Psychological Research (2001) 65: 71±80
Ó Springer-Verlag 2001
ORIGINAL ARTICLE
Axel Buchner á Melanie C. Steens
Simultaneous learning of different regularities in sequence learning tasks: limits and characteristics
Received: 3 March 2000 / Accepted: 2 November 2000
Abstract Two experiments are reported which were designed to investigate the generality and the power of the mechanisms underlying sequence learning. In both experiments, participants reacted to systematic sequences of tones. They were informed that there was a tone systematicity. Participants were not told that the interval between a response to a tone and the onset of the subsequent tone (response-signal interval, RSI) also varied according to a ®xed regularity. Experiment 1 showed that the unattended RSIs were learned when they were uniquely related to the tone sequence, but not when the relation was ambiguous. Experiment 2 showed that, on the basis of the traditional reaction time performance measure, participants who learned the RSIs by attending to their systematicity could not be distinguished from those in an incidental learning condition in which the RSI systematicity was unattended. However, a model-based analysis of the processes contributing to judgements about the event sequences suggested that the two groups had acquired qualitatively dierent knowledge.
Introduction A common assumption in the implicit learning literature is that the mechanisms underlying implicit knowledge acquisition can be characterised as an unselective and automatic aggregation of contingency information (e.g. Berry & Broadbent, 1988; Reber, 1989). It appears that such powerful learning mechanisms may indeed be assumed to underlie the acquisition of sequential patterns in sequence learning tasks (Frensch & Miner, 1995;
A. Buchner (&) á M. C. Steens University of Trier, FB I ± Psychologie, UniversitaÈtsring 15, 54286 Trier, Germany Tel.: +49-651-2012959; Fax: +49-651-2012955 e-mail:
[email protected]
Mayr, 1996; Schmidtke & Heuer, 1997). In the present article, we explore further the generality and the power of sequence learning mechanisms. Sequence learning tasks have evolved into one of the major experimental paradigms for studying implicit learning processes (cf. Berry & Dienes, 1993; Shanks & St. John, 1994). In these tasks, people learn about contingencies by reacting to systematic patterns of events with speeded discriminative responses. For instance, people learn to press a key corresponding to a spatial position as soon as a visual signal appears in that position. The sequence with which signals appear in the relevant positions follows a regular pattern. The knowledge acquired about the regularity is usually conceived as implicit (and often referred to as phenomenally unconscious) if it can only be expressed in immediate task performance, that is, in a speed-up of the reaction times and/or a decrease in error rates when the regularity is present. Whether or not ``truly'' implicit learning can be observed with healthy adults is controversial (e.g. Buchner, Steens, & Rothkegel, 1998; Cohen & Curran, 1993; Perruchet & Amorim, 1992; Willingham, Greeley, & Bardone, 1993; Willingham, Nissen, & Bullemer, 1989). However, the fact that people suering form chronic or temporary amnesia are able to learn sequential patterns supports the conceptualisation of sequence learning as implicit (Knopman, 1991; Nissen & Bullemer, 1987; Nissen, Knopman, & Schacter, 1987; Nissen, Willingham, & Hartman, 1989). Recent evidence has suggested that the mechanisms underlying sequence learning may be very general and quite powerful. For instance, Schmidtke and Heuer (1997) concluded from their ®ndings that implicit sequence learning could be characterised as a basic and nonselective learning of all potentially relevant relations between a person's actions and the stimuli in the environment. Their participants responded to systematic sequences of visual events with manual key presses. They also reacted to systematic sequences of auditory events interspersed between the manual responses and the subsequent visual signals by pressing, or preventing to
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press, a foot pedal, depending on which of two dierent tones was presented. The data indicated that participants were able to learn the systematicities inherent in the visual and auditory sequences as an integrated visual-auditory sequence. Frensch and Miner (1995) presented data indicating that participants who respond to sequences of alternating letters and unpronounceable graphical symbols may also learn both kinds of sequences simultaneously. However, in this case the sequences of letters and of graphical symbols appeared to have been learned independently of each other. The authors assumed that the learning of the sequences of letters and graphical symbols involved a phonological and a visual working memory, respectively, as explicated in the working memory model of Baddeley and Hitch (1974). Along very similar lines, Mayr (1996) demonstrated that people can simultaneously learn two independent sequential patterns. One pattern consisted of a regular sequence of objects to which participants reacted with a discriminative response. Successions of locations at which these objects could appear constituted the other pattern. The two sequences were uncorrelated. Nevertheless, even participants classi®ed as unaware of the patterns learned both of them, and the acquisition of one of the sequences did not interfere with the acquisition of the other. Mayr (1996) interpreted his ®ndings as indicating that functionally distinct systems were involved in the learning of the spatial and object patterns ± a spatial orienting system and a system responsible for visual-motor co-ordination. Thus, although the speci®ed systems diered, the idea that two separate systems were involved in the simultaneous learning of the sequences runs parallel to Frensch and Miner's (1995) account. However, Mayr (1996) hinted that ``a very ¯exible, unitary sequence learning device with a capacity for multiple sequences'' (p. 356) could also explain his data. The experiments reported in this article were designed to explore this issue further. In particular, we wanted to submit the idea of a very ¯exible, unitary sequence learning mechanism to an empirical test by looking at whether two dierent regularities could be learned simultaneously not only when the systems involved were obviously distinct as in the case of Mayr (1996), Schmidtke and Heuer (1997), and Frensch and Miner (1995), but also when it could be assumed with some plausibility that largely the same processing mechanisms were involved in the learning of the two types of regularities. As an operationalisation of this latter goal, two different regularities were instantiated in the same event sequence and mapped onto the same type of response. Speci®cally, participants reacted to sequences of tones diering in pitch with a discriminative response. They knew that the pitch sequences were systematic. They were not told that a second systematicity was implemented in the same stream of events. The interval between a response to a tone and the onset of the subsequent tone (response-signal interval, RSI) also
varied according to a ®xed regularity. In Experiment 1, we tested the conditions under which the RSI systematicity could be learned parallel to the systematicity inherent in the tone sequence. The learning of the tone sequence itself was not assessed because the learning conditions for that sequence were identical to those in Experiment 2 of Buchner, Steens, Erdfelder, and Rothkegel (1997) in which successful learning of the particular tone sequence used here has already been demonstrated. Experiment 2 extended the results of Experiment 1 and investigated possible qualitative dierences between (a) the (potentially implicit) knowledge acquired in an incidental RSI sequence learning situation such as that of Experiment 1 and (b) the explicit sequence knowledge acquired in an intentional learning situation.
Experiment 1 As is typical of sequence learning experiments, the event systematicity was established by continuously repeating the same ten-element sequence. In two conditions, the tone and RSI sequences were uniquely related such that knowledge about one pattern was informative about the structure of the other pattern. In a third condition, the relation between the two sequences was not unique. Table 1 illustrates this point. In the RSI-pitch condition, each RSI uniquely predicted the pitch of the next tone, but a particular pitch could be followed by at least two dierent RSIs. In the pitch-RSI condition, the pitch of each tone uniquely predicted the next RSI, but a particular RSI could be followed by at least two tones diering in pitch. These two conditions are referred to as unidirectional-unique. Finally, in the (bidirectional) ambiguous condition, the pitch levels and RSIs did not uniquely predict each other, with the single exception of RSI level 2, which was always followed by a tone of pitch level 4. One possible outcome of Experiment 1 is that the amount of observable sequence learning does not dier between the RSI-pitch and pitch-RSI conditions on the one side and the ambiguous-relations condition on the other, despite the fact that there are important dierTable 1 Illustration of the ten-element RSI-pitch patterns used in Experiment 1. The numbers from 1 to 4 denote tones of dierent pitches in the tone pattern, and dierent distances between a reaction to a tone and the next tone (RSI) in the RSI pattern Condition
Sequence events
RSI-pitch Tone RSI
1
Pitch-RSI Tone RSI
1
1
1
2 2
2
2
3 3
3
3
1 1
1
1
4 4
4
4
2 2
2
2
1 1
1
1
3 3
3
3
4 4
4
4
3 3
3
3
Ambiguous relations Tone 1 2 3 1 4 2 1 3 4 3 RSI 3 4 3 1 2 3 1 4 2 1
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ences between these two types of sequences. In the RSI-pitch and pitch-RSI conditions, the pitch and RSI patterns can be `merged' into one homogeneous stream of events in which sequences of pitch-RSI `feature combinations' can be learned. In the ambiguous-relations condition, in contrast, the RSI systematicity cannot be learned as one aspect of a feature combination. Rather, it must be learned independently, if at all. Note that the temporal and pitch systematicities were instantiated in the same perceptual event (rather than in distinct phonological and graphical events as in Frensch & Miner, 1995) and involved the same response system (rather than attentional shifts and object decisions as in Mayr, 1996). The ®nding of equal learning in the two unidirectional-unique conditions on the one side and the bidirectional ambiguous condition on the other would, therefore, support the hypothesis that two independent regularities can be learned simultaneously even if they do not involve obviously distinct processing mechanisms (such as phonological and visual-spatial working memory structures as in Frensch & Miner, 1995, or attentional orienting and object identi®cation systems as in Mayr, 1996). In more general terms, this could be counted as evidence in favour of an unselective and powerful, unitary sequence learning mechanism. In contrast, if distinct cognitive mechanisms were indeed necessary for the simultaneous acquisition of sequential patterns, then the learning should be con®ned to the unidirectional-unique conditions. However, evidence for the learning can only be observable if the relationship between the RSI and pitch patterns in the unidirectional-unique conditions is picked up so that a repeating sequence of feature combinations can be learned. Therefore, a third possible outcome of this experiment is that if the covariation between an intentionally learned systematicity and a second systematicity cannot be picked up, then no learning of the RSI pattern will be observed.
With respect to tones and RSIs 1±4, the ten-trial sequence that were used can be characterised as 1-2-3-1-4-2-1-3-4-3. The three dierent experimental conditions were created by shifting the RSI sequence relative to the tone sequence, as described above (see Table 1 and related text). In the unidirectional-unique conditions, the highest tone thus coincided with the shortest RSI, the secondhighest tone coincided with the second-shortest RSI, and so forth. During two of the ten experimental blocks (i.e. Blocks 7 and 8), pseudo-random sequences of RSIs replaced the systematic RSIs. The pseudo-random sequences matched the systematic sequences with respect to the frequencies of the dierent RSIs. No RSI could occur twice in a row in a pseudo-random sequence. The tone sequence remained systematic throughout Blocks 7 and 8.
Method
Design
Participants
The dependent variables were the dierences in participants' reaction times and error frequencies between blocks with pseudo-random RSIs (Blocks 7 and 8) and the adjacent blocks with systematic RSIs (Blocks 6 and 9). The independent variable was the RSI-pitch relation which de®ned the three dierent conditions during the acquisition phase. An a priori power analysis suggested that, given a b .05 and the goal to detect ``large'' eects between the three experimental groups ( f 0.4, cf. Cohen, 1977), we needed N 102 participants.1 A multivariate approach was used for the within-subject comparisons. As a consequence, no MSE values are reported for within-subject variables with more than two levels. In our applications, all multivariate test criteria correspond to the same (exact) F statistic that is reported. In addition, the Pillai-Bartlett V is reported as a multivariate measure of eect size for statistically
Participants were 102 undergraduate students, 61 of whom were female. They were paid for participating. Their age ranged from 19 to 36 years (M 23). The students were tested individually and were assigned at random to one of the three experimental conditions with the restriction of about equal sample sizes in each of the three groups: the RSI-pitch group (n 35); the pitch-RSI group (n 34); and the ambiguous-relations group (n 33). Materials For the sequence learning task, the stimuli were brief computersynthesised tones displayed binaurally through stereo headphones which were plugged directly into a Macintosh AV computer. Four dierent tones and RSIs were used. Tones 1, 2, 3, and 4 were 30 ms sine tones with carrier frequencies of 300, 675, 1,552, and 3,565 Hz, respectively. Each tone was assigned to a unique key on the computer keyboard. RSIs 1, 2, 3, and 4 were 200 ms, 400 ms, 600 ms, and 800 ms, respectively.
Procedure All participants started with a training phase to become familiar with the tone-key mapping. Their task was to press the key corresponding to a particular tone as fast as possible but without making errors. The acquisition phase began after a pre-set learning criterion for the tone discrimination performance had been reached. For one trial in the acquisition phase, the sequence of events was as follows. During the second half of the interval de®ned by the RSI of a particular trial, a ®xation cross appeared in the centre of the computer screen. Next, the appropriate tone was presented. As soon as participants reacted to the tone by a keypress, the screen was cleared if the reaction was correct, or else an appropriate error message appeared. The duration of the interval during which a blank screen or an error message was presented was half of the RSI speci®ed for the subsequent trial. During the second half of this interval, the ®xation cross appeared again. If participants' reaction time was over 2,000 ms, they were informed that this reaction was too slow. All participants received ten blocks of 50 trials composed of ®ve repetitions of the ten-trial sequence of tones. In Blocks 1±6 and 9±10, the RSIs were also systematic, as described in Table 1. In Blocks 7 and 8, the systematic RSI sequences were replaced by pseudo-random sequences of RSIs in all conditions. For each block, a random position in the ten-trial tone sequence was selected as the starting position. After each block, participants received feedback about their error rates and their average reaction times during that block. From Block 2 on, the feedback also included the corresponding values from the preceding block. Participants initiated the next block of trials at their own discretion. After the experiment, all participants were oered an explanation as to the purpose of the experiment.
1
All power calculations reported in this paper were conducted using the GáPower program (Buchner, Faul, & Erdfelder, 1996; see Erdfelder, Faul, & Buchner, 1996).
74 signi®cant eects. In all other cases in which H0 had to be rejected, partial R2s (R2p ) are reported.
Results Figure 1 illustrates the reaction time and error data for the three experimental groups. All three groups improved over the ®rst six blocks, and the pattern of improvement appears very similar for all groups. A 3 ´ 6 MANOVA on participants' average reaction times with groups as between-subjects variable and blocks as within-subject variable con®rmed this impression in that there was only a signi®cant eect of blocks, F(5, 95) 41.23, V 0.68, but neither the condition main eect nor the interaction between both variables was signi®cant, both Fs < 1. The same result emerged for the error data, with a signi®cant eect of blocks, F(5, 95) 10.45, V 0.35, and no other signi®cant eects, both Fs < 1. Thus, no dierences emerged between the groups during training, so that the results based on the learning measure can be interpreted without further complication. Fig. 1 Reaction times and error frequencies in Experiment 1 as a function of the RSI-pitch relation which de®ned the three dierent conditions during the acquisition phase (see Table 1 for an illustration of the three conditions) (RSI responsesignal interval)
As a measure of the learning of the RSI systematicity, we computed the dierence in the mean reaction times between the blocks with pseudo-random RSIs (Blocks 7 and 8) and the adjacent blocks with systematic RSIs (Blocks 6 and 9). This dierence was 77, 62, and 2 ms for the RSI-pitch, the pitch-RSI, and the ambiguousrelations conditions, respectively. An ANOVA showed that the three groups diered on this measure, F(2, 99) 5.69, MSE 9,165.03, R2p .10. Planned orthogonal contrasts showed that the RSI-pitch and the pitch-RSI conditions did not dier from each other, t < 1, but that these two conditions diered from the ambiguous relations condition, t(99) 3.31, R2p .10. Separate t-tests showed that the learning eect was signi®cant for both the RSI-pitch and the pitch-RSI conditions, t(34) 5.13, R2p .44 and t(33) 3.49, R2p .27, respectively. The same dierences, but for the learning measure based on error scores, were 0.71, 0.62, and )1.94 errors for the RSI-pitch, the pitch-RSI, and the ambiguousrelations conditions, respectively. Thus, whereas the RSI-pitch and the pitch-RSI conditions showed an increase in error rates on the random relative to the
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systematic blocks, the ambiguous-relations condition exhibited an improvement. An ANOVA showed that the three groups diered on this measure, F(2, 99)=3.29, MSE=23.10, R2p .06. The RSI-pitch and the pitchRSI conditions did not dier from each other, t < 1, but the error dierence for these two conditions was signi®cantly larger than the error dierence for the ambiguous relations condition, t(99) 2.56, R2p .06. The latter condition was the only one in which the error rate dierence was signi®cantly dierent from zero, t(33) )2.08, R2p .12.
Discussion Experiment 1 showed that the RSI systematicity could be learned, and the knowledge of the systematicity was expressed in a typical performance measure, but that learning did not occur independently of the acquisition of the tone systematicity. Only the learning of the RSI systematicity in the bidirectional ambiguous condition would have indicated that two systematicities could be learned even though they were instantiated in the same event sequence and mapped onto the same type of response. Taken together with results of Frensch and Miner (1995) and of Mayr (1996), this result suggests that clearly distinct processing mechanisms must indeed be involved if two independent feature patterns are to be learned simultaneously. The present results are compatible with the hypothesis that participants were able to learn, and take advantage of, the RSI systematicity when it ran parallel to the pitch systematicity in the unidirectional-unique conditions. Interestingly, it did not appear to matter exactly how the RSI and tone systematicities were related as long as they were uniquely related so that a sequence of feature combinations could be learned. This is evident from the ®nding that performance did not dier signi®cantly between the RSI-pitch and the pitchRSI conditions. In other words, it was not important whether the RSI predicted the pitch of the subsequent tone, or vice versa. The present experiments do not address the issue of RSI systematicity learning per se. Therefore, one may speculate that an RSI sequence could perhaps not be learned even if it was the only systematicity present. However, this seems unlikely given data from an as-yetunpublished experiment from our laboratory in which intentionally or incidentally learning participants acquired an RSI sequence by basing their discriminative responses on the RSIs (the systematicity was instantiated in the same ten-trial sequence that was used here). The measure of learning was again the dierence in reaction times between sequences with systematic and sequences with random RSIs. This dierence was 58 ms and 69 ms for participants learning under incidental (N 26) and intentional (N 29) instructions. The learning eect was statistically signi®cant and actually quite large in terms of a standardised eect size measure,
F(1, 53) 63.46, MSE 1751.61, R2p .54, and did not dier as a function of the learning condition, F < 1. Thus, RSI systematicities can be learned if they constitute the only systematicity present. Given the result of Experiment 1 that the learning of RSI-pitch-contingencies was indeed possible, it seemed the next logical step to explore more closely the nature of that learning. More precisely, we were interested in the status of the acquired knowledge with respect to the implicit-explicit distinction which is at the heart of one of the most prominent debates in sequence learning research (e.g. Buchner et al., 1998; Perruchet & Amorim, 1992; Shanks & St. John, 1994; Willingham et al., 1989). We therefore thought it important to investigate, for one of the conditions of Experiment 1 in which the RSI systematicity (as part of pitch-RSI feature combinations) appeared to have been learned well, whether the RSI knowledge could be conceptualised as explicit in the sense that it could be used for controlled judgements about the sequences.
Experiment 2 To investigate the status of the knowledge acquired about the RSI component of the event systematicity, we used an adaptation of the process dissociation procedure and the measurement model suggested for that situation by Buchner et al. (1997). For brevity, we provide only a sketch of both the adapted procedure and the measurement model (the sequence identi®cation measurement model, SIMM). Details of the procedure as well as the SIMM and its empirical evaluation can be found in Buchner et al. (1997, 1998). In the adapted procedure, participants ®rst learn the systematicity as in a typical sequence learning experiment by reacting to a continuously repeating pattern of events. Subsequently, participants react to, and then judge, three dierent types of relatively short test sequences: (a) test sequences that follow the learned systematicity (the acquisition phase test sequences); (b) test sequences that are systematic but do not follow the learned systematicity (the new systematic test sequences); and (c) distractor sequences that do not follow a systematicity. Participants in the `inclusion condition' are to respond `yes' to all systematic sequences, and `no' to distractor sequences. In contrast, participants in the `exclusion condition' are to respond `yes' only to systematic sequences that do not follow the learned systematicity. In other words, they are to reject the acquisition phase test sequences whenever they can, that is, whenever they recollect the learned systematicity. The measurement model allows the observed judgements to the three types of test sequences in the inclusion and exclusion test conditions to be distinguised into six different component processes, two of which represent the memory processes that are of interest here. More precisely, the SIMM provides for parameters representing the `recollection of the systematicity' of which the
76
training sequence was composed (parameter c) and the accepting of a nonrecollected sequence as an instantiation of the training systematicity as a result of increased `perceptual or motor ¯uency' due to the prior processing of the sequence (parameter uc-). The four remaining model parameters represent the detection of systematicity in the absence of recollection (parameter s), the rejecting of distractor sequences (parameter d), and the guessing that a sequence requires a yes response in the absence of any other information about the sequence (parameters gi and ge in the inclusion and exclusion test conditions, respectively). The SIMM has been evaluated successfully in a series of experiments (cf. Buchner et al., 1997). For instance, it was shown that the recollection parameter c was higher in an intentional than in an incidental learning condition. Also, a manipulation of processing ¯uency, inspired by research on ``illusions of familiarity'' in the memory domain (e.g. Jacoby & Whitehouse, 1989), was accurately re¯ected in the model's parameter uc-, in that this parameter was higher for sequences which were processed more ¯uently, and lower for sequences which were processed less ¯uently. For the present purpose, these two parameters are of greatest importance because they represent memory processes. If the knowledge about the RSI component of the event systematicity is to be regarded as explicit, then parameter c should be above zero. Parameter uc- should be above zero to the degree to which the RSI knowledge contributes to the ¯uency with which sequences can be responded to. To obtain a `reference' group in which knowledge of the RSI component of the event systematicity would most likely be explicit, half of the participants were assigned to an `intentional learning condition'. These participants were informed about the presence of an RSI systematicity and encouraged to identify it. The other half of the participants did not receive this instruction. They were assigned to the `incidental learning condition', which is just the learning situation of Experiment 1. By comparing these two conditions one can assess possible qualitative dierences in the RSI knowledge acquired as a function of the learning instruction (a) in the traditional reaction-time based performance measure, and (b) in the memory measures of the SIMM. One problem in sequence learning tasks is that blocks of random trials among the blocks of systematic trials may distort the post-task assessment of the sequence knowledge, particularly if the blocks of random trials occur towards the end of the experiment (cf. Buchner et al., 1997). Therefore, the design of Experiment 2 comprised two dierent groups of participants within each of the learning conditions. In one group, blocks of pseudo-random event sequences were used, just as in Experiment 1, so that we could assess the reaction timebased performance measure. In the other group, the blocks of pseudo-random event sequences were replaced by a test phase in which participants judged event sequences as required by the adapted process dissociation procedure. From these responses we were able to esti-
mate the parameters representing the memory (and other) processes as conceptualised by the SIMM.
Method Participants Participants were 193 undergraduate students, 117 of whom were female. They were paid for participating. Their ages ranged from 19 to 39 years (M 23). The students were tested individually and were assigned at random to one of the four experimental conditions, with the restriction of roughly equal sample sizes in each of the four groups: the indirect test, intentional group (n 48); the indirect test, incidental group (n 47); the direct test, intentional group (n 49); and the direct test, incidental group (n 49). In the direct test, intentional group, 25 and 24 participants were in the inclusion and exclusion conditions, respectively; 24 and 25 of the participants in the direct test, incidental group were in the inclusion and exclusion conditions, respectively. Materials The tones and RSIs were identical to those used in Experiment 1. The relationship between the RSI and the tone patterns was identical to that of the RSI-pitch group in Experiment 1 (see Table 1). Two dierent six-trial repeating sequences were used. With respect to tones and pitches 1±4, Sequence 1 can be characterised as 1-3-42-3-2. Sequence 2 was derived from Sequence 1 by exchanging tones 1 and 2 as well as tones 3 and 4. Thus, Sequence 2 was 2-4-31-4-1. Half of the participants in each condition received Sequence 1 during the acquisition phase, the other half received Sequence 2. The test sequences for the direct test groups were composed as follows. The acquisition phase test sequences consisted of two repetitions of the sequence which participants had experienced during practice. The new systematic sequences consisted of Sequence 2 or Sequence 1, depending on whether Sequence 1 or Sequence 2 had been experienced during practice. The distractor test sequences were pseudo-random sequences that matched the acquisition phase test sequences with respect to the frequencies of the dierent RSIs. No RSI could occur twice in a row in a distractor sequence. Procedure The individual trials, the training phase, the task description, and the criterion for switching from training to the acquisition phase were all identical to the corresponding elements of Experiment 1. Participants in the two `indirect test' groups reacted to six blocks of systematic sequences, two blocks of sequences in which the RSIs were pseudo-random, and two more blocks of systematic sequences. The pseudo-random sequences matched the systematic sequences with respect to the frequencies of the dierent RSIs, and no RSI could occur twice in a row in a pseudo-random sequence. Participants in the indirect test, intentional learning condition, but not participants in the indirect test, incidental learning condition, were also told that the sequence of tones was not random, and that they could perform faster and with fewer errors if they identi®ed and memorised the repeating pattern. Participants in the two `direct test' groups reacted to six blocks of systematic sequences after which the test phase began. One of the groups received intentional, the other received incidental learning instructions. After the acquisition phase, one half of the participants in each learning condition were assigned to the inclusion test condition, and the other half to the exclusion test condition. All participants received a total of 27 test blocks of 12 trials each. Nine of these blocks were composed of the acquisition phase sequence, nine were composed of the new systematic sequence, and nine were distractor sequences. A random position was selected in each
77 sequence as the starting position. The 27 test blocks were presented in a random order. After the 12 trials in each test block, participants judged the sequences. In the inclusion test condition, participants were instructed to respond `yes' if they recognised the current sequence as representing the same RSI systematicity as the sequences in the acquisition phase, or if they felt that the test sequence conformed to some other RSI systematicity. They were told to reject a sequence and to respond `no' if the RSIs did not appear systematic but rather seemed random and chaotic. In contrast, only the new systematic RSI sequences required a yes response in the exclusion test condition. Test sequences which followed the acquisition-phase RSI systematicity were to be excluded. Participants were thus asked to respond yes if an RSI sequence seemed systematic, but only if the RSI systematicity was not the one underlying the acquisition phase sequences. Otherwise, they were asked to respond no. After the experiment, all participants were oered an explanation as to the purpose of the experiment. Design For the two indirect test groups, the dependent variables were the dierences in participants' reaction times and error frequencies between blocks with pseudo-random RSIs (Blocks 7 and 8) and the adjacent blocks with systematic RSIs (Blocks 6 and 9). For the direct test groups, dependent variables were the parameter esti-
Fig. 2 Reaction times and error frequencies in Experiment 2 as a function of learning and test conditions
mates derived from participants' recognition judgements during the test phase. The independent variables were (a) the test groups as de®ned in the previous paragraph, and (b) the learning instruction (intentional vs. incidental). Given a .05, the power to detect `large' eects ( f 0.4, cf. Cohen, 1977) between the intentional (n 48) and incidental learning conditions (n 47) in the reaction time performance measure was 1)b .97.
Results Figure 2 illustrates the reaction time and error data for the four experimental groups. All four groups improve over the ®rst six blocks, and the pattern of improvement appears similar for all groups. A 2 ´ 2 ´ 6 MANOVA on participants' average reaction times with test and learning conditions as between-subjects variables and blocks as within-subject variable con®rmed this impression in that there was only a signi®cant eect of blocks, F(5, 185) 122.97, V 0.77. No other main eect or interaction was signi®cant, all Fs(1, 189) < 2.15 and all Fs(5, 185) < 1.10. A similar result emerged for the error data, with a signi®cant eect of blocks,
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F(5, 185) 26.83, V 0.42, and an eect of test condition, F(1, 189) 4.46, MSE 72.94, R2p .10. No other main eect or interaction between both variables was statistically signi®cant, all Fs(1, 189) < 0.64 and all Fs(5, 185) < 1.62. Participants in the direct test conditions made more errors during training. However, the improvement during training of participants in the two test conditions was equivalent, which is implied by the lack of an interaction between the test condition and the blocks variable (the improvement for the indirect and the direct test conditions was 237 ms/4.27 errors and 232 ms/4.64 errors, respectively). Therefore, the dierences in the absolute error levels do not complicate the analysis of the learning measure. As in Experiment 1, we ®rst computed, for all participants in the indirect test groups, the dierence in the mean reaction times between the blocks with pseudorandom RSIs (Blocks 7 and 8) and the adjacent blocks with systematic RSIs (Blocks 6 and 9). This dierence was 68 ms and 112 ms for the intentional and the incidental learning conditions, respectively. The dierence between these conditions just missed statistical signi®cance [t(93) 1.83, p > 0.07]. Still, it appears somewhat surprising that this measure should indicate more learning in the incidental than in the intentional learning condition. However, inspection of Fig. 1 shows that the dierence between groups in this measure is primarily due to the fact that participants in the incidental learning condition responded considerably faster than participants in the intentional learning condition as soon as the RSI systematicity was re-established in Block 9. One reason for this may be that participants who intentionally searched for the RSI systematicity were led Fig. 3 Estimates of the SIMM parameters representing controlled (c), uncontrolled (uc-), systematicity detection (s), distractor rejection (d), and guessing processes (gi and ge for the inclusion and exclusion conditions, respectively) as a function of the learning conditions in Experiment 2. The error bars represent the 95% con®dence intervals (SIMM sequence identi®cation measurement model)
to induce incorrect rules in Blocks 7 and 8, which made it more dicult for these participants to re-adjust to the situation in which the RSIs followed the old systematicity again (cf. Reber, 1989, for related ®ndings from the area of implicit grammar learning). This in itself appears to be an interesting ®nding, but its most important implication in the present context is that we need to supplement our analysis with a measure of learning that avoids measuring adjustment speed by taking into account only the dierence in performance between Blocks 6 and 7. The reaction time dierence between these blocks was 47 ms and 42 ms for the intentional and the incidental learning conditions, respectively. The two groups did not dier signi®cantly from each other, t < 1. The same analysis, but for the error scores, also showed no group dierence, t(93) 1.43. Thus, we may conclude that intentional and incidental learning conditions could not be distinguished on the basis of the indirect performance measure. Given that the indirect performance measure is `blind' with respect to the dierences between the intentional and incidental learning conditions, would the learning instruction dierences be re¯ected in the memory measures of the SIMM? Figure 3 illustrates the estimates of the parameters representing the memory and judgement processes as speci®ed by the SIMM. Parameters representing memory processes (i.e. parameter c representing sequence recollection and parameter uc- representing perceptual or motor ¯uency due to the prior processing of the sequence) are of importance here. If the knowledge about the RSI component of the event systematicity is to be regarded as explicit, then param-
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eter c should be above zero. In contrast, if the RSI knowledge contributed to the ¯uency with which sequences can be responded to, then parameter ucshould be above zero. We ®rst ®tted the SIMM to the data with the restriction that c(intentional) 0. This restricted model had to be rejected, G2(1) 13.66.2 In contrast, the model with the restriction that c(incidental) 0 did not have to be rejected, G2(1) 0.65. Thus, we may conclude that participants in the intentional, but not participants in the incidental learning conditions, had acquired explicit knowledge about the RSI component of the event systematicity. Further, the model with the restriction that uc)(intentional) 0 did not have to be rejected, G2(1) 1.14, whereas the model with the restriction that uc)(incidental) 0 was incompatible with the data, G2(1) 4.74, and therefore had to be rejected. We conclude from these results that participants in the incidental, but not participants in the intentional learning conditions, had acquired RSI knowledge that in¯uenced their ¯uencybased judgements.
Discussion The purpose of Experiment 2 was to investigate, for one of the conditions in which the RSI component of the event systematicity was learned in Experiment 1, whether the RSI knowledge could be conceptualised as explicit in the sense that it could be used for controlled judgements about the sequences. The present data suggest that explicit knowledge was acquired only if participants were informed of the presence of, and instructed to identify, the systematicity in the RSIs. In contrast, when participants learned about the RSIs incidentally, no such explicit knowledge was acquired, although participants obviously acquired RSI knowledge. However, the knowledge seems to have been tacit in the sense that it was expressed only in ¯uency-based processes in¯uencing the sequence judgements. Thus, participants in the intentional and incidental learning conditions appeared to have acquired qualitatively dierent knowledge about the RSI component of the event systematicity, at least with respect to how this knowledge could be used to make judgements about the test sequences. This stands in contrast to the indirect performance measure according to which the two groups 2
The log-likelihood goodness-of-®t statistic G2 is asymptotically chi-square distributed with degrees of freedom indicated in parentheses (see Hu & Batchelder, 1994, for details). All model-based statistical analyses reported in this article were conducted using the AppleTree program by Rothkegel (1996), an implementation of the algorithms described by Hu and Batchelder (1994) for the Apple Macintosh. Note that the model parameters have to be within the open interval (0, 1) so that, strictly speaking, they cannot be set equal to zero. The model tests were therefore performed by setting the parameters equal to 0.0001.
of participants could not be distinguished: Both groups showed equivalent performance decrements when the RSI systematicity was lacking.
General discussion The experiments presented here were designed to explore the generality of the mechanisms underlying sequence learning. Following up on the work of Frensch and Miner (1995), Mayr (1996), and Schmidtke and Heuer (1997), we explored whether two dierent regularities can be learned even when they do not involve clearly distinct processing mechanisms (dierent working memory structures, object identi®cation and attentional orienting systems, etc.). The present results suggest that this is not the case: Unrelated sequences of RSIs and of tones diering in pitch could not be learned when they were instantiated in the same event sequence and mapped onto the same type of response. This con®rms the interpretations of Frensch and Miner (1995) and of Mayr (1996) that clearly distinct cognitive mechanisms must be involved if independent learning of more than one systematicity is to be observed. Parallel results have been reported by Shin and Ivry (1999). They found that RSI sequences were learned only in a condition with a highly predictive relationship between the spatial locations of visual events and the subsequent RSIs. It is worth noting that in their study, the learning of both the spatial and the RSI sequence was incidental, whereas in our experiments the learning of the pitch sequence was intentional and only the learning of the RSI sequence was incidental. Thus, learning of the RSI sequence does not seem to vary as a function of the ``intentionality level'' associated with the learning of the other sequence. RSI knowledge was acquired when it was systematically related to the pitch pattern, so that the pitch and RSI systematicities could be merged into one homogeneous stream of RSI-pitch combinations, the regularity of which could be learned. In a relatively narrow sense, then, the sequence learning mechanism appears powerful in that it can exploit uniquely predictive relationships independently of whether the (attended) pitch sequence predicts the (unattended) RSI sequence or whether the reverse is true. Experiment 1 helped to identify the conditions under which RSI knowledge can be acquired. Based on those results, Experiment 2 was designed to analyse the status of the acquired knowledge with respect to the implicitexplicit distinction. We found that implicit and explicit learning conditions may lead to qualitatively dierent types of knowledge of the RSI component of the event systematicity. This qualitative dierence would have gone unnoticed had we only relied on measuring the ``amount'' of learning as re¯ected in the traditional indirect performance measure. A model-based analysis of participants' sequence knowledge revealed explicit
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RSI knowledge as a consequence of intentional learning, but no evidence of explicit RSI knowledge in a group of participants learning under incidental conditions, that is, under conditions that were also present in Experiment 1. In contrast, incidental, but not intentional, learners acquired knowledge that seems to have been tacit in that it could only be used to discriminate between sequences on the bases of ``processing ¯uency''. The important point is that dierent learning situations may well induce the learning of dierent types of (in the present case: RSI) knowledge, but whether or not such qualitative dierences are revealed depends on whether or not a suitable measure of sequence knowledge is available. Acknowledgements We would like to thank JoÈrg Kunz for programming the experiments, and Christina Baum, GuÈnter Ebersberger, Julia Hensen, Tanja Jagberger, Gabriele Klein, JoÈrg Kunz, and Inge Maurer for their assistance with data collection. The research reported in this article was supported by a grant from the Deutsche Forschungsgemeinschaft to Axel Buchner (Bu 945/1-2).
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