Psychol Res (1997) 60:72-86
© Springer-Verlag 1997
ORIGINAL ARTICLE
Axel Cleeremans
Sequence learning in a dual-stimulus setting
Received: 17 July 1996 / Accepted: 7 November 1996 Abstract Is sequence learning an autonomous process that relies on independent resources? In this paper, I attempt to answer this question by exploring whether sequence learning occurs even despite the availability of reliable explicit information about the material to be learned. I report on a series of experiments during which participants performed a sequential choice reaction task. On each trial, participants were exposed to a stimulus and to a cue of varying validity which, when valid, indicated where the next stimulus would appear. Participants could therefore optimize their performance either by implicitly encoding the sequential constraints contained in the material or by explicitly relying on the information conveyed by the cue. Some theories assume that implicit learning does not rely on the same processing resources as those involved in explicit learning. Such theories would thus predict that sensitivity to sequential constraints should not be affected by the presence of reliable explicit information about sequence structure. Other theories, by contrast, would predict that implicit learning would not occur in such cases. The results are consistent with the former theories, but simulation work meant to enable the implications of these contrasting theories to be explored suggests that such results are also obtainable in architectures in which two processing pathways share resources.
Introduction Implicit learning is typically defined as the process whereby participants appear capable of acquiring new information without concomitant awareness of what is
A. Cleeremans Laboratoire de PsychologieIndustrielle et Commerciale, Universit6 Libre de Bruxelles CP 122, Avenue F.-D. Roosevelt, 50, 1050 Bruxelles, Belgium, e-mail:
[email protected]
being learned. Even though this definition is currently very controversial (e.g., Reber, 1993; Shanks & St. John, 1994; Cleeremans, in press), there is now a large body of evidence suggesting that improvements in performance at a given task are not systematically accompanied by similar improvements in participants' ability to express or use the acquired knowledge in an explicit way. For instance, Artificial Grammar Learning studies have shown that participants can classify strings of letters as grammatical or not after practice at memorizing similar strings and without being able to report on the rules that define grammatically (e.g., see Berry & Dienes, 1993, for a review). Sequence learning studies, on which this paper will focus, have demonstrated that participants can become sensitive to the regularities contained in sequences of stimuli presented in a choice-reaction setting despite remaining unable to report on the sequence or to perform well in other direct tests such as generation, where participants are asked to predict the next stimulus instead of reacting to the current one (e.g., Cleeremans, 1993a; Nissen & Bullemer, 1987, Jim~nez, M6ndez, & Cleeremans, 1996). Implicit learning is assumed to have a number of features that distinguish it from explicit learning. However, because the existence and nature of implicit learning is controversial, there is currently no agreement in the field about which features have been empirically established. For instance, some authors claim that implicit learning is an independent unconscious process that can result in abstract knowledge (e.g., Reber, 1993). Writings by Reber (e.g., Reber & Lewis, 1977; see also Reber, 1989), by Lewicki and his collaborators (e.g., Lewicki, Czyzewska, & Hoffman, 1987), or by Curran and Keele (1993) contain clear characterizations of implicit learning as involving such a separable learning system. For instance, Reber (1990) states that '"Specifically, [implicit systems] (a) ought to be fairly cleanly dissociable from explicit systems, (b) should have perceptual and cognitive functions that operate largely independent of consciousness and (c) ought to show greater resiliency to and resistance to insult and injury"
73 (p. 342). Another example can be found in Broadbent's (e.g., Berry & Broadbent, 1988) notion that learning may either be selective (S-mode learning) or unselective (U-mode learning), although this theory puts more emphasis on the processes involved than on architectural distinctions. For others, however, implicit learning is essentially explicit exemplar-based learning (e.g., Shanks & St. John, 1994). These issues have been extensively explored empirically, and I believe that it is fair to say that they remain largely unsolved at this point (see Cleeremans, in press). In this paper I would like to focus on an assumption that often underpins the others but that has seldom been addressed directly, which is that implicit learning is an autonomous process that relies on specific, independent processing resources. Three positions on this issue have been expressed in the implicit learning literature. First, some authors (e.g., Perruchet & Amorim, 1992) argue that performance in implicit learning tasks does not necessarily reflect the operation of an independent implicit learning system. Rather, performance would be mostly based on explicit processing, but the resulting knowledge is fragmented enough that verbal reports probing for general information are unlikely to reveal the extent of participants' knowledge. Other authors (e.g., Curran & Keele, 1993; Knowlton, Ramus, & Squire, 1992) assume that implicit learning and explicit learning are supported by different memory systems, and that these systems are completely independent from each other. Implicit learning and explicit learning would thus proceed parallel and without interacting. They produce different kinds of knowledge and are most likely to operate efficiently in contrasted settings. Finally, there may be an intermediate position where one assumes that implicit and explicit processing indeed rely on distinct memory systems, but some interactions between the two systems are allowed, and some processing resources are shared (e.g., Cleeremans, 1993b). Typically, these issues have been approached by placing participants in dual-task settings or by manipulating the degree to which they possess or apply explicit knowledge about the task. For instance, Keele and his collaborators (e.g., Cohen, Ivry, & Keele, 1990; Curran & Keele, 1993) used sequential reaction time (SRT) tasks coupled with a secondary tone-counting task. The rationale of these experiments was to determine whether learning of the sequential structure of the stimulus material can still occur despite attentional resources being recruited by the secondary task. In other experiments, Curran and Keele also manipulated participants' explicit knowledge by letting them study the sequence beforehand. In general, the results of these and other studies have shown that the availability of explicit knowledge results in better performance under single-task conditions, but that the presence of a secondary task results in all participants performing at the same level regardless of
whether or not they possess explicit knowledge of the sequence. An important issue in this context is to determine whether the presence of a secondary task hinders learning of the sequential structure or whether it merely prevents knowledge about the sequential structure to be expressed. This issue is important because it has implications about the underlying mechanisms and architecture. Indeed, if learning itself is impeded by the presence of a secondary task, then one would tend to conclude that the secondary task competes in some way with the processes involved in learning about the sequential structure - for instance, because the latter processes are also influenced by the availability of attentional resources. If, on the other hand, the secondary task merely blocks the expression of knowledge about the sequential structure, then one would tend to conclude that the underlying learning processes are not sensitive to the scarcity of attentional resources, and hence that learning of the sequential structure can proceed independently of the fact that the cognitive system is otherwise engaged. Curran and Keele (1993, Exp. 3) explored this issue by asking participants to perform the RT task first under dual-task conditions and then subsequently under single-task conditions. The results showed that participants exhibited the same degree of sequence learning under dual-task and under single-task conditions. This result, together with the results from other experiments, prompted Curran and Keele to suggest that sequence learning actually involves at least two learning mechanisms: one that is sensitive to the availability of attentional resources and that is therefore impaired under dual-task conditions, and another, non-attentional mechanism, which is not sensitive to the presence of a dual task. This latter mechanism would be responsible for the results observed by Curran and Keele in their Exp. 3. To summarize, results like these have often been interpreted as yielding support to the notions (1) that sequence learning involves both explicit and implicit learning, and (2) that at least one form of sequence learning relies on mechanisms that are independent from short-term memory and from the availability of attentional resources, both of which are assumed to be crucially important for explicit learning to occur. The dual-task methodology has a number of problems, however. First, it is difficult to assess how much attentional capacity the secondary task requires. For instance, the difficulty of keeping track of how many tones of a particular kind have been presented so far may vary with the number of tones presented. This problem makes it hard to draw strong inferences about the relative independence of the processes responsible for learning the sequential structure. Second, it is not clear exactly what the effects of the secondary task are. Frensch, Buchner, and Lin (1994) reported that participants differ in how they schedule execution of the primary and secondary task responses: Some participants tended to first execute the RT re-
74
sponse and process the tone later, whereas some other participants tended to first process the tone and then answer the RT task. Hence, it may be hard to ensure that the secondary task is treated in the same way by all participants. Further, Stadler (1995) showed that the effects of the tone-counting secondary task were more similar to the effects of disrupting the temporal organization of the sequence by allowing RSI variability than to the effects of increasing the memory load by asking participants to memorize unrelated material for the duration of the task. Finally, it may also be the case that the secondary task prevents participants from applying explicit knowledge that they have acquired about the sequential structure of the material over the course of training or through direct instructions to memorize the sequence (Cleeremans, 1993b). Third, it is hard to determine how much explicit knowledge participants possess and actually use during the task. Assessing explicit knowledge is notoriously difficult in implicit learning experiments, but even in clear cases such as the task explored by Curran and Keele (1993), in which participants were asked to memorize the simple sequence that they would be subsequently exposed to during the RT task, there is no way of determining whether and to what extent participants actually used this knowledge during the task. These three problems all conspire to hinder exploration of the interaction between attentional requirements, explicit knowledge, and sequence-learning performance. As described above, a secondary task in typical sequence-learning situations may exert any or all of several different effects on performance, including (1) disrupting participants' subjective organization of the material, (2) capturing attentional resources otherwise necessary to sustain performance on the main RT task, or (3) capturing short-term memory resources necessary for the development or the application of explicit knowledge about the stimulus material. Importantly, the different effects that a secondary task may have on RT performance have different implications about the processes involved in sequence learning. In this paper, I do not aim to solve these problems but to report on a different way of addressing the same issues. Instead of placing participants in a dual-task setting, I placed them in a dual-stimulus setting where at each trial, two sources of information are available to compute the response: the sequential context set by previous elements of the sequence on the one hand, and a cue indicating where the next stimulus will appear, on the other hand. Hence, at each trial, participants can rely on the temporal context and/or the information conveyed by the cue to prepare for the next event. This design was inspired by work on overshadowing in conditioning experiments with animals (e.g., Matzel, Schachtman, & Miller, 1985). Overshadowing occurs when the "presentation of a salient cue (A) in compound with a less salient cue (X) immediately prior to the onset of an unconditioned stimulus (US) results in less conditioned responding to X than would be manifest if X had
been paired with the US independently" (Matzel, Schachtman, & Miller, 1985, p. 398). In other words, a salient stimulus that is strongly associated with a given response tends to hinder learning of the association between another weaker stimulus and the same response. Just as in the sequence-learning literature briefly reviewed above, an issue of debate in the conditioning literature is to determine whether the overshadowing stimulus actually hinders learning of the association between the overshadowed stimulus and the response, or whether it merely prevents knowledge about this association to be expressed as long as the overshadowing stimulus is present. Some authors (e.g., Rescorla & Wagner, 1972), have interpreted the phenomenon of overshadowing in terms of competition between different cues for a limited amount of associative strength. Other authors (e.g., Matzel et al., 1985) have pointed out, however, that one can also observe "recovery from overshadowing," where the association between the overshadowed stimulus and the response returns to normal without further reinforcement when the overshadowing stimulus is removed. This latter result suggests that overshadowing, far from preventing learning of the association between the overshadowed stimulus and the response, merely masks its expression in performance. The dual-stimulus cuing situation I describe hereafter is an almost direct adaptation of the overshadowing paradigm. In this situation, the cue plays the role of the overshadowing stimulus, and the sequential structure of the stimulus material plays the role of the overshadowed stimulus. Do participants learn about both dimensions (i.e., the information transmitted by the temporal context and the information transmitted by the cue), or is learning of one dimension hindered or modulated by learning of the other? If implicit sequence learning is an autonomous process that relies on independent processing and memory systems, then one should expect to obtain such learning even in conditions where more reliable and fully explicit, direct sources of information about the relevant material are available. If, on the other hand, information about the sequential structure is processed in just the same way and with the same resources as information about the cue, then one would expect little or no sequence learning whenever the response can be computed based on a much more reliable source of information. In the following, I shall first describe experimental work that implements this design. Next, I shall report on simulation work that illustrates how one can explore these issues within the connectionist framework.
Experiment 1 Method The experiment consisted of two conditions. Each consisted of ten training sessions during which participants were exposed to a sixchoice SRT task. Each session consisted of 20 blocks of 150 trails each, for a total of 30,000 trials. After training, all participants were
75 exposed to 3 blocks of a generation task in which they were asked to predict the location at which the next stimulus would appear. On each trial of the SRT task, a stimulus could appear at one of six positions arranged horizontally on a computer screen, and partici pants were to press the key corresponding to the current location of the stimulus, as fast and as accurately as possible. Participants were kept unaware that the material was structured sequentially. The sequential structure of the material was manipulated by generating the sequence based on a noisy finite-state grammar, as described below. In cued blocks, a cue consisting of an X appearing under one of the six stimulus positions was presented concurrently with the stimulus. This cue could be either valid or invalid. If valid, it indicated the location at which the next stimulus in the sequence would appear. During generation, the same stimulus material was presented, but participants were instructed to try to predict the next stimulus instead of merely reacting to the current one. No explicit feedback was provided during generation performance to minimize withingeneration learning. In the Low Validity (LV) condition, each session consisted of 2700 cued trials followed by 300 neutral trails for which the cue was absent. Cue validity was set at 20%. In the High Validity (HV) condition, the same design was used but cue validity was considerably higher (80%).
Participants. Twelve participants, all undergraduate psychology students at the Universit6 Libre de Bruxelles and aged 20-27, were tested. Six participants were randomly assigned to each of the two conditions. Participants were paid about $65 for their involvement in the experiment and could earn an additional bonus of $34-$62 based on performance in the SRT task (see below). Apparatus and display. The experiment was run on Macintosh computers. The display consisted of six dots arranged in a horizontal line on the computer's screen and separated by intervals of 3 cm. Each screen position corresponded to a key on the computer's keyboard. The spatial configuration of the keys was fully compatible with the screen positions. The stimulus was a small black circle 0.35 cm high that appeared on a white background, centered 1 cm below one of the six dots. The cue was a small X appearing at the same locations as the stimuli. The RSI was set at 120 ms.
Tasks. The experiment consisted of two tasks presented successively: an SRT task and a generation task. The SRT task was carried out during ten training sessions, each consisting of 20 blocks of 155 trials, of which 150 were recorded (see below). After the last session, participants performed a generation task during which they were required, at each trial, to predict the location of the next stimulus by pressing the corresponding key. The generation tasks consisted of 465 neutral trials over 3 blocks of 155 trials each. The generation task material followed was generated and based on exactly the same procedure as used for the RT task material. Neither the RT task nor the generation task provided explicit feedback. During generation, however, participants could receive feedback about their performance by comparing their predictions with the actual stimuli, just as they could receive feedback about the accuracy of their responses during the SRT task.
the low validity condition were also told that the cue could often be invalid. After receiving the instructions appropriate to their condition, all participants were given three practice blocks of 15 random trials each, during which they were given a chance to ask questions and to interact with the experimenter about the task setting. The first experimental session was then initiated. To eliminate early variability in performance, each block began with a set of 5 random and unrecorded trials. Participants were then exposed to 150 structured trials. Successive blocks were separated by a rest break, the length of which participants could control. During each break, participants were presented with feedback about their performance. The computer displayed information about the participants' accuracy and mean reaction time during the last block and also displayed information about how much bonus money had been earned during the last block and up till then in the experiment. Each correct response associated with an RT faster than 550 ms earned 0.016 BEF. Each incorrect response resulted in 0.016 BEF being subtracted from the running total. After the final experimental session, all participants received instructions about the generation task. They were told that the sequence of stimuli had followed a pattern during the SRT task and that they would now have to try to predict where the next stimulus would appear instead of responding to the current stimulus. The instructions emphasized the fact that the sequential structure of the material would be the same in the generation task as it had been in the SRT task. Participants were also told that reaction times would no longer be recorded and that they could receive feedback on their performance by comparing the location that they had indicated by their key press with the actual location at which the next stimulus appeared. Finally, the importance of being as accurate as possible was again emphasized by pointing out that accuracy during generation would be used to compute a number that would multiply earnings during the SRT task. This parameter was set at 1.0 plus the proportion by which accuracy exceeded chance level during generation.
Stimulus and cue generation. Stimulus generation followed the general design described in Cleeremans and McClelland (1991) and in Jim6nez et al. (1996). Stimuli were generated based on the noisy finite-state grammar used by Jim6nez et al. and illustrated in Fig. 1. Finite-state grammars consist of nodes linked by labeled arcs. A sequence of labels can be generated by following a path through the grammar and by recording the labels associated with the arcs. To assess learning of the sequential constraints set by the grammar, one can compare performance on stimuli that follow the rules of the grammar versus random stimuli. To enable these comparisons to be conducted in a continuous manner and throughout training,
C/"
A~
~D
~'~
/
AJ
Procedure. Each participant performed the task at a rate of two sessions per day, with the additional constraint that no more than two days could elapse between any two sessions. The generation task was presented immediately after the last session of the SRT task. All participants were told that the goal of the experiment was to analyze the effects of extended practice on motor performance. They were also told about the structure of the experiment and about how to place their fingers on the keyboard. Both accuracy and speed were emphasized as means of increasing earnings. Participants in both conditions were told about the cue. Participants in
~F
%.B
E
Fig. 1 Finite-state grammar (from Jim6nez et al., 1996) used to generate the stimulus material. Note that the first and last nodes are one and the same
76 the random stimuli were interspersed with the legal stimuli by replacing a small proportion (20%) of the stimuli generated based on the grammar (grammatical stimuli) with random stimuli (nongrammatical stimuli), as described below. A total of 30,000 structured trials were presented to each participant. On each trial, stimulus generation proceeded in three phases. First, an arc coming out of the current node was randomly selected and its label recorded. The current node was set at zero on the first trial of any block and was re-adjusted at each trial to the node pointed to by the selected arc. Second, there was a 20% chance of substituting a randomly selected label for the recorded one. (Identity substitutions, as welt as any substitution that would result in a stimulus being repeated or legal at the current node, were not allowed). Third, the label was used to determine the screen position at which the stimulus would appear by following a 6 x 6 Latin square design, so that each label corresponded to each screen position for exactly one of the six participants in each condition. Note that each label appears twice in the grammar and may be followed by different successors on different occurrences. Maximally reducing the uncertainty associated with each label requires encoding up to three elements of temporal context. Cue generation proceeded independently. On each trial, a cue corresponding to the next stimulus was generated. This valid cue could be suppressed entirely (neutral blocks) or be replaced by an invalid cue in either 20% (HV condition) or 80% (LV condition) of the trials. Substitution consisted of selecting a random location for the cue to appear at, with the constraints that this location could be neither the location of the current stimulus nor that of the next one. To summarize, this generation procedure results in six types of trials defined by crossing the Grammaticality (Grammatical or Non-grammatical) and Cue validity (Valid, Invalid, or Neutral) factors. A given trial was thus categorized as "valid" if the location at which the stimulus had appeared on that trial had indeed been validly primed by the cue that had appeared on the previous trial. Similarly, a given trial was categorized as "grammatical" if the stimulus that had appeared on that trial was consistent with the generation rules expressed by the finite-state grammar. Finally, note that the sequence-generation procedure makes it impossible for any stimulus to be involved in a direct repetition of itself, This guarantees that RT effects are not contaminated by short-term priming effects, which have large facilitatory effects on performance that are completely independent from the factors of interest in this research (see Bertelson, 1961; Cleeremans & McClelland, 1991; Hyman, 1953).
Results Participants were exposed to ten sessions of an R T task and were subsequently asked to try to predict each successive event in a generation task. I shall first present the R T data.
Reaction time task performance. Figure 2 represents average R T s for correct trials over the ten sessions of training and for each trial type, in the L V (left panel) and H V (right panel) conditions. Consider first the data for the H V condition (right panel). It is obvious that cue validity has a large effect on p e r f o r m a n c e , as R T s elicited by valid trials were considerably faster than those elicited by b o t h neutral and invalid trials. Similarly, one can see that participants also m a d e m a n y m o r e errors on the invalid trials t h a n on either neutral or valid trials (Fig. 3). This p a t t e r n o f results therefore indicates that participants are indeed using the cue to anticipate the location at which the next event will a p p e a r and to
specifically p r e p a r e their response accordingly. Despite the massive i m p a c t o f cue validity, small effects o f g r a m m a t i c a l i t y are also present at all levels o f cue validity and seem to have a p p r o x i m a t e l y the same m a g nitude in each case. These impressions were confirmed by an A N O V A with Practice (10 levels) x Validity (valid, invalid, or neutral) x G r a m m a t i c a l i t y ( g r a m m a t i c a l vs. n o n - g r a m m a t i c a l ) as factors and R T as a d e p e n d e n t variable. T h e analysis yielded significant m a i n effects of practice IF(9, 45) = 27.38, p < .001, MSE = 64494.03], of cue validity IF(2, 10) = 162.38, p < .001, MSE = 1849184.17], and o f g r a m m a t i c a l l y I F ( l , 5 ) = 39.68, p < .01, MSE = 29322.22], as well as a significant interaction between practice and cue validity IF(18, 90) = 8.68, p < .001, MSE = 7443.18]. There was no significant interaction between cue validity and g r a m m a t i c a l i t y (p > .05). Figure 3 represents accuracy data (percentage of correct responses) for b o t h conditions (left panel: LV condition; right panel: H V condition). Considering the H V data first (right panel), an A N O V A revealed significant effects of cue validity [ F ( 2 , 1 0 ) = 55.28, p
