cognitive psychology 2, 57-98 (1971.)

What Kind of Computer is Man? 1 '2

Earl Hunt3

University of Washington

In computing systems information handling components are organizedinto a system architecture which is exercised fry a program. A system

architecture and componentry for simulating human information processing

is described. The system is characterized by a number of input channelscontaining buffer memories connected in series and a central computing

device vvhich monitors the channels. The central system contains a shorttc.,,. memory for information seen in the past few seconds and an inter-

mediate term memory vvhich holds an abstract interpretation of eventsobserved in the past few minutes. Both the central system and theperipheral channels have access to a very large memory for permanentlystored

information,

but onlv- the central device can write into long term

memory.

Psychological studies of short term memory, language comprehen-sion, and problem solving are interpreted as tasks for the described system.

This essay will attempt the ambitious and impossible task of describinga computing system vvhich thinks like a man. Once- there was great en-thusiasm lor such machines, but it waned as th - diffi rences betvyeen bio-logical systems and digital computers became apparent (Von Xeuma1958). Next we saw the development of tin "information processingapproach vvhich used computer programs to model specific tasks. Aftertie seminal work of Newell.

Shaw,

and Sim - ) cm the construeof computer programs to solve symbolic logic problems, a verv largeliterature developed. If one includes those artificial intelligence pa]

relevant to Psychology', there are ovi t a thousand papers on sit.-,

Although tin re are numerous reviews and collections ol readings (Feigen-

■

n . . ; the papei v. is supported in part In the Nation.il SI. | e ( a No. 87-143SR and in part bv the Air Force O

■■

of Seientifi,

I. . h. An System

Command,

Grant VKOSR 70-1944.

[1 I,

."111

..»»

.iivm

v..*' m. »»«..",

- \ numbei ol people have i d to my ideas, although none- ot ill,

held responsibl, lor ni> errors. The fine editorial and cevllegial comments of Vi

licit, ,,ai,

have much improved the paper. 1 have been influci ■■"" bv theol

10, 1,.,,,1

Atkinson, James McCatigh, Ulcn Newell. and llerbeit Simon. 1 »als0 like to thank m> research associates at the l'niversit> ol Washington who satthrough H nuiulie-r of seminars while I talked out my id,

a,. si- for reprints should be seat to \'.o:\ Hunt. University ol Washington,

sis Hall

\i,nev.

Seattle, Washington 95105.57

WHAT KIND

OF

COMPUTEB is MAX? 59

58

banm 1968- Feigenbaum & Feldman, 1963; Hunt, .1968; Minsky, 1965;

STiis), with the exception of Reitman s ( 1965) book, there has been

little attempt at theoretical integratiHere such a theoretical integration is presented. Its theme is Aatth.oe

is a valid analogv between a human being and a computing system

System the k£ word, the analogy is between the iiiterrclattcmship

among components in a large computing system and the interplay of

mnan capabilities. In the language el computer science we can s^thanalogv i to svstem architecture, not system components. References" h be made to processors, memories, and buffers without presenting

retailed descriptions of how thev work. Thus, the approach to be used- between two other approaches frequently used in W^°^

theory. Most forma! models are strictlx applicable * *"^^Jas paired-associates learning or recognition memory Fr°m **we abstract principles of organization, such as M.llei. Galanter and

r, , Yicrsm TOTF unit Neisser's (1967) view of perception andPribram s (I960) Kilt-

unit, i\ei

.n- /niPture of

memory as constructive processes, or Normans (1968, 1969) piciui ol

lion and memory, which is very close to the picture to be presentedSuch ideas can be thought of as philosophies of information processing

which must be realized by a particular svstem chi^Ctlire . ftFirst I will present a broad view ot the system, then I will deal with

itstxToma components and their subcomponents. Plans for information

t^feri' ,11 be laid out as if 1 wen- dealing with an caigmeei

.ma svstem th, „ , ,„all argue that tin y are reasonable psychological "

"Vort the claim evidence will b. taken from three sources; theoietiea

I c in both psychologv and computer science of how systems simlaito he proposed one work, data from psyehologiealslud.es of man o

upportth "idea that a,ood model should work this way, and on occasion

d\,T from physiological psychologv suggesting that the ideas arc b,o

Wv realistic. Since illustrations vvill be picked and ehosim te sho

the ti 1.1 Pan "

'

I -h, the paper „ ap o R, eUu

■

ofcooiil are solved, (bvouslys

«*> " intuitively plausible ,deas v..

To avoid circumlocations, I will refer to the model as a whole as theDistributed Memory model. It is diagrammed in Fig. 1. The cetcomponent is a Long-Term Memory (LTM) in which information isstored permanently. A hierarchy of peripheral, temporary memories, or

buffers surrounds LTM. Each of these buffers has associated with it acomputing device, i.e., some neural circuitry capable of examining infor-mation in the buffer. Two types of buffers are postulated. Sensory buffers,al the outermost level, receive raw information from the environment andcode this information in a fixed manner. Thev tire little; affected by hing except, perhaps, over long periods of time. The coded data are passedthrough a sequence of identical intermediate buffers. Each of the inter-mediate buffers reeodes data, but this time the coding is under the controlof programs and dala stored in LTM. Examples of the coding at this levelare the transitions from collections of lines to letters, from letters to lettc rgroupings, and finally to the recognition of words and sentences. Suchcodings are obviously automatic in the adult. Thev are also obviouslylearned.

|

Parallel tracks of buffers are shown to indicate our ability to monitorseveral sensory paths. They converge at the level ei a single conmemory, which contains a processing unit and two memory areas. Short-Term Memory (STM) and Intermediate-Term Memory (ITM). Unlike

Conscious thought

I

eiL

tIconicbuffei

Mill jSensoiyI

buffei

y

I PI

li . . 1 I I '.,!.I

I,

EARL HUNTOVERVIEW OF THE MODEL

■-

! Short term memory

■

J.Intermediote term memory

Iconic , Icon

buff,-,

Lon^ ,erm memor*Ibuffer

I1 Sensory

,11,

-l Ii ,- is at the 1l\ ere we to l«

.-"iment

1\ Inleriiiitii - 1 ' '"

WHAT KIND

OF COMPUTER IS

MAX? 61EARL HUNT60

the lower order memories, conscious memory is able to receive mpu from

c -eral sources, and thus must have some blocking mechanism so that i

;, an me;sagcs from onc source while another is aettve. All mpu

to coLious memory is through the STM. which is to be thought of as a

leh smaller, faster access memory than ITM. ITM has he unique

capabilitv of being the only unit which can transmit coded data into

LTM Roughlv ITM stores a general picture of what is going on at the

time while STM holds an exact picture of very recently received input

The model bears a noncoineidental resemblance to a number oimemory models in mathematical psychology (Atkinson & Sehiffm, 1968

Norman 1965; Schiffrin ex Atkinson, 1969). The ideas it suggests can be

used as 'a framework in which to catalog facts about human information

processing (Hunt ex Makous, 1969). It remains to be shown that the

model can tie together a significant number of disparate studies withoutthrusting them into a Procrustean bed.

ntermediate , ,, Long term memorybuffer,

I

Environment

Peripheral Memory Components Fig. 2. Structure of the sensors' buffer.

The Purpose of a Peripheral System

The messages of light, sound, and pressure must first be transducer!into a digital'electrical signal. When tins has been accomplished we find

hat our environment consists mainly of highly redundant informationwhich, if responded to in detail, would quickly swamp our minds.Thevisual svstem alone can transmit data to the brain tit the rate of 4.3 10

bits per second. On the- other hand, silent reading-intuitively one of on.

fastest ways of understanding the environment-proceeds at abou 4o bits

pea- second. Even if we assume that a person comprehends and recalls

word he reads, we still have to account for the fact that only one

ou, of every 10,000 bits input to the brain remains there. It does not der)

intuilion to sav that man sees much and thinks hide. We appear to have a

she, subtle computer in our head, surrounded by a number of high-

capacity parallel input transmission lines. Feigenbaum (196/ has

pointed out thai il such a computing system is going to control its en

vironment, instead of being controlled bv it. the compute must be ablt0 decouple from the input sii Bj this he means that there must be

a peripheral device which screens importanl information from dross and

provi(Jcs the central computer with an orderly queue ot data. Ilnsisthefunction of ll" peripheral memory system.

touches the outside world, a memory register, and a feature detectionunit. The transducer accomplishes a coding, without interpretation, ofthe physical input from an analog to a digital signal, which is then storedin the

sensory

register. The feature detector examines the register, look-ing for features in the digital code. A feature is defined as a subpatternof zeroes and ones. or. equivalent!)', a configuration of "oft" andelements. If the sensory message is held by n elements that are "on' or"off." there will be at most 2" different sensory codes. Assume that thereexists a set of k

features,

each specifying a particular coi tion i asubset of the n bits of die register. These features need not be dimasks of the sensory code, some of them could receive as inputs a signalindicating presence or absence of other

features,

so that a feature i

be defined bv a logical combination of other features. While, for the mostpart, the combinations would be of more primitive features, die possibilityof features defined by lateral or feedback signals from othei i

should net be overlooked. The crucial point is that the set of feature islived at each stage of the organism's development. The output olsensory buffer is simply a property lis! of those features which arematched. The sensory buffer, then, accomplishes a first-stage rewriting

ol the input, signal from the dictionary <>! 2' possible inputs to Ipossible property lists.

Si motion and India! I Will feature detection work? The messages entering the- brain must bcclassified in such a way that the size ol the incoming mess.tee is reducedThe sensory buffer vvhich mak,-s our first contact with tl, environn

is shown in detail in Fi !. It contains a trai luc-inp han.s.ii, which

— Feature detector

t ,Digitol code Sensory

I register

Transducer

V^ Analog signals

WHAT KIND

OF COMPUTER IS

MAN'? &362 EARL HUNT

feature detector can react to the presence of a few general patterns any-where in the sensory space. The linear machine cannot do thi

We would be willing to accept the less powerful, but simpler, linearmachine if its capacities were adequate for our world. It appears thatthey arc not. A number of different pattern recognition experiments havebeen conducted (Bongard, I960; Uhr, 1965; Watanabe, J 969) w]

indicate that a satisfactorily performing program must contain a feature-detection mechanism as a first step. The work on machine patternrecognition also suggests that feature detection alone is not enough.Many interesting patterns are not characterized by the presence orabsence of features, but rather by the context in which the feat

"without less of essential information. Is feature detection a good first step

in sue1

,!

a process? It would bc even nicer if we were able to show thai it

Mas an essential step. Conversely, it would be bad if we could show that(hero exists a simpler and equally satisfactory procedure. We can describe,

such a simpler procedure, the linear discriminant machine, but it doesnot seem to work.

I

1101 ill-11l

ivy »>

v.i

.x.

Our receptors adjust their analog to digital conversion procedures to

the level of the stimulation they receive. For example, at high levels ofillumination the receptors of the eye integrate the amount of light arriv-ing overroughly a (punter of a second and transmit information about its

average intensity at the expense of information about the arrival of

individual photons (Hunt & Makous. 1969). The signals which arrivecentrally from the transducers, then, are monotonic but not linear trans-

formations of the stimulus intensity at the receptor site. Taken together,

the signals from the receptors define a point in Euclidean internal signalspace. In terms of the contents of the m< mory register, we could regard a

sensory buffer as holding m < n numbers, each indicating on a digitalscale the state of an appropriate receptor. We could then design a

machine which would classify this space directly. Let

.v,

be the intensity

of stimulation recorded from the ith receptor. A discriminant machine is

defined as a device with k sets of weights, [W] j= I, . . . k, each withan associated threshold, Oj. The machine classifies the sensory input bysending a k bit output word in which the ?th bit is 1 if and only if

appear.The biological case is also strong. Feature detectors have been found in

the visual system of a number of animals, including the iron (Lettvinet al, 1961), the eat (Hubel & Weisl, 1959) and the monkej I Hubel kWeisl, 1965; Weisl & Hubel, 1966). It is generally true that complex,specific visual detectors located at the periphery are characteristic otanimals low in the phylogenetic scale, while the higher vertebrates havemore general feature detectors located in the cortex (Weisstein, 1969':.Thus, the frog has retinal cells suited to bug detection, while the eat hasvertical line detectors in the cortex. While we cannot make the physio-logical measures necessary to settle the issue conclusively, it seemsreasonable to assume that man has generalized edge detectors, at leastfor horizontal and vertical lines. Although the evidence is scanty, weshall assume that feature detection is characteristic of the- sensory systemas a whole, and not unique to vision.6V „-, i (1)

i Considering tin- complexity of the patterns man recognizes, it isunlikely that feature detectors are used to categorize stimuli directly.Innate Volkswagon detectors just do not make sense. It seems more likehthat feature detectors are used to break input messages into "probabhmeaningful" units which are then analyzed, in intermediate buffers, b\ a

and is zero otherv ise.It is easy to imagine a simple system oi idealized neurons which would

achieve the threshold detection requin 1 ). Sine- there are 2* pos-sible; states ol the output word, th< tl linant machine can achieve an

equivalent reduction oi informal: hat produced b\ the featuredetector. In the case of the diserii ii nl machine, however, only thestimuli which fall within a continuous r :ion "I the Fuclidean descriptionspac.( . , ;n , be grouped together. 'I ' each stimulus classification is

equivalent to a region of the signal sp.i - bounded b) hyperplanes. Ties

makes the discriminant machine in " to interactions between theval„( .s o| sensation scales for <]^^ eceptors. The Feature detectormachine <an l>< ' usitive to such . tcrai lion. Fn particular, it is

ible to design ;i f< atnn tli tecb > h. I>\ receiving input Iroin otherI, ,,,,, , ,an react to the i > ol a parti! ulai stimulus

5 Rosenblatt (1958. 1965), Solfridge (1959), and subsequently mam others haveinvestigated the properties ol a discriminant machine which received the output of afeature detector as input. Although a number of interesting perceptual propcan bc illustrated in such devices, Minsky and Papert I 1969) hav< proven that theyare limited in the categorizations which the) can make. In particular, the) (

recognize am classification which depends on memon ol prior classicalwhether an arbitrarily large sensor) field contains an even or odd number oi ficuresol

,i

type tin machine can recognize) n.n can the) classify figures in the eontiothei figures,

'Thompson ct til. (1970) have found single cortical cells callable ol nspccifii numbers of repetitions oi stimuli- -i.e., "twice detectors" and "thrice detec-tors" up to

se\ in,

in the cat.„,yv hen within the rci ipi ' I Bongard 1970), Thus, a

64 EARL HUNT WHAT KIND

OF COMPUTER IS

MAN?

65

:.

■

■,

TM

different mechanism. This use of features is illustrated in Fig. 3, whichshows a hypothetical first step in recognizing handwritten script. Theactual physical stimulus is a wavy, discontinuous line. Eventually, areader will have to search his memory to sec if a particular segment ofthe line approximates his idea of a script "a" or "b." If search is an expen-sive process, it should only be attempted when it is likely to succeed.Figure 3 shows a set of features which represent characteristic breaksbetween letters. These features themselves need not be present directly inthe visual system, since they could be built from logical combinations ofmore primitive features, such as edge detectors in various orientations.By finding where the break features match the stimulus, a relativelysimple machine could segment a line of script into probably meaningfulsegments which would then be subjected to a more complex analysis.

/7' o<o Xi -iAa, ZJf*4s

Data

Charocter Break FeaturesFig. 3. Features for detecting letters.

We have no direct evidence for segmentationby feature detection, butthere are two puzzles in the literature for which feature detection mightprovide an answer. Preschool children can distinguish letters from non-letters before they can recognize the individual characters, a talent theyachieve without explicit training (Gibson, .1970), yet it is unlikely thatchildren are bom with innate feature detectors for parts of letters! It maybc that humans are born with a tendency to develop, very early in life,detectors which mimic the patterns which occur frequently in theirenvironment.7

Phonetic analysis also poses a segmentation problem, While (Ik; pho-nemes of

;i

language have psychological reality to the listener, analysisof the acoustic signal shows that individual phonemes are not associatedwith invariant features of the auditory stimulus. Liberman (1970:Liberman <l al.. 1%7 ) pointed out that this is not surprising, since thephysical stimulus produced by a person trying to output a given phonemewill depend upon tin prioi stale ol the musculature used, which in turn

( " rtai I - : ii ll experiences an- evident)) required it the cat i-. t<> developverticaland ' ■: Spinolli, 1970), Algorithm for detecting com

ol tin envin ' ivilhoul relying on feedback signals (learning?) canhe I )nda, I 1

depends on the speaker's preceding sounds. The problem is to accofor the fact that we recognize phonemes at all. An answer consistthe thesis here is that the auditory cortex contains feature dcapable of recognizing the breaks introduced when the speechshift from production of one phoneme to another,' thus enabllistener to segment the speech stream into units which, as in f!writing example of Fig. 3. can be subjected to a detailed analysis.Liberman et al. pointed out, the final identification of a phonemeto be a complex task which involves the detection of a stringphonemic features and a parsing of this string. Wc cannot nowthis is how the human recognizes speech, but we can say that the i

is a reasonable approach, since there exists a computer-controlled i

recognition system which segments its input by detecting(Reddy, 1967; Vicens, 1969). Within a restricted vocabulary it ]well, though it is not at all up to human standards.

Intermediate Buffers and Higher Order Recognition

In the intermediate buffer system we see a further refinement ofidea that each level of organization takes as input the output oi 1levels. The major difference between processing at the iii!' i

buffer level and at the sensory buffer level is that in the higher 1 uirecent learning and feedback control play a much more prominent :

In Fig. 1, the intermediate buffer system is shown as a pair atracks into conscious memon-. Each buffer can be thought of asa specific level in one of the tracks. It is assumed that all buffersactive simultaneously, including simultaneous retrieval from I."Within a buffer, however, information processing is strictlv serial.may be affected both by traces of previous activity within th.and by information sent to tin- butler while it is processing data.

A buller at lex el i on will receive information from the buffer

;

.i— 1 and transmit information to the buller at t-f 1. Thus the si -buffer could be considered to be level 0 and the short-termregister (STM) to be level i .To describe the logical relation 1

tween the buffers at different levels, the notion of lexical anabe used. A lexical analysis is performed when a stream ol input i

actors is grouped into a string ol units in some higher ordei(e.g., letters into words) which is then subjected to a parse, ortactical analysis, to determine the structure of the string. Notearbitrariness ol the terms; what is lexical analvsis at one level I

"If a language has n phonemes, there will lie at most n" break patterns. Sinassumes that any phoneme can follow any other phoneme, the estimate is rstoo high.

WHAT KIND

OF COMPUTER IS

MAN? 6766 EABL HUNr

syntactical analysis at another. The relationship between the intermediatebuffers is governed by the lexical-syntactical relationship. A buffer allevel i conducts a syntactical analysis of the contents of its own registerin order to provide a lexical analysis lor the buffer at level i'-|- 1. Theaction el' the system cannot be- described by a formal linguistic model,however, because of the time dependencies involved. In particular,feedback from a buffer at level / may modify the parsing in more peri-pheral buffers.

Figure 4 presents a schematic of an intermediate buffer, showing amemory register, a processing unit, and an addressing ("store and fetch")unit. The store and fetch unit is a device that maps from the finite set ofpossible states of the memory register into the set of addresses of areas''

Coded

date

Conscious

memory

Iconic

buffer

Fig. 4. Expanded view of iconic buffer.

of long-term memory (LTM). LTM itself contains a very large numberof permanent records, each of which is divided into recognition informa-tion and an output code. Typically there will be a number ol records cor-

respO 0 a ]>,uli< til.iv stimulus thai has been seen in the past. Whenan activation signal from an intermediate buffer is received in a givenarea of L'l \I the records stori d in thai an a are read back to the buffi r

which issued the signal. The processing unit ol the buffer then selects theactivated record whose recognition informatio ost closeh resemblesthe contents of the intermediate buffer register, and compares the two.lii rti.it irison the contents ol the intermediate buffer registeiwill be changed, so that alter the comparison the registei will hold a

statement ol f I ti i I between tin : i the belle] ;ln<] |] )t .

I;

* The term ... lilv imai

( Dntig 1 I.lain Vie.l intei piotcd

;i

tin- . Ins h are sensitive I >'"

mation in a particular LTM record. The processor then examines thecontrast information to determine how successful the match was. Inthe simplest case, the register contents will indicate that the matchvery close, which corresponds to recognition of a previously obsesituation. Tin- output code of the LTM record is then sent to threelocations; the intermediate buffer register involved (for use: in therecognition), the buffer register immediately above the active unita lexical item at a higher level), and the buffer register immediabelow the active unit (as a feedback signal to guide syntactic ana!;.

The case in which the LTM record docs not match the buffer infor-mation is a good deal more complex. Two subcases can bc distinguished-situations in which there is no discernible correspondence between th<record and the input, and situations in which there is an orderly contrast.In the first situation the system has simply made a mistake. Either thestimulus information is truly new or, for some reason, previous actionshave sent the intermediate processor to a section of LTM containingirrelevant records. When such an error is detected the processor sendssignals both up and down the intermediate buffer system. The upwardsignals, headed toward conscious memory, serve to alert central memorythat an unusual signal lias been received on some input channel, and thatadded processing power is needed to analyze it. Whether the analysiswill actualb he made, how ever, depends on what the more central unitsare doing at the time. The outward signals can serve as a request th.itmore peripheral parses be rechecked, so that, if possible, the buffer unitin trouble can be provided with a new lexical analysis that might lead toa sensible parse. Intuitively, this sort of system would be capable olphenomenon ol startle followed by instantaneous re-recognition, in iin which a central unit received a signal that a peripheral unit was introuble, but the trouble was corrected by a reanalvsis by the time thecentra] unit's attention had been attracted.10

The ease in which the input signal .)<.^) LTM record mismatch in anorderlv vvav is the most interesting. The description of the misnibe used to compute a new address in LTM and thus io loeati newrecords foi comparison. Note that tl we have a sequence of such act:the information in the intermediate buffer registei becomes a history ola trace through LTM, rather than a stiieth stimulus bound eodi

Those lann'liar with compute! based information retrieval svstoms will.

"In si >im eases the central m light simpb oidei that the pmred l 'in is a '.. ■! ihe follow ini . .

,'.

given b) Donald Norman ... "I nto spi > ! Me\ico abonl tin relation between memoiy and attention." There idill eu!t\ in understanding this sentence, since tin ii word

'

Mi vico" iIn lln- Ii ilonci .

68 EARL HUNT WHAT KI.VD

OF

COMPUTER

IS

MAN?

69

recognize the search mechanism proposed la-re as an example of "hashcoding," in which the features of the input signal determine the locationin which it is to be stored in memory. The general model itself, with itsemphasis on a hash code mechanism and on a sequential retrieval systemin which each query of LTM is determined by the results of previousqueries, is very similar to the models proposed by Norman (1968) andSchiffrin and Atkinson (1969). The main difference between this modeland Norman's proposal is in the treatment of hierarchies of storage.

Neither Norman nor Schiffrin and Atkinson discussed the possibilitythat certain LTM records may be activated by a simulus at one time,and others at another, due to "in context" recognition guided by feed-back signals from a higher order, parallel-processing recognition mech-anism. The distributed memory model makes a great deal of hierarchial,feedback-controlled interrogations of memory. It should be added, how-ever, that nothing in the ideas of either of the other authors rules out

feedback control in a hierarchial system. In particular, Norman's ideaof control of memory search by pertinence appears consistent with theLTM interrogation technique proposed here.

We can picture the sequential search through LTM, with each stepguided by the results of the- previous step, by using Feigenbaum's EPAM(Elementary Perceivcr and Memorizer: Feigenbaum, 1961; Simon &Feigenbaum, 1964: Hintzman, 1965) simulation program. The process ofgoing from the original input in an intermediate buffer to the productionof an output code can be diagrammed as a tree of tests or. in Feigen-baum's terms, a discrimination net. Ignoring for the moment feedbacksignals and errors, the action of the intermediate buffer system on a giventrack can be thought ol as a lineal sequence of sortings through nets, amechanism proposed by Simon and Feigenbaum (1961) to account forrecognition of items in verbal learning. The idea is illustrated in Fig. 5,which shows a progressive grouping of lines into letters, and letters intowords, using discrimination nets.

Granted that a computing system that works like this could be designed, and granted thai il would be sensible to organize computers thiswav. wliv should we believe that tin same principles are involved in

human memory? In pall this has to be answered bv faith ... it seems\n me, and lo others (Feigenbaum, 1907; Norman. 19GS), that theprim iple of distributing inform ition "' ei a nunibet ol t< niporary mi moi a s is dictated by the information rates in the environment in which wi-

lls, ii it applii

■

■ quail) well to humans a to i (imputing systemsI n addition, it in ' ologii all) lln idea dial there tire

inber of si of n mot Ii ; received a good deal ol support from

Fig. 5. EPAM nets for discriminating Utters and words.

(McGaugh, 1966). John (1966) has proposed that information is tscribed from a temporary neural memory to a permanent engram depen-dent upon the molecular structures of nerve membranes. The neuronscontaining the engram ate likelv to become active ii they receive im-pulses similar to those that established the digram in them. Even if thedetails of John's mechanism are wrong, it is hard to imagine a physio-logical memory mechanism which would not have the functional i

acteristies of a self-addressing storage mechanism.

Parsing and FeedbackThe term "parsing" will now be justified.At each slop in the LTM interrogation process, an LTM record is

matched against the current contents of the intermediate buller register.This register contains information from three sources; the in|contrasts between the stimulus and previous 1 I M matches, and feed-back and historical signals ol items provioush recognized bv the iunder consideration

,w^^

bv higher order buffers. Since re<pends upon the match between the conti nts ol the intcimediate hihowever derived and the fixed LTM record, thi fact that the buffet isdependent on all thesi sources ol information males the inforii,| nuns. hi !■> a vai'iet) ol tcchniq

WHAT KIND

OF

COMPUTER IS MAX? '. !EARL HUNT70

processing system itself capable of recognition of an item in the context

of other, previously recognized, items. In a word, recognition dependsupon a local memory of what has been recognized before. This is very

important, because it moves the model from the class of simple featuredetectors to the class of finite state automata, machines which are cap-able of accepting quite sophisticated grammars. In particular, thesedevices are capable of executing a parsing strategy for some phrase-structure grammars. The EPAM discrimination net could be presentedequally well as a diagram for such a strategy, because of the "historical"characteristic of the intermediate buffer register, the parsing strategy

can in general be made to be context sensitive.Consider the following conjectures. Suppose we think of Distributed

Memory as a network of finite state automata. Hie rob- of a signal fromone automaton to another will reset the receiving automaton to a newstate, thus permitting the system to correct a device which has started on

an erroneous parsing. Now. in general, finite-state automata withoutconceptual^ infinite "storage are not suitable for analyzing transforma-tional grammars. A collection of finite-state automata would be capableof handling a transformational grammar, however. In

fact,

a finite-stateautomaton""model for handling transformational grammars has been pro-posed and appears to be reasonably successful (Thome, Bratcly, ec

Dewar, 1968; Bobrow & Eraser. 1969). The psychological plausibility ofthe Thome et al. model of language should be explored further.

We also want to show that the Distributed Memory model can solveproblems that cannot be solved bv sin,,lie feature-detection patternrecognizers. We have a good idea of the limitations of feature detectorsthat operate without feedback (Minsky & Papert, 1969). and it is intui-

tively clear that the performance of the buffered system described hereexceeds their performance. But what about feature detectors which use

feedback signals in making a classification:' Ho they provide a simplerand equally powerful alternative to the buffered memory system that hasbeen described?"

A basic psychological assumption that runs through om reasoning is

that context-sensitive classification based upon sequential decision mak-ing is a i haracteristic mode of operation in man not a special feature ol

This proposal has been made before. Koi example. Neisser,1, si iil.ed perception as an active process in whii h tin pi reeivet

1,,,

.. )( , imposi structure on the stimulus bv synthesis, much as is done in. ' („,, do' ii - hemes ol the analysis ol compuli i I mguages ( Hop

Itosi ' " i Lid coupled | ptrons are

'

good, 1969). Liberman and his colleagues have made this point ex]for speech perception. They feel a syntactical analysis is needed torecognize the phonemes. Much of the recent computer science work onclassification is also moving toward this view (Evans, 1969). I:that in order to classify stimuli of the. complexity which man obviouslycan handle, one must use a syntactical analysis of the patterns to besorted.

Errors

What sort of errors would the hypothetical human computing systemmake? At this point the analogy to an actual digital computing systembreaks down.1 " In most computer applications an event is called "similar"to another event if and only if some precisely defined relations betweenthe two hold. Since the environment of the human computer neverrepeats itself exactly, a stochastic recognition procedure is more appro-priate. At times such decisions will be wrong, and event A will be per-ceived as event Y.

Set errors occur when the system is required to make a classificationwithout a sufficiently well-chosen set of choices. Recall that at anythe intermediate buffer will contain a set, A. of information -derived from an analysis of the stimulus and a second set, R, of informa-tion elements derived from the previous LTM records matched to thestimulus. Tlie addressing mechanism can be thought of as a functionwhich maps from the pair (A. R) into the set A ot areas, i.e.. a set of setsof addresses of records in LTM. Formally, we have

where the YA, are records in LTM and /is determined by the addressingfunction. In correct recognition we would reach an A such thai X isidentical to the recognition code ol some 1 , . so .V would be recognized.But suppose the buffer register contains a set of information, R°, suchthai

and A" does not include the needed 1, ". but does include a) . wrecognition code sufficiently resembles A so that the mat

1 \t least the analouv to a rapid, veiv accurate di.nit.il processoi is not appropriate.111. nervous system i-- betlei described .is

,1

redundant decision na parallel i ment lo stitch istii dei ision elements I \ on Ni a:. isin. reason that eunineers could not build sin Ii a system, bill il is not an ei

competitive one jiiven todav s tochnolon} the properties v\ Inch stochaslsvstcnis would have il tlnv wen' to lie built have been considered in some detail(Gaines I'.m.'ii

.1 /(.V. It) = !V„. )-.,- E.u! [2

.r - j\x. ir A

72 WHAT KirsT) OF COMPUTER IS MAN? 73EARL HUNT

passed. Then X will be mistaken for Y.,, ° in the context of R*. R° itself,however, will be determined by previous matches between X and recordsin LTM. The point is that once a sequential decision process makes awrong turn, forced misrecognition is possible. A more elegant psycho-logical wa) to state this is to say that we see those things which we ex-pect to sec.

signal, Bs- Assume that the correct answer to Rx is a function of X, andthat the filler stimulus sequence, Y, . . . Y,. may be null.

The process may be illustrated by considering how several reportedexperiments might be explained. It is well known that for a fraction of asecond after a visual stimulus is removed attention can be selectivelydirected to parts of the information presented, permitting selective neuralreadout from memory. The explanation offered is that if the recall signalarrives while information from the visual stimulus is still in relativelyuninterpreted form in a peripheral buffer, then the signal from consciousmemory will direct attention to a particular part of that buffer. This, ofcourse, is the usual explanation of the visual memory studies. It is alsoknown that if verbal material is presented visually, and memory testedseconds or moments later, then confusions are determined by auditorysimilarity. This indicates that visually presented verbal material is re-written from a xisual to an auditory code, a point to which we shallreturn in a moment. If the stimulus-test interval were extended furtherwe might expect modifications of the auditory code. Indeed, this is whathappens. Kintsch and Buschke H969) used Waugh and Norman's (1965)technique to divide responses on recall into responses from primary andsecondary memory systems. They found that primary memory wasresponsive to phonetic similarity between items while secondary memorywas responsive to semantic similarity. This supports the idea of progres-sive reeoding.

I

i

Psychophysical errors occur because of a different probabilistic mecha-nism. Suppose that A contains, in addition to the correct Y, a number ofother Y's whose recognition code matches A closely. Since the matchingprocess is itself probabilistic, the more closely the information part of a Yrecord matches the information contained in the intermediate buffer, thegreater the chance that X will bc confused with Y. In this case, however,the confusions will be predictable on the basis of the resemblance of thepresent stimulus, which gave rise to A. to the past stimulus which wasoriginally responsible for laying down the original record of Y in LTM.This suggests that the confusions can be used to map a similarity spacewhich should resemble some identifiable similarity space for the stimulithemselves.

Task Interruption

A model of human information processing cannot assume that informa-tion will be received in a smooth flow for orderly processing by indepen-dent!)' functioning buffers. Psychology must allow for panics! More pre-cisely, allowance must be made for interruption of orderly data processingto deal with high priority situations, followed by a return to the orderlyroutine when the emergency passes.

When B r is presented it will be recognized peripherally as a signalthat must be analyzed immediately, and hence it will be passed throughthe buffer system quickly, somewhat like an express train which shuntsaside the freights ahead of it. When the coded form of R. reaches con-scious memory the system can decide what information it must assenfrom within its various memories in order to construct a response. In theparticular experiment being described (his information (i.e.. the recordsof stimuli seen v cry recently) would be located in the peripheral buffi is,

in incompletely coded form. Conscious memory then issues a signal.traveling outward from il to the peripheral buffers, for the needed data.These dala must bc retrieved in the properly coded form. In particular,il signal A" is still in the bullei system. H may be necessary to performfurther coding on it before 11. cm be answered.

Two special capabilities are proposed to allow for interruptions inDistributed Memory. Peripheral bulls rs must be aide to recognize highpriority signals. Tin's can easily be handled, by storing in LTM information about a signals priority, so that win n an input signal is associatedwill) an LTM record its priority is also identified. (The fact that ;m inputcannot be matched to an LTM record might itself be considered a highpriority signal, since it indicates that an unexpected event has occurred.)In addition, central memory units, and in particular conscious memory,are assumed to be able to preempt p i pheral buffers. During the pre-emption the memory areas of the peripheral buffers tin made availableto tlit: ( enIraI units. While the peripheral buffer is preempted, then- mustbe ti limited capability for retaining in! irmation about the interruptedtask.

Some more complicated recognition and recall studies may be nplained in terms of reeoding within Distributed Memory. Recall dependspartly upon what a person remembers direetlv and partlv upon what hecan fill in, either bv appropriatecoding or bv using his knowledge ol tl-,.order inherent in the world. (If you remember that you saw tin Englishwon! beginning with "Q" vou know the second letter,) A study by (.'raw-

Imagine a (verj common) experinn ntal situation in which a sequencetimuli. A, )',. )....);. an- presented, followed by a "question"

74 EAEL HUNT WHAT KIND

OF

COMPUTER

IS

MAN? 75

ford. Hunt, and Peake- (.1966) shows how such coding may develop overtime. They displayed sentences visually for fractions of a second, thenasked subjects to recall the entire sentence from .1 to 10 sec later. Recallaccuracy increased as the stimulus-recall interval increased. The Dis-tributed Memory explanation is that tit the shorter intervals recall was

based upon a fading trace of physical or acoustical traces of the stimuli,while tit the longer intervals recall was based upon information coded to

represent the thought behind the sentence, and hence was a more accu-rate code than a collection of lines or sounds. The improved recall effectwas not found if the stimuli were- derived from the sentences by changingthe same word or letter order, thus destroying the sentence meaning.One should be able to produce the opposite effect, progressive loss ofinformation over time-, if subjects were- asked to recall idiosyncraticfeatures of individual characters, such as broken lines or unusual curves,

since these will be lost as the visual trace is replaced by auditory andsemantic codes.

Structure and Programs

The way in which the explanation of the Crawford et al. study wasgenerated is perhaps more important than the explanation itself. Follow-ing the logic introduced by Atkinson and Schiffrin (1968) in their dis-cussion of memory structure and control processes, a particular experi-mental situation and system architecture have been taken as given. Theexplanation is, in

effect,

a program by which the assumed system can beused to handle the experimental task. It may be there will be severalprograms that could handle a particular experimental situation. The taskfor psychologists becomes one of designingexperimental situations whichcan simultaneously test structure and memory programs. Since this pointis basic to the approach, it will be amplified upon in discussing a series olreaction time studies bv Posner and his associates.

In the basic experimental situation ti stimulus letter (e.g.. "A ) is pre-sent! d. followed w ithin less than 2 see- by a probe stimulus (".A.' "R, or

"a i. The subject is asked il the two stimuli arc the same or different.Wina, the probe follows hnnicdiatcl) after the first stimulus (Inter-stimulus interval, or

ISI,

ol zero I physicalb identical pairs (A A) w ill berecognized more quickly than name identical pairs ( A a) (Posner &Miti hi 11. 1967 ;. This mav not be true il the ISI is exli tided. Before eon

sjdering (la- data, however, let us consider a program thai a subjectmight use;

|, '("lie stimulus is presented and its proi i ins.

probe stimulm is pn i lib d. lie hn t ol i si i ond stimulus is

gnal tlittt a ri'sponsi tin lb constructed

3. The probe stimulus is rushed through processing to conscious mem-ory, where the coded data are used to construct a query to be directed toDistributed Memory, in order to select the response. Specifically, thisquery consists of the visual and the auditory (name) codes of the probestimulus. If either of these codes can be located in a peripheral memory,then the "Same" response is appropriate. A "Different" response is appro-priate if neither code can be located.

4. The query is broadcast through the memory system. If the test andprobe stimuli are physically identical (A-A) then a positive answer willbe returned. If a negative, answer is returned, however, tin's means eitherdrat the stimulus and probe are different or that they are name identical(A-a ) but the first stimulus has not vet been processed to the name codelevel. Processing of the first stimulus is, therefore, completed and thequery repeated. If the second query returns with negative results, thenthe "Different" response can bc given.

We now see why the ISI length is crucial. Long ISP's allow for process-ing of the first stimulus to a name level by the time that the- probi ispresented. If the ISI is long enough the difference between A-A and A-aqueries should disappear, since the1 name code' of the first stimulus will bepresent when the probe is analyzed. This happens if the ISI is more thanI sec (Posner & Keele, 1967; Posner, Boies, Eichelman, & Taylor, 11Experiment I ).

If the program analogy is correct, chancing the experimental situationshould change the use of memory components. Pour experiments bvPosner, Boies, et al. indicate that this can indeed be done. Civ en a longISI in the experiment as described, with both stimuli presented visuall).a subject should always carry stimulus coding to the name level, since hecannot tell whether the probe will be "A" or "a." Further, once tin- namecoding is complete, there is no need to hold the now redundant phvsiealcode, rims, in long 1M conditions we would expect the response time tobe determined soleb bv the- time needed to buna (he probe to the namecode level, regardless of the basis oi identity. \( short ISI intervals,physical matches should be fastest, since all that is needed to constructa query which will recognize an A-A pair is to bring the probe to thephysical code level. In fact, the time required to make an A A matchincreases as the ISI interval increases bom 0 '"> to 3 see ii the sub]lin ire! against the A a trials, but suppose the subject is promised that tillpans will be either A \ oi A B pans. The elevet Distributed Memory

rammer would (hen never enter the name code level lor tinhis, and therefore \ A matches should not lake longo as the1 ISI

increases. In hut. undei thesi conditions (lav do not ( Posnei ('/ (//.,I vpei ai 111 Mill moo- i. .ni1.. 1

o\cr

the program is possible il

WHAT KIM)

OT

COMPUTER

IS

MAX? 77

EARL HUNT76nately, we may appeal to physiological data showing that the ca]exists even if the interrupting task involves substantial physical di:tion of brain activity. Rabadeau (1967) has shown that the procetconsolidation of information into memory can be suspended in se'.structures while the electrical activity of the cortex is temporaripressed. A. similar phenomenon can bc observed if the memorydation process is disrupted by electroconvulsive shock (ECS;. \Duncan and I trained animals in a passive avoidance task, then .them ECS. Idle amnesia typically associated with ECS developi dafter several hours, indicating that information was retained in a, Iporary storage area for some time. We also found that the consolid:process could be restarted if the animals were given strychnine seafter the ECS, confirming a previous report by McGaugh and Hart (per-sonal communication). While the details of the physiological pnare certainly not clear, the evidence now available shows thatof interruptable memory processes is not at all unwarranted.

first stimulus is presented auraZny-that is. the name code and physicalcode are identical. Now the controlling factor in response constructionshould be- the lime needed to move the probe through the physical code-name code sequence, but, again, consider what the clever programmermioht do. lie could use the "dead time" of a long ISI to convert the firststimulus from a name code, to a physical code, thus improving the effi-

ciency of response construction. Apparently this is what subjects do afterconsiderable experience with the situation (Posner et al, ExperimentIV). , . u

Intuitively, one might think that the shorter the ISI, the easier it wouldbe to make the match. The argument presented states that this is not

the case . . . since coding must be completed. At a 0 ISI responses

cannot be constructed until at least physical coding of both the initialand probe stimulus have been completed. Again, the data support theargument, for at a 0 ISI the reaction time is at a maximum. This alsosuggests that only one stimulus car, be name coded on a channel at onetime. , ,

Now, let us connect these facts with the general argument. Hie Dis-tributed Memory model assumes a system architecture in the waymemory components are laid out. It also assumes that the syste■:., <

ponents have certain capabilities. As the example illustrates, phvsiologicalstudies should be sought to support assumptions about component c;

bilities. It seems unlikely that physiological studies will supply us withvery much information about system architecture, and certainly anstudies will seldom provide information about task-specific- programsexecuted by human computers.

. i i \rThe program presented here is not the only Distributed Memory

proeram which could be used to account for Posner's results, althoughit appears to me to be the best one. A case can also be made for a pro-gram which assumes that all coding can be completed within 0.5 sec,

and that delavs thereafter tire solely due to the need to carry the probecoding to a higher level in order to answer some queries. Note the differ-ence between the two programs— one requires a specific assumption

about processing speed while the other requires an assumption aboutthe abilitv of peripheral memory buffers to suspend and reinitiate pro-cessing of interrupted tasks. They arc really programs for different ma-chines. The challenge to the psychologist is clear—can he design expertmc-nts which discriminate between mac-hie

LearningMan's environment contains subtle patterns of events that can ot

recognized by sophisticated pattern-detection procedures but whichcause they transmit information of moat utility, must be reiquickly. The lexical components ol speech ate examples. Obviouslv tlarc patterns which we learn to recognize verv we'll indeed. ILa Distributed Memory system do this?

Example of the Use oj Physiological Data.Justification of Interrupt CapabilitiesIt nitty often be verv hard to find behavioral situations which can

highlight a difference between machine structures that cannot be hiddenby program differences. Al this point the cognitive psychologist wouldprobabl) do well to considei more deepb than usual the implicationsof v ,n„ of the finding' t on- erning the physiology ol memon

'I j,r, poi„t is espe< i ill) n h vanl to consideration ol a DistributedMemory. V\ lielher or not buffi is have the i apacil) to suspend and startprocesses is clearlj goinu to di l« nnim the p ;rains whit It can bewritten for the Di tribuli (1 Memo \l first,i, .fuming ihi lit) is an i x< n ise in ad hoc theorizing. Fortu

The answer to this question is based on two assumptions about LTM:that it can be read quick!) and concurrently bv several inter!processors, and that data can be written into 1 .'I'M only bv tlmemory. In othet words, learning is controlled In eisince what is leai ii;d is identical with the data in LTM. Learnine is alsoseen as tm error-correction procedure. When a peripheral cidelects an ciiot thai il cannot resolve, it sends an alertcentral (conscious)

memory.

I! the conscious memon processoi is avail-able at that time il will be used to construct the modi to 1 1 M

79WHAT KIM)

OF COMPUTER IS

MAX?EARL HUNT78

records needed to correct the error. Otherwise the error will sunply be

isnorod. It is further assumed thai the process of reading data into Ll M

take-s time so that during the initial stages of learning a complex task

manv errors will be ignored as the system tries to fix a few items: in

memory, while errors in the later stages of learning, being less frequent,

are likelv to receive prompt attention.Phis position is intermediate between the positions that learning is

gradual and that it is all or none; learning is seen as being all or none

at the level of correction of individual terms in the peripheral discrimi-

nation nets, but gradual in the sense that a complex task may require

t ,u. construction of manv nets, each consisting of several decision points

Feigenbaum and Simon (1962) have shown that the assumptions that

learning requires a finite fixation time for items, and that tomngjall or none at the item level but component by component at the task

level are sufficient to account for the serial position curve, one ot 1sy-

chologv's few firm empirical laws. Their analysis has been extendedconsiderably in simulation studies by Laughery (Laughery, 1969

I au-herv & Pincus, 1969) in which experiments on immediate recall

wore mimicked bv assuming that memorization took place by fixing more

and more information about an item as it was held in a rehearsal buffer.Up to the point of storage into LTM. which is not directly representedin l.auglierv's model, their simulation could be considered a simplifica-tion of part'of the Distributed Memory model.

Physiological psychology also supports the idea that a slow fixation

time 'is characteristic of the LTM component. Numerous experiments

have- shown that memon in rodents, cats, and probably monkeys consistsoj a l;lbile phase which is easily disrupted bv physiological insult (e.g.,

electroconvulsive shock or localized shocks in the limbic system), but

that after a short period of time the labile phase is passc-d and memon

is almost impervious to further attack ( McCaugh, 1966),- unless the

disrupting treatment interferes will, protein synthesis in the neurons

fCurowitz 1960i Presumably, when protein synthesis is disrupted one

av Its lla permanent code- in memory, as well as disrupting the process

bv which information is consolidated.

Comment ,m the. Intermediate Memory System

\), ]„„,..|, the iinalvsis here is incomplete, il raises a number ol cpiej

,;,„,.

Teat the design ol man. II would be possible to build a sill I.intermedial, processing tmil lo be- shared bv several buffer in .r.es

,-,,. . „f „ 1., t'l i '

■

'> -I"" 1 ' 1 " 1 '" ! !i " ,; " '" """Itnvfi

ft

its dm

In engineering terms this saves hardware but constrains the amount ofparallel activity possible in the buffers. Now, what is the appropriatesimulation of man? Just what can a human do in parallel? What de-termines how long a piece of information can be held in an intermedi tebuffer? Is it the rate at which new information is presented lo the system(retroactive interference), the sheer passage of time (decay), or thenature of information in the system before the stimulus is presented(proactive interference)? Certainly all these variables affect the systemas a whole, but which of them affect which buffers? Finally, how shouldthe system handle interruptions when its processors are busy? Anecdotesabout our ability to monitor cocktail party conversations tire legion, andconceptually interrupt servicing poses no problem. Can we devise ex-periments thiit move us from the anecdotal level to a scientific analysisof how man monitors the background environment while engaged ina complex task?

CENTRAL MEMORY

GeneralMany people, though perhaps not most psychologists, would

sa\

thatwe are thinking only when we tire making a conscious effort to under-stand our world. Distributed Memory provides the three areas shownin Fig. 6 for the data storage needed to construct such understanding.Short-term

memory

(STM) holds an immediate perception of thi v orld

FromShort teem

memory:

Lexicol itemsIntermediateSystem IE I

Conscious

memory

processoi

I 1Intermediate tei m memory :

Knowledge Molecules

Long term

memory

' ic nets

iluonr. ri'llipu la-liven ul aI ii. (i I in alien nl (lata lionetuivi i

WHAT KIND

OF COMPUTER IS

MAX?

81

EARL HUNT80

Intuitively each item in STM is a recognized entity which wc couldname if asked Distorting slightly an analogy of Laughcry's (1969), xve

may think of STM as a window on the outside world.- Recognizable

items pass through it. As they do they are incorporated into a general

record located in intermediate-term memory (ITM) that relates items

now in the window to items that have previously passed by. Since diemanner in which new information is to be incorporated with old may

be quite complex, the construction of the ITM data structure will haveto be under the control of a program for analyzing situations. Obviously,

many

such programs are learned, so they must exist as data stored in

LTM. This gives LTM two roles, one as a repository for descriptionsof what we have seen and one as a repository for rules for interpretingdata. In computer science terminology, LTM has a library of programsfor problem solving.

Many of the efforts at simulation can be described as attempts to

find out what is in this library. In particular, simulations of tasks suchas decision making and the algebraic problem solving are analogous

lo the mathematical and statistical packages found in a computer center'slibrary. Such programs may have a logic of their own which is quiteindependent of the characteristics of the machine on which they arc-

to be executed. Similarly, in a simulation stud)- the logic ol a problem-solving strategy may be divorced from considerations of the physiologicalmechanisms which must eventually execute it. This approach dominatedthe initial studies of the simulation of cognition (Newel! eV Simon. 1961).

The major point of the remainder of this paper is that models of struc-

ture and ol process cannot be so neatly separated. 15 To continue theanalogy to a computer library, application programs must interface withthe physical system, bv means ol programs known as operating .systems

and data-managementsystems. How much the internal logic of the appli-cation program will be controlled by the interface design depends upon

"This picture of STM is quite did.-nut I th. picluri given nl SIM in which

i,

is assumed that it can hole! onlv two or thro, nonsense syllables. There are two|„, the discrepancy. Many ol the short term memory tasks which have been

,(,„];,,! .., ..,

,iti

m 11 tasks performed by what, in the DistributedMemory terminology, might be called the buffei system. Tins system prohahh has a

I

■

■ I'1 '" [<" ■'" l >-'> """ '

:,,,lliil

environment where he must devot. some tiin. and ellorl to learning codes loi stimuli: ; id "I'll' 1" fore In can attack llie exporimi nl il tasl Outsidi th.

. . . 5,!,,0,,l cod. are tin rule rathe, th m, human". | ibil.l

"

Ii ...ch I I laboratory i-stiinal

hen Simon ( V-* I' much the samepoint i ; I their en-

what the application is. One can write a FORTRAN program to add 10numbers with almost no knowledge of the computing system on whichthe program is to be executed. One can also write, completely in FOR-TRAN, a program to conduct Computer-Aided Instruction, but toit to run one must know a good deal about the data-management pro-cedures in the computing system to be used. The same reasoning holdsfor man. While diere may be some tasks that we could handle regardlessof our limited abilities to keep track of several things at once, or torecall, on demand, all the things weknow about a topic, such tasks arelikely to be trivial. In more interesting situations man's choice in selectinghis problem-solving strategies is constrained by his ability to manthe data needed to solve the problem. He must find relevant data, bothfrom his environment and from his long-term memory, and he must findworkspace lo hold temporary data while lie attacks the problem.

The point will first be illustrated by a discussion of how we mightcomprehend speech if, indeed, we are described by the DistributedMemory model. Discussion here will be fairly detailed since vicomprehension is the most uniquely human thing we do. A number ofother programs in man's library (certainly not all of 'them) will then bediscussed brief!)', to show how they are controlled bv data managementin memory.

Verbal ComprehensionHow does a listener make sense of what he is told0 Let us limit

selves to responses to verbal

information,

even though imagery doi -play a role in verbal tasks (Paivo, 1969; Hebb. 1965). Figure 6 siSTM accepting a stream of coded inputs from the peripheralsystem, The coded units are (he lexical items of speech. Were we askedto repeat word for word what a person had just said, we would replvalmost entirely on the basis of what was contained in SIM. Clearl)replv would be limited to at most a sentence or two. If we were n"What is he saving':'

'

however, we would show comprehension over awider span. In terms ol the Distributed Memory model, we understandinformation abstracted bom STM and recorded in int.. termmemory (ITM), ITM, then, will contain a data structure based onagglutination ol information passed through STM without . - in-formation about individual words. Ohvioush tin construction olITM record is a complex process controlled by data and a program forthe conscious memory processor,

The above sentences Wi-te eaielullv phrased to avoid th. '-voidslike "syntax, "grammar," "semantics," or "association." Peoplehend in eognitivi units which are j{ lust looselv tied to gramma

WHAT KIND

OF

COMPUTER IS MAX?

83

IvVltL HUNT82

units although thev also are not merely associations of previously pre-

sented ideas'. 1 will argue that comprehension is a sloppy process in

which syntactical rules are used as a crutch to resolve conflicts when

there are several possible semantic analyses, and are ignored when syntax

and semantics disagree. .This is not a denial of the importance of syntax. The semantic analysis

must be guided bv a set of rules which state possible relations between

semantic entities. These rules are themselves a grammar for an mter-

Hnaua of inner thought which, presumably, is common for all men ot

similar experience, regardless of the language they happen to speak.

The form of a possible- interlingua will now be outlined, drawing heavily

on the work of Schank and his colleagues (Schank, 1969; Schank & Tess-

ler 1969' Schank Tessler, & Weber, 1970) and ol Quilhan (1968, 1969)

on computer comprehension of natural language, and to a ser extent

on studies bv Thompson (1966; Craig et al. 1966), Siklossy (1968) and

Thome (Thome et al. 1968; Dewar et al. 1969). The reader should be

warned at the outset that the picture of comprehension that wall be devel-

oped is supported bv the performance of programs which at best illustrateselected points. The programs are not general language-processing tools.

The- evidence for the approach is not that it works, but that it seems

reasonable. . .To describe the ITM data structure an analogy to chemical structures

will be- used. A thought which is complete in itself will be called a

molecule of thought. Molecules are constructed by linking together ideaswhich we possess and which, though understandable, tire not coherent

abate These will be called atoms of thought. Thus. ""President Nixonordered troops into Cambodia" is a molecule, while "Nixon. "Cam

bodia » and "ordered" are atoms. Submolecules are structures composed

of atoms linked together in a certain way, so thai thev form a vital part

of a molecule, but do not form a coin-rent thought in themselves. Die

phrase "ordered troops into Cambodia" is an example' ol an Action sub-m()lccule II speed,, s that something has been don-- to something, but

( loes „ol sp-calv the actor. There is a rough correspondence between,ltoms ,„„! wools submolecules and pleases, and molecules and sen-

(,.,„, . but there an many i xceptions. The sentences

■

p,,.,,,!, t Xixon ordered troops into Cambodia. Manv studentsdemonstrated to prote I his action."

provide two grammatical units and a single cognitive one since the()hjcct ol the sijcond sentence is mil) int. Iligible il ihe first sentence isInn , \\'()rd -is es n lei lo inon conipli s structures (ban a single;

,toin

'Smoker' a l'h« heav) cigai smoker . . ." names the

Actor molecule person, smokes x rather than to an indivisible ref. .of thought.

The basic structures of each thought molecule are the linked Aand Action submolccules. These can, in turn, be modified by olstructures. Fig. 7 presents a diagram of the sentence

"The man saw the blue book."

using what Schank (1969) called conceptual dependency analdiagram the relationships between basic concepts in an idea.. In tinexample an actor, "man." is linked to an action, "see book, aare appropriately qualified. The Actor

«->

Action link is a basic one. -every thought is assumed to contain the information that something i

something. Actor and Action submolecules can be further divided, butonly certain substructures are permitted in each. For exampli a ; rmis-sible component structure for Action is transitive verb —» concrete olIn turn, onlv- certain structures can replace transitive verb. etc.. untilwe reach items in the lexicon. The analogy to chemistry is valid in think-

PastMan / v| \ / See

The

Book/ \

The Blue

Fig. 7. Example ot structure of a sentence

ing of the structure-substructure hierarchy, but breaks down when weconsider the number of different types of links. Comprehension requiresa more varied linkage than the simple valences of chemistry. One of themajor lines eif inquiry in conceptual dependency analysis is the studyof the number ol different types of substructures and linkages requiredto express the thoughts ol a language (Schank. Tessler. s\ Weber. 1970b

Coordination between the conceptual dependency analysis ol an ideaand its expression in an external language is achieved bv a set ol repre-sentation rules that are language specifi. . The rules required fot conhension appear to be much simpler than the inks usualb considered

necessary

to describe the grammar of a language. Schank i 1909 1 pievided examples ol representation rules which map statements Iidiverse languages as English and Quiche, a Mavan dialect, into a con-ceptual depend, nev analysis, Obviously, it would be nice ' eneraltheory ol psvcholinguistics il representation rules were- universalisll is not eleai that lies is so. Mtlioueh Schank regards them as hol sccondaiv importance Siklossy's (1965) studies ol translations Ivarious natural languages into a single internal langu -ests that

84

EARL HUNT WHAT KIND OF COMPUTER

IS

MAN?

in some, cases they need to bc, quite detailed. Siklossy observed that

whether or not it is

easy

to find translation rules depends upon the de-

gree of compatibility between the natural language and the internal one.This implies that if, in fact, we have a single internal language, then

natural languages should vary in the degree to which they are hard to

learn as first languages.In comprehension one goes from the lexical items in the speech stream

to the thought molecule, rather than the other way around. Lexical items

must be selected from STM, their referents and semantic propertiesestablished by searching data in LTM, and the resulting codes must befitted into the molecule being developed in ITM. Consider the sentence

'"Die big bear growled angrily."

The sequence of actions in Distributed Memory is(1) "The" is identified as a determiner. It cannot be placed in the

thought molecule, so it is held in STM.(2

_) "big" is identified as an adjective specifying size. There is no

niche for it in an ITM molecule, so it is also held in STM.(3) "bear" is located in LTM and identified as a concept, and hence

a potential actor. It can be placed in ITM as the nucleus of a molecule.STM is examined and it is found that there are two qualifiers whichwould be appropriate for "bear." They arc attached to the Actor sub-structure now in ITM.suuecuiu lion ah i

j. ..*.

liii(4) "growled" is identified as an action. The ITM molecule already

has a niche waiting for an Acfmn-submolecule.(5) "angrilv" is identified as an action modifier which can be fitted

into the ITM molecule.Syntactic rules have not been applied al all. A practically identical

analysis would have handled the Spanish translation,

"El oso grande gruno colcricamente."

although the: order of action would have been slightly different, since"grande' follows the-

n0,,,,

il modifies, while "big" precedes

,1s

noun.

thc psychologist, Hie interesting fact is thai the use ol th, differentmemory components doc s not depend upon the grammar ol the language.SI \j al

,-, ■

acts as a scratchpad for input items while their semanticCOC3

C

is being establish! d, lai s. rat. hpad lor semantic codestha) c,,,,,, at be fitted into the II \1 molecule as th,-. an produced.

'1,,,. ,„,!, i ( task; in construction ol the ITM moleculi ma) vary

;

- ..,,, gy used In th. < sample v.. In can in locating

an Al tOT,

la,

i il., ''" t" 11 '"' con

ceptual analysis. 18 Though there is no explicit reliance on synta:phemics, the identification of special endings and function wordsbe used as markers to indicate that certain words were toas a group in the conceptual analysis. Partial parsings i

in an analysis. This scheme can work even if the input sentence ■

grammatical . . . and a good percentage of speech is not. ConDistributed Memory might fail to complete an analysis of a ]grammatical sentence if the parsing overloaded a memory (

Conceptual, dependency analysis is concerned with the conof ITM molecules from STM items. This overlooks an important marment task. Before the atoms and submolecules can be pthere must be a translation, in context, from S'PM words to appropimolecules. This is far from a trivial transformation. Somehow the lexstored. in LTM must be searched to interpret the words in a sentence.

Although humans are very good at this sort of recognition, itproved very difficult to develop an adequate procedure for in conretrieval of the meaning of words. The approach usually taken in com-puter comprehension is to represent the lexicon as a graph whose nodesare morphemes and whose arcs represent connections between mor-phemes. Following Quillian (1965. 1969). let us call such a graph asemantic net. Figure S shows a portion of a semantic net di law-yer" as a subset of the class "persons" and as an appropriate Actor foithe Action of giving advice to a "client" who is also a "person.'' To dis-cover the meaning of a word we need a program which when givena word in STM. can find an appropriate atom for ITM bv examiningsemantic net. Obviously, this routine must consider the noc]c correspond-ing to the word and the nodes connected, directly or indirectly, to it.The problem is to decide when a connection is important and when itis not. In Quillian's terms, we want the restricted meaning of a word inthe contexl in which it is used.

An intuitively plausible technique for finding restricted meaningcan be outlined. It is based on Quillian's ( 19651 technique for localrestricted meaning, but as presented here it is modified (or rather, itis shown how it would have to bo modified) to operate in conjunction

'"In theii most recent vvaik. Seliank tl al. (1970) suggested that [1should In- initiated with the Action unit.

" W lie, then, do we have a grammai it ill? One reason has h.m-'i maj govern the production of speech Grammar also pronre>chinelanl

messages.

The striotlj grammatical speakei will provide more anas Istructure ol his thought than the listener needs, providing that the listenei receivesand correctly perceives even word

Sue!,

atvnraej is unlikely, so grammai ,..

vide a check t<> avoid misunderstanding.

WHAT KIM)

OF

COMPUTER

IS

MAN"?

87

86 EABL HUNT

As it was in recognition, time must' be crucial in comprehension. Timeis needed both to search the semantic ne-t and to construct the ITMknowledge molecule. In each case the amount of time- needed will de-pend upon the complexity of the task to be done. This conforms bathto experimental data and to common sense. Collins and Quillian (19anel Meyers (1970) have- shown relationships between the lime requiredto comprehend a statement and the amount of hypothesized search re-quired in a semantic net. Equally to the point, speed reading is notrecommended for technical material. In terms of the Distributed-Memorymodel, the rate at which data are' placed in STM cannot exceed thespeed will) which the knowledge molecule is constructed for any lengthof time (although it can exceed it briefly), for STM. being

finite,

can-not serve as a holding station forever. Before we can

carry

the analysisfurther, however, we need studies of comprehension similar to those ofPosner on recognition.Fig. 8. Definition of LAWYER in a semantic net.

with a conceptual dependency analysis. Assume that a meaning-extrac-

tion procedure selects a word from STM and presents it to its node mthe semantic net. The presentation initiates transmissions along the aresemanating from the node and then, in turn, from each other node as it

is reached (This could be achieved by a mechanism similar to the LIM

search mechanism used by the peripheral memory.) Each transmissionalong an arc takes time, the exact amount being inversely proportionalto the amount of traffic: on the arc in the recent past. A node ts stud to

be activated when it is reached. The activated subgraph is the set of all

nodes together with their arcs, that have been activated at some time

after a presentation. The identity of the activated subgraph wall change

as new nodes are reached. Now suppose that the meaning-extraction

routine has a pattern-recognition capability, so that it can recognize

when the activated subgraph assumes the- shape ol a submoleeultu con-

stituent At this point the meaning-, xtraction routine copies ihe activatedsubgraph and present-, il to the molecule-construction program, finally,

make the- assumption that the meaning-extraction routine- can start thepresentation of one word lo the semantic net before the meaning of a

previously presented word is extracted. Given this arrangement, themeaning'found for a word will depend upon the identity ol the otherwords whose meaning is being sought.

If the reader thinks these ideas sound plausible, he should be awarethat they hide a great many problems. The biggest one is the establish-ment' of an adequate definition of restricted meaning. A pattern-recogni-tion program to do this was blithely assumed, but its details left uns]fied. Construction of a working pattern-recognition program to findappropriate submolecules in a semantic net would be no small researchproject! The general experience of those, including myself, who haveinvestigated ideas similar to those of Quillian's is that most intuitivelyplausible schemes do not suffieienth limit the size of the nets activatedby words in context of other words. Perhaps insisting that the subgraphto be retrieved fit into an ITM molecule would be a sufficii ut restriction.bul at present no one has shown that the1 approach will work. Whatstudies of computer comprehension now have 1 to offer the psychologistis a collection ol programs which work on selected, more or less impsive. examples ol speech tasks. In this section an attempt has been madeto show that these programs might be tied together to handle1 the data-management problems inherent in verbal comprehension. At best thisis a suggestion. It certainly is not a solution.

Problem Soh ing

When psychologists proposed constructing computer programs tosin,, date thought, computer science was forced to respond with a nunol formal models ol what "problem solving means, since without sucha itiodel. one can hardlv construct a program. Prohabh the most interestilea, idea thai was developed is from the psychologist's point '' : view.thai ol state-space searching Slate-space searching is a model deserilthe sort ol problem solving achieved bv the General Problem Solvvi ol

"The question ol how vvid. a context must be considered to rosolv. an.liiKi.it.es is

,-,„

;,1 |,er. We slimiest that mil> limil l""l sea,

am,"

is needed

I!,,

eon

, , |, | ,; ,„ letk-al result sful |m m mis In . vtrael r. f. lenees

from bodies of text need , i '<>">->« "' ; ' ''"' 'Stone (personal eoi.i.i ct,,,,,) l.a: ' ' .««' I- '"< >'< ' ''"'"-' ' ' ' "" ,

„

„,„„|l. |,e olit; I l>> e„ 'I '"'; » I ' 1" 1 " l" t """'l,ul 1,1,w ,"',<"" '""'>'

WHAT KIND

OF

COMPUTER

IS

MAN? 89

88 EAFL HI'XT

(Nilsson, 1971; Slagle & Dixon, 1969; Sandewall, 1969). Little concernhas been expressed over the size which the temporary information listsmay attain, since, within the range of most of the problems studied,these lists do not reach the bounds of available computer memory space.

Newell Shaw, and Simon (1959; Newell ek Simon, 1961; Ernst & Newell,1969) and its derivatives (Ernst, 1969. Quinlan, 1969; Quinlan & Hunt,

1968 1969). Nilsson (1971) has developed a formal theory describingboth' the problem of and solution algorithms for slate-space searching

from the viewpoint of computer science. What difficulties would be

posed if a Distributed Memory machine was lo be used for stale-space

searching? .The basic idea is that problem solving is equivalent to passing from

a given starting point to one of several first intermediate points, then to

a second intermediate point, and so or, until the goal point is reached.To get a rough idea of the process, imagine that you are trying to hikealong a network of trails without a map. At each trail intersection you

must decide which path to take next. Having made this decision, at thenext intersection you must decide whether to take a branch from it or

go back to investigate one of the untried paths leaving the first inter-

section." What search process should you follow?Movement from state to state is achieved by applying an "operator

to a state which has been reached previously, in order to find a newstate In the lost woodsman example the act of walking down the trailwas the. sole operator. In simple algebra problems, if we develop the

If Distributed Memory is a fair picture of man, however, simulationprograms have to be very concerned with temporary memory space.Specifically, a simulation program must arrange for a division of thetemporary information between STM, ITM, and LTM. As a first approxi-mation, it seems reasonable to assume that STM will contain the datadescribing the states and operators being considered at the moment,ITM will contain the list of states visited and some information aboutfrequently used operators, and LTM will hold the rules defining opera-tors and states.

Note the contrast between the location of potential bottlenecks hereand their location in verbal comprehension and recognition experiments,where the problem for the user of a Distributed Memory was to analyzedata as fast as they came in. In state space problem solving the systemitself has control over the rate of entry of data into STM, so this sort ofoverload can be avoided. ITM becomes the most vulnerable point. Ifthe search process generates an unwieldy record of candidates, ITM maybe overloaded, thus forcing the problem solver to repeat paths alreadyexplored and generally to take a disorderly approach toward solution.There are two defenses against this sort of confusion. The problem solvermay use an orderly algorithm for placing information into ITM and mov-ing it from ITM to STM. thus minimizing the chance of overload withina given representation, or the whole way of looking at the problem maybe changed, in an effort to find a state space in which the search istrivial.

stateX + (Y - 2 ■ Z)

Mv colleagues20 and 1 have conducted some preliminary studies illus-trating this point. We used tin experimental technique modeled afterone developed by Haves (1965), in which the subject is given the nameof a sttit ting node, a target node-, and the nodes emanating from thetarget nodes He then chooses one of these nodes as his first step. Theexperimenter tells him which nodes are connected to the chosen cThe process continues until the goal node is reached. We found thatthose piohlc ins are quite caw il the- subject use's a graph-searching ruleknown as "depth first" search, din's rule says "follow a given path to itsend, then back up to the last branch point, follow that path to its end.etc, until von come to a solution." ITM data management is simple, be

A crucial step in state space searching is deciding what to do next; i.e.,choosing a state that has been r. , and then choosing an operator

t0 apply to it. To do this effei li '

■

problem solver must maintaincertain temporary records whil - en route to a solution; details ofthe state- and operator with wh ii presently working, a list ol the

states that have been reached lisl oi the operators which he can

US

e. Computer science has focu ittcntion on algorithms whichminimize the number ol I before a solution is reached

"The experiments were conducted with the assistance ol Bruce rhornpson.Stephen Smythe programmed the simulation ol problem solvi

and have available the operators

.1 + B = BP A; A - 4 C) = (A A B) 4 C

we can then reach the states

(Y + 2-Z) + X;X + C2-Z+ Y); (X + Y) + 2-Z

■"The I-' !est discrepanc; betwei n *< Ismail i il ! i t":»1 pa,!,

l.,

a

solvin ■' " '"""! ,! ' 'ts wnictliinp I-- wall

' '

...;.. tatement at virtually no cost

90 EARL HVN'TWHAT KINH

OF

COXtl'iriKi; is MAN 0 91cause (he order in which items are taken out of ITM is the inverse ofthe order in which they are put in. In case of confusions, subjects pre-ferred to go back to the. starting point and try again, thus repeatingsome paths, than to make the effort of remembering how the differentpoints visited fitted together. This is an interesting demonstration of thedifference between simulation and computer-oriented problem solving.It can be proven (Nilsson, 1971) that, in general, "depth first" is notan efficient algorithm in terms of minimizing the number of pointsreached before a solution is achieved. People- evidently must conserveI I'M storage, even at the expense of taking longer to solve the problem.In a second stud)- we tried (o make it difficult to store and retrieveitems from ITM. The task given the subjects was similar to the one usedin the first study, but the names of the nodes were varied. Although thedata arc not striking, it appears that if the nodes have names vvhichsound alike—thus maximizing the opportunity for acoustic confusion inshort-term memory—then problem solving is characterized bv frequentand logically unnecessary repetition of steps. The explanation offeredis that acoustic: similarity makes it difficult for subjects to manage longlists of "visited" nodes in STM and ITM.

In a third experiment we simulated solution of the "eights puzzle."In this popular novelty game eight blocks, numbered 1 through S areplaced haphazardly on a 3 ;< 3 board. Die problem is to rearrange theblocks so that trie numbered blocks are in normal reading order onthe board and the blank space is in the lower right-hand corner, withoutlifting any block from the board. A program was written to solve theeights puzzle using any of a number of graph-searching algorithms ineluding those- analyzed by Xilsson (1971). Die number of steps theprogram required to solve the

cliff,

nut problems was compared to thebme people took to solve llu- sam, problems. No correspondence w itsfound except when the program used a searching algorithm which kept(he list of states v isited within a fixed size. Such an algorithm may requirethat the program repeat steps winch it has taken before-, but (-rased fromits temporary memory ol slops tab nUllil '' l1 "'" studies are certainly not definitive, they indicate that theDislributed-Memory analysis of problem solving is a reasonable one.On the other hand, they byjias- a cruci d question in the psychologicaltheon ol problem solving. This is the question -ol representation. \ rep-resentation is defined .-is a choice ol a d, finition lot the stale space itself.Tru] - ''' "-" ll problem .olviim i cha let. riz.ed h\ a choice of a repn sen

ll" ' ;

'

' : it,vial. Plodders use ordorh data1 ufortunati ly, we have almost

110 " !

;

rf'Pri "'alious rated, although we do have some

rule

fo,

evaluating them once they are presented (Ammol, 1968) Sadlytoday psychologists cannot go far beyond Polya's (.1957, cryptic adSSto problem solvers . . . 'Think of a good analogy."Concept Identification

Hie last example of application of Distributed Memory to a cognitivetask will be the simulation of concept learning. In the typical 'Optionparadtgm concept-learning study the subject is shovvTa sequence o"objects which can be described in terms of their values on known' attnbutes (e.g., Border color = red. Size = big, Shape = triangle) Each; n>;:s as;Ti to a ciass -rg some ruie ~ *»-"» »*^£If 1 ,° , °Ut What lhis rule is (Bourne> 1965

;

Hunt, 1962)If the rule to be learned is based simply on the presence or absenceBower eVT 1 Sf16"' " Callod a —PWdentification study(Howe, & Trabasso, 1964), since what the subject must learn is that the

1 sullc "l

IS

TTfng TP°nSeS °n the basis °f a discrimination thate subject already knovvs how to make. A more interesting case occurswhen the classification rule is based upon a Boolean combination of thepresence or absence of several features, since ever, though the sub,';;;^". ,x; wel] r are of the features - he

">' — Ls^ £particular combination which he must come to noticeA Boolean classification rule can be depicted as a sequential decisionbee, as shown m Fig. 9. The distinction between this tree and a discZbnation ne that i„ Fig. 9 paths terminate with nonunique class nan""-v could a Distributod-Memory system construct such a graph' Thenecessary program and data path locations are shown in FV 10 Weassum,. that the learner observes objects of known classification fromthe envnonment. After pass,,,, through the peripheral-memory svslemJ^Phon of the object will arrive in STM. The features used to esta^hsh tins description

may

in part be determined by feedback signals from-">-""' memory which set the peripheral memory to look for ehaacte"sues vvhich^the currently held hypothesis indicates are relevantThe hypothesis exists as a decision rule in ITM. When the descriptionof " arrives in STM the hypothesis is used to classify it. If The

i

Circle?, L ___,Yes No

, lue?1ue? Triangles[ | ! _*1

GEK NotGEK GEK Not GEK

'" " «f « Vision tree for concept learning with Boolean concepts.

WHAT KIND

OF COMPUTER IS

MAX? 93

EARL HUNT92Williams (1971) has combined these ideas into a single program which

simulates the use of memory in concetpt learning. In spile of having tomake a number of ad hoc decisions about rules for deciding when itemsshould be held in STM and about how rapidly ITM-recorded biases wereto bc changed, she was able topredict a number of the fine grain featuresof data from experiments on conjunctive-concept learning. This includedpredictions of the changes that subjects would make in their hypothesesunder different stimulus-response contingencies. Her results are perhapsthe strongest data in support of the Distributed-Memory analysis of con-cept learning.

IntermediateJ

Shorl

m9""" )lII i

'

t

■

ii

n -.i:u

l- i

system ""*" Records ot some

of

items seen system

~11

Conscious

memory

i

processor

ITM [Current hypothesis

Guesses about attnb.

CONCLUSIONDistributed Memory has been offered as a framework into which to

fit a number of studies from different content areas of Psychology. If wewere to begin work on a supersimulation of man. we would soon findmany questions diat have been left unanswered. Answering them wouldadd to our knowledge of human behavior, so this, in itself, is no disaster.We might even try to build the simulation just in order lo find out whatthe questions were. Would this be a reasonable way to proceed?

Long term memory

r InitialJ

CLS

or other | Dios.es toword ■

progroms

c ... butes

Fic. 10. Location of data in inductive problem solving.

classification is correct, a new object is sought. If the classification isincorrect, a new hypothesis is constructed based upon the current eon-tents of STM. In constructing the new hypothesis some use maybe madeof ITM records indicating that certain attributes appear to be relevant,or LTM records ("biases") of attributes that have proved useful on pastproblems. The program which constructs the new hypothesis willjiea strategy for learning, similar to those discussed by Bruner et al. (1956)and Hunt et al (1966). When (if) the correct classification rule is de-veloped, the process will stabilize, for no more errors will be made. Thus,a long run of error-free responding can serve as a signal that the ITMhypothesis should be copied into LTM.

'Die Distributed-Memory model of concept learning places bottleneckswhere the data indicate they are. Die time required to classify an objectcorresponds somewhat to the depth of the path along a sequential dis-crimination tree- which must be traversed in order to make the classifica-tion (Trabasso et al, in press). Bourne and Bundcrson (1963) showedthai concept learning is inhibited il the interval between the signalindicating the correct classification of an objeel and the presentation ofthe next object to be ,1 issified is short. According to the- model presentedhere, this is the: time during which the learner must do the processingrequired lo check his classification and perhaps develop a new rule.Finally, ii should be possible to overload ITM b) presenting a classifica-tion problem which requires thai a verv complex tree be learned. \nurnbri of -.indies (Bourne, 1967; Hunt, Mann, &

Stone,

1966; Neissci

It would be an expensive effort. Man needs access to a huge database. Identifying it and finding a way to handle it within a computerwould involve the psychologist in a long and psychologically uninterest-ing project . . . but if a simulation is going to be

successful,

the programhas to know that rain is wet. and a thousand other facts besides. TheDistributed-Memory model implies a great chad of parallel proces:which would have to lie simulated on the serial computers provided bytoday's technology. The computing bill is going to be high. Will theresults be worth it?

There is a more intellectual hazard. Distributed Memory is really aset of principles to guide the construction of a simulation. We want tostud)- the effect of these principles, but very large programs have a wayof becoming bogged down in details. It may be that it will be hard tofind out what causes the simulation to behave in a certain

way.

Ofcourse, we can claim that if we have a running program, we must haveii formal model bul this is not enough. The model is supposed to aidour understanding, not increase our confusion. We already have oneblack box, man, and hardly need another made ol IBM cards. In addi-tion I, at K-iist. join with Simon (1969) in suspecting a theory whichsays thai man is complex. Simon stated that man is simple and that hisinteraction with a varied environment i-- what makes bun appear com-plex. This seems a good article ol faith for the behavioral scientist.

In spite ol such reservations, a Distributed Memory simulation shouldg. We. i . : .hovvn that the diffii ult) ol concept learnjndei d i

'i.t,

': of de. ision tree,

WHAT KIND

OF COMPUTER IS

MAX? 9594 EABI, HUNT

be built. We need to have a wholistic framework for thinking aboutman as well as the reductionist approach inherent in modeling miniaturesituations. Il is doubtful that we will ever be able to prove that sucha broad model is reallv a model for man. In most situations we will haveto settle for an existence proof; if both man and the model achieve a

verv complex task, it will be hard to imagine that the task can be donein more than one way. In such case any successful program is a presump-tive psychological model. Besides, we will feel that we have learnedsometiling about cognition if we build a thinker.

It is not at all clear that experimental psychologists will or shouldaccept this view. If thev reject it, it should be because they have founda better wax of explaining cognition, and not because they have decidednot to think about thinking.

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Atkinson. R. k

Schiffrin,

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(Accepted September 14, 1970)