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ORIGINAL ARTICLE

Does familiarity or conXict account for performance in the word-stem completion task? Evidence from behavioural and event-related-potential data

Florian Klonek · Sascha Tamm · Markus J. Hofmann · Arthur M. Jacobs

Received: 15 January 2008 / Accepted: 5 September 2008 / Published online: 27 November 2008! Springer-Verlag 2008

Abstract The conXict monitoring theory (CMT) assumesthat word-stems associated with several completions shouldlead to crosstalk and conXict due to underdeterminedresponding situation (Botvinick et al. in Psychol Rev108(3):624–652, 2001). In contrast, the Multiple-Read-Out-Model (MROM) of Jacobs and Grainger (J Exp Psy-chol 20(6): 1311–1334, 1994) predicts a high level ofgeneral lexical activity (GLA) for word-stems with manycompletions, indicating a higher stimulus familiaritybecause these stems are more probable to be read. We com-pared word-stems with several completions against word-stems with one possible completion while measuringresponse times and electrophysiological recordings. Slow-est response times and a distinct FN400 component, whichhas previously been related to the concept of familiarity(Curran in Memory Cogn 28(6):923–938, 2000), wereapparent for word-stems that could only be associated witha single response. These Wndings support the claims of theMROM. Furthermore, the lack of the N2-component forword-stems with several completions continues tochallenge the EEG-extension of the CMT (Yeung et al. inPsychol Rev 111(4):2004).

Introduction

In the word-stem completion task, participants are asked tocomplete a series of word-initial letters (e.g., ZOM) withthe Wrst word that comes to their mind (e.g., ZOMBIE).

As most word-stems oVer several possible completionsand participants have to “choose from a set of responses,none of which is more obvious or compelling than theothers,” the task creates underdetermined response situationswhich lead “to the parallel activation of multiple incompat-ible response pathways, resulting in crosstalk during theperiod between stimulus presentation and response deliv-ery” (Botvinick, Braver, Barch, Carter, & Cohen, 2001,pp. 627–628). According to Botvinick et al.’s (2001) con-Xict monitoring hypothesis, this crosstalk and task-inherentneed for control is monitored by a speciWc cognitive com-ponent that “serves to translate the occurrence of conXictinto compensatory adjustments in [attentional] control”(p. 625). The conXict monitoring theory (hereafter, CMT)of Botvinick et al.’s (2001) is a unifying framework thatattempts to explain the need for cognitive control in experi-mental tasks that require participants to override prepotentbut task-irrelevant responses (e.g., Stroop task), that lead toresponse errors (e.g., Eriksen Xanker task, Simon task) orthat create a situation of underdetermined responding (e.g.,the word-stem completion task). The CMT claims that acti-vation in the anterior cingulate cortex (ACC) is a possibleneuronal substrate for this conXict monitoring function, byindicating the amount of conXict present and forwarding thesignal to the cortical units associated with executive control(Botvinick et al. 2001; Botvinick, Cohen, & Carter, 2004).The activation of the ACC in CMT has been further linkedto distinct components of the event-related-potential(ERP), namely the error-related negativity (ERN or Ne)and the N2. While the ERN is response-locked followingerrors, the N2 is stimulus-locked and correlated with theamount of pre-response conXict on trials with correctresponses (van Veen & Carter, 2002; Yeung, Botvinick, &Cohen, 2004). Yeung et al. contrasted stimulus-locked ERPsin the Erikson Xanker task for congruent and incongruent

F. Klonek (&) · S. Tamm · M. J. Hofmann · A. M. JacobsDepartment of Psychology, Freie Universität Berlin, Habelschwerdter Allee 45, 14195 Berlin, Germanye-mail: fklonek@gmx.de

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trials. In this task participants are required to identify acentral stimulus in a brieXy presented stimulus array. In tri-als in which the distracter stimuli are congruent with thecentral stimulus (e.g., <<<<<), the Xankers map to the sameresponse as the central target, whereas in trials with incon-gruent distracters (e.g., <<><<) the Xankers signal a diVer-ent response. The N2 component appeared around 250 msafter stimulus onset and peaked between 300 and 400 ms. Itwas larger on incongruent trials and had a fronto-centraltopography.

The claims of Yeung et al. (2004) are supported by stud-ies based on other conXict paradigms (Carriero, Zalla,Budai, & Battaglini, 2007; Donkers & van Boxtel, 2004).While Yeung et al.’s work is based on the Xanker task,Donkers and van Boxtel provide similar evidence for theN2 reXecting conXict monitoring in a Go/No-Go task, argu-ing that the N2 can solely be attributed to conXict process-ing. In a similar vein, Carriero et al. (2007) used a spatialversion of the Simon task in which participants have torespond to stimulus features while ignoring their spatialposition. In incongruent trials in which the spatial positioninterferes with stimulus attributes, an N2 component wasfound before response execution. Dipole analysis of ERPstudies conWrmed the ACC as the likely source of the N2potential (Carriero et al., 2007; van Veen & Carter, 2002;Yeung et al. 2004).

From the behavioural perspective, a common Wnding inparadigms that require cognitive control are prolongedresponse times (RTs) and higher error rates on incongruenttrials, likely reXecting the greater degree of conXict (seeJensen & Rohwer, 1966; MacLeod, 1991, for a review ofthe Stroop task; Erikson & Erikson, 1974; Sanders &Lamers, 2002, for the Eriksen Xanker task; and Simon &Small, 1969; Zorzi & Umiltà, 1995, for the Simon task).

SpeciWcally in regard to word-stem completion tasks,Botvinick et al. (2001) used computer simulations to showthat words in comparison to their stems lead to lower acti-vation of HopWeld Energy, a measure used in computa-tional models that is thought to reXect the extent of conXictin a cognitive system. They concluded that “stems that acti-vate one completion much more strongly than any otherwill be associated with the least conXict,” and further:

Considered in the context of the conXict monitoringhypothesis, this leads to the prediction that stem com-pletion should engage the ACC more strongly whenthe stem presented is associated with several comple-tions than when the stem is associated with onestrongly preferred response. (Botvinick et al., 2001,p. 633)

Taken together, the assumptions of Yeung et al. (2004)and Botvinick et al. (2001) suggest that word-stems withseveral possible completions should not only lead to stronger

activation of the ACC, but also to increased N2 amplitudesover fronto-central regions in ERP analysis, in contrast toword-stems with only a single possible completion.

Another computational framework relevant to the pres-ent study is the Multiple Read-Out-Model (MROM; Grain-ger & Jacobs, 1996). The MROM is a localist connectionistmodel based on the interactive activation model by McClel-land and Rumelhart (1981) and the semistochastic variantof this model (SIAM; Jacobs & Grainger, 1992). It has pri-marily been used to predict performance in implicit wordrecognition tasks, such as the lexical decision and percep-tual identiWcation tasks (Jacobs, Graf, & Kinder, 2003).The MROM assumes that the identiWcation of a stimulus isbased on two intralexical sources of information. The Wrstsource is the activation of an individual single word repre-sentation of the stimulus. If a single node in the artiWcialmental lexicon yields above-criterion activation, the stimu-lus is identiWed. The second source refers to all nodes in themental lexicon that are partially activated by the stimulus[Global lexical activity (GLA)]. A lexical decision can bemade if either of these activation levels exceeds a responsecriterion. A temporal deadline mechanism that monitorstime from stimulus-onset determines the non-identiWcationof the stimulus when neither the activation of a single wordnode nor the GLA yields above-criterion activation.

The functionality of the MROM relates to dual-processtheories of explicit memory by assuming that two types ofinformation are crucial for recognition judgements: famil-iarity and recollection (Yonelinas, 2002). While familiarityconstitutes “the product of a global matching process thatrepresents the similarity between a test item and all studiedinformation …. recollection involves the retrieval of quali-tatively speciWc information about individual items”(Curran, 2004, p. 1089). It has been previously stated that“the MROM provides [via the GLA] a convenient means ofquantifying … the familiarity dimension [as] stimulusfamiliarity is assessed by the sum of activity of all worddetectors at a given cycle of processing time…”(Jacobset al., 2003, p. 428).

Behaviourally, subjects are quicker to recognize wordswith a high predicted GLA level, that is, words that appearto be very familiar, and produce fewer identiWcation errors,in a lexical decision task than words with comparativelylow GLA (Braun et al., 2006; Grainger & Jacobs, 1996). Inaddition, stimulus familiarity has been reported to system-atically aVect ERPs (Braun et al., 2006; Curran, 1999,2000). Braun et al. (2006) found a parametric eVect of GLAfor nonwords in the lexical decision task on the N400 (timewindow: 450–550 ms) with higher N400 amplitudes forfamiliar (i.e., high-GLA) nonwords, in contrast to unfamil-iar (i.e., low-GLA) nonwords. A more frontally distributedN400, termed the FN400, has been directly related to theconcept of familiarity (e.g., Curran, 1999, 2000; see also

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Tendolkar et al., 1999). In a study-test experiment, Curran(2000) varied the similarity, and thus the familiarity,between words by means of their number, for example thestudied word was CAT while the new word was CATS.Subjects were Wrst asked to learn a list of words, and then ina second part, given a list of items and had to decide whichof them had been in the study list before. In the test phase,new words that had appeared in their number-reversedforms in the study phase were assumed to reXect highfamiliarity. Curran found an enhanced negative deXectionbetween 300 and 500 ms bilaterally in fronto-centralregions (viz., the FN400) for these words in contrast to newwords that did not morphologically resemble words fromthe study phase.

In the present study, we assumed that word-stems diVer-ing in their number of completions should also diVer instimulus familiarity, since stems that have several possiblecompletions are more frequent in the language than thosethat have only one completion. In other words, as word-stems with many possible completions are more likely to beread, heard, or spoken—in sum, explicitly or implicitlystudied—they should also appear more familiar than word-stems that have few completions. To our knowledge, therehave been no stem completion studies that have examinedRTs or ERPs to word-stems diVering in the number of pos-sible completions. The approach taken in the present studywas straightforward. Participants had to perform a word-stem completion task, in which the number of possible stemcompletions was varied in three diVerent levels: Word-stems having only a single completion were contrasted withthose having several completions. This allowed us to testpredictions from both the CMT and the MROM frame-work.

In the CMT, the diVerence in the number of completionswas seen as a basis of the amount of conXict: word-stemswith several completions are assumed to constitute a highconXict condition and RTs are expected to be prolonged incontrast to word-stems that have only a single response.Moreover, such high-conXict conditions, that is, word-stems with several completions, should be marked by a pro-nounced N2 negativity.

The MROM, by quantifying stimulus familiarity viaGLA, would make contrasting predictions. Word-stemswith several completions should elicit high levels of GLAand word-stems with only one completion should elicit alow GLA. We tested for this hypothetical model behaviourin Simulation study 1 using the MROM described in Grain-ger and Jacobs (1996). As faster RTs can be assumed whenstimulus familiarity is high, we subsequently conducted abehavioural experiment using the stimuli from the simula-tion study. To test for the model’s generality across diVer-ent stimulus sets (cf., horizontal generality as described inJacobs and Grainger, 1994), a diVerent stimulus set was

presented to the model in Simulation study 2. Apart fromreplicating the behavioural Wndings of the Wrst experiment,another aim of Experiment 2 was to investigate whetherboth models’ hypothesized mechanisms can also be sup-ported by electrophysiological data.

Experiment 1

This study used an unprimed implicit word-stem comple-tion task: no explicit study phase, in which subjects have tolearn a list of words, preceded the stem completion task,and subjects were asked to write down the Wrst word thatcame to mind in response to the test stems (see Schacter,1987, for a review). Selection of word-stems, experimentalprocedure and analysis strategy was thus guided by previ-ous work that investigated the inXuence of lexical factors inthe unprimed word-stem completion task under implicitinstructions (Graf & Williams, 1987; Meier & Eckstein,1998; Shaw, 1997). These studies showed that participantshad diYculties in completing word-stems shorter than threeletters (Meier & Eckstein, 1998), that word length and fre-quency showed to have only minor eVects on word-stemcompletion probability, and that subjects tended to com-plete word-stems with short and rather frequent words(Graf & Williams, 1987; Shaw, 1997). A corpus analysisby Shaw (1997) showed that although—on average—thelexical frequency and the completion probability of thecompleted word were moderately correlated, the strengthand direction of the correlation strongly varied acrossdiVerent stems. The CMT hypothesizes a greater amount ofconXict monitoring for word-stems with several comple-tions, as they create situations of underdetermined respond-ing with crosstalk interference from the co-activation ofmultiple lexical entries, in contrast to word-stems with onlyone possible response. A very common behavioural Wndingis that in high-conXict conditions of tasks that require cog-nitive control (e.g., Stroop task, Simon task, or the Eriksentask) subjects show prolonged RTs and fail more frequentlyto produce the right response. Whether this hypotheticalinterference also aVects RTs in the word-stem completiontask was investigated in Experiment 1.

As we assumed that word-stems with a diVerent numberof completions also diVer in stimulus familiarity, we Wrstsimulated the GLA activation in the MROM for our stimu-lus material. In a previous neurocomputational study, wefound that recognition of words in the lexical decision taskis facilitated when the GLA associated with the words ishigh (Braun et al., 2006). In Experiment 1, we also exam-ined whether this behavioural facilitation is characteristic ofthe word-stem completion task. A minor aim of Experiment 1was to replicate Wndings about stem- and word-related vari-ables that were reported to inXuence task performances, that

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is, the number of possible completions and word frequency(Graf & Williams, 1987; Shaw, 1997). Going beyond thescope of these previous studies, we also examined howresponse times were aVected by the frequency of the com-pleted words.

Method

Design

We used a one-factorial repeated-measures design, with thewithin-subjects factor “number of completions” havingthree levels: one completion (1), three to Wve completions(3–5), and eight or more completions (¸8). The number ofcompletions refers to the number of words that could bepossibly associated with the word-stem according to theCELEX word corpus (Baayen, Piepenbrock, & Gulikers,1995). Response latencies and qualitative answers wererecorded.

Materials

Ninety (90) three-letter word-stems were drawn from anoriginal corpus of 51,728 German words. Words wereexcluded from the corpus (1) if they had less than Wveletters, (2) if their Wrst three letters formed a three-letterword from the corpus and (3) if they were longer than eightletters. The Wnal word corpus contained 16,265 words.After this preselection, the Wrst three letters of each wordwere extracted and the number of occurrences of eachword-stem was counted. This served as a measure of thenumber of words that are theoretically associated with theword-stem. This variable will be called the number ofcompletions in further analyses. Three classes of 30 itemseach that diVered signiWcantly in the number of comple-tions [F(2, 87) = 76.44, P < 0.01] were created: one possi-ble completion (M = 1.0, SD = 0), three to Wve possiblecompletions (M = 3.86, SD = 0.77) and eight or morepossible completions (M = 15.33, SD = 8.19). The threeclasses were matched on the mean frequency of theirassociated words.

Simulation study

The MROM was implemented with a lexicon of 277 4–6letter German words, which constituted all possible com-pletions of the word-stems described in the material sec-tion. Since the MROM can not process umlaut letters, theGerman letter ß, and is restricted to the processing of 1–6letter words, words containing these letters or thoselonger than six letters had to be excluded. Model parame-ters were set as described in Grainger and Jacobs (1996).

Each word was simulated only once, even if it existed inseveral grammatical word classes. The resting levels ofword nodes were set according to the freqency/1 millionor to the summed freqency/1 million of several words incase the word existed in several word classes. Each word-stem was processed by the model for 40 cycles and activa-tion values for GLA were recorded. Two word-stems withoscillating GLA were excluded from further analyses(“MAR” and “KLE”). GLA activation levels were ana-lyzed with repeated measure analyses of variance(ANOVA), with three levels of number of completions(1, 3–5, and ¸8). SigniWcance level was determined to! < 0.05. Greenhouse and Geisser (1959) corrections wereapplied if necessary and post-hoc pairwise t-tests wereBonferroni-corrected. Table 1 shows the average GLAactivation between cycle 20 and 40 for the three word-stem categories: diVerent levels of GLA were predictedfor word-stems diVering in the number of completions.Word-stems with several completions generate a higherGLA in the artiWcial mental lexicon than word-stems withfew or only one completion.

The chosen cycle range (20–40) revealed signiWcantGLA diVerences between word-stem categories[F(2, 87) = 24.20, P < 0.01]. Pairwise comparisons showedthat word-stems from the 1-completion category diVeredsigniWcantly from 3–5 (P < 0.01) and ¸8-completions(P < 0.01) word-stems, but 3–5 and ¸8-completionsword-stems did not diVer signiWcantly (P = 0.26). Wehypothesize that a higher GLA, that is, stimulus familiarity,facilitates stimulus identiWcation, empirically observable infaster RTs and/or more successful stem completions bysubjects.

Participants

Ten male and seven female subjects (N = 17) ranging in agefrom 22 to 33 years (M = 27.52, SD = 3.92) participated inthe study. All had normal or corrected-to-normal vision,and all were native German speakers. Six subjects wereable to type blindly on a standard computer keyboard (i.e.,“touch typing”) whereas the rest (N = 11) had to look at thekeyboard in order to type proWciently. Subjects participatedeither voluntarily in the study or in order to accomplishcourse requirements.

Table 1 Mean GLA activation between cycle 20 and 40 for the threeword-stem categories. Standard deviations are indicated in parentheses

1 3–5 ¸8

GLA 0.62 (0.22) 0.84 (0.20) 0.93 (0.06)

Number of stems 30 30 28

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Procedure

Participants were tested individually in front of a laptop in aquiet room. They were seated at a distance of about 50 cmfrom the screen and asked to carefully read the instructions.Participants were asked to read the three-letter word-stemspresented on the screen and to complete them with the Wrstword that came to their mind. They were allowed to useevery word class (e.g., nouns, verbs, adjectives). Softwareprogramming discouraged subjects from typing more thanseven letters, thus ten letter-words in combination with theword-stems, by jumping to the next trial in case this letter-maximum was exceeded. However, fast typing could occa-sionally bypass this barrier. The 90 three-letter word-stemsin the task were presented using Presentation software(Neurobehavioral Systems, Version 9.0). Response laten-cies and the generated words were recorded. Ten trainingtrials were given before measurement took place. If sub-jects were not sure whether they had understood the task,additional training was given. All training stimuli wereexcluded from further analysis. All stimuli were presentedin Times New Roman font, 20 pt, on a 15! screen. Word-stems were printed in black on a white background. Eachtrial was initiated by the appearance of a Wxation cross “+”centered on the screen. After 700 ms, the Wxation cross wasreplaced by three uppercase letters comprising the word-stem. Maximal presentation duration of the word-stemsbefore initiation of the next trial was 10 s. Participants com-pleted the word-stem by directly typing the letters on thekeyboard. Response latency was measured from stimulus-onset until the Wrst letter was typed. Interstimulus intervalwas jittered between 500 and 1,500 ms. The order of the 90experimental stimuli was randomized for each participantand none of the three-letter word-stem appeared more thanonce. The entire experiment lasted approximately 15 min.After the experiment was over, participants received a shortdebrieWng about the goals and intentions of the study.

Results

Behavioural data reduction

Before RTs were analyzed statistically, responses werevisually inspected for orthographic misspellings and forcompletions that were either proper names, loan words orpseudo-words (e.g., ZUFT or ZUVFT). Responses wereorthographically corrected, and if this changed the Wrstthree letters of the answer (e.g., ATE-RIE ! ARTERIE,ABD-END ! ABE-ND, ATL-ETH ! ATH-LET) theresponse was excluded from RT analysis or regarded asinvalid in the analysis of completion probability. Comple-tions that did not occur in the word corpus of the CELEXwere cross-checked by means of another, more recent

corpus that was available from http://www.wortschatz.uni-leipzig.de (Leipzig Corpus). All answers were testedbefore and after orthographic correction and were regardedas valid when an entry in the Leipzig Corpus was found.Completions that according to the Leipzig Corpus wereproper names (e.g., DAG-MAR or DAG-OBERT), brandnames (e.g., DUP-LO, HIL-TON, REE-BOK) or notknown (DEG-ATO, DUP-SEN) were not included inresponse time analysis and regarded as invalid. When inX-ected words were completed to the stimulus (GLI-EDER) atransformation to the nominative singular mode was done(GLI-ED). Mean RTs and the mean number of entries werecomputed for each valid word. The completion probabilityper condition, P (completion), was calculated for each sub-ject by dividing the number of valid word entries throughthe number of stems. In addition to that, completion proba-bility for each word was calculated by dividing the numberof entries of the same word by the sample-size of partici-pants that had completed the related word-stem. RTs and P(completion)-measures were then analyzed with repeatedmeasures analyses of variance (ANOVA), with three levelsof number of completions (1, 3–5, and ¸8). SigniWcancelevel was determined to ! < 0.05. Greenhouse and Geisser(1959) corrections were applied if necessary and posthocpairwise t-tests were Bonferroni-corrected.

Following the corpus analyses of Shaw (1997), the num-ber of diVerent responses given to a stem was correlatedwith the number of possible CELEX completions. Further,word frequencies were correlated with word completionprobabilities. Valid word-stem completions that were notlisted in the CELEX were assigned a frequency of zero,whereas words that were part of the CELEX but whichwere not completed by participants were assigned a wordcompletion probability of zero. Finally, mean responsetimes of valid words were analyzed as a function of theirCELEX frequency. Therefore, Wfty per cent of the wordswith highest frequencies were assigned to a high frequencygroup (M = 63.08, SD = 117.38) and the remaining wordswere assigned to a low frequency group (M = 0.24,SD = 0.43).

Response time data

Participants’ RTs in the stem completion task diVered sig-niWcantly across the three categories [F(2, 32) = 13.26,P < 0.01, partial "! = 0.45]. Subjects responded slowest inthe 1-completion condition (M = 2,330 ms; SD = 613 ms)and became continually faster when more completions werepossible (M = 2,002 ms; SD = 488 ms in three- to Wve-completions condition, and M = 1,767 ms; SD = 383 in ¸8-completions condition). Pairwise comparisons showed thatthese diVerences were signiWcant between the 1-completioncondition and the ¸8-completions condition (P < 0.01), as

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well as between the 1-completion and the three- to Wve-completions condition (P < 0.05).

Completion probability data

The number of completions was a signiWcant factor in theanalysis of stem completion probability [F(2, 32) = 73.66,P < 0.01, partial "! = 0.82]. In contrast to RTs, the proba-bility to complete a stem increased with an increasednumber of completions. Subjects completed only 70.4%(SD = 8.7%) of stems with 1-completion, 82.0% (SD = 6.9%)in the three- to Wve-completions condition, and 93.0%(SD = 5.9%) in the ¸8-completions condition. Pairwisecomparisons showed that diVerences between all conditionswere statistically signiWcant (P < 0.01). Figure 1 summa-rizes the descriptive results for our behavioural data.

Analyses of corpus

The number of diVerent responses given to a stem and thenumber of possible CELEX completions revealed a correla-tion of r = 0.73 (P < 0.01). Word frequencies and wordcompletion probabilities were correlated with r = 0.21(P < 0.01). Further, response times diVered signiWcantly asa function of the frequency of the completed word (t = 5.56,P < 0.01): RTs to word-stems were slower when it wascompleted with a low frequency word (M = 2,574 ms;SD = 1,514 ms), whereas the response was faster when thecompletion was a high frequency word (M = 1,854 ms;SD = 853 ms).

Discussion

The results of the Wrst experiment indicated that word-stems that diVer in the number of completions have diVer-ential eVects on RTs and word-stem completion probabil-ity. Subjects were faster for word-stems with severalcompletions and had little diYculty to complete them with

an appropriate word, and they were signiWcantly slower forword-stems with fewer possible completions and failedmore frequently to complete them. Additionally, a correla-tion of r = 0.73 between the number of alternative stemcompletions made by participants and the number of wordsin German that share the stem replicated results from previ-ous studies that reported correlations of r = 0.63 (Graf &Williams, 1987) and r = 0.77 (Shaw, 1997) between thesetwo measures. The correlation between word frequency andword completion probability is also similar to correlationsreported by these previous studies—even though, thecoeYcients are a bit smaller. Interestingly, response timesto word-stems varied also as a function of the frequencyof the completed word. This result underpins the necessityto control for word frequency—as done in the presentexperiment.

From the conXict monitoring perspective, word-stemswith multiple alternative completions should have activatedequally possible responses in the lexicon resulting in cross-talk in the cognitive system and longer RTs. However, thiswas not observed in the present experiment. The monotonicdecrease in RTs and increase in completion probabilityseems to indicate facilitation eVects for stems withincreased number of completions which might be explainedin terms of a higher stimulus familiarity. The higher stimu-lus familiarity was predicted by the MROM in the simula-tion study showing higher levels of GLA for word-stemswith a greater number of completions. The MROMassumes that word-stems with several completions activatemore word nodes in the artiWcial mental lexicon than word-stems with only a single completion, hence leading to fasteridentiWcation of the stimulus. Although the prolonged RTswe found in this experiment are a very common result instandard conXict paradigms and are plausible in the senseof crosstalk interference, they do not directly reXect theactivation of the ACC. It might still be possible that theACC was activated, that is, conXict was monitored, in thehigh-conXict conditions. Thus, we conducted a second

Fig. 1 Mean response times and probabilities to complete a stem to a German word for stems that diVered in number of comple-tions. Whiskers indicate one standard deviation

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experiment using the same design as the Wrst, and measuredbrain electrical activity by means of EEG.

Experiment 2

As the CMT has been applied to data from ERP studiesclaiming that the amount of pre-response conXict is indi-cated by the N2 component (Yeung et al., 2004), weexpected the N2 to be higher on conXict trials, that is, forword-stems with several possible completions. Addition-ally, we ran a second simulation using the MROM for anew stimulus set in order to check whether the MROM canreplicate the GLA predictions from Experiment 1. Theword-stems from Simulation study 2 were also used inExperiment 2. We were further interested in the issuewhether diVerences in the number of completions, assum-edly reXecting stimulus familiarity, were visible in ERPsvia the FN400 component. Word-stems with a high GLAshould be accompanied by a more positive FN400 compo-nent and were expected to have faster RTs than those with alow GLA.

Method

Design

Since the general design for Experiments 1 and 2 was iden-tical, only the diVerences pertaining to experiment 2 will bereported in the material, simulation study, procedure anddata analysis sections.

Materials

One hundred and Wfty (150) three-letter word-stems wereused. Words were excluded from the corpus according tothe same criteria as reported in Experiment 1 except the Wrst:Words with less than four letters (Wve letters in Experiment1) were excluded. This criterion was changed becauseExperiment 1 showed that subjects also completed word-stems to four-letter words. The Wnal word corpus contained13,314 words. Since the preprocessing of behavioural datain Experiment 1 showed that some word-stems were pri-marily completed with proper names (MAR ! MAR-THA, MAR-KUS, MAR-IA), we tried to control for this byexcluding word-stems that could be associated with com-mon German names. One-hundred Wfty eight word-stemshad to be excluded that could theoretically completed to284 German names that we chose from http://lexikon.beli-ebte-vornamen.de. In addition, all word-stems that could beassociated with words that appeared in the standardizedinstruction were also excluded for stimulus selection. Thiswas done to avoid priming eVects. The experimental factor

number of completions diVered signiWcantly across all threeconditions [F(2, 147) = 165.32, P < 0.01]. Characteristics ofstimulus material are given in Table 2.

Simulation study

The MROM was implemented with a lexicon of 401 4–6letter German words. Four word-stems with oscillatingGLA were excluded from further analyses (“SAL”,“HAR”, “KOR”, “TAP”). Figure 2 shows that MROM pre-dictions are very similar to those reported in the simulationpart of Experiment 1. Word-stems with several completionsgenerate a higher GLA in the artiWcial mental lexicon thanword-stems with little or only one completion.

Analyses of variance for the chosen cycle range (20–40)revealed signiWcant GLA diVerences between word-stemcategories [F(2, 145) = 51.60, P < 0.01]. Pairwise comparisonsshowed that word-stems from the 1-completion categorydiVered signiWcantly from 3–5 (P < 0.01) and ¸8-comple-tions (P < 0.01) word-stems, but 3–5 and ¸8-completionsword-stems did not diVer signiWcantly (P = 0.201).

Table 2 Characteristics of stimulus material for Experiment 2

Number of completions

1 3–5 ¸8

Number of completions 1 (0) 4.14 (0.8) 13.76 (6.28)

Number of stems 50 50 50

Mean frequency of all completions

13.1 (73.38) 10.17 (23.68) 6.98 (10.2)

Mean length of all completions

6.48 (1.24) 6.33 (0.76) 6.53 (0.56)

Fig. 2 GLA-activation during the Wrst 40 cycles for word-stemsdiVering in the number of completions

0 5 10 15 20 25 30 35 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Cycle

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Participants

Nine male and twelve female subjects (N = 21) subjectsranging in age from 19 to 49 years (M = 23.14, SD = 6.27)participated in the study. Five subjects were able to typeblindly on a standard computer keyboard (i.e., “touchtyping”) whereas the rest (N = 16) had to look on thekeyboard in order to type proWciently.

Procedure

Participants were tested individually in front of a computerin an EEG laboratory. They were seated at a distance ofabout 80 cm from the screen. In addition to the instructionsfrom Experiment 1, participants were directed to avoidcompletions to proper names or loan words that do not existin German. In contrast to Experiment 1 participants wereinstructed to Wrst press the space bar when they identiWed aword and then to take their time to type in the answer. Thiswas done to exclude the possibility that searching for cer-tain letters on the keyboard would inXuence response laten-cies between conditions. The 150 three-letter word-stems inthe task were presented using Presentation software(Neurobehavioral Systems, Version 11.0). All stimuli werepresented in Courier New on a 19! screen. Programming ofthe Presentation software was also changed thusly fromExperiment 1: First, stimulus presentation discouraged subjectsfrom typing more than Wve letters (eight-letter words) bydisplaying Wve underscores next to the stimulus. Second,RTs were measured from stimulus-onset until the required“space bar”-press. The trial-scheme is depicted in Fig. 3.

Interstimulus interval was 500 ms. The entire experi-ment lasted approximately 25 min.

EEG recordings

Participants were seated in a comfortable chair in an acous-tically shielded chamber. The electroencephalograph wasset to record from 27 standard 10–10 system scalp locations

(FP1, FP2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8,P7, P8, Fz, Cz, Pz, FC1, FC2, CP1, CP2, FC5, FC6, CP5,CP6) and referenced to the right mastoid. The vertical EOGwas recorded from electrodes placed over and below theright eye. The horizontal EOG was recorded from positionsat the outer canthus of each eye. Electrode impedanceswere kept below 5 k!, and impedances of eye electrodesbelow 10 k!. Signals were continuously digitized at500 Hz (BrainAmp, Brain Products).

EEG data reduction

EEG analyses were conducted with Vision Analyzer soft-ware (Brain Products). Raw data Wles were Wrst visuallyinspected for artefacts, such as muscle artefacts, excessiveeye movements and ampliWer blocking. After that, an auto-matic artefact rejection procedure was used to discardepochs if they contained artefacts or bad channels (ampli-tudes diVerences greater than 200 "V within 200 ms,changing more than 30 "V between samples, reachingamplitudes over 100 "V or under ¡100 "V). Contaminatedepochs were excluded from further analyses to ensure EEGdata are as artefact-free as possible. Blinks were correctedusing Gratton, Coles, and Donchin (1983) ocular correctionalgorithm as implemented in Vision Analyzer. Signals wereWltered with a 0.5305 Hz highpass and 30 Hz lowpass Wlter(at 12 dB/oct). EEG recordings were rereferenced oZine tothe average of all electrodes including left mastoid. Thistransformation was used to minimize the eVects of refer-ence-site activity and accurately estimate the scalp topogra-phy of the measured electrical Welds (Curran, 2000; Dien,1998). The EEG recording was segmented to 1,200 msepochs and baseline-corrected with respect to a 200 msprestimulus recording interval. All segments were scannedoZine again for artefacts. Trials in which subjects could notcomplete the stem (no space-bar-press) were excluded fromERP analysis. Data from Wve subjects who had less than 15trials in any condition were not included in the Wnal analy-ses. The Wnal number of subjects retained was 16.

Fig. 3 Trial scheme for word-stem completion task

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ERP analysis strategy

The segments were averaged for each subject according tothe word-stem categories (1, 3–5, ¸8). Analyses focused onregions and time windows in which the N2 and FN400 havebeen reported to be maximal (Curran, 2000; van Veen &Carter, 2002; Yeung et al., 2004). The conXict-related N2eVect was reported to have a frontal distribution and to bemost prominent on the FCz electrode (between Fz, Cz,FC1, FC2) appearing between 300 and 400 ms (c.f., Yeunget al., 2000; van Veen & Carter, 2002). The FN400 familiarityeVect should also appear frontally in the anterior superiorregion between 300 and 500 ms most closely to F3, F4,FC1, FC2 and Fz electrodes (cf., Curran, 2004).

In the present study, repeated measures ANOVAs wereseparately performed comparing voltages across six elec-trode sites (F3, F4, FZ, FC1, FC2 and CZ) chosen to coverthe scalp areas known to be the focus of N2 and FN400 andacross the three conditions (1, 3–5, ¸8). Degrees of free-dom were corrected using Greenhouse and Geisser (1959)epsilon values.

Results

Behavioural data reduction

There were no diVerences in data reduction procedures toExperiment 1. Due to problems with Presentation softwareprogram (Neurobehavioral Systems, Version 11.0), behavi-oural data of only 20 subjects could be recorded.

Response time data

Participant’s RTs in the stem completion task diVered sig-niWcantly across conditions [F(2, 38) = 44.67, P < 0.01,partial "! = 0.70]. Subjects took longest in the 1-completioncondition (M = 2,305 ms; SD = 543 ms) and became con-tinually faster when more completions were possible(M = 1,874 ms; SD = 425 ms in three- to Wve-completionscondition, and M = 1,762 ms; SD = 414 ms in ¸8-comple-tions condition). Pairwise comparisons showed that thesediVerences were signiWcant between the 1-completion con-dition and the ¸8-completions condition (P < 0.01), as wellas between one-completion and the three to Wve-comple-tions condition (P < 0.01). The diVerence between the 3–5-completions and more than ¸ 8-completions condition wasnot signiWcant.

Completion probability data

The probability to complete a stem within a certain condi-tion diVered signiWcantly, increasing when more comple-tions were possible [F(1.53, 29.07) = 259.83, P < 0.01,

partial "! = 0.932]. Subjects completed 41.0% (SD =12.0%) of word-stems with one-completion, 78.9% (SD =10.2%) in the three to Wve-completions condition, and90.6% (SD = 7.2%) in ¸8-completions condition. Pairwisecomparisons showed that diVerences between all conditionswere statistically signiWcant (P < 0.01).

Analysis of corpus

The number of diVerent responses given to a stem and thenumber of possible CELEX completions revealed a correla-tion of r = 0.66 (P < 0.01). Word frequencies and wordcompletion probabilities were correlated with r = 0.22(P < 0.01). RTs diVered signiWcantly as a function of thefrequency of the completed word (t = 4.26, P < 0.01): RTsto word-stems were slower when the stems were completedwith a low frequency word (M = 2,294 ms; SD = 1,103 ms),whereas RTs were faster when the completion was a highfrequency word (M = 1,918 ms; SD = 920 ms).

The N2 and FN400

The upper panel of Fig. 4 plots stimulus-locked grand-aver-age waveforms for the three word-stem categories (1, 3–5,¸8) for frontal electrodes (F3, F4, FZ, FC1, FC2, CZ).Inspection of the grand-averaged waveforms revealed thatthe second negativity was between 300 and 400 ms. In linewith previous studies (cf., van Veen & Carter, 2002; Yeunget al., 2004), this time window was chosen to analyse theN2 component. The time range for the FN400 was setbetween 400 and 600 ms to exclude an overlapping of com-ponents. An enhanced diVerence in this time range was evi-dent for word-stems with only one possible completion incontrast to word-stems with several completions. The lowerpanel of Fig. 4 depicts the average scalp topography at400–450 ms for the diVerence wave of the 1-completionand ¸8-completions word-stems, as well as the diVerencebetween the 1- and 3–5-completions word-stems.

In the time N2-time window (300–400 ms) electrodelocation reached signiWcance [F(5, 75) = 2.42, P < 0.05],but neither number of completions [F(2, 30) = 1.74,P = 0.19], nor an interaction [F(3.65, 54.76) = 1.45,P = 0.23] was signiWcant. In the FN400-time window elec-trode location [F(3.26, 48.89) = 4.25, P < 0.01, partial"! = 0.22] and number of completions [F(2, 30) = 3.44,P < 0.05, partial "! = 0.19] were signiWcant. The factors didnot interact [F(2.49, 37.32) = 0.54, P = 0.62, partial"! = 0.04] indicating that the FN400 was apparent acrossthe whole region of interest. Later time windows (600–800,800–1,000 ms) revealed no signiWcant diVerences exceptfor the electrode channel variable [F(5, 75) = 4.18, P < 0.01in the 600–800 ms time window; F(5, 75) = 2.56, P < 0.05in the 800–1,000 ms time window].

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Discussion

The second experiment replicated behavioural results fromExperiment 1 and identiWed a distinct ERP component thathas been related in previous studies to stimulus familiarity(cf., Curran, 2000). Subjects needed longer to completeword-stems with a single theoretical completion and alsohad a greater proportion of failures. The corpus analysisshowed very similar results to those from Experiment 1 andreplicated results reported in previous studies (cf., Graf &Williams, 1987; Meier & Eckstein, 1998). The MROMagain predicted lower GLA levels for the one-completionword-stems than for word-stems with several completions.EEG recordings revealed a negativity that appeared 400 msafter stimulus-onset, peaked around 500 ms and slowlydecreased without changing polarity. The negativity wasvery similar in morphology and scalp distribution to theFN400 component reported in Curran (1999, 2000). Thecomponent appeared about 50–100 ms later than reportedin the previous studies by Curran (1999, 2000), who pre-sented complete word stimuli to participants. This Wnding isin line with previous ERP results that have been reported intasks that use degraded stimulus material (Matsumoto,Iidaka, Nomura, & Ohira, 2005; Holcomb, 1993). Holcomb(1993) presented degraded word stimuli to participants andreported a delayed N400 for this type of stimuli in contrastto intact words. Matsumoto et al. (2005) reported the samedelay but in the word-stem completion task and argued thisto be a characteristic of the task. The FN400 amplitude wasgreatest for word-stems with only a single completion,

indicating low familiarity and more cognitive eVort to Wnda lexical solution for the stimulus.

In contrast, we failed again to Wnd indicators of conXictmonitoring in the second experiment. The time window ofthe N2 (300–400 ms) did not show diVerences for thediVerent word-stem categories. In sum, the results on bothdependent measures are in agreement with the MROM pre-dictions, but could not provide evidence for ConXict Moni-toring during word-stem completion.

General discussion

The aim of the present study was to test predictions aboutperformance in the word-stem completion task from the per-spective of two diVerent computational model frameworks.

For word-stems with several completions, the CMThypothesizes inhibitory eVects; conversely, the MROMpredicts high levels of GLA, and therefore facilitatoryeVects. Both Experiments 1 and 2 showed reliablydecreased RTs and a higher proportion of word identiWca-tion for word-stems with several completions. Additionally,in Experiment two, word-stems with only a single comple-tion showed an increased FN400 component, which hasbeen hypothesized to represent cognitive processing ofunfamiliar items (Curran, 1999, 2000). Concurrently, theMROM predicted these single-completion word-stems tohave a low level of GLA. Since GLA estimates familiarity(Jacobs et al., 2003), the present results are in line with thepredictions of the MROM. The subsidiary corpus analysis

Fig. 4 Top: ERPs for mid-fron-tal scalp electrodes (F3, F4, FZ, FC1, FC2, and CZ). For reasons of presentation the data were 10 Hz low-pass Wltered. Bottom: Scalp topography for the diVerence between the three word-stem conditions observed 450–500 ms after stimulus presentation

-200 200 400 600 800

-6

-4

-2

2

4

Left Hemisphere Central

Fz

CzFC1 FC2

F4F3

1 ! 3-5 1 ! "8

450 ! 500 ms

Right Hemisphere

13-5 "8

ms

µV

No. of completions

Difference Difference

-1.5 µV

"0 µV

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of this study provided results about the inXuence of stem-and word-related variables on behavioural task perfor-mances. These results were similar to previous studies (e.g.,Shaw, 1997). However, we also showed that response timesvary as a function of word frequency. Since frequency of allpossible word completions was kept equal across all word-stem conditions, the possibility can be excluded that system-atic response time diVerences in the word-stem completiontask were due to a confound between word-stem conditionsand the frequency distribution of their associated words.

The CMT has a tremendous scope, given that it providesone unifying theoretical framework to account for Wndingsin very heterogeneous paradigms, such as the EriksenXanker task, the Simon task, or the Stroop task, and alsoproposed hypotheses concerning the word-stem completiontask. Following the functional overlap approach of Jacobsand Grainger (1994), the conXict monitoring mechanismsconstitutes the intersection between the three diVerent typesof processing demands (response override, error commis-sion, and underdetermined responding) that are claimed tobe present during tasks requiring cognitive control. How-ever, given this high degree of model generality (cf., Jacobs& Grainger, 1994), it appears natural that there are speciWcobservations that cannot be explained by the CMT.

Given that our study failed to Wnd evidence for the CMT,it seems worthwhile to discuss how the present resultsmight revise or constrain the scope of the CMT.

First, we would like to point out that Botvinick et al.’s(2001) claim that word-stems with only one completionshould create more cognitive conXict and activate the ACCmore strongly than word-stems with several completions isbased upon their results from a simulation comparing con-Xict resulting from whole-word and word-stem stimuli.Therefore, the simulation by Botvinick et al. (2001) maynot reXect cognitive conXict, but rather a diVerence in pro-cessing word-stems and words. This diVerence mightexplain why we could not Wnd indices of conXict monitor-ing in the present study.

Additionally, we would like to argue here that the natureand/or extent of conXict in the Eriksen and Stroop tasksdiVers from the nature of stem-stem conXict assumedlypresent in the word-stem completion task. The crucialdiVerence between these tasks is that participants can com-mit response errors (i.e., false key presses) in the formertasks, while this is not possible in the word-stem comple-tion task. Consequently, the interference in the former taskscan be observed even on a level of motor preparation (Grat-ton, Coles, & Donchin, 1992). In the word-stem completiontask lexical entries might inhibit themselves at a cognitivelevel of processing. However, we preclude the possibilitythat this conXict is forwarded to areas concerned withresponse preparation because participants could only pressone key.

Assuming that the amount or nature of conXict in theword-stem completion task diVers compared to paradigmslike the Eriksen Xanker or Stroop task, it is also possible thatwe simply were not able to detect those diVerences with ourmethods at hand (i.e., EEG). Instead, it may be necessary tomeasure ACC activation directly by means of neuroimagingmethods (e.g., fMRI or NIRS, cf., Hofmann et al., 2008). Ifsuch studies revealed ACC activity during stem completion,while EEG fails to do so, not so much CMT itself would bechallenged, but rather the EEG-related extension of Yeunget al. (2004). In regards to this, a previous study by Smith,Johnstone, and Barry (2007) has already questioned the linkbetween the N2 and the ACC, and Masaki, Falkenstein,Stürmer, Pinkpank, and Sommer (2007) have questionedwhether the ERN is related to cognitive conXict. Anothercrucial diVerence between the present work and the work ofYeung et al. (2004) is the diVerence in stimulus material:While in the Eriksen Xanker task, participants had to processnon-lexical material, they were confronted with lexical stim-uli in the present study. It can be assumed that the number ofcognitive operations required for stimulus processing is sig-niWcantly diVerent for these tasks. The word-stem comple-tion task not only requires participants to partiallypreprocess the word-stems, but also to generate word candi-dates. Thus, the temporal window for the N2 (300–400 ms)chosen to monitor conXict in the Xanker task therefore mightnot coincide with the occurrence of conXict in a task thatuses lexical stimuli. We therefore assume that conXict moni-toring in our paradigm might be temporally delayed. Indica-tors that lead us to this assumption are the participants’ RTsand EEG data. Subjects in the present study needed not onlymore time to respond compared to non-lexical paradigms(e.g., in the Eriksen task: 421 ms in incongruent conditions,352 ms in congruent conditions, cf. Yeung et al., 2004), butalso compared to RTs reported in lexical tasks (e.g., in thelexical decision task typically between 600–900 ms, cf.Braun et al., 2006; Kuchinke, Võ, Hofmann, & Jacobs,2007). In addition to the behavioural data, the peak latenciesof the FN400 also appeared 50–100 ms later than in previ-ous studies (Curran, 2000), in accordance with a previousstudy of the word-stem completion task (Matsumato, Iidaka,Nomura, & Ohira, 2005).

Taken together, it appears necessary to resolve thesequestions with future studies, for example by means of neu-roimaging methods. Such studies could also provide furtherinsight into the neural generators of the FN400 familiaritycomponent.

In conclusion, there is still missing evidence for theCMT in respect to the word-completion task. In contrast,the MROM provides a plausible account of the mechanismsinvolved in processing during task: Word-stems with severalcompletions are more familiar to participants because theyare more frequently read or heard. The enhanced familiarity

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helps participants to associate them with an appropriate wordand thus to complete the task eYciently and faster comparedto word-stems that are unfamiliar. The MROM predictionswere in line with the results obtained from both behaviouralmeasures as well as the ERPs. In summary, we conclude thatthe MROM better recapitulates the cognitive processesinvolved in the word-stem completion task than CMT.

Acknowledgments We would like to thank Niklas Krumm for care-ful proofreading of the manuscript and for his helpful comments.

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