Learning the Structure of Event Sequencespal/pdfs/pdfs/cleeremans-mcclelland...Emile and David Servan-Schreiber for several insightful discussions. Arthur Reber and an anonymous reviewer

Journal of Experimental Psychology: General1991, Vol. 120, No. 3, 235-253

Copyright 1991 by the American Psychological Association, Inc.0096-3445/91A3.00

Learning the Structure of Event SequencesAxel Cleeremans and James L. McClelland

Carnegie Mellon University

How is complex sequential material acquired, processed, and represented when there is nointention to learn? Two experiments exploring a choice reaction time task are reported. Unknownto Ss, successive stimuli followed a sequence derived from a "noisy" finite-state grammar. Afterconsiderable practice (60,000 exposures) with Experiment 1, Ss acquired a complex body ofprocedural knowledge about the sequential structure of the material. Experiment 2 was anattempt to identify limits on Ss ability to encode the temporal context by using more distantcontingencies that spanned irrelevant material. Taken together, the results indicate that Ssbecome increasingly sensitive to the temporal context set by previous elements of the sequence,up to 3 elements. Responses are also affected by priming effects from recent trials. A connectionistmodel that incorporates sensitivity to the sequential structure and to priming effects is shown tocapture key aspects of both acquisition and processing and to account for the interaction betweenattention and sequence structure reported by Cohen, Ivry, and Keele (1990).

In many situations, learning does not proceed in the explicitand goal-directed way characteristic of traditional models ofcognition (Newell & Simon, 1972). Rather, it appears that agood deal of our knowledge and skills are acquired in anincidental and unintentional manner. The evidence support-ing this claim is overwhelming: In his recent review article,Reber (1989) analyzes about 40 empirical studies that docu-ment the existence of learning processes that do not necessar-ily entail awareness of the resulting knowledge or of thelearning experience itself. At least three different "implicitlearning" paradigms have yielded robust and consistent re-sults: artificial grammar learning (Dulany, Carlson, & Dewey,1984; Mathews et al., 1989; Reber, 1967, 1989; Servan-Schreiber & Anderson, 1990), system control (Berry &Broadbent, 1984; Hayes & Broadbent, 1988), and sequentialpattern acquisition (Cohen, Ivry, & Keele, 1990; Lewicki,Czyzewska, & Hoffman, 1987; Lewicki, Hill, & Bizot, 1988;Nissen & Bullemer, 1987; Willingham, Nissen, & Bullemer,1989). The classic result in these experimental situations isthat "subjects are able to acquire specific procedural knowl-edge (i.e., processing rules) not only without being able toarticulate what they have learned, but even without beingaware that they had learned anything" (Lewicki et al., 1987,p. 523). Related research with neurologically impairedpatients (see Schacter, 1987, for a review) also provides strongevidence for the existence of a functional dissociation between

This research was supported by a grant from the National Fundfor Scientific Research (Belgium) to Axel Cleeremans and by aNational Institute of Mental Health Research Scientist DevelopmentAward to James L. McClelland.

We thank Steven Keele for providing us with details about theexperimental data reported in Cohen, Ivry, and Keele (1990) andEmile and David Servan-Schreiber for several insightful discussions.Arthur Reber and an anonymous reviewer contributed many helpfulcomments.

Correspondence concerning this article should be addressed toAxel Cleeremans, Department of Psychology, Carnegie Mellon Uni-versity, Pittsburgh, Pennsylvania 15213. Electronic mail may be sentto [email protected].

explicit memory (conscious recollection) and implicit memory(a facilitation of performance without conscious recollection).

Despite this wealth of evidence documenting implicit learn-ing, few models of the mechanisms involved have been pro-posed. Reber's (1989) analysis of the field, for instance, leavesone with the impression that little has been done beyondmere demonstrations of existence. This lack of formalizationcan doubtless be attributed to the difficulty of assessing sub-jects' knowledge when it does not lend itself easily to verbali-zation. Indeed, although concept formation or traditionalinduction studies can benefit from experimental proceduresthat reveal the organization of subjects' knowledge and thestrategies they use, such procedures often appear to disrupt oralter the very processes they are supposed to investigate inimplicit learning situations (see Dulany et al., 1984; Dulany,Carlson, and Dewey, 1985; Reber, Allen, & Regan, 1985, fora discussion of this point). Thus, research on implicit learninghas typically focused more on documenting the conditionsunder which one might expect the phenomenon to manifestitself than on obtaining the fine-grained data needed to elab-orate information-processing models.

Nevertheless, a detailed understanding of such learningprocesses seems to be an essential preliminary step towarddeveloping insights into the central questions raised by recentresearch, such as the relationship between task performanceand "verbalizable" knowledge, the role that attention plays inunintentional learning, or the complex interactions betweenconscious thought and the many other functions of the cog-nitive system. Such efforts at building simulation models ofimplicit learning mechanisms in specific experimental situa-tions are already underway. For instance, Servan-Schreiberand Anderson (1990) and Mathews et al. (1989) have bothdeveloped models of the Reber task that successfully accountfor key aspects of learning and classification performance.

In this article, we explore performance in a different exper-imental situation, which has recently attracted increased at-tention as a paradigm for studying unintentional learning:sequential pattern acquisition. We report on two experiments,which investigate sequence learning in a novel way that allowsdetailed data on subjects' sequential expectations to be ob-

235

236 AXEL CLEEREMANS AND JAMES L. McCLELLAND

tained, and explore an information-processing model of thetask.

Sequence Learning

An increasingly large number of empirical studies havebegun to explore the conditions under which one might expectsubjects to display sensitivity to sequential structure despitelimited ability to verbalize their knowledge. Most of thesestudies have used a choice reaction time paradigm. Thus,Lewicki et al. (1988) used a four-choice reaction time (RT)task during which the stimulus could appear in one of fourquadrants of a computer screen on any trial. Unknown tosubjects, the sequential structure of the material was manip-ulated by generating sequences of 5 elements according to aset of simple rules. Each rule defined where the next stimuluscould appear as a function of the locations at which the twoprevious stimuli had appeared. As the set of sequences wasrandomized, the first 2 elements of each sequence were un-predictable. By contrast, the last 3 elements of each sequencewere determined by their predecessors. Lewicki et al. (1988)hypothesized that this difference would be reflected in re-sponse latencies to the extent that subjects are using thesequential structure to respond to successive stimuli. Theresults confirmed the hypothesis: A progressively wideningdifference between the number of fast and accurate responseselicited by predictable and unpredictable trials emerged withpractice. Furthermore, subjects were exposed to a differentset of sequences in a later part of the experiment. Thesesequences were constructed using the same transition rules,but applied in a different order. Any knowledge about thesequential structure of the material acquired in the first partof the experiment thus became suddenly useless, and a sharpincrease in response latency was expected. The results wereconsistent with this prediction. Yet, when asked after the task,subjects failed to report having noticed any pattern in thesequence of exposures, and none of them even suspected thatthe sequential structure of the material had been manipulated.

Obviously, repeated exposure to structured material elicitsperformance improvements that depend specifically on thefact that the material is structured (as opposed to generalpractice effects). Similar results have been described in differ-ent tasks. For instance, Miller (1958) reported higher levels offree recall performance for structured strings over randomstrings. Hebb (1961) reported an advantage for repeatedstrings over nonrepeated strings in a recall task, even thoughsubjects were not aware of the repetitive nature of the material.Pew (1974) found that tracking performance was better for atarget that followed a consistent trajectory than for a randomtarget. Again, subjects were unaware of the manipulation andfailed to report noticing any pattern. More recently, Lewickiet al. (1987) reported improved performance in a search taskwhen combinations of trials as remote as six steps containedinformation about the location of the target. Other subjectsgiven as much time as they wished to identify the crucialinformation failed in doing so, thereby suggesting that therelevant patterns were almost impossible to detect explicitly.

However, lack of awareness, or inability to recall the ma-terial, does not necessarily entail that these tasks require no

attentional capacity. Nissen and Bullemer (1987) demon-strated that a task similar to that used by Lewicki et al. (1988)failed to elicit performance improvements with practice whena memory-intensive secondary task was performed concur-rently. More recently, A. Cohen et al. (1990) refined this resultby showing that the ability to learn sequential material underattentional distraction interacts with sequence complexity.Only sequences composed entirely of ambiguous elements(i.e., elements that cannot be predicted solely on the basis oftheir immediate predecessor) are difficult to learn when asecondary task is present.

To sum up, there is clear evidence that subjects acquirespecific procedural knowledge when exposed to structuredmaterial. When the material is sequential, this knowledge isabout the temporal contingencies between sequence elements.Furthermore, it appears that the learning processes underlyingperformance in sequential choice reaction experiments do notentail or require awareness of the relevant contingencies,although attention is needed to learn even moderately com-plex material. Several important questions, however, remainunanswered.

First, it is not clear how sensitivity to the temporal contextdevelops over time. How do responses to specific sequenceelements vary with practice? Does sensitivity to more or lessdistant contingencies develop in parallel, or in stages, withthe shortest contingencies being encoded earlier than thelonger ones? Is there an upper limit to the amount of sequen-tial information that can be encoded, even after considerablepractice?

Second, most recent research on sequence processing hasused very simple material (but see Lewicki et al., 1987),sometimes even accompanied by explicit cues to sequencestructure (Lewicki et al., 1988). Are the effects reported inthese relatively simple situations also observed when subjectsare exposed to much more complex material involving, forinstance, some degree of randomness, or sequence elementsthat differ widely in their predictability?

Third, and perhaps most important, no detailed informa-tion-processing model of the mechanisms involved has beendeveloped to account for the empirical findings reviewedabove. In other words, what kind of mechanisms may underliesequence learning in choice RT situations?

In the rest of this article, we explore the first 2 questions byproposing an answer to the third. We first describe a paralleldistributed processing (PDF) model in which processing ofevents is allowed to be modulated by contextual information.The model learns to develop its own internal representationsof the temporal context despite very limited processing re-sources and produces responses that reflect the likelihood ofobserving specific events in the context of an increasinglylarge temporal "window." We then report on two experimentsusing a choice RT task. Unknown to subjects, successivestimuli followed a sequence derived from a "noisy" finite-state grammar, in which random stimuli were interspersedwith structured stimuli in a small proportion of the trialsthroughout training. This procedure allowed us to obtaindetailed data about subjects' expectations after specific stimuliat any point in training. After considerable practice (60,000exposures) with Experiment 1, subjects acquired a complex

LEARNING SEQUENTIAL STRUCTURE 237

body of procedural knowledge about the sequential structureof the material. We analyze this data in detail. Experiment 2attempts to identify limits on subjects' ability to encode thetemporal context by using more distant contingencies thatspanned irrelevant material. Next, we argue that the mecha-nisms implemented in our model may constitute a viablemodel of implicit learning in sequence learning situations andsupport this claim by a detailed analysis of the correspondencebetween the model and our experimental data. Finally, weexamine how well the model captures the interaction betweenattention and sequence structure reported by A. Cohen et al.(1990).

A Model of Sequence Learning

Early research on sequence processing has addressed tworelated but distinct issues: probability learning situations, inwhich subjects are asked to predict the next event in a se-quence, and choice reaction situations, in which subjectssimply respond to the current stimulus, but nevertheless dis-play sensitivity to the sequential structure of the material.Most of the work in this latter area has concentrated onrelatively simple experimental situations, such as two-choicereaction time paradigms, and relatively simple effects, suchas repetition and stimulus frequency effects. In both cases,most early models of sequence processing (e.g., Estes, 1976;Falmagne, 1965; Laming, 1969; Restle, 1970) have typicallyassumed that subjects somehow base their performance on anestimation of the conditional probabilities characterizing thetransitions between sequence elements, but failed to showhow subjects might come to represent or compute them.Laming (1969), for instance, assumed that subjects continu-ously update running average estimates of the probability ofoccurrence of each stimulus, on the basis of an arbitrarilylimited memory of the sequence. Restle (1970) emphasizedthe role that explicit receding strategies play in probabilitylearning, but presumably this work is less relevant in situationsfor which no explicit prediction responses are expected fromthe subjects.

Two points seem to be problematic with these early models.First, it seems dubious to assume that subjects actually basetheir performance on some kind of explicit computation ofthe optimal conditional probabilities, except possibly in situ-ations in which such computations are required by the instruc-tions (such as in probability learning experiments). In otherwords, these early models are not process models. They maybe successful in providing good descriptions of the data, butfail to give any insights into how processing is actually con-ducted.

Second, it is not clear how the temporal context gets inte-grated in these early models. Often, an assumption is madethat subjects estimate the conditional probabilities of thestimuli given the relevant temporal context information, butno functional account is provided of how the context infor-mation—and how much of it—is allowed to influence proc-essing of the current event.

In the following paragraphs, we present a model that learnsto encode the temporal context as a function of whether it isrelevant in optimizing performance at the task. The model

consists of a simple recurrent network (SRN; see Cleeremans,Servan-Sehreiber, & McClelland, 1989; Elman, 1990). TheSRN (Figure 1) is a standard, fully connected, three-layer,back-propagation network, with the added property that thehidden unit layer is allowed to feed back on itself with a delayof one time step, so that the intermediate results of processingat Time t — 1 can influence the intermediate results ofprocessing at Time /. In practice, the SRN is implemented bycopying the pattern of activation on the hidden units onto aset of "context units" that feed into the hidden layer, alongwith the input units. All the forward-going connections in thisarchitecture are modified by back-propagation. The recurrentconnections from the hidden layer to the context layer imple-ment a simple copy operation and are not subject to training.

At first sight, this architecture appears to be a good candi-date for modeling implicit learning phenomena. Indeed, asother connectionist architectures, it has a number of basicfeatures that seem to make it highly appropriate for modelingimplicit learning phenomena. For instance, because all theknowledge of the system is stored in its connections, thisknowledge may only be expressed through performance, acentral characteristic of implicit learning. Furthermore, theback-propagation learning procedure implements the kind ofelementary associative learning that also seems characteristicof many implicit learning processes. However, there is alsosubstantial evidence that knowledge acquired implicitly isvery complex and structured (Reber, 1989), that is, not thekind of knowledge one thinks would emerge from associativelearning processes. The work of Elman (in press), in whichthe SRN architecture was applied to language processing,demonstrated that the representations developed by the net-work are highly structured and accurately reflect subtle con-tingencies, such as those entailed by pronominal reference incomplex sentences. Thus, it appears that the SRN embodiestwo important aspects of implicit learning performance: ele-mentary learning mechanisms that yield complex and struc-tured knowledge. The SRN model shares these characteristicswith many other connectionist models, but its specific archi-tecture makes it particularly suitable for processing sequentialmaterial. In the following paragraphs, we examine how theSRN model is able to encode temporal contingencies.

OUTPUT UNITS : Element t+1

CONTEXT UNITS INPUT UNITS : Element t

Figure 1. The simple recurrent network (SRN).


As reported elsewhere (Cleeremans et al., 1989), we haveexplored the computational aspects of this architecture inconsiderable detail. Following Elman (1990), we have shownthat an SRN trained to predict the successor of each elementof a sequence presented one element at a time can learn toperform this "prediction task" perfectly on moderately com-plex material. For instance, the SRN can learn to predictoptimally each element of a continuous sequence generatedfrom small finite-state grammars, such as the one representedin Figure 2.1 After training, the network produces responsesthat closely approximate the optimal conditional probabilitiesof presentation of all possible successors of the sequence ateach step. Because all letters of the grammar were inherentlyambiguous (i.e., optimal predictions required more than theimmediate predecessor to be encoded), the network musthave developed representations of entire subsequences ofevents. Note that the network is never presented with morethan one element of the sequence at a time. Thus, it has toelaborate its own internal representations of as much temporalcontext as needed to achieve optimal predictions. Throughtraining, the network progressively comes to discover whichfeatures of the previous sequence are relevant to the predictiontask.

A complete analysis of the learning process is beyond thescope of this article (a full account is given in Servan-Schre-iber, Cleeremans, & McClelland, 1988), but the key pointsare as follows: As the initial articles about back-propagation(e.g., Rumelhart, Hinton, & Williams, 1986) pointed out, thehidden unit patterns of activation represent an "encoding" ofthe features of the input patterns that are relevant to the task.In the SRN, the hidden layer is presented with informationabout the current letter, but also—on the context layer—with

x, *5

#6

Figure 2. The finite state grammar used to generate the stimulussequence in Experiment 1. (Note that the first and last nodes are oneand the same.)

an encoding of the relevant features of the previous letter.Thus, a given hidden layer pattern can come to encodeinformation about the relevant features of two consecutiveletters. When this pattern is fed back on the context layer, thenew pattern of activation over the hidden units can come toencode information about three consecutive letters, and soon. In this manner, the context layer patterns can allow thenetwork to learn to maintain prediction-relevant features ofan entire sequence of events. Naturally, the actual processthrough which temporal context is integrated into the repre-sentations that the network develops is much more continu-ous than the above description implies. That is, the "phasesof learning" outlined above are but particular points on acontinuum.

To summarize, learning and processing in the SRN modelhave several properties that make it attractive as an architec-ture for sequence learning. First, the model only developssensitivity to the temporal context if it is relevant in optim-izing performance on the current element of the sequence. Asa result, there is no need to make specific assumptions regard-ing the size of the temporal window that the model is allowedto receive input from. Rather, the size of this self-developedwindow appears to be essentially limited by the complexityof the sequences to be learned by the network. Representa-tional resources (i.e., the number of hidden units available forprocessing) are also limiting factors, but only marginal ones.Second, the model makes minimal assumptions regardingprocessing resources: Its architecture is elementary, and allcomputations are local to the current element (i.e., there isno explicit representation of the previous elements). Process-ing is therefore strongly driven by the constraints imposed bythe prediction task. As a consequence, the model tends tobecome sensitive to the temporal context in a very gradualway and will tend to fail to discriminate between the succes-sors of identical subsequences preceded by disambiguatingpredecessors when the embedded material is not itself de-pendent on the preceding information. We return to this lastpoint in the General Discussion section.

To evaluate the model as a theory of human learning insequential choice reaction time situations, we assumed (a)that the activations of the output units represent responsetendencies and (b) that the RT to a particular response isproportional to some function of the activation of the corre-sponding output unit. The specific instantiations of theseassumptions that are adopted in this research are detailedlater. With these assumptions in place, the model producesresponses that can be directly compared with experimentaldata. In the following sections, we report on two experimentsthat were designed to allow for such detailed comparisons tobe conducted.

' In a finite-state grammar, sequences can be generated by ran-domly choosing an arc among the possible arc emanating from aparticular node and by repeating this process with the node pointedto by the selected arc. A continuous sequence can be generated byassuming that the grammar loops onto itself, that is, that its first andlast nodes are one and the same.


Experiment 1

Subjects were exposed to a six-choice RT task. The entireexperiment was divided into 20 sessions. Each session con-sisted of 20 blocks of 155 trials. On any of the 60,000 recordedtrials, a stimulus could appear at one of six positions arrangedin a horizontal line on a computer screen. The task consistedof pressing as fast and as accurately as possible on one of sixcorresponding keys. Unknown to subjects, the sequentialstructure of the stimulus material was manipulated. Stimuliwere generated using a small finite-state grammar that definedlegal transitions between successive trials. Some of the stimuli,however, were not "grammatical." On each trial, there was a15% chance of substituting a random stimulus to the oneprescribed by the grammar. This "noise" served two purposes.First, it ensured that subjects could not simply memorize thesequence of stimuli and hindered their ability to detect regu-larities in an explicit way. Second, because each stimulus waspossible on every trial (if only in a small porportion of thetrials), we could obtain detailed information about what stim-uli subjects did or did not expect at each step.

If subjects become increasingly sensitive to the sequentialstructure of the material over training, one would thus predictan increasingly large difference in the RTs elicited by predict-able and unpredictable stimuli. Furthermore, detailed anal-yses of the RTs to particular stimuli in different temporalcontexts should reveal differences that reflect subjects* pro-gressive encoding of the sequential structure of the material.

Method

Subjects. Six subjects (Carnegie Mellon University [CMU] staffand students), aged 17-42, participated in the experiment. Subjectswere each paid $100 for their participation in the 20 sessions of theexperiment and received a bonus of up to $50 on the basis of speedand accuracy.

Apparatus and display. The experiment was run on a MacintoshII computer. The display consisted of six dots arranged in a horizontalline on the computer's screen and separated by intervals of 3 cm. Ata viewing distance of 57 cm, the distance between any two dotesubtended a visual angle of 3°. Each screen position corresponded toa key on the computer's keyboard. The spatial configuration of thekeys was entirely compatible with the screen positions (i.e., theleftmost key corresponded to the leftmost screen position, and so on).The stimulus was a small black circle 0.40 cm in diameter thatappeared centered 1 cm below one of the six dots. The timer wasstarted at the onset of the stimulus and stopped by the subjects'response. The response-stimulus interval was 120 ms.

Procedure. Subjects received detailed instructions during the firstmeeting. They were told that the purpose of the experiment was to"learn more about the effect of practice on motor performance."Both speed and accuracy were stressed as being important. Afterreceiving the instructions, subjects were given three practice blocksof 15 random trials each at the task. A schedule for the 20 experi-mental sessions was then set up. Most subjects followed a regularschedule of 2 sessions a day.

The experiment itself consisted of 20 sessions of 20 blocks of 155trials each. Each block was initiated by a get ready message and awarning beep. After a short delay, 155 trials were presented to thesubjects. The first 5 trials of each block were entirely random so as

to eliminate initial variability in the responses. These data pointswere not recorded. The next 150 trials were generated according tothe procedure described below (in the Stimulus material section).Errors were signaled to the subjects by a short beep. After each block,the computer paused for approximately 30 s. The message rest breakwas displayed on the screen, along with information about subjects'performance. This feedback consisted of the mean RT and accuracyvalues for the last block and of information about how these valuescompared with those for the next-to-last block. If the mean RT forthe last block was within a 20-ms interval of the mean RT for thenext-to-last block, the words as before were displayed; otherwise,either better or worse appeared. A 2% interval was used for accuracy.Finally, subjects were also told about how much they had earnedduring the last block and during the entire session up to the last block.Bonus money was allocated as follows: Each reaction time under 600ms was rewarded by .078C, and each error entailed a penalty of 1.11 <t.These values were calculated so as to yield a maximum of $2.50 persession.

Stimulus material. Stimuli were generated on the basis of thesmall finite-state grammar shown in Figure 2. Finite-state grammarsconsist of nodes connected by labeled arcs. Expressions of the lan-guage are generated by starting at Node #0, choosing an arc, recordingits label, and repeating this process with the next node. Note that thegrammar loops onto itself: The first and last nodes, both denoted bythe digit 0, are actually the same. The vocabulary associated with thegrammar consists of six letters (T, S, X, V, P, and Q), each representedtwice on different arcs (as denoted by the subscript on each letter).This results in highly context-dependent transitions, as identicalletters can be followed by different sets of successors as a function oftheir position in the grammar (For instance, Si can only be followedby Q, but 82 can be followed by either V or P). Finally, the grammarwas constructed so as to avoid direct repetitions of a particular letter,because it is known (Bertelson, 1961; Hyman, 1953) that repeatedstimuli eEcit shorter RTs independently of their probability of pres-entation. (Direct repetitions can still occur because a small proportionof the trials were generated randomly, as described below.)

Stimulus generation proceeded as follows. On each trial, three stepswere executed in sequence. First, an arc was selected at randomamong the possible arcs coming out of the current node, and itscorresponding letter recorded. The current node was set to be Node#0 on the sixth trial of any block and was updated on each trial to bethe node pointed to by the selected arc. Second, in 15% of the cases,another letter was substituted to the letter recorded at Step 1 bychoosing it at random among the five remaining letters in thegrammar. Third, the selected letter was used to determine the screenposition at which the stimulus would appear. A 6 x 6 Latin squaredesign was used, so that each letter corresponded to each screenposition for exactly 1 of the 6 subjects. (Note that subjects were neverpresented with the actual letters of the grammar.)

Postexperimental interviews. All subjects were interviewed aftercompletion of the experiment. The experimenter asked a series ofincreasingly specific questions in an attempt to gain as much infor-mation about subjects' explicit knowledge of the manipulation andthe task.

Results and Discussion

Task performance. Figure 3 shows the average RTs oncorrect responses for each of the 20 experimental sessions,plotted separately for predictable and unpredictable trials. Wediscarded responses to repeated stimuli (which are necessarilyungrammatieal) because they elicit fast RTs independently oftheir probability of presentation, as discussed above. Figure 3


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Session15 20

Figure 3. Mean reaction times for grammatical and ungrammaticaltrials for each of the 20 sessions of Experiment 1.

shows that a general practice effect is readily apparent, as isan increasingly large difference between predictable and un-predictable trials. A two-way analysis of variance (ANOVA)with repeated measures on both factors (practice [20 levels]by trial type [grammatical vs. ungrammatical]) revealed sig-nificant main effects of practice, F(19, 95) = 9.491, p < .001,MSe = 17710.45, and of trial type, F(l, 5) = 105.293, p <.001, MS, = 104000.07, as well as a significant interaction,F(19, 95) = 3.022, p < .001, MSC = 183.172. It appears thatsubjects become increasingly sensitive to the sequential struc-ture of the material. To assess whether the initial differencebetween grammatical and ungrammatical trials was signifi-cant, a similar analysis was conducted on the data from thefirst session only, using the 20 blocks of this session as thelevels of the practice factor. This analysis revealed that therewere significant main effects of practice, F(19, 95) = 4.006, p< .001, MSC = 2634.295, and of trial type, F(l, 5) = 8.066, p< .05, MS; = 3282.914, but no interaction, F(19,95) = 1.518,p > .05, MSC = 714.558. We provide an interpretation forthis initial difference when examining the model's perform-ance.

Accuracy averaged 98.12% over all trials. Subjects wereslightly more accurate on grammatical trials (98.40%) thanon ungrammatical trials (96.10%) throughout the experiment.A two-way ANOVA with repeated measures on both factors(practice [20 levels] by trial type [grammatical vs. ungrammat-ical]) confirmed this difference, F(l, 5) = 7.888, p < .05, MSe

= .004. The effect of practice did not reach significance, F(19,95) = .380, p > .05, MSC = .0003; neither did the interaction,F(19, 95) = .727, p > .05, MSC = .00017.

Postexperimental interviews. Each subject was inter-viewed after completion of the experiment. We loosely fol-lowed the scheme used by Lewicki et al. (1988). Subjects werefirst asked about "whether they had anything to report regard-ing the task." All subjects reported that they felt their perform-ance had improved a lot during the 20 sessions, but muchless so in the end. Two subjects reported that they felt frus-trated because of the lack of improvement in the last sessions.

Next, subjects were asked whether they "had noticed any-thing special about the task or the material." This questionfailed to elicit more detailed reports. All subjects tended torepeat the comments they had given in answering the firstquestion.

Finally, subjects were asked directly whether they "hadnoticed any regularity in the way the stimulus was moving onthe screen." All subjects reported noticing that short sequencesof alternating stimuli did occur frequently. When probedfurther, 5 subjects were able to specify that they had noticedtwo pairs of positions between which the alternating patternwas taking place. On examination of the data, it appearedthat these reported alternations corresponded to the two smallloops on Nodes #2 and #4 of the grammar. One subject alsoreported noticing another more complex pattern betweenthree positions, but was unable to specify the exact locationswhen asked. All subjects felt that the sequence was randomwhen not involving these salient patterns. When askedwhether they "had attempted to take advantage of the patternsthey had noticed in order to anticipate subsequent events,"all subjects reported that they had attempted to do so at times(for the shorter patterns), but that they felt that it was detri-mental to their performance as it resulted in more errors andslower responses. Thus, it appears" that subjects only hadlimited reportable knowledge of the sequential structure ofthe material and that they tried not to use what little knowl-edge they had.

Gradual encoding of the temporal context. As discussedearlier, one mechanism that would account for the progressivedifferentiation between predictable and unpredictable trialsconsists of assuming that subjects, in attempting to optimizetheir responses, progressively come to prepare for successiveevents on the basis of an increasingly large temporal contextset by previous elements of the sequence. In the grammar weused, the uncertainty associated with the next element of thesequence can, in most cases, be optimally reduced by encodingtwo elements of temporal context. However, some sequenceelements require three or even four elements of temporalcontext to be optimally disambiguated. For instance, the pathSQ (leading to Node #1) occurs only once in the grammarand can only be legally followed by S or by X. In contrast,the path TVX can lead to either Node #5 or Node #6 and istherefore not sufficient to perfectly distinguish between stim-uli that occur only (in accordance with the grammar) at Node#5 (S or Q) and stimuli that occur only at Node #6 (T or P).One would assume that subjects initially respond to thecontingencies entailed by the shortest paths and progressivelybecome sensitive to the higher order contingencies as theyencode more and more temporal context.

A simple analysis that would reveal whether subjects areindeed basing their performance on an encoding of an increas-ingly large temporal context was conducted. The analysis'general principle consists of comparing the data with theprobability of occurrence of the stimuli, given different am-ounts of temporal context.

First, we estimated the overall probability of observing eachletter as well as the conditional probabilities (CPs) of observingeach letter as the successor of every grammatical path oflength 1, 2, 3, and 4, respectively. This was achieved bygenerating 60,000 trials in exactly the same way as during the


experiment and by recording the probability of observingevery letter after every observed sequence of every length upto four elements. Only grammatical paths (i.e., sequences ofletters that conform to the grammar) were then retained forfurther analysis. There are 70 such paths of length 4, eachpossibly followed by each of the six letters, thus yielding atotal of 420 data points. There are fewer types of shorter paths,but each occurs more often.

Next, the set of average correct RTs for each successor toevery grammatical path of length 4 was computed, separatelyfor groups of four successive experimental sessions.

Finally, 25 separate regression analyses were conducted,using each of the five sets of CPs (0-4) as predictors, and eachof the five sets of mean RTs as dependent variables. Becausethe human data are far from being perfectly reliable at thislevel of detail, the obtained correlation coefficients were thencorrected for attenuation. Reliability was estimated by thesplit-halves method (Carmines & Zeller, 1987), using datafrom even and odd experimental blocks.

Figure 4 illustrates the results of these analyses. Each pointon the figure represents the corrected r2 of a specific regressionanalysis. Points corresponding to analyses conducted with thesame amount of temporal context (0-4 elements) are linkedtogether.

If subjects are encoding increasingly large amounts of tem-poral context, we would expect the variance in the distributionof their responses at successive points in training to be betterexplained by CPs of increasingly higher statistical orders.Although the overall fit is rather low (note that the verticalaxis only extends to 0.5), Figure 4 nevertheless reveals theexpected pattern: First, the correspondence between humanresponses and the overall probability of appearance of eachletter (CP-0) is very close to zero. This clearly indicates thatsubjects are responding on the basis of an encoding of theconstraints imposed by previous elements of the sequence.Second, one can see that the correspondence with the first-order CPs tends to level off below the fits for the second,third, and fourth orders early in training. By contrast, thecorrespondence between the data and the higJher order CPskeeps increasing throughout the entire experiment. The fitsto the second-, third-, and fourth-order paths are highlysimilar in part because their associated CPs are themselveshighly similar. This in turn is due to the fact that only a smallproportion of sequence elements are ambiguous up to thethird or fourth position. Furthermore, even though the datamay appear to be most closely consistent with the secondorder CPs throughout the task, a separate analysis restrictedto the first 4 sessions of training indicated that the first-orderCPs were the best predictor of the data in the first 2 sessions.Finally, it is still possible that deviations from the second-order CPs are influenced by the constraints reflected in thethird- or even fourth-order CPs. The next section addressesthis issue.

Sensitivity to long-distance temporal contingencies. To as-sess more directly whether subjects are able to encode threeor four letters of temporal context, several analyses on specificsuccessors of specific paths were conducted. One such analysisinvolved several paths of length 3. These paths were the samein their last two elements, but differed in their first elementas well as in their legal successors. For example, we compared

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Figure 4. Correspondence between the human responses and con-ditional probabilities (CP) after paths of length 0-4 during successiveblocks of four simulated sessions.

XTV with PTV and QTV and examined RTs for the lettersS (legal only after XTV) and r(legal only after PTV or QTV).If subjects are sensitive to three letters of context, their re-sponse to an S should be relatively faster after XTV than inthe other cases, and their response to a T should be relativelyfaster after PTV or QTV than after XTV. Similar contrastingcontexts were selected in the following manner: First, asdescribed above, we only considered grammatical paths oflength 3 that were identical but for their first element. Specificungrammatical paths are too infrequent to be representedoften enough in individual subject's data. Second, some pathswere eliminated to control for priming effects to be discussedlater. For instance, the path VTV was eliminated from theanalysis because the alternation between V and T favors asubsequent T. This effect is absent in contrasting cases, suchas XTV, and may thus introduce biases in the comparison.Third, specific successors to the remaining paths were elimi-nated for similar reasons. For instance, we eliminated S fromcomparisons on the successors of SQX and PQX becauseboth Q and S prime S in the case of SQX but not in the caseof PQX. As a result of this residual priming, the response toS after SQX tends to be somewhat faster than what would bepredicted on the basis of the grammatical constraints only,and the comparison is therefore contaminated. These succes-sive eliminations left the following contrasts available forfurther analysis: SQX-Q and PQX-T (grammatical) versusSQX-T and PQX-Q (ungrammatical); SVX-Q and TVX-Pversus SVX-P and TVX-Q; and XTV-S, PT V-T, and QTV-T versus XTV-T, PTV-S, and QTV-S.

Figure 5 shows the RTs elicited by grammatical and un-grammatical successors of these remaining paths, averagedover blocks of 4 successive experimental sessions. The figurereveals that there is a progressively widening difference be-tween the two curves, thereby suggesting that subjects becomeincreasingly sensitive to the contingencies entailed by ele-ments of the temporal context as removed as three elements


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Figure 5. Mean reaction times for predictable and unpredictablesuccessors of selected paths of length 3 and for successive blocks offour experimental sessions.

from the current trial. A two-way ANOVA with repeatedmeasures on both factors (practice [four levels] by successortype [grammatical vs. ungrammatical]) was conducted onthese data and revealed significant main effects of successortype, F(l, 5) = 7.265, p < .05, MSC = 530.786, and of practice,F(4,20)= 11.333,p<.001,MSe= 1602.862. The interactionjust missed significance, F(4, 20) = 2.530, p < .07, MSC =46.368, but it is obvious that most of the effect is located inthe later sessions of the experiment. This was confirmed bythe results of a one-tailed paired / test conducted on thedifference between grammatical and ungrammatical succes-sors, pooled over the first 8 and the last 8 sessions of training.The difference score averaged -11.3 ms early in training and-22.8 ms late in training. It was significantly bigger late intraining, ;(5) = -5.05, p < .005. Thus, there appears to beevidence of a gradually increasing sensitivity to at least threeelements of temporal context.

A similar analysis was conducted on selected paths of length4. After selecting candidate contexts as described above, thefollowing paths remained available for further analysis:XTVX-S, XTVX-Q, QTVX-T, QTVX-P, PTVX-T, andPTVX-P (grammatical) versus XTVX-T, XTVX-P,QTVX-S, QTVX-Q, PTVX-S, and PTVX-Q (ungrammat-ical). No sensitivity to the first element of these otherwiseidentical paths of length 4 was found, even during Sessions17-20: A paired, one-tailed t test on the difference betweengrammatical and ungrammatical successors failed to reachsignificance ?(5) = .076, p > .1. Although one cannot rejectthe idea that subjects would eventually become sensitive tothe constraints set by temporal contingencies as distant asfour elements, there is no indication that they do so in thissituation.

Experiment 2

Experiment 1 demonstrated that subjects progressively be-come sensitive to the sequential structure ot the material and

seem to be able to maintain information about the temporalcontext for up to three steps. The temporal contingenciescharacterizing this grammar were relatively simple, however,because in most cases, only two elements of temporal contextare needed to disambiguate the next event perfectly.

Furthermore, contrasting, long-distance dependencies werenot controlled for their overall frequency. In Experiment 2, amore complex grammar (Figure 6) was used in an attempt toidentify limits on subjects' ability to maintain informationabout more distant elements of the sequence. In this grammar,the last element (A or X) is contingent on the first one (alsoA or X). Information about the first element, however, has tobe maintained across either of the two identical embeddingsin the grammar and is totally irrelevant for predicting theelements of the embeddings. Thus, to accurately prepare forthe last element at Nodes #11 or #12, one needs to maintaininformation for a minimum of four steps. Accurate expecta-tions about the nature of the last element would be revealedby a difference in the RT elicited by the letters A and X atNodes #11 and #12 (A should be faster than X at Node #11and vice versa). Naturally, there was again a 15% chance ofsubstituting another letter for the one prescribed by thegrammar. Furthermore, a small loop was inserted at Node#13 so as to avoid direct repetitions between the letters thatprecede and follow Node #13. One random letter was alwayspresented at this point; after which there was a 40% chanceof staying in the loop on subsequent steps.

Finally, to obtain more direct information about subjects'explicit knowledge of the training material, we asked them totry to generate the sequence after the experiment was com-pleted. This "generation" task involved exactly the samestimulus sequence generation procedure as during training.On every trial, subjects had to press on the key correspondingto the location of the next event.

Method

The design of Experiment 2 was almost identical to that of Exper-iment 1. The changes are detailed below.

M

Figure 6. The finite state grammar used to generate the stimulussequence in Experiment 2.


Subjects, Six new subjects (CMU undergraduates and graduates),aged 19-35, participated in Experiment 2.

Generation task. Experiment 1 did not include any strong test ofsubjects' verbalizable knowledge about the stimulus material. In thepresent experiment, we attempted to remedy this situation by usinga generation task inspired by Nissen'and Bullemer (1987). Aftercompleting the 20 experimental sessions, subjects were informed ofthe nature of the manipulation and asked to try to predict thesuccessor of each stimulus. The task consisted of three blocks of 155trials of events generated in exactly the same way as during training.(As during the experiment itself, the 5 initial random trials of eachblock were not recorded.) On each trial, the stimulus appeared belowone of the six screen positions, and subjects had to press on the keycorresponding to the position at which they expected the next stim-ulus to appear. Once a response had been typed, a cross 0.40 cm inwidth appeared centered 1 cm above the screen position correspond-ing to the subjects' prediction, and the stimulus was moved to itsnext location. A short beep was emitted by the computer on eacherror. Subjects were encouraged to be as accurate as possible.

Results and Discussion

Task performance. Figure 7 shows the main results ofExperiment 2. They closely replicate the general results ofExperiment 1, although subjects were a little bit faster overallin Experiment 2. A two-way ANOVA with repeated measureson both factors (practice [20 levels] by trial type [grammaticalvs. ungrammatical]) again revealed significant main effects ofpractice, F(19,95) = 32.01 \,p< .001, MS, = 21182.79, andof trial type, F(l, 5) = 253.813, p < .001, MSC = 63277.53,as well as a significant interaction, F(19, 95) = 4.670, p <.001, MS, = 110.862. A similar analysis conducted on thedata from only the first session again revealed significant maineffects of practice, F(19, 95) = 4.631, p < .001, MS, =1933.331, and of trial type, F(l, 5) = 19.582, p< .01, MS, =861.357, but no interaction, F(19, 95) = 1.383, p > .1, MS,= 343.062.

Accuracy averaged 97% over all trials. Subjects were againslightly more accurate on grammatical (97.60%) than onungrammatical (95.40%) trials. However, a two-way ANOVAwith repeated measures on both factors (practice [20 levels]by trial type [grammatical vs. ungrammatical]) failed to con-firm this difference, F(l, 5) = 5.351, p > .05, MS, = .005.The effect of practice did reach significance, F(19, 95) =4.112, p < .001, MSe = .00018, but not the interaction, F( 19,95) = 1.060, p > .05, MS, = .00008. Subjects became moreaccurate on both grammatical and ungrammatical trials asthe experiment progressed.

Sensitivity to long-distance temporal contingencies. Ofgreater interest are the results of analyses conducted on theresponses elicited by the successors of the four shortest pathsstarting at Node #0 and leading to either Node #11 or Node#12 (AJCM, AMLJ, XJCM, and XMLJ). Among those paths,those beginning with A predict A as their only possiblesuccessor and vice versa for paths starting with X. Becausethe subpaths JCM and ML/ undifferentially predict A or Xas their possible successors, subjects need to maintain infor-mation about the initial letter to accurately prepare for thesuccessors. The RTs on legal successors of each of these fourpaths (i.e., A for AJCM and AMLJ and X for XJCM and

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Figure 7. Mean reaction times for grammatical and ungrammaticaltrials for each of the 20 sessions of Experiment 2.

XMLJ) were averaged together and compared with the ave-rage RT on the illegal successors (i.e., X for AJCM and AMLJand A for XJCM and XMLJ), thus yielding two scores. Anysignificant difference between these two scores would meanthat subjects are disciminating between legal and illegal suc-cessors of these four paths, thereby suggesting that they havebeen able to maintain information about the first letter ofeach path over three irrelevant steps. The mean RT on legalsuccessors over the last four sessions of the experiment was385, and the corresponding score for illegal successors was388, A one-tailed paired t test on this difference failed toreach significance, t(5) = 0.571, p > .05. Thus, there is noindication that subjects were able to encode even the shortestlong-distance contingency of this type.

Generation task. To determine whether subjects were bet-ter able to predict grammatical elements than ungrammaticalelements after training, a two-way ANOVA with repeatedmeasures on both factors (practice [three levels] by trial type[grammatical vs. ungrammatical]) was conducted on the ac-curacy data of 5 subjects (one subject had to be eliminatedbecause of a technical failure).

For grammatical trials^ subjects averaged 23.00%, 24.40%,and 26.20% correct predictions for the three blocks of prac-tice, respectively. The corresponding data for the ungrammat-ical trials were 18.4%, 13.8%, and 20.10%. Chance level was16.66%. It appears that subjects are indeed better able topredict grammatical events than ungrammatical events. TheANOVA confirmed this effect: There was a significant maineffect of trial type, F(l, 4) = 10.131, p < .05, MS, = .004, butno effect of practice, F(2, 8) = 1.030, p > .05, MSt = .004,and no interaction, F(2, 8) = .1654, p > .05, MS, = .001.Although overall accuracy scores are very low, these resultsnevertheless clearly indicate that subjects have acquired someexplicit knowledge about the sequential structure of the ma-terial in the course of training. This is consistent with previousstudies (A. Cohen et al, 1990; Willmgham et al., 1989) andnot surprising given the extensive training to which subjects


have been exposed. At the same time, it is clear that whateverknowledge was acquired during training is of limited use inpredicting grammatical elements, because subjects were onlyable to do so in about 25% of the trials of the generation task.

Simulation of the Experimental Data

Taken together, the results of both experiments suggest thatsubjects do not appear to be able to encode long-distancedependencies when they involve four elements of temporalcontext (i.e., three items of embedded independent material);at least, they cannot do so under the conditions used here.However, there is clear evidence of sensitivity to the last threeelements of the sequence (Experiment 1). Furthermore, thereis evidence for a progressive encoding of the temporal contextinformation: Subjects rapidly learn to respond on the basis ofmore than the overall probability of each stimulus and be-come only gradually sensitive to the constraints entailed byhigher order contingencies.

Application of the SRN Model

To model our experimental situation, we used an SRN with15 hidden units and local representations on both the inputand output pools (i.e., each unit corresponded to one of thesix stimuli). The network was trained to predict each elementof a continuous sequence of stimuli generated in exactly thesame conditions as for human subjects in Experiment 1. Oneach step, a letter was generated from the grammar as de-scribed in the Method section of Experiment 1 and presentedto the network by setting the activation of the correspondinginput unit to 1.0. Activation was then allowed to spread tothe other units of the network, and the error between itsresponse and the actual successor of the current stimulus wasthen used to modify the weights.

During training, the activation of each output unit wasrecorded on every trial and transformed into Luce ratios(Luce, 1963) to normalize the responses.2 For the purpose ofcomparing the model's and the subjects' responses, we as-sumed (a) that the normalized activations of the output unitsrepresent response tendencies and (b) that there is a linearreduction in RT proportional to the relative strength of theunit corresponding to the correct response.

This data was first analyzed in the same way as for Exper-iment 1 subjects and compared with the CPs of increasinglyhigher statistical orders in 20 separate regression analyses. Theresults are illustrated in Figure 8.

In stark contrast with the human data (Figure 4; note thescale difference), the variability in the model's responses ap-pears to be very strongly determined by the probabilities ofparticular successor letters given the temporal context. Figure8 also reveals that the model's behavior is dominated by thefirst-order CPs for most of the training, but that it becomesprogressively more sensitive to the second- and higher orderCPs. Beyond 60,000 exposures, the model's responses cometo correspond most closely to the second-, then third-, andthen finally fourth-order CPs.

Figure 9 illustrates a more direct comparison between themodel's responses at successive points in training with the

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Figure 8. Correspondence between the simple recurrent network'sresponses and conditional probabilities (CP) after paths of length 0-4 during successive blocks of four simulated sessions.

corresponding human data. We compared human and simu-lated responses after paths of length 4 in 25 separate analyses,each using one of the five sets of simulated responses aspredictor variable and one of the five sets of experimentalresponses as dependent variable. The obtained correlationcoefficients were again corrected for attenuation. The resultsare illustrated in Figure 9. Each point in the figure representsthe corrected r2 of a specific analysis. One would expect themodel's early performance to be a better predictor of thesubjects' early behavior and vice versa for later points intraining.

It is obvious that the model is not very good at capturingsubjects' behavior: The overall fit is relatively low (note thatthe vertical axis only goes up to .5) and reflects only weaklythe expected progressions. It appears that too much of thevariance in the model's performance is accounted for bysensitivity to the temporal context.

However, exploratory examination of the data revealed thatfactors other than the conditional probability of appearanceof a stimulus exert an influence on performance in our task.We identified three such factors and incorporated them in anew version of the simulation model.

The Augmented SRN model

First of all, it appears that a response that is actuallyexecuted remains primed for a number of subsequent trials(Bertelson, 1961; Hyman, 1953; Remington, 1969). In thelast sessions of our data, we found that if a response follows

2 This transformation amounts to dividing the activation of theunit corresponding to the response by the sum of the activations ofall units in the output pool. Because the strength of a particularresponse is determined by its relative, rather than absolute, activation,the transformation implements a simple form of response competi-tion.


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Figure 9. Correspondence between the simple recurrent network's(SRN's) responses and the human data during successive blocks offour sessions of training (Experiment 1).

itself immediately, there is about 60 to 90 ms of facilitation,depending on other factors. If it follows after a single inter-vening response (as in VT-V in Experiment 1, for example),there is about 25 ms of facilitation if the letter is grammaticalat the second occurrence and 45 ms if it is ungrammatical.

The second factor may be related: Responses that aregrammatical at Trial / but do not actually occur remainprimed at Trial t + 1. The effect is somewhat weaker, averag-ing about 30 ms.

These two factors may be summarized by assuming (a) thatactivations at Time t decay gradually over subsequent trialsand (b) that responses that are actually executed become fullyactivated, whereas those that are not executed are only par-tially activated.

The third factor is a priming, not of a particular response,but of a particular sequential pairing of responses. This canbest be illustrated by a contrasting example, in which theresponse to the second X is compared in QXQ-X and VXQ-X. Both transitions are grammatical; yet the response to thesecond X tends to be about 10 ms faster in cases similar toQXQ-X, in which the X follows the same predecessor twicein a row, than it is in cases similar to VXQ-X, in which thefirst X follows one letter and the second follows a differentletter.

This third factor can perhaps be accounted for in severalways. We have explored the possibility that it results from arapidly decaying component to the increment to the connec-tion weights mediating the associative activation of a letter byits predecessor. Such "fast" weights have been proposed by anumber of investigators (Hinton & Plaut, 1987; McClelland& Rumelhart, 1985). The idea is that when X follows Q, theconnection weights underlying the prediction that X willfollow Q receive an increment that has a short-term compo-nent in addition to the standard long-term component. Thisshort-term increment decays rapidly, but is still present in

sufficient force to influence the response to a subsequent Xthat follows an immediately subsequent Q.

In the light of these analyses, one possibility for the relativefailure of the original model to account for the data is thatthe SRN model is partially correct, but that human responsesare also affected by rapidly decaying activations and adjust-ments to connection weights from preceding trials. To testthis idea, we incorporated both kinds of mechanisms into asecond version of the model. This new simulation model wasexactly the same as before, except for two changes.

First, it was assumed that preactivation of a particularresponse was based not only on activation coming from thenetwork, but also on a decaying trace of the previous activa-tion:

i](f - 7)ravact[i](Z) = act[i](0 + (1

where act(0 is the activation of the unit based on the networkat Time t, and ravact(t), that is, running average activation attime /, is a nonlinear running average that remains boundedbetween 0 and 1 . After a particular response had been exe-cuted, the corresponding ravact was set to 1.0. The otherravacts were left at their current values. The constant k wasset to 0.5, so that the half-life of a response activation is onetime step.

The second change consisted of assuming that changesimposed on the connection weights by the back-propagationlearning procedure have two components. The first compo-nent is a small (slow t = 0.15) but effectively permanentchange (i.e., a decay rate slow enough to ignore for presentpurposes), and the other component is a slightly larger (fast e= 0.2) change, but which has a half-life of only a single timestep. (The particular values of « were chosen by trial and error,but without exhaustive search.)

With these changes in place, we observed that, of course,the proportion of the variance in the model accounted for bypredictions based on the temporal context is dramaticallyreduced, as illustrated in Figure 10 (compare with Figure 8).More interesting, the pattern of change in these measures aswell as the overall fit is now quite similar to that observed inthe human data (Figure 4).

Indeed, there is a similar progressive increase in the corre-spondence with the higher order CPs, with the curve for thefirst-order CPs leveling off relatively early with respect tothose corresponding to CPs based on paths of length 2, 3,and 4.

A more direct indication of the good fit provided by thecurrent version of the model is given by the fact that it nowcorrelates very well with the performance of the subjects(Figure 11; compare with the same analysis illustrated inFigure 9, but note the scale difference). Late in training, themodel explains about 81% of the variance of the correspond-ing human data. Close inspection of the figure also revealsthat, as expected, the SRN's early distribution of responses isa slightly better predictor of the corresponding early humandata. This correspondence gets inverted later on, therebysuggesting that the model now captures key aspects of acqui-sition as well. Indeed, at almost every point, the best predic-tion of the human data is the simulation of the correspondingpoint in training.


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Figure JO. Correspondence between the augmented simple recur-rent network's responses and conditional probabilities (CPs) afterpaths of length 0-4 during successive blocks of four simulated ses-sions.

Two aspects of these data need some discussion. First, thecurves corresponding to each set of CPs are close to eachother because the majority of the model's responses retaintheir relative distribution as training progresses. This is againa consequence of the fact that only a few elements of thesequence require more than two elements of temporal contextto be perfectly disambiguated.

Second, the model's responses correlate very well with thedata, but not perfectly. This raises the question as to whetherthere are aspects of the data that cannot be accounted for bythe postulated mechanisms. There are three reasons why thisneed not be the case. First, the correction for attenuationassumes homogeneity, but because of different numbers oftrials in different cells there is more variability in some cells

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Figure 11. Correspondence between the augmented simulated re-current network's (SRN's) responses and the human data duringsuccessive blocks of four sessions of training (Experiment 1).

than in others (typically, the cells corresponding to grammat-ical successors of paths of length 4 are much more stable thanthose corresponding to ungrammatical successors). Second,the set of parameters we used is probably not optimal. Al-though we examined several combinations of parameter val-ues, the possibility of better fits with better parameters cannotbe excluded. Finally, in fitting the model to the data, we haveassumed that the relation between the models' responses andreaction times was linear, whereas in fact it might be some-what curvilinear. These three facts would all tend to reducethe r2 well below 1.0 even if the model is in fact a completecharacterization of the underlying processing mechanisms.

The close correspondence between the model and the sub-jects' behavior during learning is also supported by an analysisof the model's responses to paths of length 3 and 4 (Experi-ment 1). Using exactly the same selection of paths as for thesubjects in each case, we found that a small but systematicdifference between the model's responses to predictable andunpredictable successors to paths of length 3 emerged inSessions 9-12 and kept increasing over Sessions 13-16 and17-20. The difference was .056 (i.e., a 5.6% difference in themean response strength) when averaged over the last foursessions of training. By contrast, this difference score for pathsof length 4 was only .003 at the same point in training,thereby clearly indicating that the model was not sensitive tothe fourth-order temporal context.

Finally, to further illustrate the correspondence betweenthe model and the experimental data, we wanted to comparehuman and simulated responses on an ensemble of specificsuccessors of specific paths, but the sheer number of datapoints renders an exhaustive analysis virtually intractable.There are 420 data points involved in each of the analysesdiscussed above. However, one analysis that is more parsi-monious, but that preserves much of the variability of thedata, consists of comparing human and simulated responsesfor each letter at each node of the grammar. Because thegrammar used in Experiment 1 counts seven nodes (0-6), andbecause each letter can occur at each node because of thenoise, this analysis yields 42 data points, a comparativelysmall number. Naturally, some letters are more likely to occurat some nodes than at others, and therefore, one expects thedistribution of average RTs over the six possible letters to bedifferent for different nodes. For instance, the letters Fand Pshould elicit relatively faster responses at Node #0, where bothletters are grammatical, than at Node #2, where neither ofthem is. Figure 12 represents the results of this analysis. Eachindividual graph shows the response to each of the six lettersat a particular node, averaged over the last four sessions oftraining, for both human and simulated data. Because thereis an inverse relationship between activations and RTs, themodel's responses have been subtracted from 1. All responseswere then transformed into standard scores to allow for directcomparisons between the model and the experimental data,and the figures therefore represent deviations from the generalmean.

Visual examination reveals that the correspondence be-tween the model and the data is very good. This was con-firmed by the high degree of association between the two datasets: The corrected r2 was .88. Commenting in detail on each


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Simulation

Figure 12. Human and simulated responses to each of the six letters,plotted separately for each node (#0 to #6) of the grammar (Experi-ment 1). (All responses have been transformed into standard scoreswith respect to the mean of the entire distribution.)

of the figures seems unnecessary, but some aspects of the dataare worth remarking on. For instance, one can see that thefastest response overall is elicited by a V at Node #4. This isnot surprising, because the T-V association is both frequent(note that it also occurs at Node #0) and consistent (i.e., theletter T is a relatively reliable cue to the occurrence of asubsequent V). Furthermore, V also benefits from its involve-ment in a TVT-V alternation in a number of cases. On Figure12, one can also see that T elicits a relatively fast response,even though it is ungrammatical at Node #4. This is a directconsequence of the fact that a T at Node #4 follows itselfimmediately. It is therefore primed despite its ungrammati-cality. The augmented SRN model captures both of theseeffects quite adequately, if not perfectly.

The impact of the short-term priming effects is also appar-ent in the model's overall responses. For instance, the initialdifference between grammatical and ungrammatical trialsobserved in the first session of both experiments is also present

in the simulation data. In both cases, this difference resultsfrom the fact that responses to first-order repetitions (whichare necessarily ungrammatical) were eliminated from theungrammatical trials, whereas second-order repetitions andtrials involved in alternations were not eliminated from thegrammatical trials. Each of these two factors contribute towiden the difference between responses to grammatical andungrammatical trials, even though learning of the sequentialstructure is only minimal at that point. The fact that the SRNmodel also exhibits this initial difference is a further indicationof its aptness at accounting for the data.

Attention and Sequence Structure

Can the SRN model also yield insights into other aspectsof sequence learning? A. Cohen et al. (1990) reported thatsequence structure interacts with attentional requirements.Subjects placed in a choice reaction situation were able tolearn sequential material under attentional distraction, butonly when it involved simple sequences in which each elementhas a unique successor (such as in 12345 ...). More complexsequences involving ambiguous elements (i.e., elements thatcould be followed by several different successors, as in123132 ...) could only be learned when no secondary taskwas performed concurrently. A third type of sequence—hybrid sequences—in which some elements were uniquelyassociated to their successor and some other elements wereambiguous (such as in 143132 ...) elicited intermediate re-sults. A. Cohen et al. (1990) hypothesized that the differentialeffects of the secondary task on the different types of sequencesmight be due to the existence of two different learning mech-anisms: one that establishes direct painvise associations be-tween an element of the sequence and its successor, andanother that creates hierarchical representations of entiresubsequences of events. The first mechanism would requireless attentional resources than the second and would thus notsuffer as much from the presence of a secondary task. A.Cohen et al. further point out that there is no empirical basisfor distinguishing between this hypothesis and a second one,namely, that all types of sequences are processed hierarchi-cally, but that ambiguous sequences require a more complex"parsing" than unique sequences. Distraction would thenhave differential effects on these two kinds of hierarchicalcoding.

We propose a third possibility: that sequence learning maybe based solely on associative learning processes of the kindfound in the SRN.3 Through this learning mechanism, asso-ciations are established between prediction-relevant featuresof previous elements of the sequence and the next element. Iftwo subsequences have the same successors, the model willtend to develop identical internal representations in each case.

3 In work done independently of our simulations, J. K. Kruschke(personal communication, June 5, 1990) explored the possibility ofsimulating the effects of attention on sequence learning in SRNs. Inone of his simulations, the learning rate of the connections from thecontext units to the hidden units was set to a lower value than for theother connections of the network.


If two otherwise identical subsequences are followed by dif-ferent successors as a function of their predecessors, however,the network will tend to develop slightly different internalrepresentations for each subsequence. This ability of the net-work to simultaneously represent similarities and differencesled us to refer to the SRN model as an instantiation of agraded state machine (McClelland, Cleeremans, & Servan-Schreiber, 1990). This notion emphasizes the fact that, al-though there is no explicit representation of the hierarchicalnature of the material, the model nevertheless develops inter-nal representations that are shaded by previous elements ofthe sequence.

The key point in the context of this discussion is that therepresentations of sequence elements that are uniquely asso-ciated with their successors are not different in kind fromthose of elements that can be followed by different successorsas a function of their own predecessors. How, then, might themodel account for the interaction between attention andsequence structure reported by A. Cohen et al. (1990)? Onepossibility is that the effect of the presence of a secondary taskis to hamper processing of the sequence elements. A simpleway to implement this notion in our model consists of addingnormally distributed random noise to the input of specificunits of the network (Cohen & Servan-Schreiber, 1989, ex-plored a similar idea by manipulating gain to model process-ing deficits in schizophrenia). The random variability in thenet input of units in the network tends to disrupt processing,but in a graceful way (i.e., performance does not break downentirely). The intensity of the noise is controlled by a scaleparameter, a. We explored how well changes in this parameteras well as changes in the localization of the noise captured theresults of Experiment 4 of A. Cohen et al. (1990).

A Simulation of Attentional Effects in SequenceLearning

In this experiment, subjects were exposed to 14 blocks ofeither 100 trials for the unique sequence (12345 .. .)conditionor 120 trials for the ambiguous sequence (123132 ...) andhybrid sequence (143132 ...) conditions. Half of the subjectsreceiving each sequence performed the task under attentionaldistraction (in the form of a tone-counting task); the otherhalf only performed the sequence learning task. In each ofthese six conditions, subjects first received two blocks ofrandom material (Blocks 1-2), followed by eight blocks ofstructured material (Blocks 3-10), then another two blocks ofrandom material (Blocks 11-12), and a final set of two blocksof structured material (Blocks 13-14). The interesting com-parisons are between performance on the last two randomblocks (Blocks 11-12), on the one hand, and on the four laststructured blocks (Blocks 9-10 and 13-14), on the other hand.Any positive difference between the average RTs on these twogroups of blocks would indicate interference when the switchto random material occurred, thereby suggesting that subjectshave become sensitive to the sequential structure of the ma-terial.

We have represented the standard scores of the six relevantRT differences in the left panel of Figure 13. When the

sequence learning task is performed alone ("single" condi-tion), unique and hybrid sequences are better learned thanambiguous sequences, as indicated by the larger differencebetween random and structured material elicited by uniqueand hybrid sequences. The same pattern is observed when thesequence learning task is performed concurrently with thetone-counting task ("dual" condition), but overall perform-ance is much lower. In the actual data, the difference betweenrandom and structured material for the ambiguous sequenceis very close to zero. In other words, the ambiguous sequenceis not learned at all under dual-task conditions. The crucialpoint that this analysis reveals, however, is that learning ofthe unique and hybrid sequences is also hampered by thepresence of the secondary task.

To capture this pattern of results, an SRN with 15 hiddenunits was trained in exactly the same conditions as subjectsin the study by A. Cohen et al. (1990). We recorded theresponse of the network to each stimulus and separatelyaveraged these responses over the last random and structuredblocks, as described above. These mean responses were thensubstrated from one and transformed into standard scores toallow for direct comparisons with the data.

We explored three different ways of modeling the secondarytask by means of noise. One consists of adding noise to theconnections from the context units to the hidden units only.We found that this resulted in specific interference withacquisition of the ambiguous sequence. Basically, the networklearns to ignore the noisy information coming from thecontext units and minimizes the error using the main proc-essing pathway only. However, this is not what is observed inthe data: The presence of the secondary task also hamperslearning of the unique and hybrid sequences. Therefore, wefocused on two other ways of allowing noise to interfere withprocessing: adding noise to the net input of each unit of thenetwork or adding noise to the net input of each hidden unitonly. In both cases, activation propagating from the contextunits and from the input units to the rest of the network wasaffected equally.

In a first simulation, the secondary task was modeled byadding normally distributed random noise (a = 0.7) to thenet input of each unit in the network. The learning rates wereset to 0.35 (slow e) and to 0.45 (fast e). The values of theother parameters were identical to those used in our previoussimulations. The results are illustrated in the middle panel ofFigure 13. The response pattern produced by the network isquite similar to the human data. In particular, the noise (a)affected learning of all three types of sequences and (b)virtually eliminated learning of the ambiguous sequence. In-deed, the difference score for the ambiguous sequence was0.019 in the dual condition, only 1.9%. Thus, at this level ofnoise, learning of the ambiguous sequence is almost entirelyblocked, as for subjects in the A. Cohen et al. (1990) study.By contrast, learning of the unique and hybrid sequences isrelatively preserved, although the hybrid sequence was notlearned as well by the model as by the subjects.

The right panel of Figure 13 illustrates the results of asimilar analysis conducted on a simulation using higher learn-ing rates (slow « = 0.7, fast e = 0.8) and in which noise (a =1.9) was only allowed to affect the net input to each hidden


DATA : Cohen et at. (1990)[Experiment 4]

-5 o

1-1

-2Single Dual

SIMULATION : slgma * 0.7[all units]

S

iTJ

O

-2Single Dual

Condition

SIMULATION : slgma = 1.9[hidden units only]

S 1v2S

1°oS

i«

I-2

Single Dual

Figure 13. Standard scores of human and simulated mean difference scores between responses onrandom and structured material, for unique, hybrid, and ambiguous sequences, and under single- ordual-task conditions.

unit of the network. The figure shows that with these verydifferent parameters, the model still captures the basic patternof results observed in the data. The difference score for theambiguous sequence in the dual condition was 0.023, againvery close to zero. In contrast with the previous simulation,however, the hybrid sequence now appears to be learned aswell as by human subjects. The ambiguous sequence, on theother hand, seems to be learned somewhat too well with thisparticular set of parameters.

The important result is that both simulations produced aninterference pattern qualitatively similar to the empirical data.We found that quite a wide range of parameter values wouldproduce this effect. For instance, the basic pattern is preservedif the learning rates and the noise parameter are varied pro-portionally or, as our two simulations illustrate, if the noiseis allowed to interfere with all the units in the network orwith only the hidden units. This just shows that fitting simu-lated responses to empirical data ought to be done at a fairlydetailed level of analysis. A precise, quantitative match withthe data seems inappropriate at this relatively coarse level ofdetail. Indeed, there is no indication that exactly the samepattern of results would be obtained in a replication, andoverfitting is always a danger in simulation work. The centralpoint is that we were able to reproduce this pattern of resultsby manipulating a single parameter in a system that makesno processing or representational distinction between unique,hybrid, and ambiguous sequences.

To summarize, these results have two important implica-tions. First, it appears that the secondary task exerts similardetrimental effects on both types of sequences. Learning ofambiguous sequences is almost entirely blocked when per-

formed concurrently with the tone-counting task. Unique andhybrid sequences can be learned under attentional distraction,but to a lesser extent than under single-task conditions. Bothof these effects can be simulated by varying the level of noisein the SRN model.

Second, our simulations suggest that unique and ambiguoussequences are represented and processed in the same way.Therefore, a distinction between associative and hierarchicalsequence representations does not appear to be necessary toexplain the interaction between sequence structure and atten-tion observed by A. Cohen et al. (1990).

General Discussion

In Experiment 1, subjects were exposed to a six-choiceserial reaction time task for 60,000 trials. The sequentialstructure of the material was manipulated by generating suc-cessive stimuli on the basis of a small finite-state grammar.On some of the trials, random stimuli were substituted tothose prescribed by the grammar. The results clearly supportthe idea that subjects become increasingly sensitive to thesequential structure of the material. Indeed, the smooth dif-ferentiation between grammatical and ungrammatical trialscan only be explained by assuming that the temporal contextset by previous elements of the sequence facilitates or inter-feres with the processing of the current event. Subjects pro-gressively come to encode more and more temporal contextby attempting to optimize their performance on the next trial.Experiment 2 showed that subjects were relatively unable tomaintain information about long-distance contingencies thatspan irrelevant material. Taken together, these results suggest


that, in this type of task, subjects gradually acquire a complexbody of procedural knowledge about the sequential structureof the material. Several issues may be raised regarding theform of this knowledge and the mechanisms that underlie itsacquisition.

Sensitivity to the Temporal Context and SequenceRepresentation

Subjects are clearly sensitive to more than just the imme-diate predecessor of the current stimulus; indeed, there isevidence of sensitivity to differential predictions based on twoand even three elements of context. However, sensitivity tothe temporal context is also clearly limited: Even after 60,000trials of practice, there is no evidence that subjects discrimi-nate between the different possible successors entailed byelements of the sequence four steps away from the currenttrial. The question of how much temporal context subjectsmay be able to encode has not been thoroughly explored inthe literature, and it is therefore difficult to compare ourresults with the existing evidence. Remington (1969) demon-strated that subjects' responses in a simple two-choice reactiontask were affected by elements as removed as five steps, butthe effects were very small and did not depend on the sequen-tial structure of the material. Rather, they were essentially theresult of repetition priming. Early studies by Millward andReber (1968, 1972), however, documented sensitivity to asmuch as seven elements of temporal context in a two-choiceprobability learning paradigm that used structural material.In the Millward and Reber (1972) study, the sequences wereconstructed so that the event occurring on Trial / was contin-gent on an earlier event occurring at Trial t — L. The lag Lwas progressively increased from 1 to 7 over successive exper-imental sessions. The results indicated that subjects wereslightly more likely to produce the contingent response on thetrial corresponding to the lag than on any other trial, therebysuggesting that they encoded the contingency. A number offactors, however, make this result hard to generalize to oursituation. First, subjects were asked to predict the next elementof a sequence, rather than simply react to it. It is obvious thatthis requirement will promote explicit encoding of the se-quential structure of the material much more than in oursituation. Second, the task only involved two choices, whichis much fewer than the six choices used here. There is littledoubt that detecting contingencies is facilitated when thenumber of stimuli is reduced. Third, the training schedule (inwhich the lag between contingent events was progressivelyincreased over successive practice sessions) used in this studyis also likely to have facilitated encoding of the long-distancecontingencies. Finally, the differences in response probabili-ties observed by Millward and Reber (1972) were relativelysmall for the longer lags (for instance, they reported a .52probability of predicting the contingent event at Lag 7 vs. .47for the noncontingent event).

More recently, Lewicki et al. (1987), and also Stadler(1989), reported that subjects seemed to be sensitive to sixelements of temporal context in a search task in which thelocation of the target on the seventh trial was determined by

the locations of the target on the six previous trials. This resultmay appear to contrast with ours, but close inspection of thestructure of the sequences used by Lewicki et al. (1987)revealed that 50% of the uncertainty associated with thelocation of the target on the seventh trial may be removed byencoding just three elements of temporal context. This couldundoubtedly account for the facilitation observed by Lewickiet al. and is totally consistent with the results obtained here.

In summary, none of the above studies provided firmevidence that subjects become sensitive to more than three orfour elements of temporal context in situations that do notinvolve explicit prediction of successive events. It is interestingto speculate on the causes of these limitations. Long-distancecontingencies are necessarily less frequent than shorter ones.However, this should not prevent them per se from becomingeventually encoded should the regularity-detection mecha-nism be given enough time and resources. A more sensibleinterpretation is that memory for sequential material is lim-ited and that the traces of individual sequence elements decaywith time. More recent traces would replace older ones asthey are processed. This notion is at the core of many earlymodels of sequence processing (e.g., Laming, 1969). In theSRN model, however, sequence elements are not representedindividually, and memory for context does not spontaneouslydecay with time. The model nevertheless has clear limitationsin its ability to encode long-distance contingencies. The reasonfor these limitations is that the model develops representationsthat are strongly determined by the constraints imposed bythe prediction task. That is, the current element is representedtogether with a representation of the prediction-relevant fea-tures of previous sequence elements. As learning progresses,representations of subsequences followed by identical succes-sors tend to become more and more similar. For instance, wehave shown that an SRN with three hidden units developsinternal representations that correspond exactly to the nodesof the finite-state grammar from which the stimulus sequencewas generated (Cleeremans et al., 1989). This is a directconsequence of the fact that all the subsequences that entailthe same successors (i.e., that lead to the same node) tend tobe represented together. As a result, it also becomes increas-ingly difficult for the network to produce different responsesto otherwise identical subsequences preceded by disambiguat-ing elements. In a sense, more distant elements are subject toa loss of resolution, the magnitude of which depends expo-nentially on the number of hidden units available for proc-essing (Servan-Schreiber et al., 1988). Encoding long-distancecontingencies is greatly facilitated if each element of thesequence is relevant—even only in a probabilistic sense—forpredicting the next one. Whether subjects also exhibit thispattern of behavior is a matter for further research.

Awareness of the Sequential Structure

It is often claimed that learning can proceed without explicitawareness (e.g., Reber, 1989; Willingham et al., 1989). How-ever, in the case of sequence learning, as in most other implicitlearning situations, it appears that subjects become aware ofat least some aspects of the structure inherent in the stimulusmaterial. Our data suggest that subjects do become aware of


the alternations that occur in the grammar (e.g., SQSQ andVTVT in Experiment 1), but have little reportable knowledgeof any other contingencies. The loops also produced markedeffects on performance. Indeed, as Figure 12 illustrates, thegreatest amount of facilitation occurs at Nodes #2 and #4,and for the letters involved in the loops (Q at Node #2 and Vat Node #4). However, this does not necessarily entail thatexplicit knoweldge about these alternations played a signifi-cant role in learning the sequential structure of the material.Indeed, a great part of the facilitation observed for these lettersresults from the fact that they are subject to associativepriming effects because of their involvement in alternations.Furthermore, our data contain many instances of cases inwhich performance facilitation resulting from sensitivity tothe sequential structure was not accompanied by correspond-ing explicit knowledge. For instance, the results of the analysison differential sensitivity to the successors of selected pathsof length 3 (Experiment 1) clearly demonstrate that subjectsare sensitive to contingencies they are unable to elaborate intheir explicit reports. In other words, we think that awarenessof some aspects of the sequential structure of the materialemerges as a side effect of processing and plays no significantrole in learning itself. As it stands, the SRN model does notaddress this question directly. Indeed, it incorporates nomechanism for verbalizing knowledge or for detecting regu-larities in a reportable way. However, the model implementsa set of principles that are relevant to the distinction betweenimplicit and explicit processing. For instance, even thoughthe internal representations of the model are structured andreflect information about the sequence, the relevant knowl-edge is embedded in the connection weights. As such, thisknowledge is relatively inaccessible to observation. By con-trast, the internal representations of the model may be madeavailable to some other component of the system. This othercomponent of the system may then be able to detect andreport on the covariations present in these internal represen-tations, even though it would play but a peripheral role inlearning or in processing. Even so, the internal representationsof the model may be hard to describe because of their gradedand continuously varying nature.

Other aspects of the data support the view that explicitknowledge of the sequence played but a minimal role in thistask. For instance, even though the results of the generationtask, which followed training in Experiment 2, clearly indicatethat subjects were able to use their knowledge of the sequenceto predict the location of some grammatical events, overallprediction performance was very poor, particularly whencompared with previous results. A. Cohen et al. (1990), forinstance, showed that subjects were able to achieve nearperfect prediction performance in as little as 100 trials. Instark contrast, our subjects were only able to correctly predictabout 25% of the grammatical events after 450 trials of thegeneration task and 60,000 trials of training. This differencefurther highlights the complexity of our experimental situa-tion and suggests that the presence of the noise and thenumber of different possible grammatical subsequences makeit very hard to process the material explicitly. This was cor-roborated by subjects* comments that they had sometimestried to predict successive events, but had abandoned this

strategy because they felt it was detrimental to their perform-ance.

In short, these observations lead us to believe that subjectshad very little explicit knowledge of the sequential structurein this situation and that explicit strategies played but anegligible role during learning. One may wonder, however,about the role of explicit recoding strategies in task settings assimple as those used by Lewicki et al. (1988) or A. Cohen etal. (1990). In both these situations, subjects were exposed toextremely simple repeating sequences of no more than sixelements in length. But the work of Willingham et al. (1989)has demonstrated that a sizeable proportion of subjects placedin a choice reaction situation involving sequences of 10 ele-ments do become aware of the full sequence. These subjectswere also faster in the sequence learning task and moreaccurate in predicting successive sequence elements in a fol-low-up generation task. By the same token, a number ofsubjects also failed to show any declarative knowledge of thetask despite good performance during the task. These resultshighlight the fact that the relationship between implicit andexplicit learning is complex and subject to individual differ-ences. Claims that acquisition is entirely implicit in simplesequence learning situations must be taken with caution.

To summarize, although it is likely that some subjects usedexplicit recoding strategies during learning, the complexity ofthe material we used—as well as the lack of improvement inthe generation task—make it unlikely that they did so in anysystemic way. Further experimental work is needed to assessin greater detail the impact of explicit strategies on sequencelearning, using a range of material of differing complexity,before simulation models that incorporate these effects canbe elaborated.

Learning Mechanisms and Attention

The augmented SRN model provides a detailed, mechanis-tic, and fairly good account of the data. Although the corre-spondence is not perfect, the model nevertheless capturesmuch of the variability of human responses.

The model's core learning mechanism implements the no-tion that sensitivity to the temporal context emerges as theresult of optimizing preparation for the next event on thebasis of the constraints set by relevant (i.e., predictive) featuresof the previous sequence. However, this core mechanismalone is not sufficient to account for all aspects of perform-ance. Indeed, as discussed above, our data indicate that inaddition to the long-term and progressive facilitation obtainedby encoding the sequential structure of the material, responsesare also affected by a number of other short-term (repetitiveand associative) priming effects. It is interesting to note thatthe relative contribution of these short-term priming effectstends to diminish with practice. For instance, an ungrammat-ical but repeated Q that follows an SQ- at Node #1 inExperiment 1 elicits a mean RT of 463 ms over the first 4sessions of training. This is much faster than the 540 mselicited by a grammatical X that follows SQ— at the samenode. By contrast, this relationship becomes inverted in thelast 4 sessions of the experiment: The Q now evokes a meanRT of 421 ms, whereas the response to an X is 412 ms. Thus,

252 AXEL CLEEREMANS AND JAMES L. MCCLELLAND

through practice, the sequential structure of the materialcomes to exert a growing influence on response times andtends to become stronger than the short-term priming effects.The augmented SRN model captures this interaction in asimple way: Early in training, the connection weights under-lying sensitivity to the sequential structure are very small andcan only exert a limited influence on the responses. At thispoint, responses are quite strongly affected by previous acti-vations and adjustments to the fast weights from precedingtrials. Late in training, however, the contribution of theseeffects in determining the activation of the output units endsup being dominated by the long-term connection weights,which, through training, have been allowed to develop con-siderably."

With both these short-term and long-term learning mech-anisms in place, we found that the augmented SRN modelcaptured key aspects of sequence learning and processing inour task. Furthermore, the model also captured the effects ofattention on sequence learning reported by A. Cohen et al.(1990). Even though ambiguous sequences are not processedby separate mechanisms in the SRN model, they are never-theless harder to learn than unique and hybrid sequencesbecause they require more temporal context information tobe integrated. So the basic difference between the three se-quence types is produced naturally by the model. Further-more, when processing is disturbed by means of noise, themodel produces an interference pattern very similar to thatof the human data. Presumably, a number of different mech-anisms could produce this effect. For instance, Jennings andKeele (1990) explored the possibility that the absence oflearning of the ambiguous sequence under attentional distrac-tion was the result of impaired "parsing" of the material. Theytrained a sequential back-propagation network (Jordan, 1986)to predict successive elements of a sequence and measuredhow the prediction error varied with practice under differentconditions and for different types of sequences. The resultsshowed that learning of ambiguous sequences progressedmuch slower than for unique or hybrid sequences when theinput information did not contain any cues as to the structureof the sequences. By contrast, learning of ambiguous se-quences progressed at basically the same rate as for the othertwo types of sequences when the input to the network didcontain information about the structure of the sequence, suchas the marking of sequence boundaries or an explicit repre-sentation of its subparts. If one assumes that attention isrequired for this explicit parsing of the sequence to take placeand that the effects of the secondary task is to prevent suchmechanisms from operating, then indeed learning of theambiguous sequence will be hampered in the dual-task con-dition. However, the data seem to indicate that learning ofthe unique and hybrid sequences is also hampered by thepresence of the secondary task. One would therefore need toknow more about the effects of parsing on learning of theunique and hybrid sequences. Presumably, parsing would alsofacilitate processing of these kinds of sequences, although toa lesser extent than for ambiguous sequences.

In the case of the SRN model, we found that specificallyinterfering with processing of the ambiguous sequence byadding noise to the connections from the context units to the

hidden units would not produce the observed data. On thecontrary, our simulations indicate that the interference pro-duced by the secondary task seems to be best accounted forwhen noise is allowed to equally affect processing of infor-mation coming from the context units and information com-ing from the input units. Therefore, it appears that there isno a priori need to introduce a theoretical distinction betweenprocessing and representation of sequences that have a hier-archical structure and sequences that do not. Naturally, wedo not mean to suggest that sequence learning never involvesthe use of explicit receding strategies of the kind suggested byA. Cohen et al. (1990) and by Jennings and Keele (1990). Aspointed out earlier, it is very likely indeed that many sequence-learning situations do in fact involve both implicit and explicitlearning and that recording strategies play a significant rolein performance. Further research is needed to address thisissue more thoroughly.

Conclusion

Subjects placed in a choice reaction time situation acquirea complex body of procedural knowledge about the sequentialstructure of the material and gradually come to respond onthe basis of the constraints set by the last three elements ofthe temporal context. It appears that the mechanisms under-lying this progressive sensitivity operate in conjunction withshort-term and short-lived priming effects. Encoding of thetemporal structure seems to be primarily driven by anticipa-tion of the next element of the sequence. A PDP model thatincorporates both of these mechanisms in its architecture wasdescribed and found to be useful in accounting for key aspectsof acquisition and processing. This class of model thereforeappears to offer a viable framework for modeling uninten-tional learning of sequential material.

4 As Soetens, Boer, and Hueting (1985) have demonstrated, how-ever, short-term priming effects also tend to become weaker throughpractice even in situations that only involve random material. At thispoint, the SRN model is simply unable to capture this effect. Doingso would require the use of a training procedure that allows the timecourse of activation to be assessed (such as cascaded back-propaga-tion; see J. D. Cohen, Dunbar, & McClelland, 1990) and is a matterfor further research.

References

Berry, D. C, & Broadbent, D. E. (1984). On the relationship betweentask performance and associated verbalizable knowledge. QuarterlyJournal of Experimental Psychology, 36A, 209-231.

Bertelson, P. (1961). Sequential redundancy and speed in a serialtwo-choice responding task. Quarterly Journal of ExperimentalPsychology, 13, 90-102.

Carmines, E. G., & Zeller, R. A. (1987). Reliability and validityassessment (Sage University Paper Series No. 07-017). NewburyPark, CA: Sage.

Cleeremans, A., Servan-Schreiber, D., & McClelland, J. L. (1989).Finite state automata and simple recurrent networks. Neural Com-putation, 1, 372-381.

Cohen, A., Ivry, R. I., & Keele, S. W. (1990). Attention and structure


in sequence learning. Journal of Experimental Psychology: Learn-ing, Memory, and Cognition, 16, 17-30.

Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the controlof automatic processes: A parallel distributed account of the Stroopeffect. Psychological Review, 97, 332-361.

Cohen, J. D., & Servan-Schreiber, D. (1989). A parallel distributedprocessing approach to behavior and biology in schizophrenia(Tech. Rep. No. AIP-100). Pittsburgh, PA: Carnegie Mellon Uni-versity, Department of Psychology.

Dulany, D. E., Carlson, R. C, & Dewey, G. I. (1984). A case ofsyntactical learning and judgment: How conscious and how ab-stract? Journal of Experimental Psychology: General, 113, 541-555.

Dulany, D. E., Carlson, R. C., & Dewey, G. I. (1985). On conscious-ness in syntactical learning and judgment: A reply to Reber, Allen,and Regan. Journal of Experimental Psychology: General, 114, 25-32.

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14,179-211.

Elman, J. L. (in press). Representation and structure in connectionistmodels. In G. Altmann (Ed.), Computational and psycholinguisticapproaches to speech processing. San Diego, CA: Academic Press.

Estes, W. K. (1976). The cognitive side of probability learning.Psychological Review, 83, 37-64.

Falmagne, J. C. (1965). Stochastic models for choice reaction timewith application to experimental results. Journal of MathematicalPsychology, 2, 77-124.

Hayes, N. A., & Broadbent, D. E. (1988). Two modes of learning forinteractive tasks. Cognition, 28, 249-276.

Hebb, D. O. (1961). Distinctive features of learning in the higheranimal. In A. Fressard, R. W. Gerard, J. Konorsky, & J. F.Delafresnaye (Eds.), Brain mechanisms and learning (pp. 37-51).Oxford, England: Blackwell Scientific.

Hinton, G. E., & Plaut, D. C. (1987). Using fast weights to deblur oldmemories. Proceedings of the Ninth Annual Conference of theCognitive Science Society (pp. 177-186). Hillsdale, NJ: Erlbaum.

Hyman, R. (1953). Stimulus information as a determinant of reactiontime. Journal of Experimental Psychology, 45, 188-196.

Jennings, P. J., & Keele, S. W. (1990). A computational model ofattentional requirements in sequence learning. Proceedings of theTwelfth Annual Conference of the Cognitive Science Society (pp.876-883). Hillsdale, NJ: Erlbaum.

Jordan, M. I. (1986). Attractor dynamics and parallelism in a con-nectionist sequential machine. Proceedings of the Eighth AnnualConference of the Cognitive Science Society (pp. 531-546). Hills-dale, NJ: Erlbaum.

Laming, D. R. J. (1969). Subjective probability in choice-reactionexperiments. Journal of Mathematical Psychology, 6, 81-120.

Lewicki, P., Czyzewska, M., & Hoffman, H. (1987). Unconsicousacquisition of complex procedural knowledge. Journal of Experi-mental Psychology: Learning, Memory, and Cognition, 13, 523-530.

Lewicki, P., Hill, T., & Bizot, E. (1988). Acquisition of proceduralknowledge about a pattern of stimuli that cannot be articulated.Cognitive Psychology, 20, 24-37.

Luce, R. D. (1963). Detection and recognition. In R. D. Luce, R. R.Bush, & E. Galanter (Eds.), Handbook of mathematical psychology(Vol. 1, pp. 103-189). New York: Wiley.

Mathews, R. C., Buss, R. R., Stanley, W. B., Blanchard-Fields, F.,Cho, J. R., & Druhan, B. (1989). Role of implicit and explicitprocesses in learning from examples: A synergistic effect. Journalof Experimental Psychology: Learning, Memory, and Cognition,15, 1083-1100.

McClelland, J. L., Cleeremans, A., & Servan-Schreiber, D. (1990).Parallel distributed processing: Bridging the gap between humanand machine intelligence. Journal of the Japanese Society forArtificial Intelligence, 5, 2-14.

McClelland, J. L., & Rumelhart, D. E. (1985). Distributed memoryand the representation of general and specific information. Journalof Experimental Psychology: General, 114, 159-188.

Miller, G. A. (1958). Free recall of redundant strings of letters. Journalof Experimental Psychology, 56, 485-491.

Millward, R. B., & Reber, A. S. (1968). Event recall in probabilitylearning. Journal of Verbal Learning and Verbal Behavior, 7, 980-989.

Millward, R. B., & Reber, A. S. (1972). Probability learning: Contin-gent-event schedules with lags. American Journal of Psychology,85, 81-98.

Newell, A., & Simon, H. A. (1972). Human problem solving. Engle-wood Cliffs, NJ: Prentice-Hall.

Nissen, M. J., & Bullemer, P. (1987). Attentional requirements oflearning: Evidence from performance measures. Cognitive Psy-chology, 19, 1-32.

Pew, R. W. (1974). Levels of analysis in motor control. Brain Re-search, 71, 393-400.

Reber, A. S. (1967). Implicit learning of artificial grammars. Journalof Verbal Learning and Verbal Behavior, 6, 855-863.

Reber, A. S. (1989). Implicit learning and tacit knowledge. Journalof Experimental Psychology: General, 118, 219-235.

Reber, A. S., Allen, R., & Regan, S. (1985). Syntactical learning andjudgment, still unconscious and still abstract: Comment on Dulany,Carlson, and Dewey. Journal of Experimental Psychology: General,114, 17-24.

Remington, R. J. (1969). Analysis of sequential effects in choicereaction times. Journal of Experimental Psychology, 82, 250-257.

Restle, F. (1970). Theory of serial pattern learning: Structural trees.Psychological Review, 77, 481-495.

Rumelhart, D. E., Hinton, G., & Williams, R. J. (1986). Learninginternal representations by error propagation. In D. E. Rumelhart& J. L. McClelland (Eds.), Parallel distributed processing: I. Foun-dations (pp. 318-362). Cambridge, MA: MIT Press.

Schacter, D. L. (1987). Implicit memory: History and current status.Journal of Experimental Psychology: Learning, Memory, and Cog-nition, 13, 501-518.

Servan-Schreiber, D., Cleeremans, A., & McClelland, J. L. (1988).Encoding sequential structure in simple recurrent networks (Tech.Rep. No. CMU-CS-88-183). Pittsburgh, PA: Carnegie Mellon Uni-versity, Department of Computer Science.

Servan-Schreiber, E., & Anderson, J. R. (1990). Learning artificialgrammars with competitive chunking. Jounal of ExperimentalPsychology: Learning, Memory, and Cognition, 16, 592-608.

Soetens, E., Boer, L. C, & Hueting, J. E. (1985). Expectancy orautomatic facilitation?: Separating sequential effects in two-choicereaction time. Journal of Experimental Psychology: Human Per-ception and Performance, 11, 598-616.

Stadler, M. A. (1989). On learning complex procedural knowledge.Journal of Experimental Psychology: Learning, Memory, and Cog-nition, 15, 1061-1069.

Willingham, D. B., Nissen, M. J., & Bullemer, P. (1989). On thedevelopment of procedural knowledge. Journal of ExperimentalPsychology: Learning, Memory, and Cognition, 15, 1047-1060.

Received June 18, 1990Revision received November 29, 1990

Accepted December 11, 1990

Learning the Structure of Event Sequencespal/pdfs/pdfs/cleeremans-mcclelland...Emile and David Servan-Schreiber for several insightful discussions. Arthur Reber and an anonymous reviewer

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