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Sequence learning
Clegg et al. – Sequence learning
Review
Sequence learning Benjamin A. Clegg, Gregory J. DiGirolamo and Steven W. Keele The ability to sequence information is fundamental to human performance. When subjects are asked to respond to one of several possible spatial locations of a stimulus, reaction times and error rates decrease when the target follows a sequence. In this article, we review the numerous theoretical and methodological perspectives that have been used to study sequence learning. The opportunity now exists to integrate evidence from different domains of cognitive science to begin to provide a comprehensive account of sequence learning. We suggest that subjects can learn sequences based on different information in a hierarchical representation, including either sequences of stimuli or sequences of responses. This learning can occur both with and without explicit awareness of the sequence. Multiple modes of learning exist and are subserved by different neural circuits.
I
nterest in the ability to learn and use sequences for processing information and actions is by no means a recent phenomenon1. Following the seminal study of Nissen and Bullemer2, there has been an explosion of interest in sequence learning. While there are a variety of motivations for experimentation in sequence learning; and in particular the use of the serial reaction-time task (SRT) (see Box 1), three main reasons characterize much of this interest: (1) Sequencing of information and actions is a fundamental human ability We use sequences of information or sequences of actions in a variety of everyday tasks: from sequencing sounds in speech, to sequencing movements in typing or playing instruments, to sequencing actions in driving an automobile. (2) Sequence learning is an easily studied example of skill acquisition SRT learning is comparatively simple to implement and manipulate, and improvement occurs over a relatively short time. Reaction times and error rates readily and objectively assess improvement. Sequence learning has been studied in diverse populations, from neuropsychological patients to infants3. Further, the potential exists to study sequence learning in animals. (3) Sequence learning may be a complex form of implicit learning If sequence learning is accepted as sometimes occurring without awareness (see below), then it provides an intriguing example of nonconscious learning of a complex cognitive task. With the development of a simple paradigm for studying skill acquisition, it is hardly surprising that sequence learning has become a productive domain. However, variations in methodology make comparisons between studies difficult. For example, simple parametric manipulations such as varying the response-to-stimulus interval (RSI) can influence the amount of learning4.
Initially we will address separately the major questions that the field has revolved around. Ultimately we attempt to point out how an integrated approach (combining empirical analysis, cognitive theory and other converging evidence) is necessary to resolve some of the discrepancies in the literature. Representation of sequences What is being represented in sequence learning? One fundamental question concerns the very nature of the sequential representation. When we learn sequences, what type of information is actually being learned? The SRT paradigm normally involves both sequences of stimuli as well as the corresponding sequences of responses. The benefits in reaction time and error rate may be associated with the sequencing of stimuli, the sequencing of responses, or the sequencing of intermediate representations. One method used to address what information is being represented is to employ a transfer of learning paradigm. Cohen et al.5, for example, examined the transfer of sequence knowledge following changes in the response. Transfer of previous learning was observed when responding changed from using three fingers to a single finger and corresponding arm movements. The representation of the sequence cannot therefore be tied to any particular effector, which echoes the results of effector-independent production of sequences required for writing6. Furthermore, Keele et al.7 showed that in a more extreme type of response manipulation (from manual to verbal) transfer still occurs, but was now incomplete. This data suggest that sequence learning cannot be entirely stimulus-based as some learning is lost in the change from manual to verbal responses despite stimulus information remaining constant. It also suggests the learning is not entirely response-based since some transfer does occur over extreme response changes. The results, however, do not rule out the possibility that manual and verbal responses share some common, very abstract representation.
Copyright © 1998, Elsevier Science Ltd. All rights reserved. 1364-6613/98/$19.00
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B.A. Clegg and S.W. Keele are at the Department of Psychology, University of Oregon, Eugene, OR 974031227, USA. G.J. DiGirolamo is at the Center for the Cognitive Neuroscience of Attention and the Department of Psychology, University of Oregon, Eugene, OR 974031227, USA. tel: +1 541 346 1985 fax: +1 541 346 4911 e-mail: benc@oregon. uoregon.edu
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Box 1. The serial reaction-time paradigm
Reference a Nissen, M.J. and Bullemer, P. (1987) Attentional requirements of learning: evidence from performance measures Cognit. Psychol. 19, 1–32
Willingham8 has recently provided evidence for purely response-based learning. Subjects initially responded to spatial locations using an incompatible mapping of response keys. When subsequently switched to a compatible mapping, sequence learning transferred to the new condition, but only when the order of the key presses remained the same. This transfer occurred despite the fact that the stimulus sequence changed. Willingham argues that this result, combined with the effector-independence evidence, implies that the representation of sequences may relate to the egocentric representation of the key response locations. This account, however, does not explain the manual to verbal transfer results described previously. In contrast, a study by Mayr9 seems to show that sequence learning is possible independent of response. Mayr had subjects respond to four shapes that occurred in one of four locations. Both shapes and the locations were presented with co-occurring but independent sequences. Sequence learning was found for both the locations and the objects. Since the locations did not require a key response, this can be interpreted as ‘pure’ stimulus-based spatial learning. However, this interpretation might be questioned since either eye movements, or the orienting of attention, may play the role of a response under these conditions. Other experiments have found some evidence for stimulus-based learning following observation only in which no response is required10. The studies discussed above display part of the problem of determining the nature of the representation in sequence learning. The learning of spatial information and the abstract effector-independent learning can be interpreted as being either stimulus or response-related in nature. Overall the evidence strongly suggests that the representations of sequences involve response-based information, and provides support for some learning of stimulus-based information. Indeed, researchers have found evidence for both forms of learning within single studies11. However, stimulus-based
400 Random
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Nissen and Bullemera developed a simple paradigm that forms the basis of much of the research on sequence learning. In this task, participants responded to a stimulus (an asterisk) occurring at one of four locations with a key located directly below each position. Following a 500 ms response-to-stimulus interval, the next stimulus occurred. The basic design was thus a four-choice, compatible response mapping, serial reaction task. Although not informed of it, some participants were responding to an asterisk moving in a regular, repeating pattern of positions, while others responded to a random order of locations. If the positions are designated from left to right as the letters A through D, then Nissen and Bullemer’s subjects in the ‘sequence’ condition experienced a recurring loop of D-B-C-A-C-B-D-C-B-A for ten successive repetitions through this ten-item sequence per block. Subjects in the repeating sequence condition had faster reaction times and made fewer errors than those responding to random information (see Fig.).
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Block Fig. Comparison of reaction-time results for randomly presented and sequential visual stimuli. Nissen and Bullemer’s experiment 1 results (Ref. a) show additional improvement in performance over time when locations of stimuli follow a sequence. (Adapted, with permission, from Ref. a.)
learning is still subject to some debate8. It is, nevertheless, plausible that there are two (or more) distinct forms of representation, perhaps utilized differentially depending on the nature and complexity of the sequences and task. We believe the theoretical and empirical contradictions illustrate that a dichotomy of stimulus- versus responsebased representation is overly simplistic. Instead, we might consider a representation containing multiple layers of information, with some layers corresponding more closely to stimulus or response properties. In this view, a representation would include information starting from an abstract level such as overall goals or plans (which would be neither stimulus- nor response-based). Intermediate levels would specify the types of action required (perhaps specified in terms of a target location) independent of the effectors, or specify the stimulus type independent of its exact identity. Ultimately, highly specific information related to the exact stimulus and execution of the final motor output would be represented. Learning may be acquired via the sequencing of the representation at any of these levels. Such a view might gain indirect support from work in non-SRT sequencing paradigms; for example, the explicit sequencing of finger taps. There is increasing evidence that suggests that these sequential representations are organized hierarchically12 (for a review, see Rosenbaum13). One theoretical resolution of the disparate representations postulated in sequence learning would be a hierarchical system representation for multiple levels of information (such as MacKay’s theory14). Practice then has the ability to influence different representations at different levels of the hierarchy; hence, learning could occur for different types of information (see Keele et al.15). Implicit vs. explicit learning and the role of awareness Does sequence learning occur implicitly? Can we learn how to sequence information without conscious awareness of the sequential relationship of the elements?
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Initial evidence seemed to suggest we could. In Nissen and Bullemer’s original study2, subjects demonstrated sequence learning in the absence of conscious awareness of a sequence. Indeed the study also found preserved learning in amnesic patients despite their lack of awareness of the sequence. These findings rapidly led to the conclusion that sequence learning can occur implicitly16. Such conclusions link sequence learning to a parallel debate in the memory literature on whether implicit/explicit memory reflects separate memory systems17,18 or separate processes19. Within the sequence learning literature, the debate has focused around the necessity of conscious knowledge in sequence learning, and the methods for assessing the presence/ absence of explicit knowledge20–23. While awareness is generally agreed to have some unique contribution24, the level of awareness has in some circumstances been found to be irrelevant to performance25. Such results suggest that awareness itself cannot be the only, or even the primary, avenue of learning. It seems likely that there is some degree of sequential knowledge available only for performance but not explicit awareness (see Goschke26 for a more complete discussion). On a more practical level, regardless of arguments about the suitability of various criteria for assessing explicit knowledge, the SRT paradigm remains a powerful research tool. At the very least employing incidental learning allows examination of sequencing that need not be inherently tied to any explicit strategies. In our view, explicit knowledge is not necessarily an inherent part of learning sequenced information. Rather, awareness may arise from the interaction of the sequence learning systems with other cognitive systems that then produce conscious knowledge of the sequence. As we will discuss below, we believe this sequence learning issue can be resolved through an integrated approach, combining behavioral data with neuroimaging data. Multiple systems for sequence learning Are there multiple forms of sequence learning? Is there more than one way to learn sequences? Are the processes that underlie sequence learning the same regardless of the conditions under which we learn? Much work suggests there is more than one way to learn sequences. Pursuing Nissen and Bullemer’s original finding2 that an interleaved tone-counting task disrupted sequence learning, Curran and Keele25 examined sequence learning under dual-task conditions. In the single-task condition, they found that differences in performance between subjects were related to their awareness of the sequence. These differences disappeared when a secondary task was added. Based upon this finding and other data, Curran and Keele suggested that separate attentional and non-attentional forms of sequence learning exist. There are, however, other accounts for the data in dualtask sequence learning. One claim is that dual-task learning merely reflects the diminished effects of explicit knowledge on performance27 (but see Curran and Keele). Other evidence suggests that tone-counting in the dual-task experiments may not relate to attention, but instead interfere with the temporal organization of the sequence owing to the resulting changes in the interval between primary task signals with the addition of the tones28.
Review
A more recent account suggests that dual-task conditions may simply be a special case of information integration. As well as using random tones interleaved with the sequenced visual signals, Schmidtke and Heuer29 had subjects respond to tones that also occurred in sequential order. They concluded that learning was disrupted in the original dual-task studies because of the occurrence of random items (tones) in the midst of sequential ones (locations). In this view, the sequence learning system attempts to extract sequences from any information present in the environment. Keele et al.30 have extended these findings to propose two independent learning systems. One system attempts to integrate all sequential information regardless of the dimensions or modalities of the information (and is therefore hampered by the presence of random information). A second system attempts to discover sequences of information within single dimensions regardless of intervening items from other modalities. Again we believe that resolution of the issues of whether multiple systems for learning sequence exist cannot be accomplished in isolation from evidence from other domains. As we will discuss in the next section, neuroimaging data very strongly support a multiple systems account. Converging evidence: other methodologies What do neural network models suggest about the potential mechanisms involved in sequencing? And what are the neural areas that subserve sequencing learning? Sequence learning has provided a natural domain for investigating the computations and neural structures involved in skill acquisition. As we have already suggested, we believe such converging evidence is essential for ultimately resolving the issues in this field. Computational models Neural network modeling has augmented our understanding of sequence learning. One broad class of models employs a simple recurrent network (SRN) based on the initial work of Elman31 (see Box 2). In SRN models, the network develops its own representation of temporal context using the association with the previous item to constrain which item follows the current one. These models have been extensively applied to sequence learning32. A related approach, based on Jordan’s model of serial order33, uses ‘plan’ units which maintain a fixed representation of the whole sequence (providing an overall context), while ‘state’ units vary with progression through the sequence using internal feedback from the output units both to preserve temporal context and to advance to the next item34. The computational approach has made two important contributions to our understanding of the nature of sequence learning. Both models provide illustrations of how acquired forms of context information can provide a basis for higherorder information. Sequencing can be achieved even when the association between two elements is insufficient to resolve the ambiguity of the same elements occurring in different orders in different parts of the sequence. The second major contribution of these computational analyses is to draw attention to potential mechanisms involved in learning. The models suggest two different mechanisms for
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Box 2. Simple recurrent network Neural network models, many derived from Elman’s simple recurrent networka (SRN), have contributed to a number of issues surrounding sequencing learning. The SRN uses an architecture which allows the network to predict the next element within a sequence based upon both the current element, and also the network’s own representation of the temporal context of the current element (see Fig.). This context is derived from a recurrent set of connections linking the network’s hidden units back to the input units. During training the network develops associations between items. The recurrent connections then enable contextual information to differentiate the parts of the sequence. Since the recurrent information contains details of the preceding item, the network moves beyond simple association to a current item, and includes both the current item and the preceding item in predicting the next item. Knowing the current and preceding items allows further unique predictions. For a more complete
discussion of the representation derived within SRN models see Cleeremansb. References a Elman, J.L. (1990) Finding structure in time Cognit. Sci. 14, 179–211 b Cleeremans,
Neuropsychology The most striking result from neuroimaging studies36–40 is the clear implication of multiple networks capable of learning sequences. Overall the results are very consistent with the complexity of systems revealed in behavioral and computational analyses. In a variety of studies, results typically show activation of non-overlapping brain areas between different sequence conditions (single versus dualtask, see Fig. 1; implicit versus explicit; aware versus unaware). Physically distinct brain networks appear to be activated when subjects become consciously aware of the sequence. These networks are not seen when subjects are unaware36, and thus support the claim that awareness is not an essential part of sequence learning. In one study38 only a single area of activation was common to both implicit and explicit conditions: the right thalamus which may reflect enhancement of the sequenced information41. As awareness emerges, additional areas of the brain become active; including, dorsal lateral prefrontal, medial frontal, and more dorsal posterior regions36. Such results would be consistent with awareness leading to the use of working memory42 to represent the sequence.
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For a variety of reasons, the interpretation of the neuroimaging studies is not straightforward43. However, while there are differences in the neural areas that are active across studies, a few common findings do apply. Under ‘implicit’ conditions there is usually activation of areas commonly associated with motor control, including motor cortex and subcortical structures in the basal ganglia (see Curran43 for a more complete discussion). The neuroimaging data might be regarded as implicating a response-based representation of sequences. But motor cortex activation may reflect sequence learning at the response level, or response preparation contingent on learning within other neural areas. As we develop a more detailed understanding of the functional properties of brain areas, we should be wary about attaching simple labels to individual brain regions (such as regarding the basal ganglia or cerebellum as ‘motor’ areas44, 45). (Comparison of some of these findings to neuroimaging data from other types of sequential behavior are shown in Box 3.) The neuroimaging data does help clarify several issues. For example, the combined evidence from the findings of behavioral and imaging studies of single versus dual-task, clearly implicates multiple systems for sequence learning. A recent neural network model46 of Curran and Keele’s findings25 of transfer in single and dual-task conditions posits a single mechanism of learning. This result would fit well with the notion of disruption of temporal organization in dual-task learning28. However, the neuroimaging results36 strongly implicate separate neural pathways for single- and dual-task sequence learning (Fig. 1) which suggests that a single mechanism explanation, however parsimonious, might be insufficient. Converging evidence can also be provided by studies of patient populations. As mentioned above, imaging data have shown recruitment of additional areas when awareness emerges. The idea that awareness may be independent of sequence learning gains further supports from evidence of intact
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Next element
Fig. Basic architecture for a simple recurrent network model of sequence learning. (See text for details.) (Adapted, with permission, from Ref. 35.)
learning, since each model structures the sequenced information differently. Results from the SRN models of Cleeremans and colleagues35 suggest that the basis of sequence learning is a form of statistical relationships between items. The strength of these models is that in addition to applying to learning fixed sequences, they also generalize to probabilistic sequences, such as those used in artificial grammar learning27. For the Jordan model, plan units might be considered superordinate levels of a hierarchical structure maintaining fixed activation for a set of successive subordinate elements. Thus the mechanism involved would lead to the chunking of items, forming independent groups of sub-units within the larger overall structure of the sequence.
A.
Connectionist Models of Sequence Processing, MIT Press
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Clegg et al. – Sequence learning
Review
sequence learning in amnesic patients with 80 80 hippocampal damage47. However, some of Lateral view this evidence has demonstrated possible Left Right deficits in the learning of higher-order associations for these patients. Sequence 80 –120 –120 80 learning impairments have also been observed in patients with striatum dysfunction associated with Huntington’s or Parkinson’s diseases48,49, although other studies have shown little deficits50. Dual task –60 –60 80 80 Future studies in patient populations Single task offer the possibility of clarifying whether multiple forms of representation exist in sequence learning. Evidence elsewhere does 80 –120 –120 80 suggest that there are multiple forms of rep10 resentation even within implicit learning . A study by Heindel and colleagues51 demLeft Right onstrated a double dissociation between Medial view Huntington’s patients and Alzheimer’s –60 –60 patients in implicit motor learning and Fig. 1 Neuroimaging evidence for multiple systems in sequence learning. Monotonic increases in regional implicit perceptual learning. This dissocicerebral blood flow (rCBF) for sequence learning under single task (areas indicated by filled circles) and dual-task (indicated by filled triangles) conditions suggest discrete brain networks. (Adapted from data in Ref. 36.) ation suggests that implicit information can affect performance at multiple levels, and that these representations are subaddressed in the literature, we do not offer simple answers served by different underlying neural structures. Consistent and many individual issues remain partially unresolved. with this view, implicit learning in sequencing information With regard to what is being represented in sequence could be based on different levels of the hierarchical structure learning, we feel the evidence suggests that a stimulus or reof representation that we proposed earlier. Moreover, Heindel sponse-based dichotomy proves insufficient. Instead repreet al. also demonstrated that deficits in the motor learning sentations are fairly complex, involving information at multask were correlated with the amount of dementia in these tiple levels of a hierarchy subserved by physically distinct patients and not the level of primary motor dysfunction. neural areas. Further, although the combined evidence These results suggest that the representation that underlies points to multiple systems for sequence learning, the exact the impairment in sequence learning following striatum characterization of the distinctions between these systems damage would be non-motoric, and may indeed correspond remains unclear. The role of awareness, often related to the to a higher level of the hierarchy than the response. issue of multiple systems in sequence learning, might prove to be orthogonal to the question of multiple systems. Conclusions Awareness might not be a manifest property of any of the Though we have attempted to divide the research on sesequence learning systems that initially develop sequential quence learning into the general questions that have been
Box 3. Imaging data: sequence learning compared to intentional sequencing Explicit sequential behavior, such as finger tapping tasks, has been studied using neuroimaging techniquesa–c. We can compare the results of these studies to neuroimaging studies under SRT-type conditions. One finding common to both kinds of studies relates to changes in the activation of motor cortex depending on the amount of practice. Initial practice of sequential behavior is accompanied by increased blood flow in areas related to responding. After extensive practice, these motor areas decrease their activationd,e. This result is reminiscent of brain changes that occur with practice in other motorf, and even, non-motor tasksg. One potential area for research might thus concern differences underlying the production of sequences during initial practice versus their long-term performance.
Organization and Locus of Change (Squire, L.R. and Weinberger, N.M., eds), pp. 95–113, Oxford University Press b Jenkins, I.H. et al. (1994) Motor sequence learning: a study with positron emission tomography J. Neurosci. 14, 3775–3790 c Karni, A. et al. (1995) Functional MRI evidence for adult motor cortex plasticity during motor skill learning Nature 377, 155–158 d Pascual-Leone, A., Grafman, J. and Hallett, M. (1994) Modulation of cortical motor output maps during development of implicit and explicit knowledge Science 263, 1287–1289 e Karni, A. et al. (1998) The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex Proc. Natl. Acad. Sci. U. S. A. 95, 861–868 f Petersen, S. et al. (1998) The effects of practice on the functional anatomy of task performance Proc. Natl. Acad. Sci. U. S. A. 95, 853–860
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perceptual Mem. Cognit. (in press)
Outstanding questions
9 Mayr, U. (1996) Spatial attention and implicit sequence learning: evidence for independent learning of spatial and nonspatial
• As with explicit sequences, are the representations of implicitly learned sequences hierarchically organized? • What is the best way to characterize the function of the cognitive and neural systems involved in sequence learning? • Is awareness an inherent property of any of the multiple sequence learning systems? • Are there differences in the way sequential knowledge is organized during initial practice compared to later performance? • Do the findings from this simplified, yet hopefully, prototypical task apply for sequencing information in more complex, real world situations?
sequences J. Exp. Psychol. Learn. Mem. Cognit. 22, 350–364 10 Seger, C.A. (1998) Multiple forms of implicit learning, in Handbook of Implicit Learning (Stadler, M.A. and Frensch, P., eds), pp. 295–320, Sage Publications 11 Fendrich, D.W., Healy, A.F. and Bourne, L.E., Jr (1991) Long-term repetition effects for motoric and perceptual procedures J. Exp. Psychol. Learn. Mem. Cognit. 17, 137–151 12 Povel, D.J. and Collard, R. (1982) Structural factors in patterned finger tapping Acta Psychol. 52, 107–123 13 Rosenbaum, D.A. (1991) Human Motor Control, Academic Press 14 MacKay, D.G. (1982) The problem of flexibility and fluency in skilled behavior Psychol. Rev. 89, 483–506
knowledge. Rather, the interaction of these systems with other neural areas could cause the emergence of explicit knowledge and explicit strategies. The absence of clear answers does not reflect some deficiency in the research itself. We believe that the resolution of discrepancies in the sequence learning literature is tractable through a cognitive science theory that includes the representation, the mechanism, and the functional anatomy. By taking an integrative perspective, we can now construct and greatly constrain theories of sequence learning. Furthermore, as we have attempted to illustrate, there is work from outside of the field of sequence learning that can greatly inform theories of sequence learning. We believe the challenge for researchers is now to devise unified accounts of sequence learning that meet all the constraints from the different domains of cognitive science.
15 Keele, S.W., Cohen, A. and Ivry, R. (1990) Motor programs: concepts and issues, in Attention and Performance: (Vol. XIII) Motor Representation and Control (Jeannerod, M., ed.), pp. 77–110, Erlbaum 16 Reed, J. and Johnson, P. (1994) Assessing implicit learning with indirect tests: determining what is learned about sequence structure J. Exp. Psychol. Learn. Mem. Cognit. 20, 584–594 17 Schacter, D.L. (1992) Understanding implicit memory: a cognitive neuroscience perspective Am. Psychol. 47, 559–569 18 Cohen, N.J. and Eichenbaum, H. (1994) Memory, Amnesia, and the Hippocampal System, MIT Press 19 Roediger, H.L. (1990) Implicit memory: retention without remembering Am. Psychol. 54, 1043–1056 20 Perruchet, P. and Amorim, M.A. (1992) Conscious knowledge and changes in performance in sequence learning: evidence against dissociation J. Exp. Psychol. Learn. Mem. Cognit. 18, 785–800 21 Shanks, D.R. and St John, M.F. (1994) Characteristics of dissociable human learning systems Brain Behav. Sci. 17, 367–447 22 Cohen, A. and Curran, T. (1993) On tasks, knowledge, correlations, and dissociations: comment on Perruchet and Amorim J. Exp. Psychol. Learn. Mem. Cognit. 19, 1431–1437 23 Willingham,
D.B.,
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and
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Dissociation in a serial response time task using a recognition measure: Acknowledgements
comment on Perruchet and Amorim (1992) J. Exp. Psychol. Learn.
We would like to thank David Rosenbaum, Ulrich Mayr, and an
Mem. Cognit. 19, 1424–1430
anonymous reviewer for their insightful comments on the manuscript. Michael Posner provided helpful ideas on an earlier version. This review was made possible by support to Ben Clegg from a University of Oregon fellowship from the Interval Research Corporation; by support to
24 Willingham, D.B., Nissen, M.J. and Bullemer, P. (1989) On the development of procedural knowledge J. Exp. Psychol. Learn. Mem. Cognit. 15, 1047–1060 25 Curran, T. and Keele, S.W. (1993) Attentional and nonattentional
Gregg DiGirolamo through funding of the Center for the Cognitive Neuroscience of Attention from the James S. McDonnell Foundation and Pew Memorial Trusts; and by support to Steve Keele from NIH grant NS-17778.
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(Gazzaniga, M.S. ed.), pp. 1009–1020, MIT Press
motor skills in Huntington’s disease patients Psychobiology 21, 173–182 50 Pascual-Leone, A. et al. (1993) Procedural learning in Parkinson’s
43 Curran, T. (1998) Implicit sequence learning from a cognitive neuroscience perspective: what, how, and where?, in Handbook of Implicit Learning (Stadler, M.A. and Frensch, P., eds), pp. 365–400, Sage Publications 44 Hayes, A. et al. (1998) Toward a functional analysis of the basal ganglia
disease and cerebellar degeneration Ann. Neurol. 34, 594–602 51 Heindel, W.C. et al. (1989) Neuropsychological evidence for multiple implicit memory systems: a comparison of Alzheimer’s, Huntington’s, and Parkinson’s disease patients J. Neurosci. 9, 582–587
The functional anatomy of word comprehension and production Cathy J. Price This review describes the functional anatomy of word comprehension and production. Data from functional neuroimaging studies of normal subjects are used to determine the distributed set of brain regions that are engaged during particular language tasks and data from studies of patients with neurological damage are used to determine which of these regions are necessary for task performance. This combination of techniques indicates that the left inferior temporal and left posterior inferior parietal cortices are required for accessing semantic knowledge; the left posterior basal temporal lobe and the left frontal operculum are required for translating semantics into phonological output and the left anterior inferior parietal cortex is required for translating orthography to phonology. Further studies are required to establish the specific functions of the different regions and how these functions interact to provide our sophisticated language system.
C
ognitive models divide the language system into a set of integrated but distinct subcomponents. The details of different models vary1–3 but the basic framework entails word comprehension and production. Figure 1 illustrates the three types of memory required for speaking and reading. ‘Phonology’ refers to knowledge of the sound structure of words; ‘orthography’ refers to knowledge of the letter combinations in written words; and ‘semantics’ refers to the conceptual knowledge required for comprehension. The different types of knowledge are intimately connected. For speech comprehension, phonology from heard words is translated to semantics (P–S); for reading comprehension,
orthography is translated to semantics (O–S); and for the production of phonology (both speaking or reading) semantics is translated to phonology (S–P). A further type of phonological operation is available for reading when orthographic input is translated directly into phonology (O–P). Conversely, during writing, orthography can be generated from phonology (P–O) and semantics (S–O). Neuropsychological studies of brain damaged patients indicate that different components of the language system can be selectively impaired. For instance, patients can have intact speech perception and comprehension but impaired speech production. This suggests that the area of the brain
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C.J. Price is at the Wellcome Department of Cognitive Neurology, Institute of Neurology, Queen Square, London, UK WC1N 3BG. tel: ⫹44 171 837 7456 fax: ⫹44 171 813 1445 e-mail: cprice@ fil.ion.ucl.ac.uk
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Sequence learning Benjamin A. Clegg, Gregory J. DiGirolamo and Steven W. Keele The ability to sequence information is fundamental to human performance. When subjects are asked to respond to one of several possible spatial locations of a stimulus, reaction times and error rates decrease when the target follows a sequence. In this article, we review the numerous theoretical and methodological perspectives that have been used to study sequence learning. The opportunity now exists to integrate evidence from different domains of cognitive science to begin to provide a comprehensive account of sequence learning. We suggest that subjects can learn sequences based on different information in a hierarchical representation, including either sequences of stimuli or sequences of responses. This learning can occur both with and without explicit awareness of the sequence. Multiple modes of learning exist and are subserved by different neural circuits.
I
nterest in the ability to learn and use sequences for processing information and actions is by no means a recent phenomenon1. Following the seminal study of Nissen and Bullemer2, there has been an explosion of interest in sequence learning. While there are a variety of motivations for experimentation in sequence learning; and in particular the use of the serial reaction-time task (SRT) (see Box 1), three main reasons characterize much of this interest: (1) Sequencing of information and actions is a fundamental human ability We use sequences of information or sequences of actions in a variety of everyday tasks: from sequencing sounds in speech, to sequencing movements in typing or playing instruments, to sequencing actions in driving an automobile. (2) Sequence learning is an easily studied example of skill acquisition SRT learning is comparatively simple to implement and manipulate, and improvement occurs over a relatively short time. Reaction times and error rates readily and objectively assess improvement. Sequence learning has been studied in diverse populations, from neuropsychological patients to infants3. Further, the potential exists to study sequence learning in animals. (3) Sequence learning may be a complex form of implicit learning If sequence learning is accepted as sometimes occurring without awareness (see below), then it provides an intriguing example of nonconscious learning of a complex cognitive task. With the development of a simple paradigm for studying skill acquisition, it is hardly surprising that sequence learning has become a productive domain. However, variations in methodology make comparisons between studies difficult. For example, simple parametric manipulations such as varying the response-to-stimulus interval (RSI) can influence the amount of learning4.
Initially we will address separately the major questions that the field has revolved around. Ultimately we attempt to point out how an integrated approach (combining empirical analysis, cognitive theory and other converging evidence) is necessary to resolve some of the discrepancies in the literature. Representation of sequences What is being represented in sequence learning? One fundamental question concerns the very nature of the sequential representation. When we learn sequences, what type of information is actually being learned? The SRT paradigm normally involves both sequences of stimuli as well as the corresponding sequences of responses. The benefits in reaction time and error rate may be associated with the sequencing of stimuli, the sequencing of responses, or the sequencing of intermediate representations. One method used to address what information is being represented is to employ a transfer of learning paradigm. Cohen et al.5, for example, examined the transfer of sequence knowledge following changes in the response. Transfer of previous learning was observed when responding changed from using three fingers to a single finger and corresponding arm movements. The representation of the sequence cannot therefore be tied to any particular effector, which echoes the results of effector-independent production of sequences required for writing6. Furthermore, Keele et al.7 showed that in a more extreme type of response manipulation (from manual to verbal) transfer still occurs, but was now incomplete. This data suggest that sequence learning cannot be entirely stimulus-based as some learning is lost in the change from manual to verbal responses despite stimulus information remaining constant. It also suggests the learning is not entirely response-based since some transfer does occur over extreme response changes. The results, however, do not rule out the possibility that manual and verbal responses share some common, very abstract representation.
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B.A. Clegg and S.W. Keele are at the Department of Psychology, University of Oregon, Eugene, OR 974031227, USA. G.J. DiGirolamo is at the Center for the Cognitive Neuroscience of Attention and the Department of Psychology, University of Oregon, Eugene, OR 974031227, USA. tel: +1 541 346 1985 fax: +1 541 346 4911 e-mail: benc@oregon. uoregon.edu
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Box 1. The serial reaction-time paradigm
Reference a Nissen, M.J. and Bullemer, P. (1987) Attentional requirements of learning: evidence from performance measures Cognit. Psychol. 19, 1–32
Willingham8 has recently provided evidence for purely response-based learning. Subjects initially responded to spatial locations using an incompatible mapping of response keys. When subsequently switched to a compatible mapping, sequence learning transferred to the new condition, but only when the order of the key presses remained the same. This transfer occurred despite the fact that the stimulus sequence changed. Willingham argues that this result, combined with the effector-independence evidence, implies that the representation of sequences may relate to the egocentric representation of the key response locations. This account, however, does not explain the manual to verbal transfer results described previously. In contrast, a study by Mayr9 seems to show that sequence learning is possible independent of response. Mayr had subjects respond to four shapes that occurred in one of four locations. Both shapes and the locations were presented with co-occurring but independent sequences. Sequence learning was found for both the locations and the objects. Since the locations did not require a key response, this can be interpreted as ‘pure’ stimulus-based spatial learning. However, this interpretation might be questioned since either eye movements, or the orienting of attention, may play the role of a response under these conditions. Other experiments have found some evidence for stimulus-based learning following observation only in which no response is required10. The studies discussed above display part of the problem of determining the nature of the representation in sequence learning. The learning of spatial information and the abstract effector-independent learning can be interpreted as being either stimulus or response-related in nature. Overall the evidence strongly suggests that the representations of sequences involve response-based information, and provides support for some learning of stimulus-based information. Indeed, researchers have found evidence for both forms of learning within single studies11. However, stimulus-based
400 Random
Reaction time (ms)
Nissen and Bullemera developed a simple paradigm that forms the basis of much of the research on sequence learning. In this task, participants responded to a stimulus (an asterisk) occurring at one of four locations with a key located directly below each position. Following a 500 ms response-to-stimulus interval, the next stimulus occurred. The basic design was thus a four-choice, compatible response mapping, serial reaction task. Although not informed of it, some participants were responding to an asterisk moving in a regular, repeating pattern of positions, while others responded to a random order of locations. If the positions are designated from left to right as the letters A through D, then Nissen and Bullemer’s subjects in the ‘sequence’ condition experienced a recurring loop of D-B-C-A-C-B-D-C-B-A for ten successive repetitions through this ten-item sequence per block. Subjects in the repeating sequence condition had faster reaction times and made fewer errors than those responding to random information (see Fig.).
300
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2
3
4
5
6
7
8
Block Fig. Comparison of reaction-time results for randomly presented and sequential visual stimuli. Nissen and Bullemer’s experiment 1 results (Ref. a) show additional improvement in performance over time when locations of stimuli follow a sequence. (Adapted, with permission, from Ref. a.)
learning is still subject to some debate8. It is, nevertheless, plausible that there are two (or more) distinct forms of representation, perhaps utilized differentially depending on the nature and complexity of the sequences and task. We believe the theoretical and empirical contradictions illustrate that a dichotomy of stimulus- versus responsebased representation is overly simplistic. Instead, we might consider a representation containing multiple layers of information, with some layers corresponding more closely to stimulus or response properties. In this view, a representation would include information starting from an abstract level such as overall goals or plans (which would be neither stimulus- nor response-based). Intermediate levels would specify the types of action required (perhaps specified in terms of a target location) independent of the effectors, or specify the stimulus type independent of its exact identity. Ultimately, highly specific information related to the exact stimulus and execution of the final motor output would be represented. Learning may be acquired via the sequencing of the representation at any of these levels. Such a view might gain indirect support from work in non-SRT sequencing paradigms; for example, the explicit sequencing of finger taps. There is increasing evidence that suggests that these sequential representations are organized hierarchically12 (for a review, see Rosenbaum13). One theoretical resolution of the disparate representations postulated in sequence learning would be a hierarchical system representation for multiple levels of information (such as MacKay’s theory14). Practice then has the ability to influence different representations at different levels of the hierarchy; hence, learning could occur for different types of information (see Keele et al.15). Implicit vs. explicit learning and the role of awareness Does sequence learning occur implicitly? Can we learn how to sequence information without conscious awareness of the sequential relationship of the elements?
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Initial evidence seemed to suggest we could. In Nissen and Bullemer’s original study2, subjects demonstrated sequence learning in the absence of conscious awareness of a sequence. Indeed the study also found preserved learning in amnesic patients despite their lack of awareness of the sequence. These findings rapidly led to the conclusion that sequence learning can occur implicitly16. Such conclusions link sequence learning to a parallel debate in the memory literature on whether implicit/explicit memory reflects separate memory systems17,18 or separate processes19. Within the sequence learning literature, the debate has focused around the necessity of conscious knowledge in sequence learning, and the methods for assessing the presence/ absence of explicit knowledge20–23. While awareness is generally agreed to have some unique contribution24, the level of awareness has in some circumstances been found to be irrelevant to performance25. Such results suggest that awareness itself cannot be the only, or even the primary, avenue of learning. It seems likely that there is some degree of sequential knowledge available only for performance but not explicit awareness (see Goschke26 for a more complete discussion). On a more practical level, regardless of arguments about the suitability of various criteria for assessing explicit knowledge, the SRT paradigm remains a powerful research tool. At the very least employing incidental learning allows examination of sequencing that need not be inherently tied to any explicit strategies. In our view, explicit knowledge is not necessarily an inherent part of learning sequenced information. Rather, awareness may arise from the interaction of the sequence learning systems with other cognitive systems that then produce conscious knowledge of the sequence. As we will discuss below, we believe this sequence learning issue can be resolved through an integrated approach, combining behavioral data with neuroimaging data. Multiple systems for sequence learning Are there multiple forms of sequence learning? Is there more than one way to learn sequences? Are the processes that underlie sequence learning the same regardless of the conditions under which we learn? Much work suggests there is more than one way to learn sequences. Pursuing Nissen and Bullemer’s original finding2 that an interleaved tone-counting task disrupted sequence learning, Curran and Keele25 examined sequence learning under dual-task conditions. In the single-task condition, they found that differences in performance between subjects were related to their awareness of the sequence. These differences disappeared when a secondary task was added. Based upon this finding and other data, Curran and Keele suggested that separate attentional and non-attentional forms of sequence learning exist. There are, however, other accounts for the data in dualtask sequence learning. One claim is that dual-task learning merely reflects the diminished effects of explicit knowledge on performance27 (but see Curran and Keele). Other evidence suggests that tone-counting in the dual-task experiments may not relate to attention, but instead interfere with the temporal organization of the sequence owing to the resulting changes in the interval between primary task signals with the addition of the tones28.
Review
A more recent account suggests that dual-task conditions may simply be a special case of information integration. As well as using random tones interleaved with the sequenced visual signals, Schmidtke and Heuer29 had subjects respond to tones that also occurred in sequential order. They concluded that learning was disrupted in the original dual-task studies because of the occurrence of random items (tones) in the midst of sequential ones (locations). In this view, the sequence learning system attempts to extract sequences from any information present in the environment. Keele et al.30 have extended these findings to propose two independent learning systems. One system attempts to integrate all sequential information regardless of the dimensions or modalities of the information (and is therefore hampered by the presence of random information). A second system attempts to discover sequences of information within single dimensions regardless of intervening items from other modalities. Again we believe that resolution of the issues of whether multiple systems for learning sequence exist cannot be accomplished in isolation from evidence from other domains. As we will discuss in the next section, neuroimaging data very strongly support a multiple systems account. Converging evidence: other methodologies What do neural network models suggest about the potential mechanisms involved in sequencing? And what are the neural areas that subserve sequencing learning? Sequence learning has provided a natural domain for investigating the computations and neural structures involved in skill acquisition. As we have already suggested, we believe such converging evidence is essential for ultimately resolving the issues in this field. Computational models Neural network modeling has augmented our understanding of sequence learning. One broad class of models employs a simple recurrent network (SRN) based on the initial work of Elman31 (see Box 2). In SRN models, the network develops its own representation of temporal context using the association with the previous item to constrain which item follows the current one. These models have been extensively applied to sequence learning32. A related approach, based on Jordan’s model of serial order33, uses ‘plan’ units which maintain a fixed representation of the whole sequence (providing an overall context), while ‘state’ units vary with progression through the sequence using internal feedback from the output units both to preserve temporal context and to advance to the next item34. The computational approach has made two important contributions to our understanding of the nature of sequence learning. Both models provide illustrations of how acquired forms of context information can provide a basis for higherorder information. Sequencing can be achieved even when the association between two elements is insufficient to resolve the ambiguity of the same elements occurring in different orders in different parts of the sequence. The second major contribution of these computational analyses is to draw attention to potential mechanisms involved in learning. The models suggest two different mechanisms for
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Box 2. Simple recurrent network Neural network models, many derived from Elman’s simple recurrent networka (SRN), have contributed to a number of issues surrounding sequencing learning. The SRN uses an architecture which allows the network to predict the next element within a sequence based upon both the current element, and also the network’s own representation of the temporal context of the current element (see Fig.). This context is derived from a recurrent set of connections linking the network’s hidden units back to the input units. During training the network develops associations between items. The recurrent connections then enable contextual information to differentiate the parts of the sequence. Since the recurrent information contains details of the preceding item, the network moves beyond simple association to a current item, and includes both the current item and the preceding item in predicting the next item. Knowing the current and preceding items allows further unique predictions. For a more complete
discussion of the representation derived within SRN models see Cleeremansb. References a Elman, J.L. (1990) Finding structure in time Cognit. Sci. 14, 179–211 b Cleeremans,
Neuropsychology The most striking result from neuroimaging studies36–40 is the clear implication of multiple networks capable of learning sequences. Overall the results are very consistent with the complexity of systems revealed in behavioral and computational analyses. In a variety of studies, results typically show activation of non-overlapping brain areas between different sequence conditions (single versus dualtask, see Fig. 1; implicit versus explicit; aware versus unaware). Physically distinct brain networks appear to be activated when subjects become consciously aware of the sequence. These networks are not seen when subjects are unaware36, and thus support the claim that awareness is not an essential part of sequence learning. In one study38 only a single area of activation was common to both implicit and explicit conditions: the right thalamus which may reflect enhancement of the sequenced information41. As awareness emerges, additional areas of the brain become active; including, dorsal lateral prefrontal, medial frontal, and more dorsal posterior regions36. Such results would be consistent with awareness leading to the use of working memory42 to represent the sequence.
Copy
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Mechanisms
of
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For a variety of reasons, the interpretation of the neuroimaging studies is not straightforward43. However, while there are differences in the neural areas that are active across studies, a few common findings do apply. Under ‘implicit’ conditions there is usually activation of areas commonly associated with motor control, including motor cortex and subcortical structures in the basal ganglia (see Curran43 for a more complete discussion). The neuroimaging data might be regarded as implicating a response-based representation of sequences. But motor cortex activation may reflect sequence learning at the response level, or response preparation contingent on learning within other neural areas. As we develop a more detailed understanding of the functional properties of brain areas, we should be wary about attaching simple labels to individual brain regions (such as regarding the basal ganglia or cerebellum as ‘motor’ areas44, 45). (Comparison of some of these findings to neuroimaging data from other types of sequential behavior are shown in Box 3.) The neuroimaging data does help clarify several issues. For example, the combined evidence from the findings of behavioral and imaging studies of single versus dual-task, clearly implicates multiple systems for sequence learning. A recent neural network model46 of Curran and Keele’s findings25 of transfer in single and dual-task conditions posits a single mechanism of learning. This result would fit well with the notion of disruption of temporal organization in dual-task learning28. However, the neuroimaging results36 strongly implicate separate neural pathways for single- and dual-task sequence learning (Fig. 1) which suggests that a single mechanism explanation, however parsimonious, might be insufficient. Converging evidence can also be provided by studies of patient populations. As mentioned above, imaging data have shown recruitment of additional areas when awareness emerges. The idea that awareness may be independent of sequence learning gains further supports from evidence of intact
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Next element
Fig. Basic architecture for a simple recurrent network model of sequence learning. (See text for details.) (Adapted, with permission, from Ref. 35.)
learning, since each model structures the sequenced information differently. Results from the SRN models of Cleeremans and colleagues35 suggest that the basis of sequence learning is a form of statistical relationships between items. The strength of these models is that in addition to applying to learning fixed sequences, they also generalize to probabilistic sequences, such as those used in artificial grammar learning27. For the Jordan model, plan units might be considered superordinate levels of a hierarchical structure maintaining fixed activation for a set of successive subordinate elements. Thus the mechanism involved would lead to the chunking of items, forming independent groups of sub-units within the larger overall structure of the sequence.
A.
Connectionist Models of Sequence Processing, MIT Press
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sequence learning in amnesic patients with 80 80 hippocampal damage47. However, some of Lateral view this evidence has demonstrated possible Left Right deficits in the learning of higher-order associations for these patients. Sequence 80 –120 –120 80 learning impairments have also been observed in patients with striatum dysfunction associated with Huntington’s or Parkinson’s diseases48,49, although other studies have shown little deficits50. Dual task –60 –60 80 80 Future studies in patient populations Single task offer the possibility of clarifying whether multiple forms of representation exist in sequence learning. Evidence elsewhere does 80 –120 –120 80 suggest that there are multiple forms of rep10 resentation even within implicit learning . A study by Heindel and colleagues51 demLeft Right onstrated a double dissociation between Medial view Huntington’s patients and Alzheimer’s –60 –60 patients in implicit motor learning and Fig. 1 Neuroimaging evidence for multiple systems in sequence learning. Monotonic increases in regional implicit perceptual learning. This dissocicerebral blood flow (rCBF) for sequence learning under single task (areas indicated by filled circles) and dual-task (indicated by filled triangles) conditions suggest discrete brain networks. (Adapted from data in Ref. 36.) ation suggests that implicit information can affect performance at multiple levels, and that these representations are subaddressed in the literature, we do not offer simple answers served by different underlying neural structures. Consistent and many individual issues remain partially unresolved. with this view, implicit learning in sequencing information With regard to what is being represented in sequence could be based on different levels of the hierarchical structure learning, we feel the evidence suggests that a stimulus or reof representation that we proposed earlier. Moreover, Heindel sponse-based dichotomy proves insufficient. Instead repreet al. also demonstrated that deficits in the motor learning sentations are fairly complex, involving information at multask were correlated with the amount of dementia in these tiple levels of a hierarchy subserved by physically distinct patients and not the level of primary motor dysfunction. neural areas. Further, although the combined evidence These results suggest that the representation that underlies points to multiple systems for sequence learning, the exact the impairment in sequence learning following striatum characterization of the distinctions between these systems damage would be non-motoric, and may indeed correspond remains unclear. The role of awareness, often related to the to a higher level of the hierarchy than the response. issue of multiple systems in sequence learning, might prove to be orthogonal to the question of multiple systems. Conclusions Awareness might not be a manifest property of any of the Though we have attempted to divide the research on sesequence learning systems that initially develop sequential quence learning into the general questions that have been
Box 3. Imaging data: sequence learning compared to intentional sequencing Explicit sequential behavior, such as finger tapping tasks, has been studied using neuroimaging techniquesa–c. We can compare the results of these studies to neuroimaging studies under SRT-type conditions. One finding common to both kinds of studies relates to changes in the activation of motor cortex depending on the amount of practice. Initial practice of sequential behavior is accompanied by increased blood flow in areas related to responding. After extensive practice, these motor areas decrease their activationd,e. This result is reminiscent of brain changes that occur with practice in other motorf, and even, non-motor tasksg. One potential area for research might thus concern differences underlying the production of sequences during initial practice versus their long-term performance.
Organization and Locus of Change (Squire, L.R. and Weinberger, N.M., eds), pp. 95–113, Oxford University Press b Jenkins, I.H. et al. (1994) Motor sequence learning: a study with positron emission tomography J. Neurosci. 14, 3775–3790 c Karni, A. et al. (1995) Functional MRI evidence for adult motor cortex plasticity during motor skill learning Nature 377, 155–158 d Pascual-Leone, A., Grafman, J. and Hallett, M. (1994) Modulation of cortical motor output maps during development of implicit and explicit knowledge Science 263, 1287–1289 e Karni, A. et al. (1998) The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex Proc. Natl. Acad. Sci. U. S. A. 95, 861–868 f Petersen, S. et al. (1998) The effects of practice on the functional anatomy of task performance Proc. Natl. Acad. Sci. U. S. A. 95, 853–860
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a Roland, P.E. et al. (1991) Structures in the human brain participating in visual learning, tactile learning and motor learning, in Memory:
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perceptual Mem. Cognit. (in press)
Outstanding questions
9 Mayr, U. (1996) Spatial attention and implicit sequence learning: evidence for independent learning of spatial and nonspatial
• As with explicit sequences, are the representations of implicitly learned sequences hierarchically organized? • What is the best way to characterize the function of the cognitive and neural systems involved in sequence learning? • Is awareness an inherent property of any of the multiple sequence learning systems? • Are there differences in the way sequential knowledge is organized during initial practice compared to later performance? • Do the findings from this simplified, yet hopefully, prototypical task apply for sequencing information in more complex, real world situations?
sequences J. Exp. Psychol. Learn. Mem. Cognit. 22, 350–364 10 Seger, C.A. (1998) Multiple forms of implicit learning, in Handbook of Implicit Learning (Stadler, M.A. and Frensch, P., eds), pp. 295–320, Sage Publications 11 Fendrich, D.W., Healy, A.F. and Bourne, L.E., Jr (1991) Long-term repetition effects for motoric and perceptual procedures J. Exp. Psychol. Learn. Mem. Cognit. 17, 137–151 12 Povel, D.J. and Collard, R. (1982) Structural factors in patterned finger tapping Acta Psychol. 52, 107–123 13 Rosenbaum, D.A. (1991) Human Motor Control, Academic Press 14 MacKay, D.G. (1982) The problem of flexibility and fluency in skilled behavior Psychol. Rev. 89, 483–506
knowledge. Rather, the interaction of these systems with other neural areas could cause the emergence of explicit knowledge and explicit strategies. The absence of clear answers does not reflect some deficiency in the research itself. We believe that the resolution of discrepancies in the sequence learning literature is tractable through a cognitive science theory that includes the representation, the mechanism, and the functional anatomy. By taking an integrative perspective, we can now construct and greatly constrain theories of sequence learning. Furthermore, as we have attempted to illustrate, there is work from outside of the field of sequence learning that can greatly inform theories of sequence learning. We believe the challenge for researchers is now to devise unified accounts of sequence learning that meet all the constraints from the different domains of cognitive science.
15 Keele, S.W., Cohen, A. and Ivry, R. (1990) Motor programs: concepts and issues, in Attention and Performance: (Vol. XIII) Motor Representation and Control (Jeannerod, M., ed.), pp. 77–110, Erlbaum 16 Reed, J. and Johnson, P. (1994) Assessing implicit learning with indirect tests: determining what is learned about sequence structure J. Exp. Psychol. Learn. Mem. Cognit. 20, 584–594 17 Schacter, D.L. (1992) Understanding implicit memory: a cognitive neuroscience perspective Am. Psychol. 47, 559–569 18 Cohen, N.J. and Eichenbaum, H. (1994) Memory, Amnesia, and the Hippocampal System, MIT Press 19 Roediger, H.L. (1990) Implicit memory: retention without remembering Am. Psychol. 54, 1043–1056 20 Perruchet, P. and Amorim, M.A. (1992) Conscious knowledge and changes in performance in sequence learning: evidence against dissociation J. Exp. Psychol. Learn. Mem. Cognit. 18, 785–800 21 Shanks, D.R. and St John, M.F. (1994) Characteristics of dissociable human learning systems Brain Behav. Sci. 17, 367–447 22 Cohen, A. and Curran, T. (1993) On tasks, knowledge, correlations, and dissociations: comment on Perruchet and Amorim J. Exp. Psychol. Learn. Mem. Cognit. 19, 1431–1437 23 Willingham,
D.B.,
Greenley,
D.B.
and
Bardona,
A.M.
(1993)
Dissociation in a serial response time task using a recognition measure: Acknowledgements
comment on Perruchet and Amorim (1992) J. Exp. Psychol. Learn.
We would like to thank David Rosenbaum, Ulrich Mayr, and an
Mem. Cognit. 19, 1424–1430
anonymous reviewer for their insightful comments on the manuscript. Michael Posner provided helpful ideas on an earlier version. This review was made possible by support to Ben Clegg from a University of Oregon fellowship from the Interval Research Corporation; by support to
24 Willingham, D.B., Nissen, M.J. and Bullemer, P. (1989) On the development of procedural knowledge J. Exp. Psychol. Learn. Mem. Cognit. 15, 1047–1060 25 Curran, T. and Keele, S.W. (1993) Attentional and nonattentional
Gregg DiGirolamo through funding of the Center for the Cognitive Neuroscience of Attention from the James S. McDonnell Foundation and Pew Memorial Trusts; and by support to Steve Keele from NIH grant NS-17778.
forms of sequence learning J. Exp. Psychol. Learn. Mem. Cognit. 19, 189–202 26 Goschke, T. (1998) Implicit learning of perceptual and motor sequences: Evidence for independent systems, in Handbook of Implicit Learning (Stadler, M.A. and Frensch, P., eds), pp. 401–444, Sage Publications
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Sci., University of Oregon 31 Elman, J.L. (1990) Finding structure in time Cognit. Sci. 14, 179–211
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The functional anatomy of word comprehension and production Cathy J. Price This review describes the functional anatomy of word comprehension and production. Data from functional neuroimaging studies of normal subjects are used to determine the distributed set of brain regions that are engaged during particular language tasks and data from studies of patients with neurological damage are used to determine which of these regions are necessary for task performance. This combination of techniques indicates that the left inferior temporal and left posterior inferior parietal cortices are required for accessing semantic knowledge; the left posterior basal temporal lobe and the left frontal operculum are required for translating semantics into phonological output and the left anterior inferior parietal cortex is required for translating orthography to phonology. Further studies are required to establish the specific functions of the different regions and how these functions interact to provide our sophisticated language system.
C
ognitive models divide the language system into a set of integrated but distinct subcomponents. The details of different models vary1–3 but the basic framework entails word comprehension and production. Figure 1 illustrates the three types of memory required for speaking and reading. ‘Phonology’ refers to knowledge of the sound structure of words; ‘orthography’ refers to knowledge of the letter combinations in written words; and ‘semantics’ refers to the conceptual knowledge required for comprehension. The different types of knowledge are intimately connected. For speech comprehension, phonology from heard words is translated to semantics (P–S); for reading comprehension,
orthography is translated to semantics (O–S); and for the production of phonology (both speaking or reading) semantics is translated to phonology (S–P). A further type of phonological operation is available for reading when orthographic input is translated directly into phonology (O–P). Conversely, during writing, orthography can be generated from phonology (P–O) and semantics (S–O). Neuropsychological studies of brain damaged patients indicate that different components of the language system can be selectively impaired. For instance, patients can have intact speech perception and comprehension but impaired speech production. This suggests that the area of the brain
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PII: S1364-6613(98)01201-7
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C.J. Price is at the Wellcome Department of Cognitive Neurology, Institute of Neurology, Queen Square, London, UK WC1N 3BG. tel: ⫹44 171 837 7456 fax: ⫹44 171 813 1445 e-mail: cprice@ fil.ion.ucl.ac.uk
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