
0.6
0.8
1.0
Participant 8
Serial Position
Prob
abili
ty C
orre
ct I
tem
Rec
all
1 2 3 4 5 6 7
Figure 3.3: “Position” (top two rows) and “item” (bottom two row) serial position functions forindividual participants in Experiment 1. Each panel shows the performance of a singleparticipant, and each line represents a single list length.
41
have a larger variance than would be expected if each individual observation was
generated by a binomial distribution wherein each individual had an identical prob-
ability of recalling the item correctly: 99% of such observations would have to fall
within an interval smaller than about 0.3 units of probability, which clearly is not
what occurred in the data. This “over-dispersion” might be caused either because
different participants used different strategies for performing the task, or because
their underlying memory structures differed. Most likely, both of these possibilities
are true.
Looking across participants, a few general trends appear. First, the effects of list
length and primacy appear to be consistent across all participants for both sets of
serial position functions. However, the recency effect is less consistent across par-
ticipants. Figure 3.4 shows the magnitude of the recency effect in the “position”
serial position functions of each individual across list lengths. Recency effects were
calculated by subtracting the probability of correctly recalling the final item from
the probability of recalling the next-to-last item. Across the four lists lengths and
eight participants, positive recency effects only occurred in 16 out of 32 cases.1 For
the “item” serial position function, the small recency effects seen in these functions’
means (Figure 3.2) may have reflected the contribution of one or two participants
with abnormally large recency effects on their “item” serial position functions. Con-
sequently, the recency effect appears to be more fragile than the other effects.
3.2.3 Position gradient functions
One way to better explore the types of errors made during immediate serial recall
is to examine the position gradients (as previously shown in Table 2.1). Position
gradients show the distribution of the positions that items from a given position
1Of the 16 cases that did not produce recency effects, four occurred because the participant recalled both the lastitem and the next-to-last item perfectly for every list they recalled of that length.
42
−0.
4−
0.2
0.0
0.2
0.4
List Length
Rec
ency
Effe
ct
4 5 6 7
Figure 3.4: Recency effects in the “position” serial position functions of each participant, for thesuppression condition of Experiment 1.
of the presented list were recalled in. The position gradients obtained under the
suppression condition are shown in Figure 3.5. Results indicate that when items
are recalled in the wrong position, they tend to appear in positions adjacent to the
correct position.
0.0
0.2
0.4
0.6
0.8
1.0
Position Gradient Functions
Serial Position
Prob
abili
ty R
ecal
l
1 2 3 4
0.0
0.2
0.4
0.6
0.8
1.0
Position Gradient Functions
Serial Position
Prob
abili
ty R
ecal
l
1 2 3 4 5
0.0
0.2
0.4
0.6
0.8
1.0
Position Gradient Functions
Serial Position
Prob
abili
ty R
ecal
l
1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
Position Gradient Functions
Serial Position
Prob
abili
ty R
ecal
l
1 2 3 4 5 6 7
Figure 3.5: Position gradient plots for lists of lengths 4 through 7, under articulatory suppression.For a given panel, each connected series of points represents the distribution of presentedpositions for a single response position.
One interesting fact shown by these serial position gradients is that near the ends
of the longer lists, the gradient flattens, and items are frequently recalled at positions
further from the initial presented positions. Also, for lists of six and seven words,
43
these gradients are not symmetric: items recalled in a given serial position are more
likely to have been presented in a later position than in an earlier one. This indicates
that items are getting dropped, pushing the items at the end of the list toward the
beginning.
3.2.4 The types of responses made during serial recall
To better understand how participants performed in this task, I have enumerated
the types of responses that participants made during task performance. Responses
can be separated into various categories that might help us determine how and why
errors were made. These responses can be organized into the following hierarchy:
1. No error (the item was recalled correctly); 2. Position error (an item from the
current list was recalled); 3. Word set intrusion (an item from the word set that was
not presented during the trial was recalled); 4. Other overt response (A word not
from the set was recalled, primarily consisting of the response “Blank”); and 5. No
response (the participant did not recall enough words; these are the positions at the
end of the list without a corresponding response). The proportion of each type of
response for each list length is shown in Figure 3.6.
0.0
0.2
0.4
0.6
0.8
1.0
No Error
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
List Error
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Word Set Error
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Other Response
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
No Response
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
Figure 3.6: Types of responses produced in the suppression condition of Experiment 1.
Results show that, similar to earlier conclusions, the majority of errors occur be-
44
cause items from the list are recalled in the wrong position. Only a small proportion
of the responses were words that were not on the current list. The right-most panel of
Figure 3.6 represents the proportion of times that no response was given in a specific
position because too few items were recalled. As list length gets longer, this type of
error becomes more and more prevalent, indicating that people are often failing to
recall the same number of items that were presented, especially for longer lists.
3.3 Discussion
In this experiment, I have reproduced some of the consistent effects found in pre-
vious literature: First, lists with more words tend to be recalled more poorly than
lists with fewer words. Second, primacy effects occurred for both “item” and “posi-
tion” serial position functions, whereas recency effects were only substantial in the
“item” serial position functions. Third, there appears to be a great deal of variability
across participants, which might indicate differences in both the underlying form of
encoding and the performance strategy. Fourth, typical position gradients occurred,
wherein erroneous responses were usually made by recalling an item in a position
close to its presented position. As lists got longer, these gradients became flatter
and more asymmetric. Fifth, by examining what type of responses participants
gave, it can be seen that recalled items were frequently correct, and if not correct
they were usually items that were presented in a different serial positions on the list.
Additionally, participants frequently recalled fewer items than were presented on the
list.
These results can help form the basis for some initial assumptions about how
the architecture of the human verbal memory system might be organized, as well
as how people might opt to perform the immediate serial recall task in the face of
45
the limitations presented by their memory. In the next two chapters, I will present
a set of models, based on the EPIC computational architecture, that explore the
relative contributions of architecture, coding format, and strategy to performance of
the immediate serial recall task. The models will be evaluated by analyzing their
performance on the same measures examined in the current chapter, comparing the
simulated performance to empirical data.
CHAPTER IV
AN EPIC MODEL OF THE ARCHITECTURALCOMPONENTS INVOLVED IN THE IMMEDIATE
SERIAL RECALL TASK
4.1 The EPIC Architecture
The EPIC (Executive Process/Interactive Control) Architecture (Kieras & Meyer,
1997; Meyer & Kieras, 1997) is a cognitive modeling system that makes specific as-
sumptions about the architectural components of human perception and motor con-
trol. Like other cognitive architectures such as ACT-R (Anderson, 1993) and Soar
(Rosenbloom, Laird, & Newell, 1993), EPIC makes an explicit distinction between
the fixed set of mechanisms and structures that support cognition, and the relatively
flexible procedures used to carry out tasks with these mechanisms. In this thesis, I
have used the terms architecture and strategy to describe these two aspects of cogni-
tion. When the EPIC architecture is operating in a virtual environment, performing
a task based on a task strategy, it is operating as a cognitive agent similar to a human
cognitive agent performing the same task in a physical environment.
This similarity stems from the fact that the EPIC architecture is designed to cor-
respond to the organization of the human cognitive system. The EPIC architecture
(depicted in Figure 4.1) is a component-based production rule system that involves
assumptions about the operation of peripheral (e.g., perceptual and motor) and cen-
46
47
tral (e.g., cognitive) processes. Each of EPIC’s components represents a functional
cognitive structure, resembling a corresponding structure in the human information
processing system. Furthermore, most of the components are related to a physical
component of the the human neural, motor, or perceptual system.
Figure 4.1: The EPIC (Execute Process/Interactive Control) Cognitive Architecture.
Task Environment
WorkingMemory
Production RuleInterpreter
Vocal MotorProcessor
VisualInput
AuditoryInput
Long−TermMemory
AuditoryProcessor
VisualProcessor
ProductionMemory
Ocular Motor
Processor
TactileProcessor
ManualMotor
Processor
SimulatedInteractionDevices
CognitiveProcessor
4.1.1 Components of the EPIC Architecture Subserving Verbal Working MemoryTasks
Although the EPIC architecture provides a comprehensive theory of many of the
cognitive and perceptual-motor functions performed by humans, I will be primarily
concerned with those components used during the immediate verbal serial recall
task. This task is perhaps the most commonly-used experimental procedure for
studying “verbal working memory” (VWM). Verbal working memory is not a well-
defined concept in the psychological literature, but for present purposes consists of
48
mechanisms that are involved in the maintenance of verbal information over a short
period of time.
As seen in Figure 4.1, the EPIC architecture has a set of components called “work-
ing memory”. These components provide stores for keeping track of information used
to perform tasks. Given their very specific role in the architecture, they do not en-
compass all of the functions that psychological researchers sometimes view as part
of working memory. For example, some have argued that “working memory” is com-
posed of a storage components and the functions used to manipulate this storage
(e.g., Miyake & Shah, 1999). In contrast, EPIC’s working memory is primarily a
storage mechanism, whereas processes that manipulate working memory are found
in other components. The multitude of processes frequently attributed to human
verbal working memory are not found in a single working memory component of the
EPIC architecture, but are rather distributed across multiple components of the ar-
chitecture (Kieras et al., 1999). These components work together to accomplish tasks
often described as “Verbal Working Memory”, but each component may be used in
other tasks that would probably not be considered “Working Memory” tasks.
These components include the auditory perceptual processor, the production rule
interpreter, the vocal motor processor, the cognitive processor, working memory,
and production rule sets that implement strategic components of a task (such as
rehearsal or recall). Each of these components may be used in other types of tasks
as well. For example, general-purpose executive functions such as goal-setting are
important for verbal working memory tasks, but they are also critical for performing
most (if not all) other tasks. Similarly, the auditory perceptual processor is used
for processing auditory stimuli in simple discrimination tasks, and the vocal motor
processor is used to make vocal responses in a variety of tasks (cf. Meyer & Kieras,
49
1997). I will briefly describe the roles of each of these components, and then discuss
several ways in which they may be insufficient or inaccurate, given the results from
the experiments reviewed in Chapter II and Experiment 1.
4.1.2 The Cognitive Processor
The cognitive processor is the central processor that controls performance of cog-
nitive tasks. It operates in a cyclical fashion, examining the current state of working
memory and initiating any new actions in parallel. Although this processor is im-
portant for verbal working memory tasks, it is also important for nearly every task
that involves more than simple reflexive processing.
4.1.3 The Production Rule Interpreter
One of the primary components of the cognitive processor is the production rule
interpreter. This component drives task performance, because it examines the cur-
rent state of working memory and determines what should occur next, based on
procedural knowledge that has been encoded as a set of production rules. A pro-
duction rule has two parts, the condition and the action. The condition represents
a state of working memory, and the action describes the processes that will occur
as a result of the conditions being satisfied. EPIC’s production rule system is said
to operate in parallel, meaning that every production rule whose conditions are sat-
isfied during a single cognitive processor cycle will “fire”, and its actions will be
performed. An example production rule is shown in Figure 4.2. This rule will be
satisfied whenever each of the statements in the condition (labeled “IF”) exist the
working memory. When this happens, the rule will “fire” and the actions (labeled
“THEN”) specified there will get performed.
50
(PERFORM-RECALL
IF
(
(STRATEGY USE GUESSING RECALL)
(GOAL DO RECALL)
(STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT)
(TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?CONTENT)
)
THEN
(
(SEND-TO-MOTOR VOCAL SAY END ?CONTENT)
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
)
)
Figure 4.2: An example production rule used in the EPIC architecture’s performance strategy forthe immediate serial recall task.
4.1.4 Working Memory
EPIC’s working memory is segregated into distinct partitions associated with dif-
ferent perceptual, motor, or cognitive subsystems. Some of the partitions of the work-
ing memory database include the auditory, motor, visual, and tag stores. Presum-
ably, the different types of information stored in EPIC’s working memory database
are maintained in different physical locations in the human brain. Each partition
stores numerous entries, and each entry is composed of a list of properties. These en-
tries are used by the production-rule interpreter to determine which production rules
should fire during any specific cycle. Some entries use a convention where properties
come in pairs: the first is a key that identifies what the purpose of the property is,
and the second is a value that identifies the state of the property. These entries are
typically constructed by the operation of production rules, or via some peripheral
sensory or motor processor.
Much of the verbal information used in immediate verbal memory tasks is stored
in the auditory partition of EPIC’s working memory. However, other information is
also used to perform a VWM task. These other types of information are critical for
51
performance of verbal working memory tasks, even though this information is stored
in different partitions of working memory. Some examples of this information include
GOALS and subgoals (called STEPS), and general-purpose book-keeping entries called
TAGs. Table 4.1 shows examples of some entries that might exist in the working
memory database during performance of the immediate serial recall task.
Table 4.1: Representative entries in the working memory databaseEntry Working Memory Database Entry1. (STRATEGY NO REHEARSAL)2. (GOAL DO RECALL)3. (STEP DO-RECALL RECALL-CHAIN-START)4. (AUDITORY SPEECH PREVIOUS 342 NEXT 343 SOURCE EXTERNAL
MARKER CONTINUE TYPE WORD CONTENT POTATO)5. (TAG DO-RECALL 342 IS TO-BE-RECALLED)6. (TAG DO-TRIAL 341 IS RECALL-CHAIN-START)
The first entry describes the rule set used to perform the current task. Each rule
involved specifically with the “no rehearsal” strategy would have the entry (STRATEGY
NO REHEARSAL) in its condition, so that it will only be able to fire when this line
exists in working memory. The next two entries describe general task goals that are
currently enabled. Any number of strategies, goals, and steps may be present at a
given time, and it is up to the rules in a given production rule set to manage these
entries. These first three entries are used across many different types of tasks.
The fourth entry contains information about a specific word (“potato”) that has
been heard during task performance. This entry includes information about the
word’s identity, as well as various other properties that keep track of other informa-
tion about the word. This entry was created by the auditory perceptual processor
when it processed speech whose SOURCE is EXTERNAL (occurring in the environment
and not produced by EPIC). Similarly, OVERT speech (actual speech produced by
EPIC’s vocal motor processor) and COVERT speech (sub-vocal speech produced by
EPIC’s vocal motor processor) would have entries in auditory working memory. This
52
type of memory maintains much of the information typically associated with “verbal
working memory”.
Two properties of this memory item are labeled PREVIOUS and NEXT. These prop-
erties take the form of uniquely generated symbols that are used to maintain order
information in the form of a chain (in Table 4.1, these symbols are simply unique
integers). The PREVIOUS property of one item is identical to the NEXT tag of the item
that immediately preceded it, allowing order information to be stored as a chain of
bi-directional references, and enabling the items immediately before or immediately
after a given item to be located with ease.
The fifth and sixth entries are TAGS, and represent meta-information about other
entries in the working memory database. In the context of EPIC models of immediate
serial recall, these items perform two important roles. First, they serve as entry points
for organizing a sequence of words. For example, a tag might indicate which entry is
the beginning of a chain, or which entry should be recalled next. Second, they encode
the status of the different auditory speech items, indicating (for example) whether
an item has been recalled or not. This type of meta-information is rarely discussed in
research on verbal working memory, and although it supports performance in models
of the immediate serial recall task, it may or may not be best considered a part of
verbal working memory.
In the version of EPIC I describe here (from Kieras et al., 1999) there is no limit on
the number of items that can be stored in the working memory database. However,
there is a functional capacity limit on amount of information that can be maintained
in auditory working memory, because AUDITORY SPEECH entries disappear from the
database according to a parameterized decay function, as has been hypothesized by
Baddeley (1986) and others.
53
4.1.5 Auditory Perceptual Processor
During the immediate serial recall task, the primary role of the auditory percep-
tual processor is to process auditory and speech events (either from the external
environment or the vocal motor processor), and produce corresponding AUDITORY
entries in the working memory database. In the version of EPIC used by Kieras
et al. (1999), this processor also determines when the item should decay, and sched-
ules its ultimate removal from the working memory database. This decay has several
properties: it is all-or-none, so that all properties of a single item disappear at the
same time; it works independently for all items, so that the presence or decay of
one item has no effect on the lifetime of other items; and it only affects AUDITORY
SPEECH items, so that other information used in the task (e.g., TAGs and GOALs) do
not disappear unless explicitly removed by a production rule.
4.1.6 Vocal Motor Processor
The vocal motor processor is operated via production rule actions. It has the
capability of producing two types of speech events: COVERT and OVERT. Overt speech
is analogous to actual speech produced by human vocal articulators. This type of
speech is present in the architecture’s virtual environment, and is used to perform
vocal recall in simulated serial recall tasks. On the other hand, covert speech is
sub-vocal, and is used for item rehearsal during the verbal memory task. The use
of covert speech has the effect of sending a speech event directly to the auditory
perceptual processor, but this speech is not made available to the external virtual
environment.
54
4.1.7 Production Rule Performance Strategy
The final major component of a model of immediate serial recall is the production
rule set that determines how the task should be performed. In Kieras et al. (1999),
this strategy managed new memory items, engaged in iterative cyclic rehearsal, and
performed recall. It also performed goal and sub-goal management. The production
rule set describes the procedural knowledge used to perform a task, and is assumed
to have been acquired via typical processes involved in skill acquisition.
4.1.8 The Task Environment
One more piece of a complete EPIC model is a virtual model of the environment
and task. This is critical because it makes explicit distinctions between what happens
inside the cognitive agent and what happens in the world. Additionally, it requires
all perceptual and motor components that occur during task performance to be
modeled explicitly. Most models of verbal memory tasks do not explicitly model the
environment or the model’s interaction with the environment, and consequently can
be based on dubious assumptions about the timing of perception and recall.
4.1.9 Summary
Together, each of the above components is used to create a model of performance
in the immediate serial recall task (e.g., Kieras, et al., 1999). The use of such an
architecture allows the distinction between architecture and strategy to be made
explicitly, and requires assumptions about each component to be described and im-
plemented. The consequences of these assumptions can then be evaluated, and it
can be determined how accurately these assumptions predict human performance in
the tasks that are simulated in the virtual environment.
55
Problems with EPIC’s auditory perceptual processor
Some of these components may embody assumptions about the human informa-
tion processing system that may not be accurate. For example, the number of items
that can be stored in verbal working memory may not only be limited by decay, but
may also be limited by interference or some type of memory capacity. Or, information
about a verbal item may not disappear in an all-or-none fashion—some information
may still be available after other information has disappeared (e.g., the phonological
information about a word may be partially available, even if the information about
that item’s position in a sequence has disappeared). It also may be unrealistic to
assume that there is no limit to the amount or type of the meta-information that can
be stored as a TAG, especially since some of this information encodes important infor-
mation about the order of items. So, although the current EPIC architecture offers
many advantages for building models of verbal working memory task performance,
some of its assumptions may lead to inaccurate models.
4.2 A Modified EPIC Architecture
As a consequence of these potential problems, I have created new components
of EPIC’s auditory perceptual processor and auditory working memory that can be
used in place of the ones described above. These components are a result of several
modifications to the previous auditory-perceptual and working memory components
of EPIC’s architecture, made with the goal of creating models that more accurately
depict the true capabilities and limitations of the human verbal working memory
system. These modifications primarily affect the role and organization of the infor-
mation produced by the auditory perceptual processor, and how this information is
used during the immediate serial recall task.
56
4.2.1 Current Limitations
There are several specific aspects of the current EPIC architecture that I believe
may require modification. The following section describes the deficiencies of each
aspect, and how these deficiencies might be overcome.
Access to verbal content information
In the current instantiation of EPIC, the content of a verbal item is directly
available to production rules. There are several reasons to believe that this may be
unrealistic.
For example, results from Sternberg’s (1975) memory-scanning experiments sug-
gest that there are limits on how the identity of items can be accessed from verbal
working memory. In this task, participants were given a target set of letters which
they were instructed to memorize. Then, they were probed with single letter that
either was or was not a member of the target set. Results showed that the time
required for a participant to answer increased with the size of the target set. These
results suggest that this type of memory search can not be performed in parallel.
However, the EPIC architecture allows for parallel search because all verbal content
information is accessible by the cognitive processor. Consequently, a set of produc-
tion rules could be devised that searches them all simultaneously.
Another reason for believing that access to verbal content may be limited is the
existence of phonological errors in serial recall. Phonological similarity has a power-
ful effect on serial recall accuracy (e.g., Mueller et al., in press). The explanation of
this effect that has received the most support is called “Redintegration”. This expla-
nation states that the phonological similarity effect arises because partially-degraded
items may be reconstructed based on long-term phonological representations (Hulme
57
et al., 1997). This system of memory decay and reconstruction is inconsistent with
EPIC’s ability to access the content directly through a production rule, because
redintegration may involve sub-symbolic effects on memory decay and recall. Since
information in the working memory database is symbolic in nature, this assumption
is somewhat incompatible with a redintegration mechanism.
One step toward accommodating both of these findings, thereby creating a more
realistic verbal memory system within the EPIC architecture, would be to provide
architectural constraints and modeling conventions for how item content can be ac-
cessed. One such constraint might be to assume that the content of verbal items
is not available to production rules directly, but this content can be deliberately
retrieved from the auditory working memory. This retrieval might include either
an automatic or an optional reconstruction (“redintegration”) phase, so that pro-
duction rules can have access to fully reconstructed symbolic entities. Furthermore
one might also assume that only one item can be reconstructed at a time, or that
only one reconstructed item at a time would be available to production rules. This
system would provide an architectural explanation for why serial search through a
target set of items might be necessary, as well as providing a system where explicit
assumptions about redintegration can be tested.
Access to adjacent items
Currently, EPIC encodes serial-order information with pairs of mutually-referential
properties. There are no architectural restrictions that limit how this type of infor-
mation might be accessed. However, as discussed in Chapter II, humans may have
limits on their ability to access adjacent items encoded in a list. For instance, it
appears to be easier to access a list in the forward direction than in the backwards
direction (e.g., Harris, 1975), and items that are closer to each other in a list appear
58
to be accessed more rapidly than items that are more distant. The representation
used by EPIC does not impose constraints that would lead to these limitations:
production rules may access items as easily in the backward direction as in the for-
ward direction, and rules can be constructed that specifically match long chains of
items, allowing direct access from one item to another with an arbitrary number of
intermediate items.
One way to address the issue of access to adjacent items is to assume that order
information is not encoded as mutually-referential properties, but rather as uni-
directional forward “pointers” that can be used to determine which item occurs
next. Furthermore, the process of following these pointers might be managed by
functions within the auditory perceptual processor, rather than directly through
production rules. A consequence of this restriction would be to limit the number
of order tags that could be bound by a single rule, so that multiple production rule
cycles would be required to determine the exact relative positions of items that are
not adjacent. Additionally, determining which item precedes another item would be
a difficult task, and may require deliberate search through items currently in working
memory to determine which one has a forward pointer that matches the identifier of
the current item. With such limitations imposed at the architectural level, strategy
can be better constrained and more accurate predictions may be possible.
Reliability of list organization tags
EPIC models of verbal memory can use TAGs to encode the PREVIOUS property
of a specific item that might represent the beginning of a list or sub-list. This tag
serves as a special entry-point into the list, allowing recall or rehearsal to begin easily
at the correct item. However, since TAGs do not decay, this leads to the prediction
that the first item on a list should almost always be recalled correctly.
59
Surprisingly, this prediction is fairly accurate. In the experiments reviewed earlier,
the first item of nearly every list length in every experiment was recalled with high
accuracy: normally greater than 90%. However, examining those experiments and
especially the data produced in Experiment 1 more closely, it is clear that the first
item of a list was not always recalled correctly. Furthermore, when it was recalled
incorrectly, the order of the first two items was frequently swapped, indicating that
the specific identity of the first item on a list was not entirely reliable.
To account for this finding, it may be necessary to allow TAGs to decay, or to
create a new type of item that explicitly keeps track of list organization (e.g., the
beginning or end of list sub-groups.)
Storage and maintenance of speech items
In previous EPIC models of VWM in Kieras et al. (1999), speech items were
maintained and disappeared independently, according to a decay distribution. As
a result, the auditory store had no upper limit on the number of verbal items that
could be maintained simultaneously. One potential limitation of such a model is that
it would predict that if recall could be performed faster than items were presented,
items that are recalled later in the list should be recalled more frequently. This should
be true at least for the “item” serial position functions. This should occur because
for these later items, less time would elapse between presentation and recall than for
earlier items. Although this prediction is modulated by whatever guessing strategy
is used, this prediction does not appear to be borne out in the data examined in
Chapters II and III. The “item” serial position functions show two consistent effects
(when forward recall is required): earlier items are recalled more frequently than
later items, and items from shorter lists are recalled more frequently than items
from longer lists.
60
Given that an account relying on time-based decay cannot explain these two
effects, this may suggest that further architectural assumptions about limitations
in encoding, storage, or maintenance of speech items may be required. Alternative
assumptions about verbal memory interference or capacity may be able to better
explain these effects, and so it may prove fruitful to investigate other ways in which
verbal memory capacity might be limited.
Of the many ways in which verbal short-term memory might be limited, few can
account for these two observations about the “item” serial position functions. Nine
such simple limitations are summarized in Table 4.2. I will next discuss each of these
potential limitations.
Table 4.2: Effects of different assumptions about short-term memory limitations.Theoretical Hypothesis Predicted Serial-Position Effect Predicted List-Length EffectTime-based decay Later items recalled better Shorter lists recalled betterEncoding Interference
(Recall produces interference) No effect Shorter lists recalled betterEncoding Interference
(Recall does not produce interference) Later items recalled better Shorter lists recalled betterProactive Encoding Interference (fatigue) Earlier items recalled better No effectOutput Interference Earlier items recalled better No effectCapacity with encoding failure Earlier items recalled better No effectCapacity with overwriting
(Recalled items not encoded) Later items recalled better Shorter lists recalled betterCapacity with overwriting
(Recalled items overwrite other items) Earlier items recalled better Shorter lists recalled betterSub-optimal guessing strategy Strategy-dependent Strategy-dependent
One simple account involves encoding interference, where later items interfere
with items currently in memory by erasing or overwriting them. This account would
explain why the “item” serial position functions of longer lists fall below those of
shorter lists, because longer lists would undergo more interference. However, this
account would also predict that later items should be recalled more frequently than
earlier items (if items did not undergo interference from recalled items) or with
roughly the same probability (if recalled items do interfere with presented items).
Neither of these predictions are consistent with the data, and so either account by
61
itself is insufficient.
Another type of interference assumes that encoding mechanisms undergo fatigue
(i.e., proactive interference) during the task, so that later items are less likely to be
successfully encoded. This account predicts that items earlier in a list should be
recalled better, because they would not be as susceptible to interference, but this
account does not explain why there should be an effect of list length, because the
probability of recalling an item would not depend on how many items follow it in
the list. Consequently, this account is also insufficient for producing the observed
effects.
A third type of interference might occur if an item’s reliability is affected by
retrieval or recall of other items. This “output interference” would predict that later
items would be recalled less accurately than earlier items, because the later items
would be interfered with more. However, it would not explain why there is an effect
of list length on item recall probability, because such interference should have similar
effects on the early items of each list, regardless of the list’s length.
None of these pure effects of interference can explain the two primary effects
on the “item” serial position functions. However, there are other ways in which
working memory might be limited. For example, the memory storage buffer might
have a limited capacity. Like limitations based on interference, limitations based on
capacity might work in several different ways. However, each version of a limited-
capacity memory assumes that only a small number of speech objects can be stored
reliably.
One way a capacity account might explain these two effects is through encoding
failure: if the capacity is exceeded, new items are not encoded. Of course, this
account does not explain how items ever leave working memory, allowing new items
62
to be encoded. However, it does predict that items near the beginning of the list
would be recalled better than items near the end, because the capacity might be
reached before all items could be encoded. Unfortunately, this account predicts that
there should be no effect of list length for the initial serial positions, which contradicts
the finding that items from longer lists are recalled less frequently. Consequently,
this capacity account is insufficient as well.
Another capacity account assumes that every item gets stored, even if the capacity
has been reached. However, when no empty slots remain for new items to be stored
in, previously-stored items will get overwritten. This account predicts that items
from shorter lists would be recalled more accurately than items from longer lists,
because for longer lists, items would be overwritten more frequently. However, its
prediction about serial position depends on whether the process of recall produces
memory traces that are encoded as new items in working memory, overwriting earlier
items. For example, when the response “dog” is given during recall, a new speech
object whose content is “dog” might be created and encoded into working memory,
possibly resulting in two distinct items whose content is “dog” (i.e., the original
stimulus and the response). The prediction of this account depends on whether this
new speech object is treated like any other newly-perceived speech item, and thus
overwrites items that already exist in a capacity-limited working memory.
If, when a response is made, no new speech item is entered into working memory
(and so no currently-stored items are overwritten) this account predicts that ear-
lier items should be recalled better than later items. This would happen because
later items would be less likely to have been overwritten. Consequently, like earlier
accounts discussed here, this account is also insufficient.
If, however, when a response is made, a new speech object is encoded and allowed
63
to overwrite earlier items, a different prediction is made. According to this account,
earlier items should be recalled better than later items, because there would be fewer
opportunities for earlier items to be overwritten before they could be recalled. For
example, if working memory had a capacity for three items and a four-item list was
presented, then the first item would have been subject to only one overwriting event
(i.e., the presentation of the fourth item). The second item would be subject to two
such events (i.e., the presentation of the fourth item and the recall of the first item),
and the third item would be subject to three such events. Of course, if these words
are getting overwritten, fewer words would be recalled, and so the ultimate effect
would depend on many specific assumptions about capacity and recall. However,
this account is unique among the explanations explored so far, in that it may be
able to explain why “item” serial position functions show both primacy effects and
list-length effects.
Although each of these accounts involves new architectural limitations, it may
be true that these effects are at least partially a consequence of strategic factors.
Such an explanation would assume that the participant will often not recall words
that he or she knew were presented. This strategy seems sub-optimal, but may
not actually be: although participants are sometimes encouraged to recall words in
the correct position, they are rarely encouraged to recall all of the words that they
remember regardless of the position. Lacking these specific instructions, they might
avoid recalling words they know to be from the list but in incorrect positions. Such a
strategy might produce the two critical effects on the “item” serial position functions,
but this is difficult to determine without specifying these strategies in greater detail.
Of course, although only one of these explanations is likely to produce both effects,
it may be possible that some combinations of these accounts will produce them. For
64
example, perhaps time-based decay in combination with a limited capacity memory
would produce data similar to Experiment 1. However, a reasonable first step would
be to determine how well the limited capacity explanation can account for the data
from Experiment 1.
Independent decay of “item” and “order” information
The models described in Kieras et al. (1999) assumed that item and order in-
formation were both maintained in a single working memory entry that was either
present or absent.1 This is probably unrealistic, as can be seen in the data from
Experiment 1. Figures 3.5 and 3.6 show that items were frequently recalled in the
incorrect position. This indicates that although information about the relative order
of items may have been lost, participants were still able to remember the content of
the word and recall it during the trial.
This suggests that item and order information may be somewhat independent.
Perhaps, if the order information about an item is no longer available, information
about the item’s content may still be available. The opposite might be true as well;
a participant in a memory task may know that some item came between two other
items, but at the same time be unable to determine what the content of that item
was.
Access to the final item in a list
Several lines of research reviewed in Chapter II have suggested that the final
item in a sequence may have some special status. In the EPIC architecture, speech
items stored in the auditory working memory buffer have a property called MARKER,
which marks whether the item came at the beginning (START), middle (CONTINUE),
1These models did not attempt to produce serial position curves or investigate other aspects of the recall eventsduring a trial. Instead, they examined variables that are known to affect the probability of correctly recalling anentire list.
65
or END of the list. These are properties of the word, and so might represent certain
stress or enunciation patterns that are embedded in the pronunciation of a word.
A production rule would be able to examine this property, and assure that initial
and final items are always recalled at the beginning and end of the list, respectively.
However, as with the previous discussion about the reliability of the tags that marked
the list beginning, it may be unreasonable to assume that the final items in lists can
always be determined unambiguously. Consequently, it may be necessary to modify
the exact nature of this type of information, both in how it is accessed and used and
in how reliable it is.
4.2.2 Modifications to EPIC’s auditory perceptual processor
In response to these potential limitations of EPIC’s current auditory perceptual
processor and auditory working memory store, I have created a modified auditory
perceptual processor that can be used within the EPIC architecture to allow more
accurate models of verbal working memory phenomena to be created. These modi-
fications address each of the limitations discussed in the previous section.
4.2.3 The primary auditory store
Previously, the auditory perceptual processor of the EPIC architecture did not
implement a specific auditory or phonological store. Instead, auditory items in the
working memory database reflected the theoretical contents of a primary auditory
store, but this primary store was not implemented because it was unnecessary for the
tasks being studied. For the current set of problems, this “virtual” auditory store is
no longer sufficient, and so the core changes I have made to the auditory perceptual
processor involve an explicit implementation of the primary auditory store. These
modifications are not theoretically important, but they enable modifications that
66
allow new theoretical questions to be examined.
One important difference that these modifications allow is for information to be
maintained that is not directly accessible to production rules. In the new auditory-
perceptual processor, only some of the contents in the primary auditory store are
mirrored directly in the EPIC’s working memory (and are thus directly accessible
by production rules). Other types of information stored in this primary auditory
store are accessible by rules only through intermediate accessor functions, or are
not directly accessible at all. This allows sub-symbolic information to be stored,
and enables architectural constraints on the access of information. Furthermore,
this organization enables decay and interference processes to operate more easily in
sub-symbolic ways.
Several major types of information are stored in this primary auditory store. The
internal representation used by the auditory perceptual processor is different from
the information accessible by the production rule interpreter in the working mem-
ory database, because the primary auditory store maintains information that is not
accessible to production rules. This information may also undergo changes that are
not externally visible. The following sections will discuss the internal representation
and processes used to maintain items in the primary auditory store, as well as how
this information is made available to the production rule interpreter via the working
memory database.
4.2.4 Internal Representation
Internally, the primary auditory store contains several types of information, bound
together by speech objects. Each speech object maintains important information
about a word, and allows access to that information. Also, this store maintains
a “speech tag” that allows the initial items of lists or sub-lists to be remembered
67
and accessed without memory search. Each speech object also presumably contains
other types of information that have not yet been implemented, such as the word’s
associated meaning or its location in a semantic network.
The sub-types of speech information maintained in the primary auditory store
are depicted in Figure 4.3. The “primary auditory store” is really just a collection
of storage buffers that maintain auditory and verbal information, as depicted in
the figure. Each buffer maintains a single type of information. On the left side of
Figure 4.3, the Speech Tag storage buffer holds speech-tags that can be accessed
using identifying labels. Speech objects are stored in the “Speech object storage
buffer”, and are accessed by their ITEM-ID properties. Speech objects allow access
to all the information that makes up a word, but a speech object does not hold
this information directly. Phonological “item” information, order information, and
semantic information are actually stored in their own primary storage buffers. The
speech object simply serves as a container for these various types of information that
are presumably subserved by independent neural mechanisms.
Speech Objects
Inside the speech object storage buffer, speech objects allow access to different
properties of a word. Some of these properties are not specifically tied to the phono-
logical information about the word or its order or position in a list. In the current
models described here, these properties include the item’s source, marker, and type.
Additionally, phonological and order information are accessed via each speech object,
although these types of information are stored in their own data sub-stores. Conse-
quently, if the speech object for a word disappears, all other information associated
with the word is lost as well. Thus, although phonological and order information can
degrade independently, both types of information will be inaccessible if the speech
68
Semantic Memory(Unimplemented)
Item-ID
Primary PhonologicalBuffer
Primary OrderBuffer
Identifying Label
Speech Tag
Item-ID
Speech Object
PhonologicalInformation
General Properties
Semantic Information
(Unimplemented)
Relative Order Tag
Item-ID
Speech Tag Storage Buffer
Speech Object Storage Buffer
Primary Verbal Storage BuffersFigure 4.3: Depiction of the different types of verbal information stored by the modified EPIC
primary auditory storage.
object disappears.
Under this new auditory processor, these speech objects may disappear for one
of two reasons. First, (similar to the previous auditory processor) all objects de-
cay independently according to a pre-specified distribution. Second, (based on the
discussion in Section 4.2.1), there may be an upper limit on the number of speech
objects that can be maintained at any given time. This limitation is implemented
as follows: whenever a new item is encoded, it overwrites a currently-stored object
with a probability related to the number of objects currently stored in the primary
store. Thus, if only a few objects are currently stored, the probability of overwriting
one of them is very low. On the other hand, if many objects are currently stored,
the probability of one of them being overwritten is very close to 1. By setting the
capacity large enough, this limitation may be nullified, leaving only the decay dis-
69
tribution of these items to have an effect. More specific details about the nature
of this limitations and the parameters of the distributions associated with them are
discussed below.
The speech object’s phonological information
One type of information maintained by the speech object involves phonological
information associated with a word. The use of an independent store for phonological
verbal information has two major benefits. First, it allows assumptions about the
nature of the representation of phonological information to be tested. Second, it
allows the reliability of phonological “item” information to be dissociated from the
reliability of “order” information about a word.
Inside the phonological store, the representation of verbal items is based on the
phonological features that make up a word. The reliability of an item’s phonological
information may depend on the content of the other items that are currently being
remembered (e.g., as in Posner & Konick, 1966). However, it is more likely that
their reliability depends on interactions between this phonological store and long-
term memory for phonological forms, via a process called “redintegration” (Hulme
et al., 1997).
Aside from these two properties (phonological representation and independence
from order information), the structure of this storage mechanism is not investigated
in the present experiments, and so will not be discussed in greater detail.
Speech-object order information
The speech object also maintains information about the relative order of items: it
encodes information about what item immediately follows another item. This order
information is basically a pointer to the item identifier of the next item, and is not
70
dependent on the phonological content of either item.2 Order information decays
according to a log-normal distribution that is independent for each item.
As seen in Figure 4.3, I assume that this information is maintained in its own
modality-specific store, and that the speech object only provides an access point to
the corresponding order information. In humans, there is evidence that this type
of order information is not specific to verbal information (e.g., Kimura & Watson,
1989), and so it is likely that the same system that maintains order for sequences of
verbal items also manages order for other modalities, such as manual movements.
Although order information is currently maintained as a set of relative associa-
tions between adjacent items, each speech object is accessed via a single identifier.
Consequently, an order memory system that encodes position in a different fashion
could be constructed and added to this auditory perceptual processor fairly easily.
Such a system might resemble “positional” or “context” theories that have appeared
in different models of immediate serial recall, (e.g., Brown et al., 2000 and Burgess
& Hitch, 1996). As will be shown in the next chapter, the use of a serial chain has
been proven sufficient for present purposes.
Speech-Tags
As discussed earlier, previous EPIC models of the serial recall task used TAG
items to maintain information about list organization. However, since these tags
were entirely reliable markers, their use produced serial position functions whose first
positions were unrealistically accurate. Consequently, I have made a new assumption:
this information is not encoded in general-purpose reliable TAGs, but in special-
2It is often assumed that “chaining” models of serial order require associations to be formed between the contentof consecutive items (cf. Henson et al., 1996). Such chaining models would predict that errors should increase afteritems that occur more than once in a list, which is not supported by empirical data. Although the model I presentis a chaining model, it does not make such predictions about repeated items in lists, because its order links are notbased on the phonological content of the item.
71
purpose unreliable SPEECH-TAGs.
These SPEECH-TAGs serve exactly the same function as the first type of TAG, except
they are managed by the auditory-perceptual processor and are subject to probabilis-
tic decay according to a pre-specified distribution. In the models discussed here, this
distribution is log-normal with two parameters describing the distributions’ median
and spread.
Additionally, these SPEECH-TAGs have a capacity limitation of sorts: only one of
a kind can exist at any given time. This limitation is not architectural, but logical:
if more than one item is marked as the beginning of a list with a speech-tag such as
(SPEECH-TAG ?ID IS RECALL-CHAIN-START), the tag is ambiguous and of little use
to a strategy that expects a single item to be marked as the start of the recall chain.
Thus, such a strategy must also manage the removal of speech-tags that are no longer
valid. In the models that will be described in the next chapter, this management is
fairly trivial, and it is likely that the immediate serial recall task will not offer much
information about what types of tag-management operations are easy or difficult.
4.2.5 External representations
The internal representations described above are mostly sub-cognitive and not
directly accessible to production rules. This new auditory perceptual processor also
implements interfaces that allow production rules to manage the contents of the
primary auditory store, and it synchronizes the working memory database so that
the appropriate information from the primary auditory store is present for production
rules to use.
Not all of the information held in the primary auditory store is available to produc-
tion rules. Some of this hidden information is implementation-dependent and has no
theoretical significance, so will not be discussed here. Other such information might
72
be considered sub-symbolic or too peripheral for general purpose production rules to
access to it. However, much of the information held in the primary auditory store is
mirrored directly in the working memory database. The information mirrored in the
working memory database is symbolic, and is available for production-rule strategies
to use. Determining what types of information are readily accessible by production
rules has some important theoretical significance, so these issues will be discussed in
the next sub-sections.
The speech object
My new auditory perceptual processor provides less information about speech
objects to the working memory database than the previous version did. The infor-
mation available to production rules also contains a slightly altered set of properties.
Example entries in the working memory database are shown in Table 4.3, which is
intended to be an analog of Table 4.1. As seen in first and second entries of Ta-
ble 4.3, the content of a word is no longer available directly to production rules, and
the PREVIOUS property has been renamed as ITEM-ID to better reflect this property’s
status as an identifier. The relative order “NEXT” property is a part of the working
memory database’s speech object, although it is managed by a separate sub-system
in the primary auditory store. This will be discussed in greater detail in the next
section.
Similarly to the previous version of the auditory perceptual processor, speech-
objects are produced either because of an external verbal stimulus, or because the
vocal motor processor generated a speech event. Items appear in and disappear
from the working memory database at the same time they appear in or disappear
from the primary auditory store. This synchronization is performed by the auditory
perceptual processor, so all manipulations to speech objects are made through the
73
interfaces provided by the auditory perceptual processor.
Table 4.3: Representative entries in the working memory database under the new auditory percep-tual processor
Entry Working Memory Database Entry1. (AUDITORY SPEECH ITEM-ID ID-E342 NEXT ID-E343 SOURCE EXTERNAL
MARKER CONTINUE TYPE WORD)2. (AUDITORY SPEECH ITEM-ID ID-E343 NEXT GONE SOURCE EXTERNAL
MARKER CONTINUE TYPE WORD)3. (AUDITORY SPEECH-TAG ID-E342 IS RECALL-CHAIN-START)4. (TAG DO-RECALL ID-E343 IS TO-BE-RECALLED)
The speech object’s order information
Although the serial order information is managed by its own sub-system in the
primary auditory store, it appears as a property of the speech object in the working
memory database. When the order information decays from the primary auditory
store, the speech object in the working memory database is changed so that its
NEXT property is given the value GONE, indicating that this information is no longer
present. The second entry of Table 4.3 shows an example of a speech object whose
corresponding order information has disappeared.
Phonological Information
As discussed earlier, phonological information is not directly available to produc-
tion rules. Of course, a production rule must be able to access the identity of a word,
even for simple tasks. A special function called (RETRIEVE-PHONOLOGICAL-INFORMATION)
enables this access. This function allows the symbolic identity of a word to be re-
trieved from the primary phonological buffer, and (depending on various system pa-
rameter settings) this function will perform “redintegration”. The function produces
a reconstructed word whose identity can be added to the working memory database
symbolically as a special tag item for use on the next trial, or passed immediately to
the vocal motor processor in order to produce a vocal response. Because this function
74
operates in the action part of the production rule, no further condition-matching can
be performed during the cycle in which the content is retrieved, so processes that
need to match the identity of a word require at least two production rule cycles to
be performed: one to identify the correct speech object and retrieve the phonological
information, and a second to use the retrieved information.
Phonological decay and reconstruction take place at a sub-symbolic level, not
directly accessible to production rules. If redintegration is used, the retrieval macro
will always retrieve some content, even if it must simply guess from among a set
of candidate words. If redintegration is not used and the phonological content has
decayed below some threshold, RETRIEVE-PHONOLOGICAL-INFORMATION will retrieve
“NIL”, indicating that all phonological information is gone.
The speech-tag
As discussed earlier, the SPEECH-TAG replaces one of the two primary functions for
which the TAG item was used in previous models. The SPEECH-TAG stores the ITEM-ID
identifier of a specific item, denoting it as the beginning of a list and allowing it to
be accessed directly. Such a SPEECH-TAG is shown as the third entry of Table 4.3,
and the normal TAG is shown as the fourth entry.
SPEECH-TAGs are created and removed from the primary auditory store via special-
purpose accessor functions that can be invoked by production rules. These functions
perform the role of adding, removing, or replacing a single speech-tag, and insure
that the proper changes are reflected in the working memory database. Currently,
these functions are entirely reliable and have an immediate effect. However, it is
very likely that such list organization tags have complicated mnemonic properties
that make them difficult to reorganize under some circumstances. Hopefully, future
research will discover more about how sequences of words can be organized, accessed,
75
and reconstructed in memory.
4.3 Implications for models using the modified auditory perceptual pro-cessor
With the newly-proposed auditory perceptual processor, there are several ways
in which models that use auditory and verbal working memory need to be altered.
These include the requirement of greater tolerance for missing information, the use
of new memory structures, and the restriction of access to some types of information.
The primary additional requirement that models must deal with when using the
new auditory-perceptual processor (in contrast to previous versions) is that they
must be able to handle and recover from a greater variety of missing information.
In this new realm, much more of the information used during verbal memory tasks
is stored in unreliable mechanisms, so greater tolerance for missing information and
error recovery is needed. For instance, during performance of the serial recall task,
the item that should be recalled next may be unknown for several reasons: it may
have decayed, it may have failed to be encoded, the serial order tag from the previous
item may have decayed, or the content may not have been retrieved. Production rule
strategies must handle all of these situations in order to recover from these errors
gracefully.
This is further complicated by the fact that some procedures used in a performance
strategy require several consecutive steps to accomplish, but information may decay
between two consecutive steps. Such multi-step operations must anticipate such loss
of “on-line” information, and this type of fault tolerance accounts for a great deal of
the complexity of the rule sets that will be described in the next chapter.
A second new requirement for models using the new auditory perceptual processor
is in the use of new memory structures. These structures and interfaces available for
76
production rules have been discussed previously, and so will not be discussed further.
A third new requirement involves the ability for production rules to access infor-
mation from these new structures. This primarily take the form of architecture-level
constraints on how information can be accessed. However, I have hypothesized ear-
lier in this chapter that there may be limitations on how serial order information may
be accessed by production rules, such that only limited look-ahead is possible for a
single rule. It may be possible to make this restriction an architectural constraint,
hiding serial order information from production rules unless explicitly retrieved using
an accessor macro similar to the (RETRIEVE-PHONOLOGICAL-INFORMATION) macro
above. Such architectural modifications would require significant reorganization of
the production rule strategies I present in the next chapter, and so I have instead
enforced this type of access by a modeling policy rather than actual architectural
constraints. Hopefully, the insights from the current models may provide better in-
formation about how this constraint could be made a part of the auditory memory
architecture.
4.4 Parameter values associated with the auditory perceptual processor
My new restructured auditory perceptual processor has a number of parameters
associated with its performance. Each parameter maps onto a psychological concept
that is associated with an aspect of an underlying theory of verbal working memory.
Although most of these parameters have been discussed earlier in their relevant
sections, it is useful to present them together in order to highlight what types of
flexibility the subsequent models will have in their ability to fit empirical data. In
the next chapter, I will examine the influence that each of these parameters can have
on performance, and how they can interact with specific components of different
77
strategies.
4.4.1 Decay Parameters
The importance of the form of the decay distribution has been frequently over-
looked in the study of verbal working memory. Theories have frequently assumed
that item decay is an all-or-none step function wherein every item lasts a fixed period
of time and then disappears (e.g., Baddeley et al., 1975; Baddeley & Lewis, 1984;
Schweickert & Boruff, 1986), or that decay follows an exponential distribution, where
the probability of an item disappearing at any point is independent of how long it
has existed (e.g., Brown & Hulme, 1995). Neither of these claims is likely to be true,
and theories that have adopted such assumptions have produced dubious conclusions
about the nature of verbal working memory.3
For the new auditory perceptual processor, each of the types of information in
primary auditory store decays according to a log-normal distribution with two pa-
rameters: a median decay time and a spread parameter that determines the shape
of the distribution. This distribution is used because its density falls entirely above
0, making it consistent with concepts of time-based decay, and because it can fairly
flexibly produce a range of distributions, from step-like density functions (when the
spread parameter is close to 0) to relatively flat density functions (when the spread
parameter is a relatively large value like 3). The decay of speech objects, speech-tags,
order tags, and phonological item information is governed by this distribution. In
the next chapter and Appendix B, the effect that each of these parameters has on
3In the first case, authors who have assumed that decay takes the form of a step function have concluded thatitems in verbal working memory have a life-span of about 2 seconds (e.g., Baddeley and Lewis, 1984; Schweickert &Boruff, 1986). In the second case, theorists adopting the assumption that decay follows an exponential distributionhave concluded (based on models of the task) that rehearsal is unnecessary in performance of the immediate serialrecall tasks. Whether the adoption of a memory-less decay distribution was the cause or the effect of this conclusion,it is interesting to note that if memory decay were actually exponential, rehearsal would have little or no effect onperformance. For an exponential decay distribution, during any arbitrary time interval every item has the sameprobability of decaying regardless of how long each had been around before this interval began. Consequently, noadvantage would be gained by engaging in rehearsal.
78
task performance under different recall strategies is systematically investigated
4.4.2 Capacity Parameters
The new auditory perceptual processor also allows capacity limits to be placed
on the number of speech objects that can be stored in the primary auditory store.
According to this version of a capacity limitation (for others, cf. Table 4.2), every
presented word is encoded into the verbal working memory store, but each newly-
encoded item will overwrite a previously encoded item with a probability that de-
pends on the number of items currently present in the verbal working memory store.
As discussed in Section 4.2.1, this version of a capacity limitation was chosen because
it is able to produce appropriate effects of list length and serial position on “item”
serial position functions.
For the current models, the probability of overwriting an earlier item follows a
log-normal distribution with parameters describing its median and relative spread.
The median parameter determines the capacity of the memory store, because the
total number of encoded items will tend to increase when fewer than the median
number of items have been encoded, and decrease when greater than this number
are present. If the median parameter is set large enough, the capacity will have
little impact, and so the performance of models without capacity limitations can be
examined. The spread parameter determines how sharp the overwriting distribution
is around the median. If this parameter is set to zero, the distribution is a step
function.
4.4.3 Time parameters
In the current models, since much of the information in the primary auditory store
is mirrored in the working memory database, all such information is available directly
79
to production rules and so has an effective access time that is no greater than one
cycle period. Similarly, for information such as the identity of the word’s content, this
can be retrieved by a rule instantaneously, although the corresponding information
cannot be used by other production rules until the next cognitive processor cycle.
Consequently, the time required to access auditory information is modulated by
the parameters that control cognitive processor cycle times, and have no other free
parameters associated with them.
No new assumptions were made about the time required to encode an external au-
ditory item into the primary auditory store or the central working memory database.
These assumptions are discussed elsewhere (e.g., in Kieras et al., 1999, and Kieras
& Meyer, 1997), and will not be enumerated here.
4.4.4 Other parameters
There are many other parameters that determine how a specific strategy might
perform the immediate serial recall task. These parameters are associated with
other components of the EPIC architecture, such as the cognitive processor (e.g., the
cognitive processor cycle time) and the vocal motor processor (e.g., the articulatory
duration of each word). Different values of these parameters could produce different
performance in verbal working memory tasks, but these parameters will not be varied
here and so will not be discussed further.
4.5 Summary
In this chapter, I first described the current implementation of EPIC’s auditory
perceptual processor. After considering this processor and its architectural limita-
tions with respect to reported empirical data, I described an enhanced alternative
auditory perceptual processor for EPIC that I have implemented. This processor’s
80
architecture is based on some new assumptions about how auditory speech informa-
tion is stored (and forgotten). Then, I described several ways in which models of
verbal working memory tasks would differ, when using the two different processors.
In the next chapter, I will describe several models of how the immediate serial recall
task may be performed on the basis of my new auditory perceptual processor. Each
model implements a different guessing strategy, and these models’ performance will
be compared to the results from Experiment 1, with the goal of validating some of
the assumptions of the new auditory perceptual processor.
CHAPTER V
RECALL STRATEGIES USED FOR PERFORMING THEIMMEDIATE SERIAL RECALL TASK
In the previous chapter, I discussed the basic architectural components used by
EPIC to perform the immediate serial recall task, and presented a new auditory
perceptual processor that allows more realistic models to be built. However, these
structural assumptions do not form a complete model of immediate serial recall.
Additionally, assumptions about how the cognitive agent uses these structures to
perform the task must be made. This chapter will consider some of these strategies,
testing several candidates that might be used to perform immediate serial recall.
5.1 The General Recall Strategy
Given the architectural constraints of the new auditory perceptual processor I
have described in Chapter IV, there are two general stages that must occur for a
word to be recalled during serial recall. First, a candidate speech object must be
identified. Once identified, the content associated with the identified speech object
must be retrieved. This reconstructed content can then be sent to the vocal motor
processor in order to produce a response during the task.
Given the assumed constraints of the architecture and the general goals of the
immediate serial recall task, there are many different strategies that could be used
81
82
to produce sequences of words based on a partially-intact memory. For initial inves-
tigative purposes, I have implemented four such strategies, in order to better assess
the relative contributions of architecture and strategy to the serial recall task. Each
strategy deals exclusively with the first stage of item recall–identifying the proper
trace to recall. For present purposes, it is assumed that if the correct speech object
can be identified, its content will be recalled correctly.
5.2 Components of task performance
Before describing in greater detail what any specific guessing strategy might be, it
will be beneficial to enumerate some specific components of task performance. These
components do not constitute an entire strategy for performance, but are some of
the important procedures that compose a complete performance strategy.
In each of the following sub-sections, I will describe a simple procedure that can
be followed during recall in order to enhance the likelihood of recalling the correct
item. Most of these procedures are performed in response to the loss of information:
if no information about a sequence of words has been lost, the cognitive agent simply
recalls the sequence of words correctly.
5.2.1 Elimination of recalled items
In the experiments reported here, words were never repeated in the sequences
presented to participants. An agent can take advantage of this fact by not guessing
from the set of items that have already been recalled. In the models discussed below,
this is accomplished by initially entering a tag into the working memory database
of the form (TAG ?ITEM-ID IS TO-BE-RECALLED), where ?ITEM-ID is the value of
an auditory speech entry’s ITEM-ID property. When an item is recalled, this tag
is removed and replaced with a (TAG ?ITEM-ID IS RECALLED) tag. Guessing only
83
occurs from the set of items with corresponding TO-BE-RECALLED tags.
5.2.2 Elimination of last item in list
When the identity of the last item of a list is known, a rational strategy should not
recall it unless all other items have been recalled. This will increase the probability of
recalling items in pre-terminal positions correctly, because there will be fewer items
to guess from. Additionally, when combined with the previous strategic component
of not guessing from among recalled items, this strategy will enhance the probability
of recalling the last item in its correct position, because it will not have been recalled
previously.
5.2.3 Elimination of items with known preceding items
One slightly more complex method for increasing the probability of recalling an
item in its correct position is for an agent to only guess from among items whose
immediate predecessors are unknown. These items are the heads of sub-chains. If the
agent determines that an item is not the head of a sub-chain, it can be eliminated
from the guessing set because the item followed a different item, and would most
likely be incorrect if it were recalled.
The procedures that eliminate such items are complex, in part because consecu-
tive items can only be accessed in a forward fashion based on the auditory perceptual
processor described in Chapter IV. In the current set of models, this sub-strategy
is accomplished with the following procedure: The first step marks all to-be-recalled
items with the tag (TAG ?ITEM-ID IS NOT-FOUND). Then, a candidate item is ran-
domly selected from among the NOT-FOUND items, and it is marked with the tags (TAG
?ITEM-ID IS CHAIN-HEAD) and (TAG ?ITEM-ID IS FOUND), and its NOT-FOUND tag
is removed. Next, the item immediately following that selected item is identified
84
and marked with a (TAG ?ITEM-ID IS FOUND) tag. If this item is also marked with
a (TAG ?ITEM-ID IS CHAIN-HEAD) tag, this tag is removed, because this item is
no longer the head of a chain. If an item has no subsequent item that is marked
with a NOT-FOUND tag, the process starts again with the random selection of a new
NOT-FOUND item. When no NOT-FOUND items remain, a single item with a CHAIN-HEAD
is selected as the next item to be recalled.
5.2.4 Fill-In
At some points during recall, one or more items from the presented list may
have entirely disappeared. For example, the agent may be required to recall seven
items, and have already recalled three, but only two TO-BE-RECALLED items remain
in auditory working memory. The agent may also have determined that these items
form a chain, and the last item in the chain was the last item in the sequence. When
this occurs, the agent will be able to recall these in their correct positions by engaging
in “Fill-In” recall, where two dummy words are recalled, and then the remaining two
words are recalled in the correct position at the end of the list. Such “dummy” words
might be selected from among a highly familiar set of words (so as to improve the
chance that these filler words are correct as well), or may occur by saying “Blank”.
This fill-in strategy requires the agent (either human or computer-based) to detect
how many items remain to be recalled and compare this to the number of words it
remembers but has not yet recalled. There are two plausible occasions when this
might happen during recall: either when all items remaining in memory form a
single unbroken chain whose final item was the final item presented, or when only a
single item remains in memory, and that item was the final item presented. If fill-in
occurs at the first occasion, the recency effect in the serial position functions should
extend several items back from the end of the list. If fill-in occurs at the second
85
occasion, the recency effect will occur for the final item only.
5.2.5 Guessing from known items
A strategy related to fill-in can be used when a small “closed” set of stimuli are
reused throughout a block of immediate serial recall trials. If a closed set of words
is used throughout the experiment, an agent may perform fill-in and other types of
guessing by choosing from only the set of relevant words, increasing the probability of
any guess being correct. If new words are used for each consecutive trial (an “open”
set of words), this strategy is less feasible, because the conditions would require a
new guessing set to be inferred on each trial. However, under these conditions, other
encoding and retrieval strategies might be used that are not possible when a closed
set of words is used: an open set might allow for semantic encoding strategies to
be used for maintaining the order and identity of presented items. Experiments on
immediate serial recall frequently use closed sets of words to diminish the use of this
type of coding strategy.
5.2.6 Error Aversion
For a number of reasons, an agent might attempt to avoid making overt errors
during recall. Instead, it may simply recall the words it is certain of, and stop as
soon as it is unsure of what word appears next in the sequence.1
5.2.7 Summary of Sub-Strategies
A single recall strategy may consist of a combination of several of these above
sub-strategies. To examine the effects of these strategic guessing techniques, I have
constructed several performance strategies that utilize combinations of them in the
1Although this strategy might appear irrational, there are several reasons a human participant in the immediateserial recall task may halt recall instead of making an overt error. He or she might be influenced by the potentialsocial embarrassment of getting something wrong, or might not receive a reward for recalling sequences partially, ormay not care enough to undertake the extra effort involved in list reconstruction, or (as in Experiment 2 reportedbelow) may be explicitly told to stop recall before an error is made.
86
context of the new auditory perceptual processor proposed in Chapter IV. These
performance strategies vary in their complexity, and are intended to demonstrate
the performance differences that might be expected if human participants engaged
in these strategies. Of course, different mixtures of these strategic components are
possible, and human participants in the immediate serial recall task may use different
strategies on different trials, or even switch strategies within one trial. If this type
of strategy mixing occurs, it may be difficult to interpret the data produced by such
performers. Nonetheless, these relatively pure recall strategies demonstrate how some
of these strategic components may work together to enable task performance. They
will be described in greater detail in the next section.
5.3 Four Strategies for Performing the Immediate Serial Recall Task
I have implemented four strategies for the immediate serial recall task. They are
performance strategies in that they manage all aspects of task performance through-
out the experimental trial, as opposed to just the components discussed in Section 5.2
However, each strategy uses the same basic procedure for performing the task, and
each one differs from the others only in how it deals with missing information. Con-
sequently, each performance strategy consists of a distinct guessing strategy, but is
identical to the other strategies in other respects.
The strategies discussed here implement increasingly complex guessing techniques
based on the available partial information. These strategies are called (from least
elaborate to most elaborate): the “Abort on Error” strategy, the “Order Recon-
struction” strategy, the “Reconstruction with Fill-in Before Last item” strategy, and
the “Reconstruction with Fill-in Before End-Chain ” strategy. The strategies are
implemented as sets of production rules that describe the operations performed by
87
the EPIC architecture in order to accomplish the immediate serial recall task. These
rules can be found in Appendix A. Before describing these strategies in greater de-
tail, I will first give a simplified example of how a set of these production rules may
be used to accomplish a given component of the recall task.
For example, suppose that during the performance of the immediate serial recall
task, the recall signal has just been detected, indicating that the agent should recall
the first item in a list of three words. At this time, working memory may contain
the entries found in Figure 5.1. This set of entries includes an overall goal for
performing the task (DO TRIAL), and a goal step indicating that the recall signal has
been received. A speech-tag that indicates which speech item was first is present, and
several speech items exist, allowing access to corresponding speech objects. Finally,
a status message indicates that the motor vocal processor is not presently producing
speech.
(GOAL DO TRIAL)
(STEP DO-TRIAL RECALL-SIGNAL RECEIVED)
(AUDITORY SPEECH-TAG ID-334 IS RECALL-CHAIN-START)
(AUDITORY SPEECH ITEM-ID ID-334 NEXT ID-335 SOURCE EXTERNAL MARKER START TYPE WORD )
(AUDITORY SPEECH ITEM-ID ID-335 NEXT ID-336 SOURCE EXTERNAL MARKER START TYPE WORD )
(AUDITORY SPEECH ITEM-ID ID-336 NEXT ID-337 SOURCE EXTERNAL MARKER START TYPE WORD )
(MOTOR VOCAL PROCESSOR FREE)
Figure 5.1: Hypothetical contents of working memory during immediate serial recall after the recallsignal has been received.
Now, suppose that the set of production rules in Figure 5.2 is being used. The first
rule (“START-RECALL”) initiates the recall procedure, and is the only rule that will
fire when the working memory entries in Figure 5.1 are present. As a consequence
of this rule, a new goal (GOAL DO RECALL) will be entered into working memory,
indicating that recall should begin. The first step of the recall goal (STEP DO-RECALL
IDENTIFY-CHAIN-START) is also added, indicating that the start of the recall chain
88
should be identified. No other rules fire during this cycle.
During the next cycle (assuming no items have been lost because of decay or
interference), the second rule (“IDENTIFY-CHAIN-START”) in Figure 5.2 will fire.
This rule identifies the first item in the chain by determining which item has an
ITEM-ID property that is identical to the one indicated by the RECALL-CHAIN-START
speech-tag. In this case, it will be the item whose ITEM-ID is ID-334. The rule adds
a tag to working memory indicating that ID-334 is the next item to be recalled, and
changes the goal step from one in which the start of the chain should be identified
to one in which the next-to-recall item should be recalled. No other rules fire during
this cycle.
During the third successive cycle, the third rule (“RECALL-NEXT-ITEM”) in Fig-
ure 5.2 fires. During the previous cycle, a tag was entered stating that ID-334 was
NEXT-TO-RECALL, so this rule uses a special function of the auditory-perceptual pro-
cessor called RETRIEVE-CONTENT to obtain the phonological content of that item,
storing it in a temporary variable called ^CONTENT. Then, it uses the SEND-TO-MOTOR
function to send this content to the vocal motor processor, which will program the
vocal motor system to actually say the word. This rule also cleans up some of the
intermediate information, in anticipation of the next step of the recall goal, which is
to identify another item to recall.
These rules are simplifications of the rule sets that appear in Appendix A, but
they capture the basic operations that a set of rules perform, and show how they
are used to embody the procedures that accomplish the immediate serial recall task.
More rules are necessary for other aspects of task performance, and other types of
information are used to guide recall and guessing behavior. In the following sections,
I describe some of the processes involved in each of the different guessing strategies,
89
(START-RECALL
IF
(
(GOAL DO TRIAL)
(STEP DO-TRIAL RECALL-SIGNAL RECEIVED)
(NOT (GOAL DO RECALL))
)
THEN
(
(ADDDB (GOAL DO RECALL)
(ADDDB (STEP DO-RECALL IDENTIFY-CHAIN-START))
)
)
(IDENTIFY-CHAIN-START
IF
(
(GOAL DO RECALL)
(STEP DO-RECALL IDENTIFY-CHAIN-START)
(AUDITORY SPEECH-TAG ?ID IS RECALL-CHAIN-START)
(AUDITORY SPEECH ITEM-ID ?ID NEXT ??? SOURCE EXTERNAL MARKER START TYPE WORD )
)
THEN
(
(DELDB (STEP DO-RECALL IDENTIFY-CHAIN-START))
(ADDDB (STEP DO-RECALL RECALL-NEXT-ITEM ))
(ADDDB (TAG ?ID IS NEXT-TO-RECALL))
)
)
(RECALL-NEXT-ITEM
IF
(
(GOAL DO RECALL)
(STEP DO-RECALL RECALL-NEXT-ITEM)
(TAG ?ID IS NEXT-TO-RECALL)
(MOTOR VOCAL PROCESSOR FREE)
)
THEN
(
(RETRIEVE-CONTENT ?ID ^CONTENT)
(SEND-TO-MOTOR VOCAL SAY ^CONTENT)
(DELDB STEP DO-RECALL RECALL-NEXT-ITEM)
(ADDDB STEP DO-RECALL IDENTIFY-NEXT-ITEM)
(DELDB (TAG ?ID IS NEXT-TO-RECALL))
)
)
Figure 5.2: Three simplified production rules for illustrating the basic operation of the rule set usedfor immediate serial recall.
90
and provide flowcharts that represent the basic procedures used to accomplish the
task. However, full understanding of these details of these procedures require careful
study of the production rules found in Appendix A.
5.3.1 The “Abort on Error” Strategy
The “Abort on Error” strategy is the simplest performance strategy I will describe.
During this strategy, the cognitive agent aborts recall whenever there is no clear
successor to an item that has just been recalled. This situation might occur for
several reasons. For example, the NEXT property from the previous item might have
decayed, so it no longer indicates what the next item is. Or, the speech object
whose ITEM-ID was referenced by that NEXT property may have entirely disappeared
from the phonological storage buffer. Even if these two pieces of information were
available, the content of the speech object might fail to be recalled. Finally, the
SPEECH TAG denoting the initial list item might have decayed.
When any of these situations occurs, an agent using this strategy will “give up”
instead of guessing and potentially making an overt error. However, this strategy
may still produce overt erroneous responses if architectural parameters are set so that
redintegration occurs during recall, and incorrect content is retrieved and recalled.
The strategy would not be able to detect this type of error, and so could not prevent
it.
A flowchart depicting the procedures involved in this strategy is shown in Fig-
ure 5.3. When recall begins, the agent first attempts to identify which item was
marked as the RECALL-CHAIN-START, and creates a tag designating that item as
NEXT-TO-RECALL. It then determines whether there is a speech object whose ITEM-ID
matches the NEXT-TO-RECALL tag, and attempts to retrieve the associated phono-
logical content of the word. Once retrieved, it recalls the content and examines the
91
"Abort On Error" Strategy
Retrieve ItemContent
Yes
No
Give Up
Is there aTO-BE-RECALLED
item marked NEXT-TO-RECALL?
Start Recall
End Trial
Recall Content
Change NEXT-TO-RECALL tothe NEXT item
Success? Yes
No
Identify NEXT-TO-RECALLitem
Figure 5.3: Flowchart depicting the “Abort on Error” strategy. During this strategy, recall isaborted whenever the next item cannot be determined.
just-recalled speech object to determine what should be recalled next. When this is
accomplished, the agent changes the NEXT-TO-RECALL tag and repeats the process.
If at any time information is found to be missing, the agent aborts recall.
With respect to the guessing procedures discussed in the previous section, this
strategy is only “Error Averse”, and does not use any more complicated consid-
erations. It can be seen in Figure 5.3 that there are several points at which the
agent expects to find information but might not succeed. These are the points at
which different guessing strategies might proceed in different ways, using the different
guessing procedures discussed earlier. The “Order Reconstruction” strategy engages
92
in guessing at some of these points.
5.3.2 The “Order Reconstruction” Strategy
The “Order Reconstruction” strategy (shown in Figure 5.4) is significantly more
complex than the “Abort on Error” strategy. The basic performance strategies are
identical, and when no items disappear during recall, the agent simply performs
the steps in the region labeled “Item Recall”. But, when critical information has
disappeared, the agent performs the processes labelled “Sublist Reconstruction”, in
an attempt to determine what the next item is. It proceeds by using several of the
guessing procedures discussed in Section 5.2.
The goal of this phase is to select an item that is likely to be correct, based on
the remaining information. During this phase, the agent performs the procedures
labeled “Item Elimination”. First, any items that have already been recalled are
removed from the guessing pool, so that only those tagged TO-BE-RECALLED are
considered (cf. Section 5.2.1). Then, sub-chains of the the remaining items are
built in order to eliminate those items that are known to immediately follow other
items (cf. Section 5.2.3). Because serial order links can only be followed forward,
this process must proceed via stochastic search through the TO-BE-RECALLED items,
where multiple sub-chains are formed by following links forward from earlier items.
Once each of the items has been placed in a chain, one of the “chain heads” is selected,
and recall proceeds until the next piece of serial-order information is missing.
One consequence of using this guessing strategy is that relatively little importance
is placed on recalling items in their original positions. To illustrate this, suppose that
a five-word list were presented, and before recall began, the speech objects associated
with both the third and fourth items disappeared. If an agent was able to successfully
recall the first two words of this sequence, it would select the fifth word for recall
93
"Order Reconstruction" Strategies
Select a "NOT-FOUND" Item andMark it as a "CHAIN-HEAD"
Is successor "NOT-FOUND"
Select Successor and changeit to "FOUND"
Yes
No Does item havea successor?
Yes
No
Remove "CHAIN-HEAD" tag from successor
Are there any"NOT-FOUND"
items?
Yes
No Tag a random"CHAIN-HEAD" as
NEXT-TO-RECALL
What type of "Chain-Head"s
are there?
If successor is "END-CHAIN-HEAD",
change tag to current item
No "CHAIN-HEAD"s
Only an "END-CHAIN-HEAD"
Tag "END-CHAIN-HEAD" as NEXT-TO-RECALL
Only non-"END-CHAIN-HEAD"
Chain-Heads
End Trial
Sublist Reconstruction
Attempt to Retrieve Contentof NEXT-TO-RECALL
from phonological storage buffer
Yes
No
Is there aTO-BE-RECALLED
item marked NEXT-TO-RECALL?
Start Recall Phase
Tag the Start item as NEXT-TO-RECALL
Recall Content
Success?YesNo
Change NEXT-TO-RECALL tagto the NEXT item
Recall "Blank"
Are there anyTo-Be-Recalled
Items?
No
End TrialYes
Mark all TO-BE-RECALLEDitems as NOT-FOUND
Item Recall
Item EliminationFill-In Stage
(Only for "Fill-In beforeEnd-Chain")
Fill-In Stage
(Only for"Fill-In before Last Item")
Figure 5.4: Flowchart depicting the “Order Reconstruction” strategy. When this strategy is used,sub-chains are reconstructed in order to identify which item should be recalled next.
94
in the third position, even if it was able to detect that the word it chose was the
final word in the sequence. The two strategies I will discuss next attempt to improve
the probability of recalling items in their initial positions, by augmenting the “order
reconstruction” strategy with “fill-in” recall as discussed in Section 5.2.4.
5.3.3 The “Reconstruction with fill-in before the last item” Strategy
The “Order Reconstruction” strategy suffers from the weakness that if a speech
object from the presented list disappears, the agent will recall too few words, and so
the final words will not be recalled in the correct position. With a slight modification
to this strategy, the probability of recalling the last item can be improved. To benefit
from this improvement, the agent must keep track of how many items remain to be
recalled, and be able to detect which item should be recalled in the final position.
Then, if the agent ever reaches a point where the only item remaining to be recalled
is a final item, but more than one slot remains to be filled, it can engage in “fill-in”
recall so that the final item will be recalled in the final position. This fill-in recall
might involve simply saying “Blank” one or more times; alternately the agent might
generate a familiar item that is likely to have been presented on the current list.
Once the agent determines that only a single slot remains to be filled, recall will
continue with the item previously determined to be the last item in the list. This
“Fill-In” stage (depicted in Figure 5.5) is inserted into the “Order Reconstruction”
strategy (depicted in Figure 5.4) at the point marked “Fill-In Stage for Last Item
Fill-In”.
This strategy improves the probability of recalling the final item in the final
position. It will produce a larger single-item recency effect than would be found in
the “order reconstruction” strategy. Another reconstruction strategy that uses item
fill-in will be discussed next.
95
Will Recalling the End itemproduce a longenough list?
Yes
No
Randomly Recall a wordfrom the current set
Fill-In Stage for "Fill-In Before Last Item" Strategy
Is theNEXT-TO-RECALL
item an "End" item?
No
Yes
Start
Continue recall withNEXT-TO-RECALL item
Figure 5.5: Flowchart depicting the “Fill-In” sub-phase of the “Reconstruction with Fill-in BeforeLast Item” strategy. This sub-phase is added to the previous “Order Reconstruction”strategy at the point indicated in Figure 5.4, insuring that the final item will be in thecorrect position.
5.3.4 The “Reconstruction with fill-in before end-chain” Strategy
Although use of the previous strategy increases the probability of recalling the
final item in the final position, an agent may be able to perform even better by
performing fill-in at an earlier point during recall. The “Reconstruction with fill-
in before end-chain” is an example of a strategy that attempts to do this. In this
strategy, a fill-in sub-phase (shown in Figure 5.6) occurs earlier in the recall phase
than for the “Reconstruction with fill-in before last item” strategy (as shown in
Figure 5.4). Instead of performing “fill-in” recall immediately before the last item,
96
this strategy performs it before recalling the last chain of items connected to the
the final item (the “end chain”). Thus, not only is the last item more likely to be
recalled in the final position, but its immediate predecessors are likely to be recalled
correctly as well.
To use this strategy, an agent must detect when it has arrived at the “end chain”,
as well as determine how many items it has failed to recall prior to this point. Given
the difficulty involved in determining this, which may involve counting or other high-
level processes, it may be unreasonable to believe human participants can perform
this strategy consistently. Thus, this strategy may represent an upper limit on how
well a human might be expected to perform if the assumptions made about the
auditory perceptual processor are correct.
Will the End-Chainproduce a longenough list?
Yes
No
Randomly Recall a wordfrom the current set
Fill-in Stage for"Fill-In Before End-Chain" Strategy
Start
Continue recall with"END-CHAIN-HEAD" as"NEXT-TO-RECALL"
Figure 5.6: Flowchart depicting the “Fill-in” sub-phase of the “Reconstruction with Fill-in BeforeEnd-Chain” strategy. This strategy adds a fill-in sub-phase, where it is determinedwhether any presented items have disappeared, and these missing items are filled in sothat the final END-CHAIN will be in the correct position.
97
5.4 Exploration of parameter settings in proposed models
Each of the performance strategies discussed in the Section 5.3 may be modulated
by different combinations of architectural parameters. For these models, the param-
eters of interest describe the decay and capacity distributions of different types of
information. There are five such distributions. They describe the decay properties of
the SPEECH TAG, the SPEECH OBJECT, the phonological content of the speech object,
the information identifying the final item, and the capacity of the primary speech
object buffer. Each of these five distributions is controlled by two parameters, for a
total of ten architectural parameters that can affect the model’s performance.2
It would be nearly impossible to systematically explore the complete parameter
space of these ten parameters under four different strategy conditions; the sheer
amount of data produced would fill volumes and would be difficult to digest. Nev-
ertheless, it is important to understand what effect changes in one parameter might
have in any given recall strategy, which cannot be determined without systematic
investigation. Consequently, I have elected to systematically examine how changes in
individual distributions can affect a model’s performance, while other distributions
are held fixed. I have selected a ’neutral’ set of parameters that can produce data
that are reasonably close to what might be found in an empirical data set. Then,
for each distribution of interest, I systematically varied the associated parameters
while holding the parameters of the other distributions constant. Simulated imme-
diate serial recall performance was produced by models using one of four strategies
under the specific parameter settings. The results of this exploration can be found
2Aside from these parameters, there are many other architectural parameters that can affect performance. Theseinclude perceptual and motor delays, speech production times, cognitive processor cycle-times, and other factors.Although these may play important roles in performance of this and other tasks, these parameters are not variedfor the current set of models and take on standard values that have been used in other models. (e.g., Kieras et al.,1999).
98
in Appendix B, which contains more detailed comments about how each of the dis-
tributions can affect the performance of the different strategies. Here, I will briefly
summarize these comments.
5.4.1 The speech-tag decay distribution
The speech-tag decay parameters affect the duration of the speech tags that mark
which item is the beginning of the list. The reliability of the speech-tag has similar
effects for each strategy: for shorter median distributions, the first few items of
longer lists are less likely to be recalled in the correct position. Additionally, this
distribution has little effect on correct “item” recall except in the “Abort on Error”
strategy, indicating that even if the first item is not clearly marked, it still gets
recalled, albeit in an incorrect position.
5.4.2 The serial order decay distribution
The serial order decay distribution parameters affect the duration of the structures
maintaining the relative order between subsequent items. This parameter has a large
effect on the shape of the “position” serial position function, and highlights some of
the differences between the simple “Order Reconstruction” strategy and the recon-
struction strategies using fill-in. This distribution has little impact on the “item”
serial position functions, except for the “Abort on Error” strategy. This indicates
that the reconstruction strategies are successful at recalling each item from the pre-
sented list somewhere in the recalled list, even when much of the order information
is gone.
5.4.3 The final item tag decay distribution
The final item tag decay distribution affects the probability being able to deter-
mine whether an item is the final item on a list. The different order reconstruc-
99
tion strategies use this information to help eliminate candidate items from guessing.
Thus, it is not surprising that this distribution has no impact on the “Abort on
Error” strategy. It does have a large impact on the recency effect of both strate-
gies that use fill-in, and shows how the point at which fill-in occurs can impact the
shape of the serial position curve. Somewhat surprisingly, this distribution appears
to have little impact on the simpler “Order Reconstruction” strategy, even though
that strategy does use the final item tag to eliminate candidate items when guessing.
If the base-line capacity distribution for speech objects had been larger, the recency
effect would probably have been affected more by the decay distribution for the final
item tag. Like the decay distributions discussed earlier, this distribution has little
effect on the “Item” serial position functions.
5.4.4 The speech object capacity distribution
The speech object capacity distribution affects the probability that a speech object
currently encoded in the the primary auditory store will be overwritten when a new
item is encoded. Unlike the other distributions discussed here, this distribution is
not dependent on time, but rather on the number of speech objects currently stored
in the primary auditory store. When the speech object disappears, access to all
other aspects of that item disappear as well, and so partial reconstruction cannot be
performed once the speech object is gone.
The reliability of the speech object is also controlled by a decay distribution similar
to the other decay distributions investigated here. However, for present purposes,
this decay distribution is set to insure that items will disappear between trials but
have no impact on performance within trials.
Because these distributions affect the probability of an item being accessible to
the cognitive agent during recall, they affect both the “position” and “item” serial
100
position functions. However, their effects are probably most directly visible in the
“item” serial position functions, because these functions are insensitive to ordering
errors.
5.4.5 The phonological content storage decay distribution
The decay distribution of the phonological content of the speech object is the
last distribution examined in Appendix B. It controls the probability of correctly
reconstructing the content of an item. Like the speech object capacity distribution,
this distribution affects both the “position” and “item” serial position functions.
When phonological content information is no longer available, the “Abort on Error”
strategy simply stops recalling. The other strategies, however, recall “Blank” and
continue recall as normal.
This distribution is presumably affected by the phonological similarity of the word
set and familiarity of the individual words. In the present models, the sub-symbolic
interactions between these factors are not addressed. However, storage mechanisms
based on phonological similarity and familiarity could be implemented, and a similar
investigation of how different assumptions influence performance under a number of
different guessing strategies could be undertaken.
5.4.6 Summary of the exploration of parameter settings
The simulated serial position functions in Appendix B serve two important pur-
poses. First, they demonstrate what influence different parameters have on perfor-
mance, and how some of these parameters interact. This is important because it can
be difficult to determine what the effects of different assumptions are when only a
demonstration of the model’s performance is provided, and this demonstration is the
the final result of fitting the model to an empirical data set. Second, the availability
101
of these parameterized theoretical performance functions enables first-order approx-
imations of parameter values to be estimated without undergoing explicit search
through the parameter space.
5.5 Models of Experiment 1 Results
Although the analysis in the previous section has shown the boundaries and range
of these models’ performance, important lessons can be learned by attempting to
fit empirical data. In the remainder of this chapter, I will undertake the task of
analyzing the models’ performance in the same ways I analyzed data produced by
human participants in Experiment 1. This will help determine which assumptions are
likely to be true, and what parameter values lead to performance in the EPIC-based
models that approximate human performance in the same task.
As a first step, parameter values for each distribution were chosen, based on the
analysis shown in Appendix B and further iterative search. The values were selected
to produce simulated data that approximated the empirical data from Experiment 1
reasonably well. These values are listed in Table 5.1.
Table 5.1: Summary of parameter values used to produce simulated data in Figure 5.7.Value
Distribution Median SpreadSpeech-tag decay 21 sec 1.00Serial order tag decay 4.5 sec 1.50Final item tag decay 5.0 sec 0.10Speech object capacity 8 items 0.30Speech object decay 20 sec 0.01Phonological content decay 20 sec 0.50Note: The speech object decay distribution was set so that it had noeffect within a trial, but items disappeared between trials.
There are a few notable conclusions that can be drawn from the parameter values.
First, phonological content appears to be much more reliable than serial order infor-
mation. Second, the models estimate that a maximum of about 8 speech objects can
102
be maintained at one time, which is consistent with Miller’s conclusion that short-
term memory has a capacity of 7 ± 2 items (Miller, 1956). Third, none of the decay
distributions have a mean near 2 seconds, which some have previously concluded
is the duration of the verbal short-term memory trace (e.g., Schweickert & Boruff,
1986). Fourth, the decay distribution of the final item tag is roughly the same as
the distribution of the serial order tag, indicating that order information about the
final item may not be any more reliable than other items, and simply the ability to
access it directly can explain the recency effects found in Experiment 1. Finally, the
speech-tag appears to be more reliable than other serial order information, indicating
that people’s ability to access the initial item of a list may be stored in a different
way than other order information.
5.5.1 Serial position functions.
The serial position functions produced by the four performance strategies pre-
sented in Section 5.3 are shown in Figure 5.7, with the corresponding empirical data
from Experiment 1. It can be seen that as the guessing strategy gets more com-
plex, performance improves. The largest improvement comes when moving from the
“Abort on error” strategy (which does not resemble the empirical data at all) to the
“Order reconstruction” strategy (which approximates the empirical data fairly well).
The two “Fill-in” strategies exhibit improved performance over the simpler recon-
struction strategy: the “Fill-in before the last item” strategy produces single-item
recency effects, and the “Fill-in before end-chain” strategy leads to a slight improve-
ment for the last few items of a list. However, these latter three strategies produce
“item” serial position functions that do not differ appreciably.
Examining Figure 5.7, there is surprisingly little difference between the three
order reconstruction strategies. This similarity occurs because very little item fill-in
103
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Aborting Strategy
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Order Reconstruction Strategy
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Reconstruction with Last−Item Fill−In Strategy
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Reconstruction with End−Chain Fill−In Strategy
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Aborting Strategy
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Order Reconstruction Strategy
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Reconstruction with Last−Item Fill−In Strategy
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Reconstruction with End−Chain Fill−In Strategy
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
Figure 5.7: Empirical serial position functions from Experiment 1 (shown in blue with solid lines andfilled circles) and simulated serial position functions produced by each recall strategy(shown in red with dashed lines and empty circles). Top row shows “Position” serialposition functions, and bottom row shows “item” serial position functions. Identicaldecay parameters are used for each strategy.
occurs, due to the fact that most items from the list are recalled on every trial,
and errors are primarily errors in ordering. The root mean squared error (RMSE)
and R2 values calculated by comparing observed and predicted data points for the
strategies are shown in Table 5.2. This table shows that the “Reconstruction with
fill-in before last item” strategy captures the empirical data slightly better than
the other reconstruction strategies, but all strategies account for the “item” serial
position functions poorly. However, the three complex guessing strategies account
for the “position” serial position functions fairly well.
The “position” serial position functions account for the empirical data fairly well,
but the “item” serial position functions only capture the observed data to a first
approximation. As discussed earlier, because the guessing strategies attempt to recall
104
Table 5.2: Goodness-of-fit measures for the four different guessing strategies, compared to serialposition functions from Experiment 1.
Order ItemGuessing Strategy RMSE R2 RMSE R2
“Abort on Error” .437 .476 .668 .446“Order Reconstruction” .072 .937 .099 .743“Fill-in before Last Item” .063 .948 .099 .727“Fill-in before End-chain” .067 .936 .099 .710
all words that exist in working memory, the shape of these functions are primarily
affected by the capacity of working memory. The capacity parameters produced
“item” serial position functions with primacy effects and with effects of list length,
but the magnitude of the effects produced by this limitation were smaller than those
observed in empirical data. Additionally, Appendix B shows that no combination of
capacity parameters can produce “item” serial position functions that account for
the empirical data any better.
This capacity limitation was selected from among various other limitations de-
scribed in Table 4.2 because it was able to produce qualitative effects of serial position
and list length. Its inability to account for these functions quantitatively indicates
that the only ’pure’ assumption from Table 4.2 that is able to explain this aspect of
the data is one assuming the effect is caused by a sub-optimal guessing strategy. If
such a strategy were used, words that appeared later in a list would sometimes not
be recalled, even if they were present in memory. What might lead to such a recall
strategy is unclear at this point. Another viable possibility is that some combination
of limitations in Table 4.2 actually exists, and together these limitations cause a
primacy effect and a list-length effect in “item” serial position functions.
Since this question cannot be resolved without further experimentation and mod-
eling, the current capacity limitation may provide a simple stand-in for whatever
processes lead items to not be recalled. Consequently, for the remainder of the anal-
105
ysis, I will use the capacity parameters that maximize goodness-of-fit. Furthermore,
in future analyses, I will examine the performance of only the “Reconstruction with
fill-in before last item” strategy, which appeared to capture the serial position func-
tions better than did the other strategies.
5.5.2 Position gradient functions
The position gradient functions for Experiment 1 produced by the “Reconstruc-
tion with fill-in before last item” strategy are shown in (Figure 5.8), overlayed with
the data obtained in Experiment 1 (seen earlier in Figure 3.5). The model accounts
quite well for the position gradient functions, with incorrect words usually recalled
adjacent to their correct positions. For longer list lengths, the distribution of items
across serial positions was more spread out, just as with the empirical data. The R2
values between observed and simulated position gradients were .994, .964, .976, and
.848 for lists of length four, five, six, and seven, and the corresponding root mean
squared deviations were respectively .034, .057, .032, and .056 units of probability.
For lists of length seven, the model under-predicts the proportion of responses that
are correct, and frequently over-predicts the proportion of items recalled in the in-
correct position. This is at least partly related to the model’s overprediction of the
“item” serial position functions, because the model is predicting that a larger pro-
portion of items should be recalled somewhere in the response list than is actually
recalled.
5.5.3 Types of responses made by the model
I have also performed an analysis of the response types produced by the model,
similar to the analysis shown in Figure 3.6 of Chapter III. The model’s responses
can be placed in five categories: correct responses, incorrect responses from the
106
0.0
0.2
0.4
0.6
0.8
1.0
Recall Position:
1
Presentation Position
Prob
abili
ty R
ecal
l
1 2 3 4
Recall Position:
2
Presentation Position1 2 3 4
Recall Position:
3
Presentation Position1 2 3 4
Recall Position:
4
Presentation Position1 2 3 4
0.0
0.2
0.4
0.6
0.8
1.0
Recall Position:
1
Presentation Position
Prob
abili
ty R
ecal
l
1 2 3 4 5
Recall Position:
2
Presentation Position1 2 3 4 5
Recall Position:
3
Presentation Position1 2 3 4 5
Recall Position:
4
Presentation Position1 2 3 4 5
Recall Position:
5
Presentation Position1 2 3 4 5
0.0
0.2
0.4
0.6
0.8
1.0
Recall Position:
1
Presentation Position
Prob
abili
ty R
ecal
l
1 2 3 4 5 6
Recall Position:
2
Presentation Position1 2 3 4 5 6
Recall Position:
3
Presentation Position1 2 3 4 5 6
Recall Position:
4
Presentation Position1 2 3 4 5 6
Recall Position:
5
Presentation Position1 2 3 4 5 6
Recall Position:
6
Presentation Position1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
Recall Position:
1
Presentation Position
Prob
abili
ty R
ecal
l
1 2 3 4 5 6 7
Recall Position:
2
Presentation Position1 2 3 4 5 6 7
Recall Position:
3
Presentation Position1 2 3 4 5 6 7
Recall Position:
4
Presentation Position1 2 3 4 5 6 7
Recall Position:
5
Presentation Position1 2 3 4 5 6 7
Recall Position:
6
Presentation Position1 2 3 4 5 6 7
Recall Position:
7
Presentation Position1 2 3 4 5 6 7
Figure 5.8: Position gradient functions produced by the “Reconstruction with fill-in before finalitem” strategy, for list lengths 4 through 7. Each row shows the observed (in blue withsolid lines and filled circles) and predicted (in red with dashed lines and empty circles)position gradient functions for a single list length. For each row, every panel shows theposition gradients for a single recall position.
107
list, incorrect responses from the word set, incorrect responses not on the word
set (in the model’s case, the response was “Blank”), and no response. The model
responded “Blank” whenever the phonological content information associated with
a word had decayed. Presumably, if a redintegration mechanism was used, some of
these responses would appear as items within the current set of words. Consequently,
the only time this model made an error by saying a word that was in the current
set but not on the current list was during item “fill-in”. The distribution of these
responses across list lengths and serial positions is shown in Figure 5.9.
0.0
0.2
0.4
0.6
0.8
1.0
No Error
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
List Error
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Word Set Error
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Other Response
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
No Response
Serial Position
Prop
ortio
n of
Res
pons
es
1 2 3 4 5 6 7
Figure 5.9: Simulated response types produced by the “Order reconstruction with fill-in before theLast item” strategy (shown in red with dashed lines and empty circles) and correspond-ing observed response types from Experiment 1 (shown in blue with solid lines and filledcircles).
The types of responses produced by the model (shown in Figure 5.9) match the
empirical responses well, with a few exceptions. The model successfully reproduces
the relative proportions of correct responses versus order errors, and manages to
predict the proportion of non-responses at the end of the list fairly accurately across
list lengths. However, the model produces too few intrusion errors, both from the
relevant set of words and other overt responses that were not part of the current
set. Again, this is a consequence of the model producing too many responses from
the presented list. Nevertheless, the R2 values between the observed and predicted
108
responses proportions were .990, .956, .956, and .85 respectively for list lengths four
through seven, with corresponding root mean squared deviations of .04, .06, .05, and
.10 units of probability. Overall, the R2 value between observed and predicted data
was .94, with a root mean-squared deviation of .07.
5.5.4 Response time measures
One important benefit of using a cognitive architecture such as EPIC is that
it can also predict how long psychological processes should take. Consequently, it
makes predictions about how long list reconstruction times should be in relation to
list length, and thus how pauses between recalled words should be affected by list
length.
The predictions about inter-word response times, as well as speech production
times made under the “Order reconstruction” strategy3 are shown in Figure 5.10.
This model predicts that the inter-word response times should increase with list
length, because the time used for searching through lists and reconstructing sublist
increases as the list gets longer. This finding is similar to what Cowan (1992) found,
although he only used lists of length 2, 3, and 4. These times were not collected for
Experiment 1, so it is unclear if the model predicts these inter-word times accurately.
5.5.5 Limitations of the model
The model captures many aspects of the data with acceptable accuracy. This
may not be surprising, given that ten architectural parameters were available for
data fitting and there was also considerable freedom in the choice of performance
strategies. However, despite this flexibility, the models failed to capture some aspects
of the empirical data.
3For the response time analysis, the “Order reconstruction” strategy was used because it does not perform “fill-in”recall. During “fill-in”, list reconstruction may not occur as frequently, which might diminish the effect of list length.
109
4.0 4.5 5.0 5.5 6.0 6.5 7.030
035
040
045
050
0
Simulated Recall Durations
List Length
Tim
e (m
s)Word Production Times
Inter−word Pauses
Figure 5.10: Inter-word response times for the “Order Reconstruction” strategy.
For example, the models presented here produce data that is much more regular
than what human participants in the immediate serial recall task produce. Although
the models can account for the mean values obtained by humans and so produce clear
explanations of why certain effects occur, they cannot provide as clear an answer to
why there is variability between people. Undoubtedly, these differences stem from
both architectural and strategic differences between human participants. The present
models do not have the power to distinguish which of these differences was most
important in Experiment 1.
Additionally, the models fail to capture several aspects of the “item” serial position
functions, and this failure is seen in aspects of several other analyses performed in
this section. Overall, the model recalled too many items from the original list.
Additionally, the effects of serial position and list length on “item” serial position
functions were smaller than for empirical data, and these functions diverge near the
end of the list (unlike the predictions). Also, these functions showed a small recency
effects. As discussed in Experiment 1, these recency effects may be spurious, but
similar recency effects have been observed in other “item” position functions (cf.
Figure 2.9). None of the strategies presented in this chapter would have produced
110
recency effects in the “item” serial position function, and there are no aspects of the
architecture that make the content of the last item more durable.
Several explanations might account for the model’s inability to capture these
aspects of the data. Perhaps, the order reconstruction strategies are more thorough
than human participants are, and are consequently more likely to recall all items
in the list. Alternately, perhaps human participants do not use a uniform encoding
strategy throughout list presentation, and sometimes ignore or fail to encode items in
later positions of the list. Or, maybe several of the limitations discussed in Table 4.2
exist, and together they lead to the observed effects on the “item” serial position
functions.
5.5.6 Summary
With a few notable exceptions, the “Order reconstruction with fill-in before the
last item” guessing strategy is able to account for most aspects of the empirical
data. The model’s predictions about empirical data could be improved somewhat
if the parameter space could be searched exhaustively, or if mixture strategies were
used to predict the data. However, given the variability among the participants who
produced these data and the number of free parameters available in these models,
such a demonstration would provide relatively little new information.
Nevertheless, this modeling effort has provided support for some of the assump-
tions made about the structural organization of verbal working memory, as well as
some of the strategic aspects of immediate serial recall. Yet I have not explored the
role of recall strategy in detail. Although the different recall strategies described
here can produce very different serial position functions (as shown in Appendix B),
the parameter values obtained for the data in Experiment 1 yielded simulated data
that were hardly distinguishable from each other. Consequently, I have designed
111
and conducted a new experiment that explicitly instructs participants to recall in
ways similar to some of the strategies described in the present chapter. In the next
chapter, I will discuss this new experiment, and examine its results in light of the
current set of models.
CHAPTER VI
EXPERIMENT 2: AN EMPIRICAL MANIPULATION OFRECALL STRATEGY
In the previous chapter, I demonstrated several different possible strategies for
guessing or list reconstruction that might be used during the immediate serial recall
task. Presumably, each participant in Experiment 1 interpreted the instructions in a
slightly different way, and may have used a slightly different guessing strategy. Addi-
tionally, it is unlikely that even a single participant used a uniform guessing strategy
throughout the task; it is more likely that their guessing strategies changed through-
out the task. Variability in these strategies (both between different participants and
across different trials for a single participant) is likely to have contributed a large
amount of variance to the measures of individual performance seen in Figures 3.3
and 3.4. Consequently, although I was able to construct a model of performance that
managed to account for the mean data fairly accurately, the mean is representative
of few (if any) individuals’ actual performance. The purpose of Experiment 2 is to
provide independent confirmation of the assumptions about how the use different
recall strategies might affect the serial position functions and other measures of im-
mediate serial recall. Additionally, it serves to further demonstrate the importance
of recall strategy in the immediate serial recall task, and highlight the importance
of understanding recall strategy.
112
113
During this experiment, participants were given explicit instructions about how
to perform the immediate serial recall task, and how to guess when they were unsure
about what item to recall next. This manipulation provides direct evidence for the
role of guessing strategy in serial recall performance, and provides better theoretical
guidelines for future investigation of immediate serial recall.
6.1 Method
6.1.1 Participants
The participants were nine undergraduate students at the University of Michigan
with normal perceptual, cognitive, and motor abilities. They were paid for their
participation, and received a bonus for performing well.
6.1.2 Apparatus
The experiment was conducted with a Pentium-class computer using special-
purpose software. Auditory stimuli were presented via headphones, and visual stimuli
were presented on the computer’s SVGA display. Performance was monitored by an
experimenter who sat next to the participant and interacted with the computer in
order to record the participant’s responses.
6.1.3 Stimuli
Testing was done with three sets of words: one set of one-syllable words, one set
of two-syllable words, and one set of three-syllable words. The words were all nouns
approximately equated for concreteness, imageability, and phonological dissimilarity.
The words in each list are shown in Table 6.1.
114
Table 6.1: Word sets used in Experiment 2.Set 1 Set 2 Set 3dare belief advantagefate delight behaviorhint glory circumstancemood judgment fantasyoath kindness miseryplea logic narrownessrush mischief occasiontruth nonsense protocolverb revenge ridiculezeal tenure upheaval
6.1.4 Design
Each participant was tested individually across four sessions on four different
days. During the first session, each participant performed two tasks: one task that
measured the spoken articulatory durations of the words in the three stimulus sets,
and one that involved the immediate serial recall task. Both tasks were performed
once for each of the three sets of words.
On the second, third, and fourth days, each participant performed the immediate
serial recall task with each word set. A single recall condition was tested during each
session. The order of word sets and recall conditions was conjointly counter-balanced
using a Greco-Latin square.
6.1.5 Procedures
Two basic experimental procedures were used during this experiment. One pro-
cedure assessed participants’ serial recall accuracy for the three sets of words under
three different instructional conditions; the other measured the articulatory duration
of those same words.
115
Articulatory duration measurement
To assess the mean articulatory duration per word, a procedure like the one of
Mueller et al. (in press) was used to measure “articulatory duration for words in
memorized sequences”. For this procedure, 16 word lists for each length 3 through
6 were created by sampling without replacement from a single set of words, so that
each word in the set occurred approximately the same number of times across the
lists. These lists were presented in a block of trials, in a randomized order. At the
beginning of each trial, a list of words was presented on a video screen until the par-
ticipant verbally indicated that he or she was ready to begin. On the participant’s
signal, the experimenter pressed a computer key that began the trial sequence. At
the beginning of this sequence, three 100-ms tones were presented at approximately
500-ms intervals. Immediately after the third tone was presented, the words disap-
peared from the screen and a computer-based timer started. Then, the participant
attempted to recall the list of words twice from memory at a clear rapid pace. When
the participant finished speaking the second list, the experimenter stopped a com-
puter timer. If speech or memory errors were made, the trial was repeated. Total
articulation times for each trial were recorded.
Immediate Serial Recall
During each session, participants engaged in the immediate serial recall task for
each of the three sets of words. During each session, participants were instructed
to perform recall according to a single recall strategy. Each session consisted of
three blocks of 16 trials, and the stimuli within each block all came from a single
word set. During each trial, the participant first heard a pre-recorded number (via
computer-controlled headphones) indicating the number of words that would occur
116
on the subsequent list. Words were then presented via a pre-recorded male voice at
1.5 second intervals between onsets. 1.5 seconds after the final onset, a recall tone was
presented, indicating that the participant should initiate recall. To minimize the role
of rehearsal and help encourage a more uniform performance strategy, participants
were required to engage in articulatory suppression during the task. Participants
were instructed to repeat the numbers “1, 2, 3, 4, 1, 2, 3, 4” at a rapid steady pace,
from the beginning of the trial until the recall beep was presented. The experimenter
monitored their counting to ensure that it was maintained at a constant pace.
During the first session, participants were instructed to recall the sequence of
words as accurately as possible, so that each word was recalled in its original position.
This block was intended to give the participants familiarity with the general task
procedure, as well as with the word sets being used in the experiment.
For each remaining session, one of three recall instructions was given. These
instructions were intended to encourage a uniform recall strategy, and determine the
extent to which recall strategy could be controlled. The three strategies were modified
versions of the rehearsal strategies discussed in Chapter V. The first strategy (“Don’t
guess”) encouraged participants to not guess when they were unsure about what the
next word was. Instead, they were instructed to abort recall immediately. A bonus
system encouraged compliance with these instructions. This strategy is similar to the
“Abort on error” strategy discussed in Chapter V. The second strategy (“Relative
order recall”) encouraged participants to insure that words were recalled in their
correct order, even if they were in the wrong position. If they were unsure about
what word to recall next, they were instructed to skip to the next word that they
were confident about, and continue recalling from that point. This strategy is most
similar to the simple “Order reconstruction” strategy. The third strategy (“Position
117
recall”) encouraged participants to recall presented words in the correct position. If
they were unsure about the position of a word, they were instructed to say “Blank”
and move on to the next serial position they were certain of. This strategy is most
similar to the “Order reconstruction with fill-in before end-chain” strategy discussed
earlier. For each strategy, bonus points were given for performance that was accurate
and complied with given task instructions.
6.2 Results
6.2.1 Articulatory Duration Measurement
Mean articulatory durations per word for each word set were calculated using the
same procedure as in Experiment 1. Results showed that for 1-syllable, 2-syllable,
and 3-syllable word sets, mean baseline articulatory duration was 260 ms, 350 ms,
and 427 ms respectively. Mean amplification factors were 1.14, 1.08, and 1.09 re-
spectively. For each participant, the baseline articulatory duration and amplification
factor were combined to estimate an overall articulatory duration for each word set,
which produced estimates of mean articulatory durations for these word sets of 306
ms, 389 ms, and 480 ms respectively. These articulatory durations were submitted
to an analysis of variance to determine if the word sets differed reliably in their
mean articulatory duration. Word set was found to be a reliable predictor of ar-
ticulatory duration (F (2, 16) = 70, p < .001). The residual standard error of this
analysis was less than 1 ms, indicating that each pair-wise difference between the
mean articulatory durations of word sets was highly reliable.
6.2.2 Overall Memory Performance
The two primary factors manipulated in this experiment were word set and re-
call instructions. The mean proportion of items recalled correctly under each recall
118
strategy for each word set are shown in Table 6.2. A within-participant analysis
of variance revealed reliable differences between the probabilities of correctly recall-
ing items (1) from different word sets (F (2, 364) = 5.2, p < .006), (2) in lists of
different lengths (F (3, 364) = 198, p < .001), and (3) under different recall instruc-
tions (F (3, 364) = 15.2, p < .001). Further statistical tests of orthogonal contrasts
showed that there was no reliable difference between the mean proportion of items re-
called for two-syllable versus three-syllable words (t(368) = 1.12, p(t) > .1), but the
difference between the mean proportion of items recalled for one-syllable and three-
syllable words together was reliably greater than that for two-syllable words (mean
difference=.04± .0187, t(364) = 3.68, p(t) < .001). The fact that these data did not
show an articulatory duration effect is not surprising given that participants engaged
in articulatory suppression during the serial recall task, and articulatory suppression
has been shown to eliminate the articulatory duration effect (e.g., by Baddeley et al.,
1975). This finding suggests that participants refrained from rehearsing during the
task, whereas the reliable differences that occurred between correct recall for differ-
ent word sets presumably stem from other factors (such as how easily a word from a
set could be reconstructed from partial information).
Table 6.2: Mean proportion of items recalled in the correct position as a function of word set andrecall instruction.
One-Syllable Two-Syllable Three-Syllable MeanDon’t Guess .690 .675 .687 .684Order Recall .681 .659 .707 .682Position Recall .765 .713 .774 .750No Instructions .627 .584 .667 .626Mean .690 .657 .709 .686
Examining the differences between recall conditions, a 99% confidence interval for
the mean performance across recall conditions was .035, indicating the proportion
of items recalled under each condition differed from every other condition, except
119
for the “Order Recall” and the “Don’t Guess” strategies, which were not reliably
different. The proportion of items recalled in the “No Instructions” condition was
smaller than in the others, but this effect may be attributable to novelty because
this condition always occurred on the first day of the experiment.
6.2.3 Serial Position Functions
The “position” and “item” serial position functions show the results of this exper-
iment in greater detail (Figure 6.1). As mentioned in the previous analysis, the effect
of list length is large and reliable; it accounts for over half of the variance within the
data of individual participants.
There are several similarities between these data and the data from Experiment 1.
First, as in Experiment 1, primacy effects occurred, with items earlier in the list being
recalled more accurately than items later in the list. Second, an effect of list length
occurred for both “position” and “item” serial position functions, across all recall
conditions. Third, recency effects occurred in at least some of the recall conditions.
However, there are also a number of notable differences. For example, in Exper-
iment 2, “item” position functions exhibit recency effects over a number of recall
conditions, even in the “No Instruction” condition, which is most compatible with
Experiment 1. Item recall appears to be poorer in this condition, which may stem
from the use of multiple word sets; in Experiment 1, participants performed all
memory tasks with a single word set, and so they may have been able to correctly
reconstruct lists more accurately. Finally, list length appeared to have little effect
on the “Don’t Guess” recall condition, except for the final item. The fact that items
at the beginning of the list were recalled equally well for all list lengths may indicate
that participants adapted their encoding strategy to accommodate the special recall
instructions.
120
By examining the differences between the “Order recall” and the “Position recall”
conditions, another conclusion can be drawn. For these two conditions, the “item”
serial position functions are very similar, although their “position” serial position
function are quite different. This indicates that participants recalled essentially the
same items in both tasks, but during the order recall the missing items were skipped,
whereas during position recall, they were filled in with “blank” responses.
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Don’t Guess
Serial Position
Prob
abili
ty C
orre
ct
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Order Recall
Serial Position
Prob
abili
ty C
orre
ct
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Position Recall
Serial Position
Prob
abili
ty C
orre
ct
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
No Instructions
Serial Position
Prob
abili
ty C
orre
ct
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Don’t Guess
Probability Correct Item Recall
Prob
abili
ty C
orre
ct
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Order Recall
Probability Correct Item Recall
Prob
abili
ty C
orre
ct
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Position Recall
Probability Correct Item Recall
Prob
abili
ty C
orre
ct
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
No Instructions
Probability Correct Item Recall
Prob
abili
ty C
orre
ct
Figure 6.1: Mean “position” (top panels) and “item” (bottom panels) serial position functions fromExperiment 2, averaged across word sets.
121
6.2.4 Participant compliance with instructed guessing strategies
These serial position functions indicate that the recall condition manipulation was
somewhat successful, but they provide only a little insight into how well participants
were able to follow the specific recall instructions. For each recall instruction, there
were some errors that were allowable, and others that were strongly discouraged.
For example, in the “Don’t Guess” condition, participants were encouraged to abort
recall rather than make an overt error. In the “Order Recall” condition, participants
were encouraged to skip items whose serial order they were unsure of, rather than
making an overt ordering error. Finally, in the “Position Recall” condition, partici-
pants were encouraged to say the word “Blank” instead of overtly recalling an item
in an incorrect position.
If recall consists of a great deal of deliberate search and reconstruction, partic-
ipants’ errors should have mainly been “acceptable”, because the “unacceptable”
errors would be errors that they were unable to detect, and thus attributable to
lower-level architectural factors.
For each of the three instructed recall conditions and four list lengths, the pro-
portion of total errors is compared to the “unacceptable” errors in Figure 6.2. The
triangles indicate the total proportion of items that were not recalled in the correct
position. Only a small proportion of total responses were “unacceptable” errors,
which indicates that participants were able to follow guessing instructions properly.
However, although most errors are “acceptable” errors, participants did commit
“unacceptable” errors occasionally. For some serial positions under some recall con-
ditions, five to ten percent of the total responses were the types of errors that the
participants were instructed to avoid.
122
"Don’t Guess"
Serial Position
Prob
abili
ty E
rror
1 2 3 40.0
0.5
1.0
Serial Position
Prob
abili
ty E
rror
1 2 3 4 50.0
0.5
1.0
Serial Position
Prob
abili
ty E
rror
1 2 3 4 5 60.0
0.5
1.0
Serial Position
Prob
abili
ty E
rror
1 2 3 4 5 6 70.0
0.5
1.0
"Order Recall"
Serial Position
Prob
abili
ty E
rror
1 2 3 40.0
0.5
1.0
Serial Position
Prob
abili
ty E
rror
1 2 3 4 50.0
0.5
1.0
Serial Position
Prob
abili
ty E
rror
1 2 3 4 5 60.0
0.5
1.0
Serial Position
Prob
abili
ty E
rror
1 2 3 4 5 6 70.0
0.5
1.0
"Position Recall"
Serial Position
Prob
abili
ty E
rror
1 2 3 40.0
0.5
1.0
Serial Position
Prob
abili
ty E
rror
1 2 3 4 50.0
0.5
1.0
Serial Position
Prob
abili
ty E
rror
1 2 3 4 5 60.0
0.5
1.0
Serial Position
Prob
abili
ty E
rror
1 2 3 4 5 6 70.0
0.5
1.0
Figure 6.2: Total errors (blue triangles) and “unacceptable” errors (red circles) in different in-structed recall conditions of Experiment 2. Total errors are all responses where a wordwas not recalled in its correct position; unacceptable errors are the proportion of re-sponses that were “unacceptable” errors according to the instructed recall strategy.
6.2.5 Discussion of Empirical Results
The current experiment serves two purposes. First, it demonstrates that humans
have a great deal of control over how they perform the immediate serial recall task.
Such flexibility is not present in most theoretical accounts of this task.
The results have some implications for the assumptions about the structural mech-
anisms that support immediate serial recall, as well as the strategies participants use
during the task. One result that has implications for the the assumptions I made
about the structure of verbal working memory mechanisms (cf. Chapter IV) is the
presence of unacceptable errors. Although these were fairly rare, their presence
123
suggests that at least some of these errors stemmed from processes that cannot be
described as “guessing”, but may be directly influenced by the structure short-term
memory for order.
Another interesting result is that “item” serial position functions for the “Position
Recall” condition and the “Order Recall” conditions were nearly identical. Their
corresponding “position” serial position functions are different, but these differences
primarily affect the last few items in the list. This similarity suggests that for both
conditions, people attempt to recall an initial sequence of items from the beginning
of the list, after which they jump to the end, filling in “blank” items in the “Position
Recall” condition. Casual inspection of the results from individual trials indicates
that this was the case.
One result that may be difficult to explain is the fact that there was almost no ef-
fect of list length in the “Don’t Guess” condition, except for the recall accuracy of the
final item. Additionally, unlike the other conditions of this experiment, no list length
effect occurred for even the first item of the list. This suggests that participants may
have used a special encoding strategy for the “Don’t Guess” condition whereby they
ignored later items in longer lists in order to avoid their potential interference with
earlier items.
To investigate these results in greater depth, this experiment also serves as a
basis for the new computational models presented in the next section. These models
confirm and extend the models of task performance strategy presented in Chapter V.
First, I present several new performance strategies that attempt to model the recall
processes required for Experiment 2. Then, I compare the simulated performance
of these strategies to the results of Experiment 2, using parameter values estimated
in Experiment 1. Finally, I re-estimate some parameter values based on the data
124
produced in Experiment 2.
6.3 EPIC Models of Strategic Guessing Performance
Although the instructions for each recall condition were inspired by a model pre-
sented in Chapter V, the instructions in the current experiment would probably
not lead to performance strategies that were identical those hypothesized in Exper-
iment 1. Although the “Abort on Error” strategy is reasonably close to the “Don’t
Guess” instructions, the “Position Recall” and “Order Recall” instructions do not
encourage a strategy that is similar to one presented in Chapter V. Consequently, I
created two new performance strategies that better followed the instructions in the
current experiment. I next discuss these strategies briefly, and then present simulated
data that they produced.
6.3.1 Task performance strategies
Each of the instruction conditions in Experiment 2 encourages the use of a specific
recall strategy. This contrasts with Experiment 1 (and most immediate serial recall
experiments) in that specific guessing instructions were given, and payoff incentives
were designed to encourage compliance to these instructions.
“Don’t Guess” Instructions
The “Don’t Guess” instructions were intended to encourage participants to per-
form similarly to the “Abort on Error” strategy of Chapter V. This strategy does
not attempt to perform any list reconstruction, but simply stops recalling whenever
information about how to proceed is unclear. Consequently, I will use the “Abort on
Error” strategy to simulate performance under the “Don’t Guess” condition.
125
“Order Recall” Instructions
During the “Order Recall” condition, participants were encouraged to not make
any errors in the order of items produced. A response was considered correct if
it was initially presented after the immediately previous response and before the
immediately subsequent response. Participants were told that if they were unsure
about what an item was, they should skip over it and recall the next item that they
were certain about.
These instructions were based loosely on the “Order Reconstruction” strategy
from Chapter V, because that strategy simply attempts to recall the existing items
in the correct order, without concern for whether they are in the correct position.
However, these instructions provide a different goal from that of the “Order Recon-
struction” strategy, because these instructions place a high penalty on out-of-order
errors.
Consequently, I have developed a new performance strategy that obeys these
instructions more accurately. This strategy attempts to recall as many words as
possible in a sequence from the beginning of the list. When it is no longer able to
determine what to recall next, it locates the chain of items at the end of the list and
continues recall from that point.
“Position Recall” Instructions
During the “Position Recall” strategy, participants were encouraged to recall items
in the original position. Consequently, rather than recalling an item in the incorrect
position, they were encouraged to say “Blank”. These instructions attempted to
encourage a version of the “Fill-In” strategies from Chapter V, but with better
control over what items were used as fillers for unknown positions.
126
I have also developed a new recall strategy to model data produced during this
condition. This model performs operations that are very similar to the model of the
“Order reconstruction with fill-in before end-chain” strategy discussed in Chapter V.
When using this strategy, an agent begins by identifying the first item of the list,
and recalling items in a chain until it cannot determine which item to recall next. It
then engages in sub-list reconstruction and identifies the remaining items. The agent
attempts to identify and eliminate each intermediate item on the list, and recall the
word “blank” for each item that it eliminates. When all items except for the items
in the final chain have been recalled, it continues recall with the final chain of items.
6.3.2 Predictive modeling of performance in Experiment 2
A model’s predictive power can be judged through a procedure called generaliza-
tion (Busemeyer & Wang, 2000). In this procedure, a model’s predictions about one
data set are based on parameter estimates obtained by fitting the model to another
data set. The current set of experiments offers an opportunity for this generalization
technique to be used, because the data from Experiment 2 can be modeled with
parameter estimates obtained in Experiment 1. Because the two experiments used
slightly different tasks, the ability of these models to predict aspects of the data
obtained in Experiment 2 will show whether the conclusions from Experiment 1 are
reasonable. Similarly, the ways in which these predictions fail to capture the data
may help determine the ways in which the original models were incorrect.
The results of these predictive models are found in Figure 6.3, which correspond
to the left-most three columns of Figure 6.1. Each curve was formed by averaging
the performance across three mean word lengths, and 1000 simulated trials were per-
formed for each list length by rehearsal condition by word length cell, for a total of
27,000 simulated trials. Mean word durations in the model were based on the articu-
127
latory duration measurements collected from the participants in Experiment 2. The
parameter values used for these simulations were identical to those from Chapter V,
and can be found in Table 5.1
These predictive models conform to some qualitative features of the data. For
example, in the “Don’t Guess” condition, observed and predicted serial position
functions were monotonically decreasing with little effect of list length. In the “Order
Recall” condition, observed and predicted “position” serial position functions were
monotonically decreasing, and “item” serial position function had recency effects,
similar to the observed data. In the “Position Recall” condition, primacy and recency
effects each occurred for both “position” and “item” serial position functions, which
happened for the empirical data as well.
However, the models under-predict the level of performance achieved by human
participants in the experiment, and their overall quantitative fits are poor. For
the three guessing strategies, R2 values between observed and predicted proportions
for “position” serial position functions were .26, .44, and .23, and for “item” serial
position functions were .27, .005, and .25 for the “Don’t Guess”, “Order Recall”, and
“Position Recall” respectively. The corresponding RMSE values were (for “position”
serial position functions) .52, .43, and .45; and (for “item” serial position functions)
.54, .44, and .48.
This under-prediction could stem from a number of sources. One explanation
is that participants in Experiment 2 had better memory than participants in Ex-
periment 1, and so the parameters estimated from Experiment 1 are biased. If
this explanation were true, it could have occurred because the tasks and word sets
were different. However, because performance in the “No instruction” condition was
roughly equivalent to the performance from Experiment 1, it is unlikely that these
128
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Don’t Guess: Position
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Order Recall: Position
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Position Recall: Position
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Don’t Guess: Item
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Order Recall: Item
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Position Recall: Item
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
Figure 6.3: Mean “position” (top panels) and“item” (bottom panels) serial position functions, fordata from Experiment 2 (shown in blue with solid lines and filled circles) and dataproduced by the modified EPIC architecture (shown in red with dashed lines and emptycircles) using three different recall strategies.
explanations are correct. Another reason why participants may have appeared to
achieve better memory in Experiment 2 is that the tasks in Experiment 2 may have
enabled special encoding strategies, or because some of the assumptions about the
structure of verbal working memory were incomplete. Either of these explanations
would have limited the generalizability of the original parameter estimates, because
these models assumed that stimulus encoding was identical in both experiments, and
that verbal items in working memory undergo decay and interference in some very
specific ways. If either of these assumptions were incorrect, the estimates may have
been biased.
129
Yet another potential explanation for why the parameter estimates from Experi-
ment 1 were biased is because they were estimated in an attempt to allow the “Order
Reconstruction with fill-in before the last item” model to match the performance of
human participants. If people actually used a mixture of performance strategies,
some of which were not as good as the pure strategy used to estimate the param-
eters, the parameters may have been systematically biased and would give smaller
estimates for capacity and decay time than would have otherwise been obtained.
It is probably impossible to determine exactly why the models under-predict the
observed data, and it is likely that all of these factors contribute to the differences
found. Next, I will estimate a new set of parameters that approximate the level of
performance achieved by participants in Experiment 2, and attempt to understand
which of these explanations is a likely cause of the under-prediction.
6.3.3 Parameter Estimation based on current data.
To determine how well these models might predict the observed data, I adjusted
several decay parameters so that the overall level of performance approximated the
observed data more accurately. By changing the serial order tag decay distribution
median from 4.5 seconds to 15 seconds, and the median of the decay distribution for
the end-item tag from 5 seconds to 10 seconds, the predicted serial position functions
in Figure 6.4 were obtained. These data show improvement in the goodness-of-fit
statistics: R2 values were .75, .92, and .74 (for “position” serial position functions)
and .71, .55, and .73 (for “item” serial position functions) respectively for the “Don’t
Guess”, “Order Recall”, and “Position Recall” conditions; corresponding RMSE
values were .31, .11, and .11 (for “position” serial position functions) and .34, .12,
and .13 (for “item” serial position functions) respectively for the “Don’t Guess”,
“Order Recall”, and “Position Recall” conditions.
130
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Don’t Guess: Position
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Order Recall: Position
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Position Recall: Position
Serial Position
Prob
abili
ty C
orre
ct R
ecal
l
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Don’t Guess: Item
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Order Recall: Item
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
Position Recall: Item
Serial Position
Prob
abili
ty R
ecal
l in
Any
Pos
ition
Figure 6.4: Mean “position” (top panels) and“item” (bottom panels) serial position functions, fordata from Experiment 2 (shown in blue with solid lines and filled circles) and data pro-duced by the modified EPIC architecture (shown in red with dashed lines and emptycircles) using three different recall strategies, under parameters estimations made specif-ically for Experiment 2.
These simulated data approximate the observed data more accurately than the
simulated data presented in Section 6.3.2, although there are still several aspects
that are mis-predicted. In these new simulated data, the recency effects on the
“item” serial position functions were fairly similar for “Position Recall” and “Order
Recall”, unlike the earlier prediction. However, the simulated data predicted an
effect of list length at more than the final serial position, and did not appear to
predict the enhanced recall accuracy for the final item. For the “Order Recall” and
“Position Recall” conditions, several distinguishing characteristics of each “position”
131
serial position function were reproduced. However, the new models failed to capture
the slope of the primacy effect, and produced recency effects that extended back from
the end of the list more than for the empirical data. This is primarily a consequence
of the fact that the order reconstruction models moved to the “end chain” when
they were unable to recall more items. The observed data show that there was a
consistent reduction in recall probability for the “item” serial position functions up
until the final item. Apparently, human participants tended to recall only the final
item when they were unable to continue recalling the initial sequence of items, rather
than reconstructing a sequence of items from the end of the list.
Although this fit was an improvement over the predictive model produced earlier,
its predictions about the shape of the serial position functions are incorrect. The
models predict that during “Order Recall” and “Position Recall”, the “item” serial
position functions should drop off quickly, and then starting around the middle of
the list, recall accuracy should begin to improve until the end of the list. What
actually happened is that recall accuracy decreased more gradually until the last
one or two items, and then improved. This is at least partly a consequence of the
guessing strategies, which attempt to reconstruct the end chain, rather than moving
on to the final item of the list. However, it may also be influenced by the stochastic
decay of serial order tags, which favors items nearer to the end of the list.
6.4 Discussion
This experiment and the subsequent computational models have shown several
new things about the structure of verbal working memory and the nature of the
performance strategies used to accomplish variations on the immediate serial recall
task.
132
One important result of this experiment is that the “item” serial position functions
of the “Order Recall” and “Position Recall” conditions were nearly identical, but
the “position” serial position functions were quite different. This manipulation was
performed by specifically encouraging participants to recall items in a certain way:
for “Order Recall”, they were told to skip items they did not know, and for “Order
Recall”, they were told to recall the word “blank” if they were unsure of a word.
For most experiments on immediate serial recall, instructions are not this specific,
and participants must induce by themselves what to do when they are unsure of
what word should be recalled next. Undoubtedly, different experimental conditions
encourage different reconstruction strategies, and influence the shape of the serial
position functions accordingly.
These two conditions may represent two extremes for how participants normally
perform immediate serial recall. Most participants’ strategies probably fall some-
where between these extremes, so that they will sometimes engage in “fill-in” recall,
but other times simply skip to the next item they are certain about. Interestingly,
the data produced under the “No Instruction” condition appear to fall somewhere
between the “Order Recall” and “Position Recall” conditions, suggesting that the
strategies participants naturally induced fell somewhere between a pure positional
recall strategy and a pure order recall strategy.
When these strategies are combined with a “fill-in” sub-phase where actual words
are recalled (instead of “blank”), we can begin to understand how the strategic com-
ponents of recall can affect the shape of the serial position function. Recency effects
will occur for “position” serial position functions whenever positional recall is en-
couraged or facilitated by experimental conditions. Such conditions might include
the use of methods of manual recall, or performance bonuses for correct positional
133
recall. Recency effects will be present in “item” serial position functions when con-
ditions discourage guessing from the set of presented words. Such conditions might
include specific instructions (e.g., as in Drewnowski & Murdock, 1980), or the use of
word sets that are difficult to learn or remember.
Another important conclusion from this experiment is that participants are easily
able to follow different recall instructions. This conclusion is based on the facts that
serial position functions were affected in predictable ways by instructional manipula-
tions, and that people were aware of very few of the errors they made. This suggests
that the processes involved in recall are quite flexible and under active strategic
control of the participant. It also serves as a warning: experimenters should provide
specific instructions about how participants should guess when they are unsure about
what word to recall next.
The results also suggest that some of the assumptions I have made about the
structure of verbal working memory are inaccurate. For example, I have assumed
that people use a uniform encoding strategy throughout the trial and across different
recall conditions. This is most likely not true. The fact that there was little effect of
list length in the “Don’t Guess” condition suggests (at least in that condition) that
people were using a special encoding strategy that focused on the beginning of the
list to the detriment of later items. The choice of encoding strategy may have a large
impact on the shape of the serial position function for other conditions as well.
Another assumption I have made is that decay of information about words (in
various forms) is a major limitation on immediate serial recall accuracy. This leads
to the prediction that items later in the list should be recalled more accurately
than earlier items whenever recall occurs more rapidly than item presentation. This
prediction has not generally been supported by the data: items later in recall tend
134
to be recalled less accurately than earlier items (except for the last item). This may
indicate that later items in the list are not encoded as accurately or durably as earlier
items.
Both of these failures suggest that participants may have some control over how
they choose to encode an item, and that they may be able to ignore some later
items in order to enhance their probability of recalling earlier items, or alternately
choose to encode later items more reliably at the expense of earlier items. Encoding
strategies may play an important role in immediate serial recall performance.
A third assumption that I have made in these models is that ordering errors are
almost solely the result of errors occurring during guessing, and not a consequence
of unreliable order maintenance codes. Although the vast majority of errors in this
experiment were “acceptable”, a non-trivial proportion of errors violated this as-
sumption. Like the failures described above, these errors may stem from problems
that occurred during encoding. Further investigation will be required to determine
the source of these errors.
In summary, this experiment has demonstrated several things. First, it has shown
that strategic factors influence the recall phase of the immediate serial recall task.
This is clear because different recall instructions affected the presence of the recency
effect in “position” serial position functions. Second, it has shown that when “fill-in”
guessing with the word “blank” is performed, the magnitude of the recency effect
in “item” serial position functions can be affected. Finally, the final item on a list
appears to be easily identified, but this will only lead to enhanced recency effects
when the participant is motivated to recall that item in the final recall position.
CHAPTER VII
GENERAL DISCUSSION
The experiments and computational models presented in this thesis have investi-
gated how both the architecture of cognition and people’s performance strategy work
together to enable performance in the immediate serial recall task. Clearly, there are
important structural limitations on how humans can remember short sequences of
words. But, just as clearly, the task performance strategies used to accomplish imme-
diate serial recall have a large influence on the results of experiments on immediate
serial recall. Both architecture and strategy are critical components of performance
in the immediate serial recall task, and neither can be ignored if we hope to under-
stand it.
7.1 New insights gained from present experiments and models
Several important conclusions can be drawn from the experiments and models in
this thesis. Clearly, recall and guessing strategy plays an important role in immedi-
ate serial recall. Human participants in the immediate serial recall task are able to
flexibly control their recall strategy, and their choice of recall strategy can be influ-
enced by experimental procedures and instructions. But these strategies are limited
by the underlying architecture of verbal memory.
For example, the immediate serial recall task with verbal recall is not simply a
135
136
task of re-ordering a set of words: a significant proportion of the presented words
are never retrieved. This loss of information can be counter-acted somewhat if the
participant engages in “fill-in” recall, and whether they do this may affect whether
a recency effect is observed. However, the participant will probably only engage
in “fill-in” recall if he or she believes that recalling items in their initial positions
is important. If he or she believes that recall in the correct order is critical, it is
likely that no recency effect will be produced. Additionally, although the presence or
absence of the recency effect can be modulated by task goals, its existence indicates
that a participant can easily identify which item is the final item in a list, and thereby
insure that he or she recalls it in the final position.
These models also provide an explanation of why inter-response intervals increase
for longer lists lengths. According to the “Order Reconstruction” model, this time
increases because of deliberate search and list reconstruction between consecutive
recall attempts. List search and reconstruction takes more time to perform for longer
lists, because there are more items to search in order to identify the next item, and
this search occurs serially through the items that remain to be recalled.
Finally, the fact that most errors in immediate serial recall are ordering errors
suggests that information about the order of words in a list is somewhat dissociable
from the information about their phonological content. Apparently, participants
are able to guess from among the items remaining to be recalled, and thus may
reconstruct a list fairly accurately.
7.2 Limitations of the current conclusions
Despite the new inferences that these experiments have allowed, there are several
assumptions made in earlier chapters that have received limited support. For exam-
137
ple, one assumption that may be incorrect is that all items are encoded equally well,
no matter where they occur in a list or what the eventual recall goals are. Addition-
ally, pure time-based decay has a limited ability to account for aspects of the serial
position function, because even when recall occurs faster than item presentation,
words at the end of the list are recalled more poorly than words near the beginning.
Finally, participants do make errors that they are not aware of, which suggests that
some aspects of these errors are not under cognitive control.
Several other assumptions remain plausible, but these experiments have provided
little direct evidence that they are true. For instance, I have assumed that the
special tags that mark the first and last item of a list undergo time-based decay.
Although this assumption was able to account for data fairly accurately, it may be
true that these types of information have different properties. For example, they may
be relatively immune to decay but susceptible to interference. As another example, I
have assumed that phonological content is reconstructed and retrieved independently
from other types of information. This assumption may be accurate as well, but the
experiments discussed here did not manipulate factors that would affect the reliability
of phonological content in predictable ways.
7.3 The value of modeling both architecture and strategy
For this thesis, I have created models that are composed of both architectural and
strategic components, and done so in a way that distinguishes between architecture
and strategy. Most other extant models of the immediate serial recall task do not
make this distinction, but instead ignore task strategy in one way or another. For
present purposes, models of the immediate serial recall task fall into four categories,
which are depicted in Figure 7.1. These categories are “Cognitive Architecture”
138
models, “Behavioral” models, “Mechanistic” models, and “Homunculus” models.
The next sections discuss these categories in greater detail.
StrategyArchitecture
"Cognitive Architecture" Model
"Mechanistic" Model
Architecture/Strategy
Strategy
Architecture
"Homunculus" Model
"Behavioral" Model
Figure 7.1: The different ways a model can incorporate strategy.
7.3.1 Cognitive architecture models
Like the models presented in Chapters V and VI, some models have made specific
distinctions between architecture and strategy. I will call these “cognitive architec-
ture” models. As seen in Figure 7.1, the complete model consists of both architectural
and strategic components, and these components have distinguishable roles within
the model. An important component of such a model is that it must be a model of
the procedures and structures involved in performing a task, and not just a model
of the underlying architectural structure.
Of course, this type of model is easiest to construct by using a ’cognitive ar-
chitecture’ modeling system such as EPIC or ACT-R. However, it may be possible
to create such models without the use of such an architecture. Additionally, some
models created with such a modeling system may not be true cognitive architecture
139
models, if they violate the principles of these model architectures. For example, if
such a model embedded a putatively strategic process inside the architecture, or con-
structed putatively architectural processes from production rules, this model could
not be considered a “Cognitive Architecture” model. Instead, it may fall into one of
the other model classes I discuss below.
7.3.2 Behavioral Models
The class “Behavioral” models may appear to make predictions about task per-
formance, but these models do not describe the underlying processes involved. Such
a model might acknowledge that both architectural and strategic components lead
to the ultimate performance, but may not make explicit assumptions about either.
This type of model is depicted in Figure 7.1. These models describe data, and do
not make assumptions about the underlying structure or processes.
A classic example of a simple “behavioral” model is typified by Miller’s (1956)
claim that the apparent capacity of human short-term memory is about seven chunks
(plus or minus two). This is a behavioral description of a consistent empirical result,
and is not a model of the processes or structures involved in a single task.1 Typically,
behavioral models appear to describe only the structure of the mechanism, yet are
often used to predict performance in tasks that putatively utilize these structures.
In another discipline of cognitive psychology, classic descriptions of Fitts’s law
provide another example of a purely behavioral model of performance. According
to Fitts’s law, the time required to make an aimed movement to a target can be
predicted by the size of the target and the distance to the target. Only more recent
accounts have hypothesized the underlying structures and processes that might lead
1The “behavioral model” described here is simply the one implicated by the “magical number seven” statement,which serves as a simplistic example. Miller did hypothesize about some of the underlying structures involved, andthese hypotheses may represent a more complex model.
140
to such a finding (e.g., Meyer et al., 1988)
The limitations of this type of model are fairly clear. Even if such a model can
accurately predict empirical data, it is unable to explain how such an effect occurs,
and so has limited value.
7.3.3 Mechanistic Models
Some models of the immediate serial recall task are “Mechanistic”, incorporating
performance procedures, but not distinguishing these procedures from other hard-
coded aspects of the model. These models do not make distinctions between volun-
tary strategic acts and involuntary reflexive ones, and are essentially automata whose
performance is tied to their underlying structure. These models are not simply be-
havioral, because they seek to understand the underlying processes involved in task
performance. However, they may be unable to distinguish between structures that
are relatively fixed, and the more flexible processes used to satisfy task demands.
Common examples of mechanistic models are found in “connectionist” neural
networks and some simplistic mathematical models. For example, so-called “com-
petitive queueing” neural-network models (e.g., Hartley & Houghton, 1996; Burgess
& Hitch, 1996) have attempted to explain serial behavior in immediate verbal recall
using special network structures. Such models can operate in a mechanistic sequence
whereby the recall of one item automatically cues the next item. This process repeats
until all items have been recalled, but the process is closely tied to the structure of
the network, implying that this process is automatic.
These models’ primary weakness is that they fail to allow for flexible performance
strategies that undoubtedly occur during these tasks. If the process of serial recall
is actually closely integrated with the underlying memory structures, one would
expect participants to have difficulty performing tasks under different instructions.
141
But, as shown in this thesis numerous times, participants’ performance can easily
be influenced by instructional manipulation, and they are quite adept at adopting
performance strategies that conform to the experimenter’s instructions.
7.3.4 Homunculus Models
Some models make assumptions (implicitly or explicitly) about task strategy, but
the assumptions are not actually considered a part of the model. Instead, this model
requires a “Homunculus” that controls task performance, but resides outside of the
framework of the model. The majority of existing models of the immediate serial
recall task are of this type (e.g., Shiffrin & Cook, 1978; Henson et al., 1996, Page &
Norris, 1998). These models have attempted to model the underlying architecture
of verbal working memory, but have often found it necessary to make additional
assumptions about how the architecture might be used to perform the task.
These models lack formal mechanisms for representing task strategy, such as pro-
duction rule descriptions of strategy found in many cognitive architecture models.
Consequently, they often neglect making explicit assumptions about strategy, and
prefer to build these assumptions implicitly into the architectural components they
have chosen to model. This is counter-productive, because these models often incor-
porate components that are clearly strategic (such as rehearsal), leading to impure
models of the underlying architecture.
7.3.5 Other models
Of course, not all models of immediate serial recall fall neatly into one of these cat-
egories. Some “models” are too poorly specified to determine what the assumptions
about architecture and strategy actually are. For example, Baddeley’s phonological-
loop “model” (1986) does make claims about executive control, suggesting it is a
142
model of cognitive architecture, but its use of a “tape loop” metaphor suggests that
in the model, rehearsal is very mechanistic. Furthermore, the model does not specify
what processes are involved during recall. Ultimately, because the theory is only
specified verbally, it is difficult to determine what specific claims it makes about the
interaction between architecture and strategy.
7.3.6 Benefits of modeling task performance strategy
Even if most models of immediate serial recall do not describe the strategic com-
ponents of a task specifically, they may still be fairly accurate and instructive. Some
of these models have achieved impressively accurate predictions about performance
in the immediate serial recall task, even without incorporating the contributions of
task strategy. Consequently, a question must be answered: What can be gained from
constructing a model that incorporates task strategy?
Clearly, the answer depends on what the goal of building these models was in
the first place. If the goal is just to be able to predict new data, then a simplistic
behavioral model in the form of a logistic regression equation may be acceptable.
However, most models attempt to go further, and try to understand the underlying
processes involved in task performance. Because task performance strategy can play
an important role in the results that are produced, a model that does not treat
these performance strategies as a distinct component is obviously incorrect. But,
such models are also subject to some pitfalls that can be avoided if a cognitive
architecture model is used.
Models that lack a vocabulary for describing the strategic components of a task
are subject to criticism on a number of levels. If the model is a “homunculus” model
that incorporates some strategic processes but does so outside of the model, we can
not know whether the model can actually perform the task as described, given the
143
information available to it. For example, Page and Norris (1998) described a rehearsal
process that occurs under recall conditions for their primacy model. This rehearsal
process is claimed to be identical to list presentation. Consequently, although the
model can recall items incorrectly, it always rehearses items in the correct order.
Clearly, the model uses information that it does not really have access to during
rehearsal (i.e., the correct order of the list). However, the model is unable to access
this same information during recall. If rehearsal was treated as a principal aspect
of this model, the rehearsal process should not have had access to this information,
and the model’s true conclusions about iterative rehearsal could be better assessed.
A related issue is that even if a model has a vocabulary for describing the strate-
gic components of a task, it may use architectural parameters to mimic the effect
of strategic processes. For example, Anderson et al. (1998) modeled an iterative
rehearsal process by manipulating an architectural parameter associated with base-
line activation. Whether this parameter actually has the same effect as the strategic
process of rehearsal is unknown and unproven. Although the model used the ACT-
R architecture, this aspect of the model provides only a behavioral description of
performance.
By using a model in which both architecture and strategy are important com-
ponents (and modeling both strategic and architectural effects at their appropriate
levels), some of these problems can be avoided.
7.4 Future directions
The empirical results and computational models presented here suggest several
directions for future research on verbal working memory. The present analysis has
focused primarily on the immediate serial recall task, and especially the strategies
144
used during the recall phase of this task. Consequently, it provides a basis two future
lines of research on the role of strategy in verbal working memory tasks.
7.4.1 Other components of the immediate serial recall task that are under strategiccontrol
Although the models of recall strategies presented here are quite complex, they
only represent one specific aspect of one fairly simple task used to study verbal
working memory. Two other major aspects of task performance are undoubtedly
influenced by strategic factors: encoding and rehearsal.
The results of Experiment 2 suggest that a participant might engage in different
encoding strategies, depending on what is supposed to be recalled. It may be that
whenever an item is perceived, a participant can elect to either encode that item or
ignore it. If they encode the item, this may interfere with their maintenance of items
already encoded, but if they choose to ignore the current item, there may be little
interference with items that had been encoded earlier.
Similarly, Experiment 1 showed that rehearsal can have a large impact on serial
recall accuracy. The strategies involved in rehearsal are undoubtedly more complex
than those involved with recall. For example, a strategy must specify how many
words should be rehearsed, which words should be rehearsed, and what to do if
items disappear during rehearsal. The choices a participant makes about how to
rehearse will probably affect the serial position function as well.
7.4.2 Other verbal working memory tasks that are modulated by strategic control
Just as there are other components of the immediate serial recall task that can
be modulated by strategic control, there are other tasks involving verbal working
memory that require strategic control as well. In fact, most other tasks involving
verbal working memory are more complex than the immediate serial recall task, such
145
as the n-back task, the reading span task, and the computation span task. These
may involve list reorganization and dual-task procedures that might affect memory
reliability. Consequently, the strategies available for performing these are likely to
be even more diverse than the ones presented in this thesis. Without accounting for
these strategies, it may be even more difficult to interpret results from these more
complex verbal working memory tasks
7.5 Conclusions
In order to understand verbal working memory, it is necessary to understand both
the architectural components used to maintain verbal information for short periods
of time, and the procedural task strategies used to perform verbal working memory
tasks. True progress in our understanding of verbal working memory will only occur
when both of these factors are understood.
APPENDICES
146
147
APPENDIX A
PRODUCTION RULES USED DURING TASKPERFORMANCE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;The following rules are used to implement the
;;;;;procedures for performing immediate serial recall with
;;;;;the modified version of the EPIC architecture described
;;;;;in the dissertation of Shane T. Mueller (July, 2002).
;;;;;They are based partly on production rules used by
;;;;;Kieras et al. (1999). Copyright Shane T. Mueller, 2002,
;;;;;Ann Arbor
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;********************************************
; STM TASK
;********************************************
;
(STM-TASK-Start
IF
(
(GOAL DO STM-TASK)
(NOT (STATUS STM-TASK UNDERWAY))
)
THEN
(
(ADDDB (STATUS STM-TASK UNDERWAY))
(ADDDB (STEP WAIT-FOR READY))
;;DO NOT set eyes for a reflex move
(SEND-TO-MOTOR OCULAR DISABLE REFLEX)
(SEND-TO-MOTOR MANUAL RESET MEMORY)
))
(READY-SIGNAL-RECEIVED
IF
(
(GOAL DO STM-TASK)
(STEP WAIT-FOR READY)
(AUDITORY RECOGNITION TYPE TONE FREQ 100 STATE ON) ;signal tone
)
THEN
(
(ADDDB (GOAL DO TRIAL)) ;do a trial
(DELDB (STEP WAIT-FOR READY))
(ADDDB (STEP WAIT-FOR TRIAL-END))
;;kludge to simplify hang detection in later rule
148
(DELDB (AUDITORY RECOGNITION TYPE TONE FREQ 100 STATE ON)) ;signal tone
))
(TRIAL-END
IF
(
(GOAL DO STM-TASK)
(STEP WAIT-FOR TRIAL-END)
(NOT (GOAL DO TRIAL))
)
THEN
(
(DELDB (STEP WAIT-FOR TRIAL-END))
(ADDDB (STEP WAIT-FOR READY))
))
;;if ready signal arrives while a trial is underway, a serious failure has occurred.
;;Abort the trial
(READY-SIGNAL-RECEIVED-DURING-TRIAL
IF
(
(GOAL DO STM-TASK)
(STEP WAIT-FOR TRIAL-END)
(AUDITORY RECOGNITION TYPE TONE FREQ 100 STATE ON) ;signal tone
(GOAL DO TRIAL)
)
THEN
(
(ADDDB (GOAL ABORT TRIAL)) ;shut down the trial
(DELDB (STEP WAIT-FOR TRIAL-END))
(ADDDB (STEP WAIT-FOR ABORT-DONE))
))
(RESTART-TRIAL
IF
(
(GOAL DO STM-TASK)
(STEP WAIT-FOR ABORT-DONE)
(NOT (GOAL ABORT TRIAL))
)
THEN
(
(DELDB (STEP WAIT-FOR ABORT-DONE))
(ADDDB (STEP WAIT-FOR READY))
))
;; ***********************
;; *** TRIAL SUBMETHOD ***
;; method ID is DO-TRIAL
;;this method processes all the input, and cleans up
;;if the trial has to be aborted.
(MFG-TRIAL
IF
(
(GOAL DO TRIAL)
(NOT (STATUS DO-TRIAL UNDERWAY))
)
THEN
(
(ADDDB (STATUS DO-TRIAL UNDERWAY))
(ADDDB (STEP DO-TRIAL WAIT-FOR STIMULUS-START))
))
149
;;This starts an execution thread that accepts each stimulus to the end,
;;and then waits for the recall signal.
;;New external items will be accepted at any time.
;;new items will have no tags
(ACCEPT-EXTERNAL-STIMULUS-START
IF
(
(GOAL DO TRIAL)
(STEP DO-TRIAL WAIT-FOR STIMULUS-START)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ??? SOURCE EXTERNAL MARKER START TYPE ??? )
(NOT (TAG DO-TRIAL ?ITEM-ID IS ???))
)
THEN
(
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS EXTERNAL-NEW))
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(DELDB (STEP DO-TRIAL WAIT-FOR STIMULUS-START))
(ADDDB (STEP DO-TRIAL WAIT-FOR STIMULUS-END))
;;separate thread to wait for recall signal
(ADDDB (STEP DO-TRIAL WAIT-FOR RECALL-SIGNAL))
(ADDDB (STEP DO-TRIAL PROCESS STIMULUS))
))
;;new items will have no tags
(MARK-EXTERNAL-STIMULUS-START-AS-CHAIN-START
IF
(
(GOAL DO TRIAL)
(STEP DO-TRIAL WAIT-FOR STIMULUS-START)
(STRATEGY NO REHEARSAL)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ?NEXT SOURCE EXTERNAL MARKER START TYPE ??? )
(NOT (TAG DO-TRIAL ?ITEM-ID IS ???))
)
THEN
(
(CREATE-AUDITORY-SPEECH-TAG DO-TRIAL ?ITEM-ID RECALL-CHAIN-START)
))
(MARK-TAG-AS-ORPHANED
IF
(
(TAG DO-TRIAL ?ORPHANED IS TO-BE-RECALLED)
(NOT (AUDITORY SPEECH ITEM-ID ?ORPHANED NEXT ??? SOURCE ??? MARKER ??? TYPE WORD ))
(NOT (TAG DO-TRIAL ?ORPHANED IS ORPHANED))
)
THEN
(
(ADDDB (TAG DO-TRIAL ?ORPHANED IS ORPHANED))
))
;;This puts the marker (TAG DO-TRAIL ??? IS PRESENT) on an item. When the item disappears,
;; PRESENT gets deleted and if it has other DO-TRIAL tags (TAG DO-TRIAL ??? IS ORPHANED) gets added.
(MARK-NEW-EXTERNAL-STIMULUS-AS-PRESENT
IF
(
(GOAL DO TRIAL)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ??? SOURCE EXTERNAL MARKER ??? TYPE WORD )
(NOT (TAG DO-TRIAL ?ITEM-ID IS ???))
)
THEN
(
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS PRESENT))
))
150
(NO-REHEARSE-AFTER-EXTERNAL-STIMULUS
IF
(
(GOAL DO TRIAL)
(STRATEGY NO REHEARSAL)
(STEP DO-TRIAL PROCESS STIMULUS)
)
THEN
(
(DELDB (STEP DO-TRIAL PROCESS STIMULUS))
))
(ACCEPT-EXTERNAL-STIMULUS-CONTINUING
IF
(
(GOAL DO TRIAL)
(STEP DO-TRIAL WAIT-FOR STIMULUS-END)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ?NEXT SOURCE EXTERNAL MARKER ?MARKER TYPE ??? )
(NOT (TAG DO-TRIAL ?ITEM-ID IS ???))
(DIFFERENT ?MARKER END)
)
THEN
(
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS EXTERNAL-NEW))
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(ADDDB (STEP DO-TRIAL PROCESS STIMULUS))
))
(ACCEPT-EXTERNAL-STIMULUS-END-NEXT-TAG-NOT-GONE
IF
(
(GOAL DO TRIAL)
(STEP DO-TRIAL WAIT-FOR STIMULUS-END)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ?NEXT SOURCE EXTERNAL MARKER END TYPE ??? )
(NOT (TAG DO-TRIAL ?ITEM-ID IS ???))
(DIFFERENT ?NEXT GONE)
)
THEN
(
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS EXTERNAL-NEW))
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(ADDDB (TAG DO-TRIAL ?NEXT IS STIMULUS-END))
(DELDB (STEP DO-TRIAL WAIT-FOR STIMULUS-END))
(ADDDB (STEP DO-TRIAL PROCESS STIMULUS))
))
(ACCEPT-EXTERNAL-STIMULUS-END-NEXT-TAG-GONE
IF
(
(GOAL DO TRIAL)
(STEP DO-TRIAL WAIT-FOR STIMULUS-END)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT GONE SOURCE EXTERNAL MARKER END TYPE ??? )
(NOT (TAG DO-TRIAL ?ITEM-ID IS ???))
)
THEN
(
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS EXTERNAL-NEW))
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(DELDB (STEP DO-TRIAL WAIT-FOR STIMULUS-END))
(ADDDB (STEP DO-TRIAL PROCESS STIMULUS))
(ADDDB (TAG DO-TRIAL MISSING IS STIMULUS-END)) ;;This might happen. MISSING is just a place-holder
))
151
(RECALL-SIGNAL-RECEIVED-NO-REHEARSAL
IF
(
(GOAL DO TRIAL)
(STRATEGY NO REHEARSAL)
(STEP DO-TRIAL WAIT-FOR RECALL-SIGNAL)
;;wait for all of stimulus to be received
(NOT (STEP DO-TRIAL WAIT-FOR STIMULUS-END))
(AUDITORY RECOGNITION TYPE TONE FREQ 500 STATE ON) ;signal tone
)
THEN
(
(ADDDB (STRATEGY RECALL EXTERNAL)) ;set recall strategy - try to use same rule set
(ADDDB (GOAL DO RECALL)) ;start recall
(DELDB (STEP DO-TRIAL WAIT-FOR RECALL-SIGNAL))
(ADDDB (STEP DO-TRIAL WAIT-FOR RECALL-COMPLETE))
))
(WAIT-FOR-RECALL-COMPLETE
IF
(
(GOAL DO TRIAL)
(STEP DO-TRIAL WAIT-FOR RECALL-COMPLETE)
(NOT (GOAL DO RECALL)) ;wait for recall to be done
)
THEN
(
(DELDB (STEP DO-TRIAL WAIT-FOR RECALL-COMPLETE))
(ADDDB (STEP DO-TRIAL WAIT-FOR TRIAL-COMPLETE))
(ADDDB (GOAL CLEANUP TRIAL-TAGS))
))
(WAIT-FOR-TRIAL-COMPLETE
IF
(
(GOAL DO TRIAL)
(STEP DO-TRIAL WAIT-FOR TRIAL-COMPLETE)
(NOT (GOAL CLEANUP TRIAL-TAGS)) ;wait for cleanup to be done
)
THEN
(
(DELDB (STEP DO-TRIAL WAIT-FOR TRIAL-COMPLETE))
(DELDB (GOAL DO TRIAL))
(DELDB (STATUS DO-TRIAL UNDERWAY))
))
;;***********************************
;;****** CLEANUP TRIAL-TAGS *********
;;***********************************
;;cleanup trial tags in two cycles - one to change to used,
;;another to delete anything no longer needed
(CLEANUP-TRIAL-TAGS-MFG
IF
(
(GOAL CLEANUP TRIAL-TAGS)
(NOT (STATUS CLEANUP-TRIAL-TAGS UNDERWAY))
)
THEN
(
(ADDDB (STATUS CLEANUP-TRIAL-TAGS UNDERWAY))
(ADDDB (STEP CLEANUP-TRIAL-TAGS CHANGE-TO USED))
))
(CLEANUP-TRIAL-TAGS-DELETE-STEP
IF
152
(
(GOAL CLEANUP TRIAL-TAGS)
(STEP CLEANUP-TRIAL-TAGS CHANGE-TO USED)
)
THEN
(
(DELDB (STEP CLEANUP-TRIAL-TAGS CHANGE-TO USED))
(ADDDB (STEP CLEANUP-TRIAL-TAGS DELETE GONE-TAGS))
))
(CLEANUP-TRIAL-TAGS-RGA
IF
(
(GOAL CLEANUP TRIAL-TAGS)
(STEP CLEANUP-TRIAL-TAGS DELETE GONE-TAGS)
)
THEN
(
(DELDB (STEP CLEANUP-TRIAL-TAGS DELETE GONE-TAGS))
(DELDB (GOAL CLEANUP TRIAL-TAGS))
(DELDB (STATUS CLEANUP-TRIAL-TAGS UNDERWAY))
))
;;changing all tags to a "used" tag.
(CLEANUP-TRIAL-TAGS-CHANGE-TO-USED
IF
(
(GOAL CLEANUP TRIAL-TAGS)
(STEP CLEANUP-TRIAL-TAGS CHANGE-TO USED)
(TAG DO-TRIAL ?ITEM-ID IS ?PREDICATE)
(DIFFERENT ?PREDICATE USED)
)
THEN
(
(DELDB (TAG DO-TRIAL ?ITEM-ID IS ?PREDICATE))
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS USED))
))
;;This rule deletes any tags for items no longer present.
(CLEANUP-GONE-ITEM-TAG-AT-TRIAL-END
IF
(
(GOAL CLEANUP TRIAL-TAGS)
(STEP CLEANUP-TRIAL-TAGS DELETE GONE-TAGS)
;;there is a tag
(TAG DO-TRIAL ?ITEM-ID IS ?PREDICATE)
;; but there is no corresponding item
(NOT (AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ??? SOURCE ??? MARKER ??? TYPE ???))
)
THEN
(
(DELDB (TAG DO-TRIAL ?ITEM-ID IS ?PREDICATE))
))
;;***********************************
;;********** ABORT TRIAL ************
;;***********************************
(ABORT-TRIAL
IF
(
(GOAL ABORT TRIAL)
(NOT (STATUS ABORT-TRIAL UNDERWAY))
)
THEN
(
(ADDDB (STATUS ABORT-TRIAL UNDERWAY))
153
(ADDDB (STEP ABORT-TRIAL CLEANUP TRIAL))
(ADDDB (STEP ABORT-TRIAL WAIT-FOR ABORT-DONE))
(ADDDB (GOAL CLEANUP TRIAL-TAGS)) ;cleanup the trial tags
(ADDDB (GOAL ABORT RECALL))
))
(ABORT-TRIAL-DONE
IF
(
(GOAL ABORT TRIAL)
(STEP ABORT-TRIAL WAIT-FOR ABORT-DONE)
(NOT (GOAL ABORT REHEARSE))
(NOT (GOAL ABORT RECALL))
)
THEN
(
(DELDB (GOAL ABORT TRIAL))
(DELDB (STATUS ABORT-TRIAL UNDERWAY))
(DELDB (STEP ABORT-TRIAL WAIT-FOR ABORT-DONE))
))
(ABORT-TRIAL-CLEANUP-TRIAL
IF
(
(GOAL ABORT TRIAL)
(STEP ABORT-TRIAL CLEANUP TRIAL)
)
THEN
(
(DELDB (GOAL DO TRIAL)) ;shut down the trial
(DELDB (STATUS DO-TRIAL UNDERWAY))
(DELDB (STEP ABORT-TRIAL CLEANUP TRIAL))
))
(ABORT-TRIAL-CLEANUP-STEPS
IF
(
(GOAL ABORT TRIAL)
(STEP ABORT-TRIAL CLEANUP TRIAL)
(STEP DO-TRIAL ?A ?B)
)
THEN
(
(DELDB (STEP DO-TRIAL ?A ?B))
))
;;tags cleaned up using method shared with normal trial termination
;;***********************************
;;********* RECALL SUBMETHOD ********
;;***********************************
;method ID is DO-RECALL
(MFG-RECALL
IF
(
(GOAL DO RECALL)
(NOT (STATUS DO-RECALL UNDERWAY))
)
THEN
(
(ADDDB (STATUS DO-RECALL UNDERWAY))
(ADDDB (STEP DO-RECALL WAIT-FOR START-ITEM))
))
154
(EXTERNAL-START-ITEM-PRESENT
IF
(
(GOAL DO RECALL)
(STRATEGY RECALL EXTERNAL)
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS))
(STEP DO-RECALL WAIT-FOR START-ITEM)
;there is something marked as chain start
(AUDITORY SPEECH-TAG ?UNUM ?ITEM-ID IS RECALL-CHAIN-START )
)
THEN
(
(DELDB (STEP DO-RECALL WAIT-FOR START-ITEM))
(ADDDB (STEP DO-RECALL START CHAIN-RECALL))
))
(RECALL-START-TAG-NOT-PRESENT-AT-RECALL
IF
(
(GOAL DO RECALL)
(STRATEGY RECALL EXTERNAL)
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS))
(STEP DO-RECALL WAIT-FOR START-ITEM)
(NOT (AUDITORY SPEECH-TAG ??? ??? IS RECALL-CHAIN-START ))
(NOT (AUDITORY SPEECH-TAG ??? ??? IS RECALL-CHAIN-START ))
(MOTOR VOCAL MODALITY FREE)
)
THEN
(
(DELDB (STEP DO-RECALL WAIT-FOR START-ITEM))
(ADDDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
))
(START-CHAIN-RECALL-AT-START
IF
(
(GOAL DO RECALL)
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS))
(STRATEGY RECALL ?SOURCE)
(STEP DO-RECALL START CHAIN-RECALL)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ?NEXT SOURCE ?SOURCE MARKER START TYPE ???)
;marked as chain start
(AUDITORY SPEECH-TAG ?UNUM ?ITEM-ID IS RECALL-CHAIN-START )
;hasn’t been already said
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(MOTOR VOCAL MODALITY FREE)
(RANDOMLY-CHOOSE-ONE ?ITEM-ID) ;in case there is more than one thread.
)
THEN
(
(RETRIEVE-PHONOLOGICAL-INFORMATION ^TO-BE-RECALLED-CONTENT ?ITEM-ID)
(ADDDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS SAY START ^TO-BE-RECALLED-CONTENT ))
(ADDDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
(REMOVE-AUDITORY-SPEECH-TAG ?UNUM)
(DELDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS EXTERNAL-NEW))
(ADDDB (TAG DO-RECALL ?NEXT IS NEXT-TO-RECALL))
(DELDB (STEP DO-RECALL START CHAIN-RECALL))
))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; These next rules deal with the 2-stage recall procedure
(RECALL-RETRIEVED-ACTUAL-CONTENT
IF
155
(
(GOAL DO RECALL)
(STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT)
(TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE ?MARKER ?CONTENT)
(DIFFERENT ?CONTENT NIL)
)
THEN
(
(SEND-TO-MOTOR VOCAL ?STYLE ?MARKER ?CONTENT)
(DELDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
(DELDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE ?MARKER ?CONTENT))
))
(RECALL-RETRIEVED-NIL-CONTENT
IF
(
(NOT (STRATEGY DO-RECALL RECONSTRUCT BY-ABORTING))
(NOT (STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-ORDER))
(GOAL DO RECALL)
(NOT (STEP DO-RECALL FINISH RECALL))
(STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT)
(TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE ?MARKER NIL)
)
THEN
(
(SEND-TO-MOTOR VOCAL ?STYLE ?MARKER BLANK)
(DELDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
(DELDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE ?MARKER NIL))
))
(DONT-RECALL-RETRIEVED-NIL-CONTENT--BY-ABORTING
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-ABORTING)
(GOAL DO RECALL)
(STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT)
(NOT (STEP DO-RECALL FINISH RECALL))
(TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE ?MARKER NIL)
)
THEN
(
(DELDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
(DELDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE ?MARKER NIL))
(ADDDB (STEP DO-RECALL FINISH RECALL))
))
;;If the marker is END, the previous recall rule may have invoked FINISH RECALL
;;If so, clean up with this rule.
;;It is necessary because there are other abort rules that enter FINISH RECALL steps which don’t
;; occur at the end of the trial. This cleans up the content steps without adding a finish recall step.
(CLEAN-UP-IF-NIL-CONTENT-IS-AT-FINAL-ITEM--BY-ABORTING
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-ABORTING)
(GOAL DO RECALL)
(STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT)
(STEP DO-RECALL FINISH RECALL)
(TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE END NIL)
)
THEN
(
(DELDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
(DELDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE END NIL))
))
156
;; IF YOU get a ’nil, don’t do anything
(DONT-RECALL-RETRIEVED-NIL-CONTENT--BY-ORDER
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-ORDER)
(GOAL DO RECALL)
(STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT)
(TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE ?MARKER NIL)
)
THEN
(
(DELDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
(DELDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ?STYLE ?MARKER NIL))
))
(ENABLE-RECALL-TO-CONTINUE
IF
(
(NOT (STRATEGY DO-RECALL RECONSTRUCT BY-ABORTING))
(GOAL DO RECALL)
(STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT)
(TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ??? ??? ???)
(NOT (STEP DO-RECALL FINISH RECALL))
)
THEN
(
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
))
(ENABLE-RECALL-TO-CONTINUE-DURING-ABORT-STRATEGY
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-ABORTING)
(STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT)
(TAG DO-RECALL TO-BE-RECALLED-CONTENT IS ??? ??? ?CONTENT)
(DIFFERENT ?CONTENT NIL)
(NOT (STEP DO-RECALL FINISH RECALL))
)
THEN
(
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
))
(START-CHAIN-RECALL-AT-START-RECALL-START-TAG-GONE
IF
(
(GOAL DO RECALL)
(STRATEGY RECALL EXTERNAL)
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS))
(STEP DO-RECALL START CHAIN-RECALL)
(NOT (AUDITORY SPEECH-TAG ??? ??? IS RECALL-CHAIN-START ))
(MOTOR VOCAL MODALITY FREE)
)
THEN
(
(DELDB (STEP DO-RECALL START CHAIN-RECALL))
(ADDDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
))
(START-CHAIN-RECALL-AT-START-ITEM-GONE
IF
(
157
(GOAL DO RECALL)
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS))
(STRATEGY RECALL EXTERNAL)
(STEP DO-RECALL START CHAIN-RECALL)
(NOT (AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ??? SOURCE EXTERNAL MARKER ??? TYPE ??? ))
;;marked as chain start
(AUDITORY SPEECH-TAG ?UNUM ?ITEM-ID IS RECALL-CHAIN-START )
;;hasn’t been already said
(MOTOR VOCAL MODALITY FREE)
(RANDOMLY-CHOOSE-ONE ?ITEM-ID) ;in case there is more than one thread.
)
THEN
(
(REMOVE-AUDITORY-SPEECH-TAG ?UNUM)
(DELDB (STEP DO-RECALL START CHAIN-RECALL))
(ADDDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
))
(CHAIN-RECALL-NEXT-ITEM
IF
(
(GOAL DO RECALL)
(STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)
(NOT (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM))
(TAG DO-RECALL ?ITEM-ID IS NEXT-TO-RECALL)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ?NEXT SOURCE EXTERNAL MARKER ?MARKER TYPE ??? );hasn’t been already said
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(MOTOR VOCAL MODALITY FREE)
(TAG DO-TRIAL ?END-ITEM IS STIMULUS-END)
(DIFFERENT ?END-ITEM ?NEXT)
)
THEN
(
(RETRIEVE-PHONOLOGICAL-INFORMATION ^TO-BE-RECALLED-CONTENT ?ITEM-ID)
(ADDDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS SAY ?MARKER ^TO-BE-RECALLED-CONTENT ))
(ADDDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS EXTERNAL-NEW))
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS RECALLED))
(DELDB (TAG DO-RECALL ?ITEM-ID IS NEXT-TO-RECALL))
(ADDDB (TAG DO-RECALL ?NEXT IS NEXT-TO-RECALL))
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
))
(CHAIN-RECALL-NO-NEXT-ITEM
IF
(
(GOAL DO RECALL)
(STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)
(TAG DO-RECALL ?ITEM-ID IS NEXT-TO-RECALL)
(NOT (AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ??? SOURCE EXTERNAL MARKER ??? TYPE ??? ));hasn’t been already said
(DIFFERENT ?ITEM-ID GONE)
(MOTOR VOCAL MODALITY FREE)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM-ID IS NEXT-TO-RECALL))
(ADDDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
))
;;Changes above rule so that it doesn’t necessarily end recall at the end item
;; Another rule handles the finish--when there are no more items to recall
(CHAIN-RECALL-NEXT-ITEM-END
IF
(
158
(GOAL DO RECALL)
(STRATEGY RECALL ?SOURCE)
(NOT (STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN))
(NOT (STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-ORDER))
(STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)
(TAG DO-RECALL ?ITEM-ID IS NEXT-TO-RECALL)
(TAG DO-TRIAL ?NEXT IS STIMULUS-END)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ?NEXT SOURCE ?SOURCE MARKER ?MARKER TYPE ??? )
;hasn’t been already said
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(MOTOR VOCAL MODALITY FREE)
)
THEN
(
(RETRIEVE-PHONOLOGICAL-INFORMATION ^TO-BE-RECALLED-CONTENT ?ITEM-ID)
(ADDDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS SAY ?MARKER ^TO-BE-RECALLED-CONTENT ))
(ADDDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS EXTERNAL-NEW))
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS RECALLED))
(DELDB (TAG DO-RECALL ?ITEM-ID IS NEXT-TO-RECALL))
(ADDDB (STEP DO-RECALL FINISH RECALL))
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
))
;;Changes above rule so that it doesn’t necessarily end recall at the end item
;; Another rule handles the finish--when there are no more items to recall
(CHAIN-RECALL-NEXT-ITEM-END-WITH-FILL-IN-TO-LAST-ITEM
IF
(
(GOAL DO RECALL)
(STRATEGY RECALL ?SOURCE)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)
(NOT (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM))
(TAG DO-RECALL ?ITEM-ID IS NEXT-TO-RECALL)
(TAG DO-TRIAL ?NEXT IS STIMULUS-END)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ?NEXT SOURCE ?SOURCE MARKER ?MARKER TYPE ??? )
;hasn’t been already said
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(MOTOR VOCAL MODALITY FREE)
)
THEN
(
(ADDDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM))
))
;;This rule only occurs when all items have been recalled
(FINISH-TRIAL-NO-ITEMS-LEFT-TO-RECALL
IF
(
(GOAL DO RECALL)
(MOTOR VOCAL PROCESSOR FREE)
(STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)
(NOT (TAG DO-TRIAL ??? IS TO-BE-RECALLED))
(NOT (STEP DO-RECALL FINISH RECALL))
(STEP DO-RECALL RECONSTRUCT-CHAINS)
)
THEN
(
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
(ADDDB (STEP DO-RECALL FINISH RECALL))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS)) ;This should only happen when you are about to keep looking
;; because the end tag has gone
(DELDB (STEP DO-RECALL MARK-LOST-ITEMS))
))
159
(FINISH-TRIAL-LAST-ITEM-JUST-RECALLED
IF
(
(NOT (STRATEGY DO-RECALL RECONSTRUCT BY-GUESSING))
(GOAL DO RECALL)
(MOTOR VOCAL PROCESSOR FREE)
(STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)
(NOT (TAG DO-TRIAL ??? IS TO-BE-RECALLED))
(TAG DO-RECALL ?NEXT IS NEXT-TO-RECALL)
(TAG DO-TRIAL ?NEXT IS STIMULUS-END)
)
THEN
(
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
(ADDDB (STEP DO-RECALL FINISH RECALL))
))
;;This rule takes care of situations where the next item in the chain has already been recalled.
;;In these situations, you don’t know what to do, so you give up.
(FINISH-TRIAL-NEXT-ITEM-IS-RECALLED-ALREADY
IF
(
(NOT (STRATEGY DO-RECALL RECONSTRUCT BY-GUESSING))
(GOAL DO RECALL)
(STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)
(TAG DO-RECALL ?NEXT IS NEXT-TO-RECALL)
(TAG DO-TRIAL ?NEXT IS RECALLED)
)
THEN
(
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
(ADDDB (STEP DO-RECALL FINISH RECALL))
))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;; BEGIN RECONSTRUCT BY-REORDERING SUBROUTINE
;;;
;;; This is the base strategy for two sub-strategies (specific rules in their own sections below)
;;; It attempts to rebuild the chains as well as it can, based on the remaining serial order information
;;; that exists. One substrategy stops when it runs out of words. Another substrategy fills in words
;;; that might have disappeared so that it can get the end of the list correct.
;;;................................................................................................
;
; strategy should go about like this:
; 1 mark-lost-items:
; 2 select-item-for-finding
; 3 walk-forward-to-not-found-item (repeat until)
; 4a walk-forward-[link-gone|to-found-item|etc]
; 5. goto 2, unless
; 4b no-more-items-to-select
;
;;the next rules help recover in case a link breaks
;; The strategy is simple: if the next item is not there, find the end and
;; walk backwards. When the backwards chain is done, look for any leftovers
;; Choose randomly from those leftovers if there are some, otherwise,
;; choose the beginning of the end-chain.
(GONE-TAG-RECOVER-INITIATE-SIGNAL-PHASE-WITH-REORDERING-STRATEGY
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(NOT (STEP DO-RECALL FINISH RECALL))
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS))
(TAG DO-RECALL GONE IS NEXT-TO-RECALL)
160
(MOTOR VOCAL MODALITY FREE)
(NOT (STEP DO-RECALL CLEANUP ???))
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-CHAINS)) ;;This should ONLY be deleted by the rule CLEANUP-
(ADDDB (STEP DO-RECALL DO-RECONSTRUCT-CHAINS-SIGNAL-PHASE))
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
))
(GONE-TAG-RECOVER-SIGNAL-PHASE--TO-BE-RECALLED-PRESENT-ITEMS-EXIST
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL DO-RECONSTRUCT-CHAINS-SIGNAL-PHASE)
(TAG DO-TRIAL ?ITEM IS PRESENT)
(TAG DO-TRIAL ?ITEM IS TO-BE-RECALLED)
(USE-ONLY-ONE ?ITEM)
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-PRESENT-ITEMS-EXIST))
))
(GONE-TAG-RECOVER-SIGNAL-PHASE--TO-BE-RECALLED-ORPHANED-ITEMS-EXIST
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL DO-RECONSTRUCT-CHAINS-SIGNAL-PHASE)
(TAG DO-TRIAL ?ITEM IS ORPHANED)
(TAG DO-TRIAL ?ITEM IS TO-BE-RECALLED)
(USE-ONLY-ONE ?ITEM)
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ORPHANED-ITEMS-EXIST))
))
(GONE-TAG-RECOVER-SIGNAL-PHASE--COMPLETE
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL DO-RECONSTRUCT-CHAINS-SIGNAL-PHASE)
)
THEN
(
(DELDB (STEP DO-RECALL DO-RECONSTRUCT-CHAINS-SIGNAL-PHASE))
(ADDDB (STEP DO-RECALL DO-RECONSTRUCT-CHAINS-DECISION-PHASE))
))
;;;The next four rules sort out what to do in the event of GONE-TAG-RECOVERY
;;; Either: finish recall (abort) or do normal chain recovery
(GONE-TAG-RECOVER-DECISION-PHASE--NO-TO-BE-RECALLED-ITEMS-EXIST
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL DO-RECONSTRUCT-CHAINS-DECISION-PHASE)
161
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-PRESENT-ITEMS-EXIST))
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ORPHANED-ITEMS-EXIST))
)
THEN
(
(DELDB (STEP DO-RECALL DO-RECONSTRUCT-CHAINS-DECISION-PHASE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(ADDDB (STEP DO-RECALL FINISH RECALL))
))
(GONE-TAG-RECOVER-DECISION-PHASE--ONLY-TO-BE-RECALLED-PRESENT-ITEMS-EXIST
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL DO-RECONSTRUCT-CHAINS-DECISION-PHASE)
(STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-PRESENT-ITEMS-EXIST)
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ORPHANED-ITEMS-EXIST))
)
THEN
(
(DELDB (STEP DO-RECALL DO-RECONSTRUCT-CHAINS-DECISION-PHASE))
(ADDDB (STEP DO-RECALL MARK-LOST-ITEMS))
(DELDB (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-PRESENT-ITEMS-EXIST))
))
(GONE-TAG-RECOVER-DECISION-PHASE--ONLY-TO-BE-RECALLED-ORPHANED-ITEMS-EXIST
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL DO-RECONSTRUCT-CHAINS-DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-PRESENT-ITEMS-EXIST))
(STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ORPHANED-ITEMS-EXIST)
)
THEN
(
(DELDB (STEP DO-RECALL DO-RECONSTRUCT-CHAINS-DECISION-PHASE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(ADDDB (STEP DO-RECALL FINISH RECALL))
(DELDB (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ORPHANED-ITEMS-EXIST))
))
(GONE-TAG-RECOVER-DECISION-PHASE--BOTH-TO-BE-RECALLED-PRESENT-AND-ORPHANED-ITEMS-EXIST
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL DO-RECONSTRUCT-CHAINS-DECISION-PHASE)
(STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-PRESENT-ITEMS-EXIST)
(STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ORPHANED-ITEMS-EXIST)
)
THEN
(
(DELDB (STEP DO-RECALL DO-RECONSTRUCT-CHAINS-DECISION-PHASE))
(ADDDB (STEP DO-RECALL MARK-LOST-ITEMS))
(DELDB (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-PRESENT-ITEMS-EXIST))
(DELDB (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ORPHANED-ITEMS-EXIST))
))
(MARK-LOST-ITEMS
IF
162
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL MARK-LOST-ITEMS)
(TAG DO-TRIAL ?ITEM IS TO-BE-RECALLED)
(NOT (TAG DO-RECALL ?ITEM IS FOUND))
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE WORD )
)
THEN
(
(ADDDB (TAG DO-RECALL ?ITEM IS NOT-FOUND))
(DELDB (STEP DO-RECALL MARK-LOST-ITEMS))
(ADDDB (STEP DO-RECALL FIND-LOST-ITEM))
))
(MARK-LOST-ITEMS--SIGNAL-TO-BE-RECALLED-TAGS-EXIST
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL MARK-LOST-ITEMS)
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ITEMS-EXIST))
))
(MARK-LOST-ITEMS--CLEANUP-TO-BE-RECALLED-TAGS-SIGNAL
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ITEMS-EXIST)
)
THEN
(
(DELDB (STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ITEMS-EXIST))
))
;;This will fire if you want to mark lost items, but none exist. It will occur
;;if things have disappeared since the time that you decided to mark the lost items.
(MARK-LOST-ITEMS--NO-LOST-ITEMS-EXIST
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-CHAINS TO-BE-RECALLED-ITEMS-EXIST)
(STEP DO-RECALL MARK-LOST-ITEMS) ;;If its still this step, there were no lost items marked
)
THEN
(
(DELDB (STEP DO-RECALL MARK-LOST-ITEMS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(ADDDB (STEP DO-RECALL FINISH RECALL))
))
(SELECT-ITEM-FOR-FINDING
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
163
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(TAG DO-RECALL ?ITEM IS NOT-FOUND)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
(RANDOMLY-CHOOSE-ONE ?ITEM)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS NOT-FOUND))
(ADDDB (TAG DO-RECALL ?ITEM IS FOUND))
(ADDDB (TAG DO-RECALL ?ITEM IS CHAIN-HEAD))
(ADDDB (TAG DO-RECALL ?ITEM IS CURRENT-CHAIN-HEAD))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL WALK-FORWARD))
(ADDDB (TAG DO-RECALL ?NEXT IS NEXT-ITEM))
))
(WALK-FORWARD-TO-NOT-FOUND-ITEM
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL WALK-FORWARD)
(TAG DO-RECALL ?ITEM IS NEXT-ITEM)
(TAG DO-RECALL ?ITEM IS NOT-FOUND)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS NOT-FOUND))
(ADDDB (TAG DO-RECALL ?ITEM IS FOUND))
(ADDDB (TAG DO-RECALL ?NEXT IS NEXT-ITEM))
(DELDB (TAG DO-RECALL ?ITEM IS NEXT-ITEM))
))
(WALK-FORWARD-TO-FOUND-ITEM
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL WALK-FORWARD)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(TAG DO-RECALL ?ITEM IS NEXT-ITEM)
(TAG DO-RECALL ?ITEM IS FOUND)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
(TAG DO-RECALL ?HEAD IS CURRENT-CHAIN-HEAD)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS CHAIN-HEAD))
(DELDB (STEP DO-RECALL WALK-FORWARD))
(DELDB (TAG DO-RECALL ?ITEM IS NEXT-ITEM))
(ADDDB (STEP DO-RECALL FIND-LOST-ITEM))
(DELDB (TAG DO-RECALL ?HEAD IS CURRENT-CHAIN-HEAD))
))
(WALK-FORWARD-LINK-GONE
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL WALK-FORWARD)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(TAG DO-RECALL GONE IS NEXT-ITEM)
164
(TAG DO-RECALL ?HEAD IS CURRENT-CHAIN-HEAD)
)
THEN
(
(DELDB (TAG DO-RECALL GONE IS NEXT-ITEM))
(DELDB (TAG DO-RECALL ?HEAD IS CURRENT-CHAIN-HEAD))
(ADDDB (STEP DO-RECALL FIND-LOST-ITEM))
(DELDB (STEP DO-RECALL WALK-FORWARD))
))
(CANT-WALK-FORWARD-NO-NEXT-ITEM
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL WALK-FORWARD)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(TAG DO-RECALL ?ITEM IS NEXT-ITEM)
(DIFFERENT ?ITEM GONE)
(NOT (TAG DO-TRIAL ?ITEM IS STIMULUS-END))
(NOT (AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE ??? ))
(TAG DO-RECALL ?HEAD IS CURRENT-CHAIN-HEAD)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS NEXT-ITEM))
(DELDB (TAG DO-RECALL ?HEAD IS CURRENT-CHAIN-HEAD))
(ADDDB (STEP DO-RECALL FIND-LOST-ITEM))
(DELDB (STEP DO-RECALL WALK-FORWARD))
))
;;If you have a CURRENT-CHAIN-HEAD and you walk forward to the STIMULUS-END, this makes CURRENT-CHAIN-HEAD
;; the END-CHAIN-HEAD, and delete the CURRENT-CHAIN-HEAD (you don’t need it anymore).
(WALK-FORWARD-END-ITEM
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL WALK-FORWARD)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(TAG DO-RECALL ?NEXT IS NEXT-ITEM)
(TAG DO-TRIAL ?NEXT IS STIMULUS-END)
(TAG DO-RECALL ?HEAD IS CURRENT-CHAIN-HEAD)
)
THEN
(
(ADDDB (TAG DO-RECALL ?HEAD IS END-CHAIN-HEAD))
(DELDB (TAG DO-RECALL ?NEXT IS NEXT-ITEM))
(ADDDB (STEP DO-RECALL FIND-LOST-ITEM))
(DELDB (STEP DO-RECALL WALK-FORWARD))
(DELDB (TAG DO-RECALL ?HEAD IS CURRENT-CHAIN-HEAD))
))
;;If you have an END-CHAIN-HEAD and you walk forward to it, this moves it backward.
(PROPAGATE-END-CHAIN-HEAD
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL WALK-FORWARD)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(TAG DO-RECALL ?ITEM IS NEXT-ITEM)
(TAG DO-RECALL ?ITEM IS FOUND)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
(TAG DO-RECALL ?HEAD IS CURRENT-CHAIN-HEAD)
165
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD))
(ADDDB (TAG DO-RECALL ?HEAD IS END-CHAIN-HEAD))
))
;;This rule should fire when you happen to (randomly) choose an item whose
;;NEXT is STIMULUS-END. There will not be a CURRENT-CHAIN-HEAD at this time,
;;because you are about to make the selected item the chain head.
(INITIATE-END-CHAIN-HEAD-WHEN-NO-CURRENT-CHAIN-HEAD
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL WALK-FORWARD)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(TAG DO-RECALL ?ITEM IS NEXT-ITEM)
(TAG DO-RECALL ?ITEM IS FOUND)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
(NOT (TAG DO-RECALL ??? IS END-CHAIN-HEAD))
(NOT (TAG DO-RECALL ??? IS CURRENT-CHAIN-HEAD))
(TAG DO-TRIAL ?NEXT IS STIMULUS-END)
)
THEN
(
(ADDDB (TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD))
))
;;if there are no not-found items that are not marked as end items
;;signal that there are NO-MORE-ITEMS-TO-SELECT. This initiates a SIGNAL phase
;;where the state of the world is assessed, and a DECISION phase where a (possibly strategy-dependent)
;;course of action is initiated.
(SIGNAL-NO-MORE-ITEMS-TO-SELECT
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS))
(NOT (TAG DO-RECALL ??? IS NOT-FOUND))
(NOT (STEP DO-RECALL CHAIN-HEAD-SELECTED))
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS SIGNAL-PHASE))
))
(NO-MORE-ITEMS--CLEANUP-SIGNAL-PHASE
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS SIGNAL-PHASE)
)
THEN
(
(DELDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS SIGNAL-PHASE))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE))
))
166
(NO-MORE-ITEMS--CLEANUP-DECISION-PHASE
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
))
(NO-MORE-ITEMS--DECISION-PHASE-COMPLETED-SUCCESSFULLY
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE)
)
THEN
(
(DELDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE))
(DELDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
(DELDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
;;If this occurred, no decision-phase-rules fired. Things disappeared
;; and you have to go back and figure this out.
(NO-MORE-ITEMS--DECISION-PHASE-COMPLETE-BUT-NOTHING-HAPPENED
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(STEP DO-RECALL FIND-LOST-ITEM)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(DELDB (STEP DO-RECALL RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE))
(DELDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
))
;;---------------------
;; SIGNAL-PHASE rules
;;---------------------
(NO-MORE-ITEMS--SIGNAL-PHASE--DETECT-NO-END-CHAIN-HEAD
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
167
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS SIGNAL-PHASE)
(NOT (TAG DO-RECALL ??? IS END-CHAIN-HEAD))
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT))
))
(NO-MORE-ITEMS--SIGNAL-PHASE--DETECT-ORPHANED-END-CHAIN-HEAD
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS SIGNAL-PHASE)
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
;;This is better than testing for ORPHANED because ORPHANED has a delay
(NOT (AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE WORD ) )
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
))
(NO-MORE-ITEMS--SIGNAL-PHASE--DETECT-NORMAL-END-CHAIN-HEAD-ORPHANED
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS SIGNAL-PHASE)
(TAG DO-RECALL ?ITEM IS CHAIN-HEAD)
;;This is better than testing for ORPHANED because ORPHANED has a delay
(NOT (AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE WORD ) )
(NOT (TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD))
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
))
(NO-MORE-ITEMS--SIGNAL-PHASE--DETECT-CHAIN-HEAD-PRESENT
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS SIGNAL-PHASE)
(TAG DO-RECALL ?ITEM IS CHAIN-HEAD)
;;This is better than testing for ORPHANED because ORPHANED has a delay
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE WORD )
(USE-ONLY-ONE ?ITEM)
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT))
))
(NO-MORE-ITEMS--SIGNAL-PHASE--DETECT-NON-END-CHAIN-HEAD-PRESENT
IF
168
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS SIGNAL-PHASE)
(TAG DO-RECALL ?ITEM IS CHAIN-HEAD)
(NOT (TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD))
;;This is better than testing for ORPHANED because ORPHANED has a delay
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE WORD )
(USE-ONLY-ONE ?ITEM)
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
))
(NO-MORE-ITEMS--SIGNAL-PHASE--DETECT-END-CHAIN-HEAD-PRESENT
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS SIGNAL-PHASE)
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
;;This is better than testing for ORPHANED because ORPHANED has a delay
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE WORD )
)
THEN
(
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-PRESENT))
))
;; No-more-items ’Decision’ Rules:
;; The rules in this section deal solely with reconstruction situations that are identical
;; for both WITH-FILL-IN and WITHOUT-FILL-IN strategies. Specific sections exist below for
;; sub-strategy specific rules.
;;This rule should fire when at least one normal chain head (non END-CHAIN-HEAD) exists
(NO-MORE-ITEMS--DECISION-PHASE--NORMAL
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(NOT (STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-POSITION))
(NOT (STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-ORDER))
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
(TAG DO-RECALL ?ITEM IS CHAIN-HEAD)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
(NOT (TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD))
(RANDOMLY-CHOOSE-ONE ?ITEM)
)
THEN
(
(ADDDB (TAG DO-RECALL ?ITEM IS NEXT-TO-RECALL))
169
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CHAIN-HEAD-SELECTED))
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
;;;................................................................................................
;;; END RECONSTRUCT BY-REORDERING general rules
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;; BEGIN RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN SUBROUTINE
;;;
;;; This is identical to the WITHOUT-FILL-IN below, except that it is aware of how many items remain
;;; to recall when only the END item left to recall. It will do fill-in recalls in that situation,
;;; until enough items have been recalled.
;;;.................................................................................................
;;;If the only chain head around (PRESENT or ORPHANED) is
;;;the END-CHAIN-HEAD, and you aren’t doing FILL-IN, recall the END-CHAIN-HEAD
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-END-CHAIN-HEAD-PRESENT--WITH-LAST-ITEM-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT))
(TAG DO-RECALL ?ITEM-ID IS END-CHAIN-HEAD)
(TAG DO-RECALL ?ITEM-ID IS END-CHAIN-HEAD)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ?NEXT SOURCE ??? MARKER ??? TYPE WORD )
(NOT (TAG DO-TRIAL ?NEXT IS STIMULUS-END))
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (TAG DO-RECALL ?ITEM-ID IS NEXT-TO-RECALL))
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
(ADDDB (STEP DO-RECALL CHAIN-HEAD-SELECTED))
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
))
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-END-CHAIN-HEAD-PRESENT--CHAIN-HEAD-IS-END-ITEM--WITH-LAST-ITEM-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(GOAL DO RECALL)
170
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT))
(TAG DO-RECALL ?ITEM-ID IS END-CHAIN-HEAD)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ?NEXT SOURCE ??? MARKER ??? TYPE WORD )
(TAG DO-TRIAL ?NEXT IS STIMULUS-END)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
(ADDDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM))
))
;;; If there are orphaned chain-heads that aren’t the end-chain head, but
;;; there are no unorphaned chain-heads that aren’t the end-chain-head,
;;; you’ve got to reassess what’s going on---clean up and do gone tag recovery again
(NO-MORE-ITEMS--DECISION-PHASE--ORPHANED-NORMAL-CHAIN-HEAD-EXISTS--WITH-LAST-ITEM-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT))
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
))
;;If there are no chain heads present (of any kind-neither PRESENT or ORPHANED),
;;ABORT
(NO-MORE-ITEMS--DECISION-PHASE--NO-CHAIN-HEADS-PRESENT--WITH-LAST-ITEM-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
171
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT))
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
(TAG DO-TRIAL ?ITEM-ID IS ORPHANED)
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(RANDOMLY-CHOOSE-ONE ?ITEM-ID)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
;;A real non-end-chain-head has just become unattached. Redo the detection phase.
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-NORMAL-CHAIN-HEAD-JUST-DISAPPEARED--WITH-LAST-ITEM-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;;; (NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
(TAG DO-RECALL ?ITEM-ID IS CHAIN-HEAD) ;;There is only 1 non-end-chain-head
(NOT (TAG DO-RECALL ?ITEM-ID IS END-CHAIN-HEAD))
(IF-ONLY-ONE ?ITEM-ID)
(TAG DO-RECALL ?HEAD IS CHAIN-HEAD) ;;non-end-chain-head has just gone poof
(NOT (TAG DO-RECALL ?HEAD IS END-CHAIN-HEAD))
(NOT (AUDITORY SPEECH ITEM-ID ?HEAD NEXT ??? SOURCE ??? MARKER ??? TYPE WORD ))
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;FILL-IN-TO-LAST-ITEM subroutine;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
172
;; These rules will fire when the (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM)
;; subgoal is set.
(FILL-IN-TO-LAST-ITEM
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM)
(NOT (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM ???))
)
THEN
(
(ADDDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM DETECT-ORPHANED-ITEMS))
))
;;If there are no more orphaned items, recall the (only) remaining end item
(FILL-IN-TO-LAST-ITEM-DETECT-NO-MORE-ORPHANED-ITEMS
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM DETECT-ORPHANED-ITEMS)
(NOT (TAG DO-TRIAL ??? IS ORPHANED))
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(MOTOR VOCAL MODALITY FREE)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ??? SOURCE ?SOURCE MARKER ??? TYPE WORD )
(RANDOMLY-CHOOSE-ONE ?ITEM-ID)
)
THEN
(
(RETRIEVE-PHONOLOGICAL-INFORMATION ^TO-BE-RECALLED-CONTENT ?ITEM-ID)
(ADDDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS SAY END ^TO-BE-RECALLED-CONTENT ))
(ADDDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(ADDDB (TAG DO-TRIAL ?ITEM-ID IS RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS EXTERNAL-NEW))
(DELDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM DETECT-ORPHANED-ITEMS))
(ADDDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM CLEANUP))
;;Do appropriate cleanup now.
(ADDDB (STEP DO-RECALL FINISH RECALL))
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
))
;;If you detect an orphaned item, do a random recall and reset.
(FILL-IN-TO-LAST-ITEM-DETECT-ORPHANED-ITEMS
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM DETECT-ORPHANED-ITEMS)
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(TAG DO-TRIAL ?ITEM-ID IS ORPHANED)
(RANDOMLY-CHOOSE-ONE ?ITEM)
(MOTOR VOCAL MODALITY FREE)
)
THEN
(
(DELDB (TAG DO-TRIAL ?ITEM-ID IS ORPHANED))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(CHOOSE-CONTENT-RANDOMLY-FROM-LTM ^CONTENT)
(SEND-TO-MOTOR VOCAL SAY CONTINUE ^CONTENT)
173
))
;;If you detect an orphaned item, do a random recall and reset.
(FILL-IN-TO-LAST-ITEM-NOTHING-IS-LEFT
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM DETECT-ORPHANED-ITEMS)
(NOT (TAG DO-TRIAL ??? IS TO-BE-RECALLED))
(NOT (TAG DO-TRIAL ??? IS ORPHANED))
(MOTOR VOCAL MODALITY FREE)
)
THEN
(
(DELDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM DETECT-ORPHANED-ITEMS))
(ADDDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM CLEANUP))
(ADDDB (STEP DO-RECALL FINISH RECALL))
))
(CLEAN-UP-FILL-IN-TO-LAST-ITEM
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM CLEANUP)
)
THEN
(
(DELDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM))
(DELDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM CLEANUP))
))
;;;................................................................................................
;;; END RECONSTRUCT BY-REORDERING WITH-LAST-ITEM-FILL-IN SUBROUTINE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;; BEGIN RECONSTRUCT BY-REORDERING WITH-FILL-IN SUBROUTINE
;;;
;;; This is identical to the WITHOUT-FILL-IN below, except that it is aware of
;;; how many items there are and how many are left. If it gets to the END-CHAIN-HEAD
;;; and there aren’t enough items to go around, it will guess randomly until the END-CHAIN-HEAD
;;; is long enough.
;;;.................................................................................................
;;If there is an END-CHAIN-HEAD and a ALL normal CHAIN-HEADs are ORPHANED, do a fill-in
;; (I know something disappeared, even though there may be some unattached items out there)
;;
(NO-MORE-ITEMS--DECISION-PHASE--ALL-NORMAL-CHAINS-HEAD-ORPHANED-WITH-END-CHAIN-HEAD--WITH-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
174
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(TAG DO-TRIAL ?ITEM-ID IS ORPHANED)
(RANDOMLY-CHOOSE-ONE ?ITEM-ID)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(CHOOSE-CONTENT-RANDOMLY-FROM-LTM ^CONTENT)
(SEND-TO-MOTOR VOCAL SAY CONTINUE ^CONTENT)
(DELDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS ORPHANED))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-NORMAL-CHAIN-HEAD-JUST-DISAPPEARED--WITH-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;;; (NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
(TAG DO-RECALL ?ITEM-ID IS CHAIN-HEAD) ;;There is only 1 non-end-chain-head
(NOT (TAG DO-RECALL ?ITEM-ID IS END-CHAIN-HEAD))
(IF-ONLY-ONE ?ITEM-ID)
(TAG DO-RECALL ?HEAD IS CHAIN-HEAD) ;;non-end-chain-head has just gone poof
(NOT (TAG DO-RECALL ?HEAD IS END-CHAIN-HEAD))
(NOT (TAG DO-TRIAL ?HEAD IS ORPHANED))
(NOT (AUDITORY SPEECH ITEM-ID ?HEAD NEXT ??? SOURCE ??? MARKER ??? TYPE WORD ))
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(CHOOSE-CONTENT-RANDOMLY-FROM-LTM ^CONTENT)
(SEND-TO-MOTOR VOCAL SAY CONTINUE ^CONTENT)
(DELDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS ORPHANED))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-CHAIN-HEAD-IS-END-CHAIN-HEAD-BUT-ITS-ORPHANED--WITH-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-FILL-IN)
175
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;;Include proper (NOT ..) and ;;; to match the proper situation
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT))
(NOT (AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE WORD ))
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(CHOOSE-CONTENT-RANDOMLY-FROM-LTM ^CONTENT)
(SEND-TO-MOTOR VOCAL SAY CONTINUE ^CONTENT)
(DELDB (TAG DO-TRIAL ?ITEM IS TO-BE-RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM IS ORPHANED)) ;This might not be there, but delete it anyway.
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-END-CHAIN-HEAD-REMAINS-BUT-ORPHANED-ITEMS-REMAIN--WITH-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
(TAG DO-TRIAL ?ORPHAN IS ORPHANED)
(TAG DO-TRIAL ?ORPHAN IS TO-BE-RECALLED)
(RANDOMLY-CHOOSE-ONE ?ORPHAN)
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(CHOOSE-CONTENT-RANDOMLY-FROM-LTM ^CONTENT)
(SEND-TO-MOTOR VOCAL SAY CONTINUE ^CONTENT)
(DELDB (TAG DO-TRIAL ?ORPHAN IS TO-BE-RECALLED))
(DELDB (TAG DO-TRIAL ?ORPHAN IS ORPHANED))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
176
;;If there are no chain heads present (of any kind-neither PRESENT or ORPHANED),
;; do a fill-in rehearsal
(NO-MORE-ITEMS--DECISION-PHASE--NO-CHAIN-HEADS-PRESENT--WITH-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT))
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
(TAG DO-TRIAL ?ITEM-ID IS ORPHANED)
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(RANDOMLY-CHOOSE-ONE ?ITEM-ID)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(CHOOSE-CONTENT-RANDOMLY-FROM-LTM ^CONTENT)
(SEND-TO-MOTOR VOCAL SAY CONTINUE ^CONTENT)
(DELDB (TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM-ID IS ORPHANED))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-END-CHAIN-HEAD-REMAINS--WITH-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITH-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
(NOT (TAG DO-TRIAL ??? IS ORPHANED))
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
)
THEN
(
(ADDDB (TAG DO-RECALL ?ITEM IS NEXT-TO-RECALL))
177
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CHAIN-HEAD-SELECTED))
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
;;;.................................................................................................
;;; END RECONSTRUCT BY-REORDERING WITH-FILL-IN SUBROUTINE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;; BEGIN RECONSTRUCT BY-REORDERING WITHOUT-FILL-IN SUBROUTINE
;;;
;;; This is identical to the BY-REORDERING strategies above, except that it is aware of
;;; how many items there are and how many are left. If it gets to the END-CHAIN-HEAD
;;; and there aren’t enough items left, it will recall the end-chain anyway
;;;
;;; This strategy for dealing with missing next items is the traditional approach:
;;; you move around and try as well as you can to reconstruct the items based
;;; only on what’s there. If an item has disappeared, you don’t know it and
;;; so you end up not recalling it.
;;;.................................................................................................
;;;
;;; If there are orphaned chain-heads that aren’t the end-chain head, but
;;; there are no unorphaned chain-heads that aren’t the end-chain-head,
;;; you’ve got to reassess what’s going on---clean up and do gone tag recovery again
(NO-MORE-ITEMS--DECISION-PHASE--ORPHANED-NORMAL-CHAIN-HEAD-EXISTS--WITHOUT-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITHOUT-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
;;;;(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT))
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
))
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-END-CHAIN-HEAD-REMAINS--WITHOUT-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITHOUT-FILL-IN)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
178
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
)
THEN
(
(ADDDB (TAG DO-RECALL ?ITEM IS NEXT-TO-RECALL))
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CHAIN-HEAD-SELECTED))
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-END-CHAIN-HEAD-REMAINS-BUT-ITS-ORPHANED--WITHOUT-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITHOUT-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESEN)))T)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
))
;;If there are no chain heads present (of any kind-neither PRESENT or ORPHANED),
;;ABORT
(NO-MORE-ITEMS--DECISION-PHASE--NO-CHAIN-HEADS-PRESENT--WITHOUT-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITHOUT-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
179
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT))
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
(TAG DO-TRIAL ?ITEM-ID IS ORPHANED)
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(RANDOMLY-CHOOSE-ONE ?ITEM-ID)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
(NO-MORE-ITEMS--DECISION-PHASE--ONLY-NORMAL-CHAIN-HEAD-JUST-DISAPPEARED--WITHOUT-FILL-IN
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING WITHOUT-FILL-IN)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;;; (NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
(TAG DO-RECALL ?ITEM-ID IS CHAIN-HEAD) ;;There is only 1 non-end-chain-head
(NOT (TAG DO-RECALL ?ITEM-ID IS END-CHAIN-HEAD))
(IF-ONLY-ONE ?ITEM-ID)
(TAG DO-RECALL ?HEAD IS CHAIN-HEAD) ;;non-end-chain-head has just gone poof
(NOT (TAG DO-RECALL ?HEAD IS END-CHAIN-HEAD))
(NOT (AUDITORY SPEECH ITEM-ID ?HEAD NEXT ??? SOURCE ??? MARKER ??? TYPE WORD ))
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
;;;.................................................................................................
;;; END RECONSTRUCT BY-REORDERING WITHOUT-FILL-IN SUBROUTINE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
180
;;; BEGIN RECONSTRUCT BY-ABORTING SUBROUTINE
;;;
;;; According to this "strategy", when the going gets rough, you hide your head under a rock.
;;; Total, complete failure (sorta). You clean up gracefully and wait for the next trial.
;;;
;;;................................................................................................
(SIGNAL-GONE-TAG-RECOVERY-NECESSARY-WITH-ABORTING-STRATEGY
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-ABORTING)
(NOT (STEP DO-RECALL FINISH RECALL))
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS))
(TAG DO-RECALL GONE IS NEXT-TO-RECALL)
(MOTOR VOCAL MODALITY FREE)
)
THEN
(
(ADDDB (STEP DO-RECALL FINISH RECALL))
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)) ;;This might not be there, but it should be deleted
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
))
;;.................................................................................................
;;; END RECONSTRUCT BY-ABORTING SUBROUTINE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;; BEGIN RECONSTRUCT BY-GUESSING SUBROUTINE
;;;
;;; According to this strategy, when you run into a problem
;;; and dont know what to do next, choose a to-be-recalled item.
;;; randomly and continue from that point.
;;;................................................................................................
(SIGNAL-GONE-TAG-RECOVERY-NECESSARY-WITH-GUESSING-STRATEGY
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-GUESSING)
(NOT (STEP DO-RECALL FINISH RECALL))
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS))
(TAG DO-RECALL GONE IS NEXT-TO-RECALL)
(MOTOR VOCAL MODALITY FREE)
(DIFFERENT ?ITEM-ID GONE)
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(AUDITORY SPEECH ITEM-ID ?ITEM-ID NEXT ??? SOURCE ??? MARKER ??? TYPE WORD )
(RANDOMLY-CHOOSE-ONE ?ITEM-ID)
)
THEN
(
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)) ;;Add this because it won’t always exist
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
(ADDDB (TAG DO-RECALL ?ITEM-ID IS NEXT-TO-RECALL))
))
;;This rule takes care of situations where the next item in the chain has already been recalled.
;;In these situations, you don’t know what to do, so you give up.
(FINISH-TRIAL-LAST-ITEM-JUST-RECALLED-BY-GUESSING
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-GUESSING)
(GOAL DO RECALL)
(MOTOR VOCAL PROCESSOR FREE)
(STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)
(NOT (TAG DO-TRIAL ??? IS TO-BE-RECALLED))
181
(TAG DO-RECALL ?NEXT IS NEXT-TO-RECALL)
(TAG DO-TRIAL ?NEXT IS STIMULUS-END)
)
THEN
(
(DELDB (TAG DO-RECALL ?NEXT IS NEXT-TO-RECALL))
(ADDDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
))
(FINISH-TRIAL-NEXT-ITEM-IS-RECALLED-ALREADY-BY-GUESSING
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-GUESSING)
(GOAL DO RECALL)
(STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)
(TAG DO-RECALL ?NEXT IS NEXT-TO-RECALL)
(TAG DO-TRIAL ?NEXT IS RECALLED)
(TAG DO-TRIAL ??? IS TO-BE-RECALLED)
)
THEN
(
(ADDDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
(DELDB (TAG DO-RECALL ?NEXT IS NEXT-TO-RECALL))
))
;;.................................................................................................
;;; END RECONSTRUCT BY-GUESSING SUBROUTINE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;; BEGIN RECONSTRUCT BY-REORDERING BY-POSITION SUBROUTINE
;;;
;;; According to this "strategy", you only recall the words you know for sure,
;;; saying "blank" in between
;;;................................................................................................
(SIGNAL-GONE-TAG-RECOVERY-NECESSARY-WITH-RECONSTRUCT-BY-POSITION
IF
(
(GOAL DO RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-POSITION)
(NOT (STEP DO-RECALL FINISH RECALL))
(NOT (STEP DO-RECALL RECONSTRUCT-CHAINS))
(TAG DO-RECALL GONE IS NEXT-TO-RECALL)
(MOTOR VOCAL MODALITY FREE)
)
THEN
(
(ADDDB (STEP DO-RECALL FINISH RECALL))
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM)) ;;This might not be there, but it should be deleted
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
))
;; If there isn’t an end-chain-head, pick a chain-head randomly,
;; mark it as ’recalled’, and recall ’BLANK’
(GONE-TAG-RECOVER--DECISION-PHASE--NEXT-ITEM-IS-UNCERTAIN-WITH-POSITION-RECALL
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-POSITION)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT)
182
(NOT (TAG DO-TRIAL ??? IS ORPHANED))
(TAG DO-RECALL ?ITEM IS CHAIN-HEAD)
(NOT (TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD))
(RANDOMLY-CHOOSE-ONE ?ITEM)
)
THEN
(
(DELDB (TAG DO-TRIAL ?ITEM IS TO-BE-RECALLED))
(ADDDB (TAG DO-TRIAL ?ITEM IS RECALLED))
(ADDDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS SAY CONTINUE blank ))
(ADDDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
))
(GONE-TAG-RECOVER--DECISION-PHASE--NEXT-ITEM-IS-UNCERTAIN--ORPHANED-ITEMS-EXIST--WITH-POSITION-RECALL
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-POSITION)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
(TAG DO-TRIAL ?ITEM IS ORPHANED)
(NOT (TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD))
(RANDOMLY-CHOOSE-ONE ?ITEM)
)
THEN
(
(DELDB (TAG DO-TRIAL ?ITEM IS TO-BE-RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM IS ORPHANED))
(ADDDB (TAG DO-TRIAL ?ITEM IS RECALLED))
(ADDDB (TAG DO-RECALL TO-BE-RECALLED-CONTENT IS SAY CONTINUE blank ))
(ADDDB (STEP DO-RECALL RECALL-TO-BE-RECALLED-CONTENT))
))
(GONE-TAG-RECOVER--DECISION-PHASE--ONLY-CHAIN-HEAD-IS-END-CHAIN-HEAD-WITH-POSITION-RECALL
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-POSITION)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-PRESENT)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
(NOT (TAG DO-TRIAL ??? IS ORPHANED))
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (TAG DO-RECALL ?ITEM IS NEXT-TO-RECALL))
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
))
;;;.................................................................................................
;;; END RECONSTRUCT BY-POSITION SUBROUTINE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
183
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;; BEGIN RECONSTRUCT BY-REORDERING BY-ORDER SUBROUTINE
;;;
;;; According to this "strategy", you only recall the words you know for sure, skipping
;;; intermediate words. It is based on the "without-fill-in" reordering strategy above, but
;;; recalls a single normal chain, then skips to the end-chain.
;;;
;;; It does the following:
;;; It will recall until there is a problem. Then, it will find the end-chain
;;; and recall that. If it can’t find the end-chain, it will give up.
;;; If redintegration fails, it will move to the next item.
;;;.............................................................................
;;If you are re-ordering and the end-chain-head exists, choose it and recall it.
(NO-MORE-ITEMS--DECISION-PHASE--END-CHAIN-HEAD-EXISTS--BY-ORDER
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-ORDER)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
;;;; (STEP DO-RECALL RECONSTRUCT-BY-REORDERING-BY-ORDER RECALL-END-CHAIN)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-PRESENT)
;;;; (NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
;;;; (NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED))
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
(AUDITORY SPEECH ITEM-ID ?ITEM NEXT ?NEXT SOURCE ??? MARKER ??? TYPE ??? )
)
THEN
(
(ADDDB (TAG DO-RECALL ?ITEM IS NEXT-TO-RECALL))
(DELDB (TAG DO-RECALL GONE IS NEXT-TO-RECALL))
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CHAIN-HEAD-SELECTED))
(ADDDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
;;If the end-chain tag exists but doesn’t currently correspond to an item,
;; begin again
(NO-MORE-ITEMS--DECISION-PHASE--END-CHAIN-HEAD-REMAINS-BUT-ITS-ORPHANED--BY-ORDER
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-ORDER)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-PRESENT)
184
;;;; (NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT))
;;;; (NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED))
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
;;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
))
;;If there are no chain heads present (of any kind-neither PRESENT or ORPHANED),
;;ABORT
(NO-MORE-ITEMS--DECISION-PHASE--NO-CHAIN-HEADS-PRESENT--BY-ORDER
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING BY-ORDER)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL FIND-LOST-ITEM)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE)
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE COMPLETE))
;;These are the possible STEPS from the NO-MORE-ITEMS--SIGNAL-PHASE.
;;Include proper (NOT ..) and ;;; to match the proper situation
(NOT (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS CHAIN-HEAD-PRESENT))
;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NON-END-CHAIN-HEAD-PRESENT)
;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NORMAL-CHAIN-HEAD-ORPHANED)
;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS END-CHAIN-HEAD-ORPHANED)
;;; (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS NO-END-CHAIN-HEAD-PRESENT)
(TAG DO-TRIAL ?ITEM-ID IS ORPHANED)
(TAG DO-TRIAL ?ITEM-ID IS TO-BE-RECALLED)
(RANDOMLY-CHOOSE-ONE ?ITEM-ID)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS DECISION-PHASE DECISION-MADE))
))
;;;.................................................................................................
;;; END RECONSTRUCT BY-ORDER SUBROUTINE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;; BEGIN CLEANUP RULES
;;;
;;; Any rules that deal with general tag cleanup during recall and reconstruction go here
;;;................................................................................................
(CLEANUP-RECONSTRUCT-CHAINS
IF
(
(STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS)
)
THEN
(
(DELDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
185
(DELDB (STEP DO-RECALL RECONSTRUCT-CHAINS))
))
(CLEANUP-RECONSTRUCT-NO-MORE-ITEMS
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS)
(STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS ?A)
)
THEN
(
(DELDB (STEP DO-RECALL CLEANUP RECONSTRUCT-NO-MORE-ITEMS))
(DELDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS ?A))
(DELDB (STEP DO-RECALL RECONSTRUCT-NO-MORE-ITEMS))
))
;;It only attempts to cleanup if an item has been successfully selected
(CLEANUP-NO-MORE-ITEMS
IF
(
(TAG DO-RECALL ?ITEM IS NEXT-TO-RECALL)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(DIFFERENT ?ITEM GONE) ;we have successfully selected an item
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL CHAIN-HEAD-SELECTED)
)
THEN
(
(DELDB (STEP DO-RECALL FIND-LOST-ITEM))
(ADDDB (STEP DO-RECALL CLEANUP RECONSTRUCT-CHAINS))
(DELDB (STEP DO-RECALL CHAIN-HEAD-SELECTED))
))
(CLEANUP-NO-MORE-ITEMS-LAST-ITEM-TAG
IF
(
(TAG DO-RECALL ?LAST IS LAST-ITEM)
(TAG DO-RECALL ?ITEM IS NEXT-TO-RECALL)
(DIFFERENT ?ITEM GONE) ;we have successfully selected an item
(GOAL DO RECALL)
(STEP DO-RECALL RECONSTRUCT-CHAINS)
(STEP DO-RECALL CHAIN-HEAD-SELECTED)
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
)
THEN
(
(DELDB (TAG DO-RECALL ?LAST IS LAST-ITEM))
))
(CLEANUP-FOUND-TAGS
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL CLEANUP FOUND-TAGS)
(TAG DO-RECALL ?ITEM IS FOUND)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS FOUND))
186
))
(CLEANUP-FOUND-TAGS-SIGNAL
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL CLEANUP FOUND-TAGS)
)
THEN
(
(ADDDB (STEP DO-RECALL CLEANUP-TAGS DONE))
))
(CLEANUP-NOT-FOUND-TAGS
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL CLEANUP FOUND-TAGS)
(TAG DO-RECALL ?ITEM IS NOT-FOUND)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS NOT-FOUND))
(ADDDB (STEP DO-RECALL CLEANUP-TAGS DONE))
))
(CLEANUP-CHAIN-HEAD-TAGS
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL CLEANUP FOUND-TAGS)
(TAG DO-RECALL ?ITEM IS CHAIN-HEAD)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS CHAIN-HEAD))
(ADDDB (STEP DO-RECALL CLEANUP-TAGS DONE))
))
(CLEANUP-END-CHAIN-HEAD-TAGS
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL CLEANUP FOUND-TAGS)
(TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS END-CHAIN-HEAD))
(ADDDB (STEP DO-RECALL CLEANUP-TAGS DONE))
))
(FINISH-FOUND-TAG-CLEANUP
IF
(
(STRATEGY DO-RECALL RECONSTRUCT BY-REORDERING)
(GOAL DO RECALL)
(STEP DO-RECALL CLEANUP-TAGS DONE)
)
187
THEN
(
(DELDB (STEP DO-RECALL CLEANUP-TAGS DONE))
(DELDB (STEP DO-RECALL CLEANUP FOUND-TAGS))
(DELDB (STEP DO-RECALL CLEANUP-FOUND-TAGS DONE))
(DELDB (STEP DO-RECALL CLEANUP-NOT-FOUND-TAGS DONE))
(DELDB (STEP DO-RECALL CLEANUP-LAST-FOUND-TAG DONE))
))
;;Get rid of found tags that no longer point at anything
(CLEANUP-ORPHANED-DO-RECALL-TAGS
IF
(
(GOAL DO STM-TASK )
(TAG DO-RECALL ?ITEM IS ?TAG)
(NOT (AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE ??? ))
(DIFFERENT ?TAG GONE)
(DIFFERENT ?ITEM GONE)
(DIFFERENT ?TAG NEXT-TO-RECALL)
(DIFFERENT ?TAG NEXT-ITEM)
(DIFFERENT ?TAG CHAIN-HEAD)
(DIFFERENT ?TAG END-CHAIN-HEAD)
(DIFFERENT ?TAG CURRENT-CHAIN-HEAD)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM IS ?TAG))
))
(CLEANUP-ORPHANED-DO-TRIAL-TAGS
IF
(
(GOAL DO STM-TASK)
(TAG DO-TRIAL ?ITEM IS ?TAG)
(NOT (AUDITORY SPEECH ITEM-ID ?ITEM NEXT ??? SOURCE ??? MARKER ??? TYPE ??? ))
(DIFFERENT ?TAG GONE)
(DIFFERENT ?ITEM GONE)
(DIFFERENT ?TAG STIMULUS-END)
(DIFFERENT ?TAG TO-BE-RECALLED)
(DIFFERENT ?TAG ORPHANED)
(DIFFERENT ?TAG RECEIVED)
)
THEN
(
(DELDB (TAG DO-TRIAL ?ITEM IS ?TAG))
))
(CLEANUP-ORPHANED-RECALLED-TAG-PAIRS
IF
(
(GOAL DO STM-TASK)
(TAG DO-TRIAL ?ITEM IS RECALLED)
(TAG DO-TRIAL ?ITEM IS ORPHANED)
)
THEN
(
(DELDB (TAG DO-TRIAL ?ITEM IS RECALLED))
(DELDB (TAG DO-TRIAL ?ITEM IS ORPHANED))
))
;;very rarely, an item will disappear right when you choose to do a fill-in recall.
;;You will attempt to delete its ORPHANED tag, which doesn’t exist, and at the same time,
;;another rule will add the orphaned tag. This cleans up, deleting all orphaned tags that do not
;;have a corresponding RECALLED or TO-BE-RECALLED tag.
188
(CLEANUP-ORPHANED-ORPHANED-TAG
IF
(
(GOAL DO STM-TASK)
(TAG DO-TRIAL ?ITEM IS ORPHANED)
(NOT (TAG DO-TRIAL ?ITEM IS RECALLED))
(NOT (TAG DO-TRIAL ?ITEM IS TO-BE-RECALLED))
)
THEN
(
(DELDB (TAG DO-TRIAL ?ITEM IS ORPHANED))
))
(RECALL-RGA
IF
(
(GOAL DO RECALL)
(STEP DO-RECALL FINISH RECALL)
)
THEN
(
(DELDB (GOAL DO RECALL))
(DELDB (STEP DO-RECALL FINISH RECALL))
(DELDB (STATUS DO-RECALL UNDERWAY))
(DELDB (STEP DO-RECALL CHAIN-RECALL NEXT-ITEM ))
))
;get rid of any tags that this method added
(RECALL-CLEANUP
IF
(
(GOAL DO RECALL)
(STEP DO-RECALL FINISH RECALL)
(TAG DO-RECALL ?ITEM-ID IS ?PREDICATE)
)
THEN
(
(DELDB (TAG DO-RECALL ?ITEM-ID IS ?PREDICATE))
))
(RECALL-CLEANUP-STEPS--FILL-IN-AND-RECALL
IF
(
(GOAL DO RECALL)
(STEP DO-RECALL FINISH RECALL)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM)
)
THEN
(
(DELDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM))
))
(RECALL-CLEANUP-STEPS--FILL-IN-AND-RECALL-PREDICATE
IF
(
(GOAL DO RECALL)
(STEP DO-RECALL FINISH RECALL)
(STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM ?PRED)
)
THEN
(
(DELDB (STEP DO-RECALL FILL-IN-AND-RECALL-LAST-ITEM ?PRED))
))
189
(ABORT-RECALL
IF
(
(GOAL ABORT RECALL)
(NOT (STATUS ABORT-RECALL UNDERWAY))
)
THEN
(
(ADDDB (STATUS ABORT-RECALL UNDERWAY))
(ADDDB (STEP ABORT-RECALL CLEANUP RECALL))
))
(ABORT-RECALL-CLEANUP-CONTROL
IF
(
(GOAL ABORT RECALL)
(STEP ABORT-RECALL CLEANUP RECALL)
)
THEN
(
(DELDB (GOAL DO RECALL))
(DELDB (STATUS DO-RECALL UNDERWAY))
(DELDB (GOAL ABORT RECALL))
(DELDB (STATUS ABORT-RECALL UNDERWAY))
(DELDB (STEP ABORT-RECALL CLEANUP RECALL))
))
(ABORT-RECALL-CLEANUP-STEPS
IF
(
(GOAL ABORT RECALL)
(STEP ABORT-RECALL CLEANUP RECALL)
(STEP DO-RECALL ?A ?B)
)
THEN
(
(DELDB (STEP DO-RECALL ?A ?B))
))
(ABORT-RECALL-CLEANUP-1STEPS
IF
(
(GOAL ABORT RECALL)
(STEP ABORT-RECALL CLEANUP RECALL)
(STEP DO-RECALL ?A)
)
THEN
(
(DELDB (STEP DO-RECALL ?A))
))
(ABORT-RECALL-CLEANUP-TAGS
IF
(
(GOAL ABORT RECALL)
(STEP ABORT-RECALL CLEANUP RECALL)
(TAG DO-RECALL ?A ?B ?C)
)
THEN
(
(DELDB (TAG DO-RECALL ?A ?B ?C))
))
;;.................................................................................................
;;; END CLEANUP RULES
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
190
APPENDIX B
PERFORMANCE OF IMMEDIATE SERIAL RECALLMODELS UNDER DIFFERENT PARAMETER
SETTINGS
The present models of working memory have a number of parameters that can be
varied in order to produce simulated results that approximate actual human per-
formance. In the current models, these parameters are associated with four decay
distributions and one capacity distribution, as described in Chapter IV. These dis-
tributions describe the decay properties of list’s initial SPEECH-TAG, the ORDER TAGs
that keep track of relative order information within a list, the ORDER TAG of the
final item (which is assumed to have its own decay distribution), and the phonologi-
cal content of each word. Additionally, another distribution discribes the capacity of
the primary auditory store for AUDITORY SPEECH items: it determines whether a new
item will displace an item that is currently encoded in primary auditory memory.
Presently, I assume that each of these distributions is a log-normal distribution
with two parameters: one describing the median of the distribution, and one de-
scribing the spread of the distribution relative to its median. This produces a total
of eight parameters that may be varied. Additionally, I have described four recall
strategies in Chapter IV. Varying these parameters has different effects for differ-
ent recall strategies, and this appendix demonstrates what affects different decay
191
distributions might produce, if all other parameters were held constant.
In each graph, “Position” and “Item” serial position functions are constructed for
simulated list lengths between 4 and 7. Each column contains simulated performance
for a single median parameter, and each row contains simulated performance under
a single spread parameter. The median decay parameter is scaled in milliseconds
(or for the speech item capacity distribution, items), and so larger numbers indicate
that the information lasted longer. The spread parameter only affects the shape of
the distribution, and has no scale unit. Values close to 0 approximate a sharp step
function decay (i.e., information is always present for M seconds, at which time it
always disappears), whereas larger values produce a distribution with a much longer
tail (i.e., information is likely to disappear nearly immediately, but some is likely to
exist for a relatively long duration.)
The simulations presented here were conducted with 500 trials for each strategy
(4) by median (5) by spread (5) by list length (4) condition, resulting in a total of
200,000 simulated trials of immediate serial recall.
B.1 The role of the speech-tag decay distribution
In the new auditory perceptual processor described in Chapter IV, the first item
on a list is marked with a special SPEECH TAG that decays in an all-or-none fashion
according to a probabilistic distribution. When this tag is gone, a guessing strategy
may still be able to determine which item belongs in the first position by eliminating
other items based on other information.
Performance for four different strategies under a parameterized range of speech-
tag decay distributions is shown in Figures B.1 through B.4. For the “Abort on
Error” guessing strategy, this distribution can have a large influence on performance
192
because this strategy will stop immediately if it is unsure about what the first item is.
The remaining three strategies produce position and item position functions that are
similar to each other: varying these parameters has little effect on the item position
functions, and has predictable effects on the “position” position functions. These
parameters do not affect the “item” functions because these strategies attempt to
reconstruct the order of items, and so even if the first item is not tagged as the first
item, it will eventually get recalled.
These two parameters do not appear to produce entirely separable effects on the
recall accuracy of the initial item: distributions with small medians and small spread
parameters produce results similar to distributions with larger medians and larger
spread parameters. This occurs because only a single speech tag is used for each
list, producing a fairly direct relationship between the decay distribution and the
probability of recalling the first item correctly. Thus, an increase in the distribution’s
median can be compensated for by a corresponding increase in the distribution’s
spread. Although these two distributions would be different (especially in their tail),
their shapes for during the time period in which the initial items of lists are usually
recalled are very similar.
193
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
5000 9000 13000 17000 21000
"Position" Serial Position Functions: Abort Strategy
"Item" Serial Position Functions: Abort Strategy
Figure B.1: “Position” and “Item” position functions for varying values of the median and spreadparameters of the speech-tag decay distribution, for the “Abort on Error” guessingstrategy.
194
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
5000 9000 13000 17000 21000
"Position" Serial Position Functions: Order Reconstruction Strategy
"Item" Serial Position Functions: Order Reconstruction Strategy
Figure B.2: “Position” and “Item” position functions for varying values of the median and spreadparameters of the speech-tag decay distribution, for the “Order Reconstruction” guess-ing strategy.
195
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
5000 9000 13000 17000 21000
"Position" Serial Position Functions: Fill−in Before Last Item Strategy
"Item" Serial Position Functions: Fill−in Before Last Item Strategy
Figure B.3: “Position” and “Item” position functions for varying values of the median and spreadparameters of the speech-tag decay distribution, for the “Fill In Before Last Item”guessing strategy.
196
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
5000 9000 13000 17000 21000
"Position" Serial Position Functions: Fill−in Before End−Chain Strategy
"Item" Serial Position Functions: Fill−in Before End−Chain Strategy
Figure B.4: “Position” and “Item” position functions for varying values of the median and spreadparameters of the speech-tag decay distribution, for the “Fill In Before End-Chain”guessing strategy.
197
B.2 The role of serial order link decay distribution
In the new auditory perceptual processor described in Chapter IV, the SPEECH-OBJECT
ORDER TAG maintains serial order information in the form of a tag pointing to the
subsequent item in a list. This type of information can disappear independently from
the general speech object. I assume that it decays in an all-or-none fashion according
to a log-normal distribution.
Performance for four different strategies under a parameterized range of order tag
decay distributions is shown in Figures B.5 through B.8. This parameter has a
large effect on the “position” serial position functions, and the effect differs based
on which strategy is being used. Generally, as the distribution gets longer and its
spread parameter gets smaller, performance gets better and the effect of list length
diminishes.
198
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3000 5000 8000 10000 15000
"Position" Serial Position Functions: Abort Strategy
"Item" Serial Position Functions: Abort Strategy
Figure B.5: “Position” and “Item” position functions for varying values of the median and spreadparameters of the decay distribution of the serial order tag, for the “Abort on Error”guessing strategy.
199
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3000 5000 8000 10000 15000
"Position" Serial Position Functions: Order Reconstruction Strategy
"Item" Serial Position Functions: Order Reconstruction Strategy
Figure B.6: “Position” and “Item” position functions for varying values of the median and spreadparameters of the decay distribution of the serial order tag, for the “Order Reconstruc-tion” guessing strategy.
200
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3000 5000 8000 10000 15000
"Position" Serial Position Functions: Fill−in Before Last Item Strategy
"Item" Serial Position Functions: Fill−in Before Last Item Strategy
Figure B.7: “Position” and “Item” position functions for varying values of the median and spreadparameters of the decay distribution of the serial order tag, for the “Fill In Before LastItem” guessing strategy.
201
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3000 5000 8000 10000 15000
"Position" Serial Position Functions: Fill−in Before End−Chain Strategy
"Item" Serial Position Functions: Fill−in Before End−Chain Strategy
Figure B.8: “Position” and “Item” position functions for varying values of the median and spreadparameters of the decay distribution of the serial order tag, for the “Fill In BeforeEnd-Chain” guessing strategy.
202
B.3 The role of final item decay distribution
In the new auditory perceptual processor described in Chapter IV, the final item
in a list is marked by creating a tag that refers to the same ITEM-ID that the final
item refers to. Thus, whenever the last item’s order tag exists, its status as the
final item in a list can be determined unambiguously. This order information has
its own decay distribution, potentially different from the decay distribution of other
(non-end) items.
Performance for four different strategies under a parameterized range of final
item decay distributions is shown in Figures B.9 through B.12. This parameter has
little or no effect on either the order or item position functions of the “Abort on
Error” guessing strategy (Figure B.9). For the “Fill In Before Last Item” strategy
(Figure B.11), the distribution parameters have little effect on the item position
functions (lower panel), but do affect the order position functions (upper panel):
distributions with longer decay medians produce larger recency effects, whereas the
spread parameter primarily affects how large of an effect list length has on the recall
accuracy of the final item.
203
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3000 5000 8000 10000 15000
"Position" Serial Position Functions: Abort Strategy
"Item" Serial Position Functions: Abort Strategy
Figure B.9: “Position” and “Item” position functions for varying values of the median and spreadparameters of the decay distribution of the end item, for the “Abort on Error” guessingstrategy.
204
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3000 5000 8000 10000 15000
"Position" Serial Position Functions: Order Reconstruction Strategy
"Item" Serial Position Functions: Order Reconstruction Strategy
Figure B.10: “Position” and “Item” position functions for varying values of the median and spreadparameters of the decay distribution of the end item, for the “Order Reconstruction”guessing strategy.
205
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3000 5000 8000 10000 15000
"Position" Serial Position Functions: Fill−in Before Last Item Strategy
"Item" Serial Position Functions: Fill−in Before Last Item Strategy
Figure B.11: “Position” and “Item” position functions for varying values of the median and spreadparameters of the decay distribution of the end item, for the “Fill In Before Last Item”guessing strategy.
vvvv
206
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3000 5000 8000 10000 15000
"Position" Serial Position Functions: Fill−in Before End Chain Strategy
"Item" Serial Position Functions: Fill−in Before End Chain Strategy
Figure B.12: “Position” and “Item” position functions for varying values of the median and spreadparameters of the decay distribution of the end item, for the “Fill In Before End-Chain” guessing strategy.
207
B.4 The role of the capacity of the primary auditory store
In the new auditory perceptual processor described in Chapter IV, there is an
upper limit to the number of speech objects that can be maintained at a given
time. This upper limit is described by a stochastic log-normal distribution with two
parameters. Every item that is perceived gets encoded into auditory speech-object
store, but with some probability (depending on how many objects are in the speech-
object store) an item currently in this store will get overwritten and replaced by the
new item.
Performance for four different strategies under a parameterized range of final item
decay distributions is shown in Figures B.13 through B.16. Not surprisingly, this
parameter has a large effect on each strategy, and affects both “position” and “item”
serial position functions.
208
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3 5 8 10 15
"Position" Serial Position Functions: Abort Strategy
"Item" Serial Position Functions: Abort Strategy
Figure B.13: “Position” and “Item” position functions for varying values of the median and spreadparameters of the capacity distribution of speech objects, for the “Abort on Error”guessing strategy.
209
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3 5 8 10 15
"Position" Serial Position Functions: Order Reconstruction Strategy
"Item" Serial Position Functions: Order Reconstruction Strategy
Figure B.14: “Position” and “Item” position functions for varying values of the median and spreadparameters of the capacity distribution of speech objects, for the “Order Reconstruc-tion” guessing strategy.
210
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3 5 8 10 15
"Position" Serial Position Functions: Fill−in Before Last Item Strategy
"Item" Serial Position Functions: Fill−in Before Last Item Strategy
Figure B.15: “Position” and “Item” position functions for varying values of the median and spreadparameters of the capacity distribution of speech objects, for the “Fill In Before LastItem” guessing strategy.
211
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
3 5 8 10 15
"Position" Serial Position Functions: Fill−in Before End−Chain Strategy
"Item" Serial Position Functions: Fill−in Before End−Chain Strategy
Figure B.16: “Position” and “Item” position functions for varying values of the median and spreadparameters of the capacity distribution of speech objects, for the “Fill In Before End-Chain” guessing strategy.
212
B.5 The role of the phonological storage decay parameters
In the new auditory perceptual processor described in Chapter IV, phonological
content of an item is maintained in a distinct storage buffer. The information de-
cays according to a log-normal distribution, and the parameters of this distribution
presumably depend on factors such as the phonological similarity and familiarity of
a words.
Performance for four different strategies under a parameterized range of phono-
logical decay distributions is shown in Figures B.17 through B.20. This distribution
affects both “item” and “position” serial position functions of all strategies, but has
a similar effect for each of the “order reconstruction” strategies because the errors
introduced by phonological decay may not be detected by these strategies.
213
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
5000 9000 13000 17000 21000
"Position" Serial Position Functions: Abort Strategy
"Item" Serial Position Functions: Abort Strategy
Figure B.17: “Position” and “Item” position functions for varying values of the median and spreadparameters of the phonological information decay distribution, for the “Abort onError” guessing strategy.
214
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
5000 9000 13000 17000 21000
"Position" Serial Position Functions: Order Reconstruction Strategy
"Item" Serial Position Functions: Order Reconstruction Strategy
Figure B.18: “Position” and “Item” position functions for varying values of the median and spreadparameters of the phonological information decay distribution, for the “Order Recon-struction” guessing strategy.
215
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
5000 9000 13000 17000 21000
"Position" Serial Position Functions: Fill−in Before Last Item Strategy
"Item" Serial Position Functions: Fill−in Before Last Item Strategy
Figure B.19: “Position” and “Item” position functions for varying values of the median and spreadparameters of the phonological information decay distribution, for the “Fill In BeforeLast Item” guessing strategy.
216
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7
0.1
0.3
0.5
1.0
2.0
5000 9000 13000 17000 21000
"Position" Serial Position Functions: Fill−in Before End−Chain Strategy
"Item" Serial Position Functions: Fill−in Before End−Chain Strategy
Figure B.20: “Position” and “Item” position functions for varying values of the median and spreadparameters of the phonological information decay distribution, for the “Fill In BeforeEnd-Chain” guessing strategy.
REFERENCES
Anderson, J. R. (1993). Rules of the Mind. Lawrence Erlbaum Associates, Hillsdale,
NJ.
Anderson, J. R., & Matessa, M. (1997). A production system theory of serial memory.
Psychological Review, 104, 728-748.
Anderson, J. R., Bothell, D., Lebiere, C., & Matessa, M. (1998). An integrated
theory of list memory. Journal of Memory & Language, 38, 341-380.
Baddeley, A. D. (1968). How does acoustic similarity influence short-term memory?
Quarterly Journal of Experimental Psychology, 20, 249-264.
Baddeley, A. D. (1986). Working memory. Oxford, UK: Oxford University Press.
Baddeley, A., & Lewis, V. (1984). When does rapid presentation enhance digit span?
Bulletin of the Psychonomic Society, 22, 403-405.
Baddeley, A. D., Thomson, N., & Buchanan, M. (1975). Word length and the struc-
ture of short-term memory. Journal of Verbal Learning and Verbal Behavior,
14, 575-589.
Brown, G. D., & Hulme, C. (1995). Modeling item length effects in memory span:
No rehearsal needed? Journal of Memory and Language, 34, 594-624.
Brown, G. D. A., Preece, T., & Hulme, C. (2000). Oscillator-based memory for serial
order. Psychological Review, 107, 127-181.
Balota, D. A., & Engle, R. W. (1981). Structural and strategic factors in the stimulus
suffix effect. Journal of Verbal Learning and Verbal Behavior, 20, 346-357.
Burgess, N., & Hitch, G. (1996). A connectionist model of STM for serial order. In
217
218
S. Gathercole (Ed.) Models of short-term memory (pp. 51-72). Hove, U. K.:
Psychology Press.
Busemeyer, J. R.& Wang, Y. (2000). Model comparisons and model selections based
on generalization criterion methodology. Journal of Mathematical Psychology:
Special Issue on Model Selection, 44, 171-189.
Cavanaugh, J. P. (1972). Relation between the immediate memory span and the
memory search rate. Psychological Review, 79, 525-530.
Cowan, N. (1992). Verbal memory span and the timing of spoken recall. Journal of
Memory and Language, 31, 668-684.
Cowan, N., Day, L., Saults, J. S., Keller, T. A., Johnson, T., & Flores, L. (1992).
The role of verbal output time in the effects of word length on immediate
memory. Journal of Memory and Language, 31, 1-17.
Cowan, N., Nugent, L. D., & Elliot, E. M. (2000). Memory-search and rehearsal
processes and the word length effect in immediate serial recall: A synthesis
in reply to Service. Quarterly Journal of Experimental Psychology, 53A, 666-
670.
Crowder, R. G., & Melton, A. W. (1965). The Ranschburg phenomenon: Failures
of immediate recall correlated with repetition of elements within a stimulus.
Psychonomic Science, 2, 295-296.
Dosher, B. A., & Ma, J. (1998). Output loss or rehearsal loop? Output-time versus
pronunciation-time limits in immediate recall for forgetting matched materi-
als. Journal of Experimental Psychology: Learning, Memory, and Cognition,
24, 316-335.
219
Drewnowski, A., & Murdock, B. B. (1980). The role of auditory features in memory
span for words. Journal of Experimental Psychology: Human Learning and
Memory, 6, 319-332.
Ericsson, K. A., & Simon, H. A. (1980). Verbal Reports as data. Psychological
Review, 87, 215-251.
Greene, R. L. (1991). The Ranschburg effect: The role of guessing strategies. Mem-
ory & Cognition, 19, 313-317.
Harris, G. L. (1975). Probe recall and short-term memory: some evidence for non-
linear search strategies. Memory & Cognition, 3, 609-613.
Hartley, T., & Houghton, G. (1996). A linguistically constrained model of short-term
memory for non-words. Journal of Memory and Language, 35, 1-31.
Henson, R. N. A., Norris, D. G., Page, M. P. A., & Baddeley, A. D. (1996). Unchained
memory: error patterns rule out chaining models of immediate serial recall.
Quarterly Journal of Experimental Psychology, 49A, 80-115.
Hulme, C., Newton, P., Cowan, N., Stuart, G., & Brown, G. (1999). Think before you
speak: Pauses, memory search, and trace redintegration processes in verbal
memory span. Journal of Experimental Psychology: Learning, Memory, an
Cognition, 25, 447-463.
Hulme, C., Roodenrys, S., Schweickert, R., Brown, G. D. A., Martin, S., & Stuart,
G. (1997). Word-frequency effects on short-term memory tasks: Evidence for
a redintegration process in immediate serial recall. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 23, 1217-1232.
Kieras, D. E., & Meyer, D. E. (1997). An overview of the EPIC architecture
220
for cognition and performance with application to human-computer inter-
action. Human-Computer Interaction: Special Issue: Cognitive Architectures
and Human-Computer Interaction, 12, 391-438.
Kieras, D. E., Meyer, D. E., Mueller, S., & Seymour, T. (1999). Insights into working
memory from the perspective of the EPIC architecture for modeling skilled
perceptual-motor and cognitive human performance. In A. Miyake & P. Shah
(Eds.), Models of working memory: Mechanisms of active maintenance and
executive control (pp. 183-223). New York: Cambridge University Press.
Kimura, D., & Watson, N. (1989). The relation between oral movement control and
speech. Brain and Language, 37, 565-590.
Lee, C. L., & Estes, W. K. (1977). Order and position in primary memory for letter
strings. Journal of Verbal Learning and Verbal Behavior, 16, 395-418.
Logie, R. H., Della Sala, S., Laiacona, M., & Chalmers, P. (1996). Group aggregates
and individual reliability: The case of verbal short-term memory. Memory &
Cognition, 24, 305-321.
Meyer, D. E., Abrams, R. A., Kornblum, S., Wright, C. E., & Smith, J. E. K.
(1988). Optimality in human motor performance: Ideal control of rapid aimed
movements. Psychological Review, 95, 340-370.
Meyer, D. E., & Kieras, D. E.(1997). A computational theory of executive cog-
nitive processes and multiple-task performance: Part 1. Basic mechanisms.
Psychological Review, 104, 3-65.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits to
our capacity for processing information. Psychological Review, 63, 81-97.
221
Miyake, A. & Shah, P. (eds.), Models of working memory: Mechanisms of active
maintenance and executive control. New York: Cambridge University Press.
Mueller, S. T., Seymour, T. L., Kieras, D. E., & Meyer, D. E. (in press). Theoretical
Implications of Articulatory Duration, Phonological Similarity, and Phonolog-
ical Complexity Effects in Verbal Working Memory. Journal of Experimental
Psychology: Learning, Memory, and Cognition.
Nairne, J. S., & Kelley, M. R. (1999). Reversing the phonological similarity effect.
Memory & Cognition, 27, 45-53.
Nicholls, A. P., & Jones, D. M. (2002). Capturing the suffix: Cognitive streaming
in immediate serial recall. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 28, 12-28.
Page, M. P. A., & Norris, D. (1998). The primacy model: A new model of immediate
serial recall. Psychological Review, 105, 761-768.
Pashler, H. (1984). Processing stages in overlapping tasks: Evidence for a central
bottleneck. Journal of Experimental Psychology: Human Perception and Per-
formance, 10, 358-377.
Penney, C. G. (1985). Elimination of the suffix effect on preterminal list items with
unpredictable list length: Evidence for a dual model of suffix effects. Journal
of Experimental Psychology: Learning, Memory, and Cognition, 11, 229-247.
Puckett, J. M., & Kausler, D. H. (1984). Individual differences and model of memory
span: A role for memory search rate? Journal of Experimental Psychology:
Learning, Memory, and Cognition, 10, 72-82.
Rosenbloom, P. S., Laird, J. E., & Newell, A. (1993). The Soar Papers: Research on
222
Integrated Intelligence. MIT Press, Cambridge, MA.
Schweickert, R., & Boruff, B. (1986). Short-term memory capacity: Magic number
or magic spell? Journal of Experimental Psychology: Learning, Memory, &
Cognition, 12, 419-425.
Shiffrin, R., & Cook, J. (1978). Short-term forgetting of item and order information.
Journal of Verbal Learning and Verbal Behavior, 17, 189-218.
Sternberg, S. (1975). Memory scanning: New findings and current controversies.
Quarterly Journal of Experimental Psychology, 27, 1-32.
ABSTRACT
THE ROLES OF COGNITIVE ARCHITECTURE AND RECALL STRATEGIES
IN PERFORMANCE OF THE IMMEDIATE SERIAL RECALL TASK
by
Shane Thomas Mueller
Chair: David E. Meyer
The immediate serial recall task has been the primary means for studying verbal
short-term working memory. Although it has been acknowledged that willful “ex-
ecutive” control is a component of working memory (e.g., Baddeley, 1986), most
theories and models of immediate serial recall focus almost entirely on the underly-
ing structural architecture of verbal short-term memory, rather than on the strategic
recall processes used to accomplish the task. In this thesis, I show that flexible
strategic recall processes can have a large impact on performance of the immediate
serial recall task, and may account for many of the effects previously attributed to
the underlying structural architecture of verbal working memory. For example, the
primacy and recency effects on the serial position functions are influenced by how
the participant chooses to guess during recall, and participants’ options for recall
are in turn influenced by instructional manipulations and task procedures. I present
several models that encompass both the structural architecture of verbal working
1
memory and the cognitive strategies for performing immediate serial recall, showing
that each is important for understanding how people perform the task. Together
with the results of two experiments, tests of these models reveal that human par-
ticipants in the immediate serial recall task have an immense amount of flexibility
in choosing how they guess during recall. Without accounting for the role of recall
strategies, theories of verbal working memory are doomed to mis-attribute the effects
of these strategic processes to structural mechanisms, and will fail to appreciate the
flexibility available for performing verbal working memory tasks.