Category learning and multiple memory systems

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1 Review TRENDS in Cognitive Sciences Vol.9 No.2 February 2005 Category learning and multiple memory systems F. Gregory shby and Jeffrey B. O Brien Department of Psychology, University of California, Santa Barbara, C 93106, US Categorization is a vitally important skill that people use every day. Early theories of category learning assumed a single learning system, but recent evidence suggests that human category learning may depend on many of the major memory systems that have been hypothesized by memory researchers. s different memory systems flourish under different conditions, an understanding of how categorization uses available memory systems will improve our understanding of a basic human skill, lead to better insights into the cognitive changes that result from a variety of neurological disorders, and suggest improvements in training procedures for complex categorization tasks. Category learning and memory To categorize is to respond differently to objects or events in separate classes or categories. It is a skill we perform countless times every day. It allows us, and all animals, to approach food or friend and to avoid toxin or trap. Upon reaching adulthood, humans have learned thousands of categories and have developed the ability to learn a wide variety of new category structures quickly. Learning is, by definition, a process of laying down some new memory trace, or of strengthening an existing trace, so a natural and important question to ask is: what memory system or systems are used during category learning? There is growing consensus that human memory is mediated by multiple qualitatively distinct systems [1 3]. For the most part, early theories of category learning did not specify an underlying memory system, at least not in the language used by memory theorists. However, virtually all theories have assumed a single learning system (see Box 1). growing body of recent evidence, however, suggests that category learning uses many, or perhaps all of the major memory systems that have been hypothesized by memory researchers. This article reviews much of that evidence. The recent category-learning literature has clearly been converging towards the multiple memory systems hypothesis. Even so, despite the intuitive appeal of this hypothesis, this is to our knowledge the first article to consider explicitly the possibility that all major memory systems contribute to category learning. Before proceeding, it is important to clarify that this article focuses exclusively on the learning of new categories. In particular, we do not consider expert Corresponding author: shby, F.G. (ashby@psych.ucsb.edu). vailable online 24 December 2004 categorization and the memory of highly experienced experts. There are many reasons to believe that the consolidation of category knowledge recruits different neural structures and pathways from those used during initial learning (e.g. no groups with known categorylearning deficits exhibit any category-specific agnosias). Declarative memory systems Declarative memories are those accessible to conscious awareness [4]. Typically this includes working memory, episodic memory, and often also semantic memory. In categorization, some sort of declarative memory presumably would mediate any learning strategy that the subject could accurately describe. Obvious candidates include the formulating and testing of explicit rules and explicitly memorizing the category labels associated with specific stimuli. Working memory Working memory is the ability to maintain and manipulate limited amounts of information during brief periods of cognitive activity [5]. It is heavily used in reasoning and problem solving, and because of this, it is often associated with a wide variety of cognitive tasks. Because working memory is effective only for brief time intervals, it cannot store a lasting category representation, but it could be the primary mediating memory system in tasks where the categories are learned quickly. There are two obvious possibilities. One is that the categories contain few enough exemplars that the process of explicitly memorizing their category labels does not exceed the span of working Box 1. Single versus multiple systems of category learning Early theories of category learning virtually all assumed a single category learning system and the focus of debate was on the nature of this system. For example, one popular theory, called exemplar theory, assumes that when a novel stimulus is encountered, its similarity is computed to the memory representation of every previously seen exemplar from each potentially relevant category, and a response is chosen on the basis of these similarity computations. During the past few years, many articles, both theoretical and empirical, have argued that human category learning is mediated by multiple, qualitatively distinct systems [65], and there have been efforts to associate some of these putative systems with well-known memory systems [21,45]. Some exemplar theorists have responded with arguments that exemplar theory can account for many of the phenomena that have been used to support the notion of multiple systems [66,67], so the single versus multiple category-learning systems debate is currently unresolved /$ - see front matter Q 2004 Elsevier Ltd. ll rights reserved. doi: /j.tics

2 84 Review TRENDS in Cognitive Sciences Vol.9 No.2 February 2005 Box 2. Category-learning tasks that might depend on declarative memory: rule-based and unstructured tasks Categories that can be learned via logical reasoning are used in rulebased category-learning tasks [65]. In the simplest case, only one stimulus dimension is relevant. For example, although athletes vary on many dimensions, they can be categorized by team simply by observing the color of their uniforms. Working memory seems ideally suited for learning in one-dimensional rule-based tasks of this type. n example that might be used in experimental research is shown in Figure Ia. In this case, the stimuli vary on 2 dimensions (bar width and orientation) and the optimal rule is one-dimensional and easy to describe verbally. ctively memorizing the category membership of specific stimuli seems an ideal strategy when the categories have only a few exemplars and lack any coherent structure that could be discovered, for instance, via logical reasoning. For example, it seems likely that categories such as my personal numbers (e.g. phone numbers, zip code, social security number) are learned via explicit memorization that depends on the medial temporal lobes. Figure Ib shows categories from an unstructured category-learning task that was used in the seminal study of Shepard, Hovland, and Jenkins [68]. Shepard et al. compared category-learning rates in six tasks where the categories were constructed by dividing the same eight threedimensional binary-valued stimuli (shown in Figure Ib) into two categories in six different ways, which included rule-based, unstructured, and information-integration tasks (see Box 3). Results showed that the one-dimensional rule-based task was easiest to learn and the unstructured task was the most difficult. (a) (b) B Orientation B Bar Width TRENDS in Cognitive Sciences Figure I. (a) Categories that might be used in a rule-based category-learning task. Note that the categorization rule has a simple verbal description (i.e. Respond if the bar width is low and B if the bar width is high ). (b) Categories that might be used in an unstructured category-learning task. memory. lthough this seems plausible, most category structures used in experimental research use more than this number of exemplars. For example, reliable error-free performance in working memory span tasks is limited to list lengths of four or less. With two categories, this implies that working memory, by itself, would be insufficient if each category contained more than a few exemplars. second possibility is that working memory could be used if the category structures were simple enough that they could be discovered quickly via a logical reasoning process. Tasks in which the categories can be learned via logical reasoning are known as rule-based tasks (see Box 2). Virtually all category-learning tasks used in neuropsychological assessment are rule-based, including the widely known Wisconsin Card Sorting Test [6] (WCST). There is now considerable evidence that working memory is crucial in rule-based tasks, both to select and apply the correct categorization rule and to interpret and process the feedback signal. Perhaps the best cognitive evidence that working memory is crucial for rule-based category learning comes from a study in which subjects performed a dual task that required working memory and executive attention at the same time that they learned either simple one-dimensional, rule-based categories or a more difficult information-integration category structure (Box 3) that required attention to three separate stimulus dimensions [7]. If the same memory system was used to learn both types of category structures then one would expect the dual task to interfere more strongly with the more difficult categorization task. In fact, the opposite result was observed. The dual task slowed learning in the onedimensional, rule-based task by 350%, whereas there was no significant effect of the dual task in the difficult threedimensional task. Thus, a dual task that requires working memory interfered with a simple rule-based task but not with a more difficult information-integration task. related study showed that rule-based category learning is significantly impaired if the time to process the feedback signal is reduced, whereas learning of more difficult information-integration categories is unaffected by the length of the delay following the feedback signal [8]. This result is consistent with the hypothesis that rulebased category learning is mediated by a conscious process of hypothesis generation and testing. ccording to this hypothesis, if the feedback indicates that a response was incorrect, then the subject must decide whether to try the same rule again, or whether to switch to a new rule, and if the latter decision is made then in addition, a new rule must be selected and attention must be switched from the old rule to the new. These operations require time, attention, and working memory. wide variety of behavioral and cognitive neuroscience evidence implicates the lateral prefrontal cortex (PFC) and the head of the caudate nucleus (a major input structure within the basal ganglia) in working memory [9 11]. Thus, if working memory is crucial in rule-based categorization then patients with PFC or basal ganglia damage should be impaired in rule-based category learning and these same structures should be activated in

3 Review TRENDS in Cognitive Sciences Vol.9 No.2 February Box 3. Category-learning tasks that might depend on non-declarative memory: information-integration and (, not ) prototype distortion tasks Procedural learning is closely associated with motor or skill learning, but many motor skills also require significant category learning. For example, an important skill in tennis is selecting the appropriate category of motor programs (e.g. backhand, topspin, cross-court) in response to the opponent s shot. Figure Ia shows the categories from an information-integration task in which accuracy is maximized only if information from the two stimulus dimensions is integrated at some pre-decisional stage [69]. Note that equal attention must be allocated to both stimulus dimensions and that there is no simple verbal description of the category decision bound. The PRS seems most likely to mediate category learning if there is only one category in which many exemplars are similar to the category prototype. popular task with these properties is called the (, not ) prototype-distortion task [70] (Figure Ib). In this task, the category exemplars are created by first constructing a category prototype. The other exemplars of the category are then created by randomly distorting the prototype. The not stimuli are just random patterns with no coherent structure. In the most popular prototype distortion task, each stimulus is a random pattern of dots. n alternative version of the task, called the (, B) prototype distortion task, creates category and B exemplars by randomly distorting two separate prototypes. (a) (b) Orientation not B Bar Width TRENDS in Cognitive Sciences Figure I. (a) Some stimuli from an information-integration category-learning task. Note that the category bound has no simple verbal description. (b) Some stimuli from an (, not ) prototype-distortion category-learning task. neuroimaging studies of rule-based categorization. substantial number of studies support these two predictions. First, as expected, patients with PFC damage have working memory deficits [12,13]. They also are impaired in rule-based category learning. For example, perseverative errors on the WCST are among the most classic of all signs of PFC damage [14,15]. nother group with wellknown deficits in both working memory and rule-based category learning is Parkinson s disease patients [16 19]. lthough later in the disease Parkinson s patients have frontal damage, the disease mainly targets the basal ganglia. Within the caudate nucleus, the region most affected appears to be the head [20], which is reciprocally connected to the PFC. Thus, the rule-based category learning deficits of frontal and Parkinson s disease patients are consistent with the hypothesis that rule-based category learning is mediated, in part, by the same frontal-striatal circuits that mediate working memory [21]. Several neuroimaging studies have used the WCST or a rule-based task similar to the WCST. ll of these have reported task-related activation in the PFC and most have also reported activation in the head of the caudate nucleus [22 26]. Episodic and semantic memory Episodic memory refers to memories of specific past events or episodes in our personal history [2]. Episodic memory provides a context-rich representation of an event that might include information from all sensory modalities. By contrast, semantic memory memory for facts [4] is context poor and typically involves only one modality. Overwhelming evidence implicates the medial temporal lobes in both these memory systems, especially hippocampal and parahippocampal structures. We know of no categorization data that disambiguate the separate contributions of episodic and semantic memory systems to category learning, so in the remainder of this section we lump these two systems together. Episodic and semantic memory systems are used during explicit memorization, so category structures that encourage memorization are especially likely to be learned via these declarative memory systems. Two conditions seem important. First, memorization is an especially effective strategy if each category contains a small number of perceptually distinct exemplars. Second, because memorizing the category labels of novel stimuli is effortful, it is important that other simpler strategies are ineffective. This is likely in unstructured categories of the type described in Box 2. Currently, there is little direct evidence from traditional cognitive experiments that category learning depends on episodic or semantic memory, although there is much indirect evidence (e.g. exemplar models provide good fits to much categorization data). The best existing direct evidence comes from neuropsychological studies using patients with amnesia. Perhaps the most striking result of these experiments is that in many cases no category learning deficits were observed. First, several studies have reported that amnesiacs are normal in rulebased category learning [27,28]. s mentioned above, an

4 86 Review TRENDS in Cognitive Sciences Vol.9 No.2 February 2005 obvious possibility is that many rule-based tasks are simple enough (e.g. the WCST) that working memory is sufficient for subjects to keep track of which alternative rules they have tested and rejected. Second, several studies have reported normal amnesic performance in (, not ) prototype-distortion tasks of the type described in Box 3 [29,30]. Third, normal amnesic learning has also been reported in an information-integration task [31]. On the other hand, some studies have reported category-learning deficits in amnesia patients, and these studies represent the best available evidence for an active role of episodic/semantic memory systems in category learning. First, amnesiacs were worse than controls at learning to name which of 3 artists created each of 36 different Italian Renaissance paintings [32]. Second, several studies have reported category-learning deficits by amnesiacs in (, B) prototype-distortion tasks (see Box 3) [33,34]. third set of studies used the weather prediction task, which is especially popular in cognitive neuroscience studies of categorization [35 37]. On each trial of this task, subjects are shown one, two, or three of four possible tarot cards and are asked to indicate whether the presented constellation signals rain or sun. Each card is labeled with a unique and highly discriminable geometric pattern and each card combination is probabilistically associated with the two outcomes. Several qualitatively different strategies are all about equally effective in this task, so successful performance, by itself, does not provide much information about what strategy was used. strategy analysis of data collected in the weather prediction task concluded that a substantial majority of subjects memorized the optimal responses to the four possible singleton card patterns and guessed for the remaining patterns [38]. Given this result, it is not surprising that amnesiacs are impaired in the weather prediction task, although the deficit appears only after 50 trials of training [37]. n obvious possibility is that it takes controls about 50 trials to memorize responses to the singletons, a strategy that is unavailable to the amnesiacs. Non-declarative memory systems Procedural memory Procedural memories are the memories of skills that are learned through practice [39]. Traditionally these have been motor skills, such as those used when playing golf or tennis. There are several signatures of procedural learning that make it qualitatively different from learning that is mediated by declarative memory systems. First, there typically is little conscious recollection or even awareness of the details of procedural memories. Second, procedural learning is slow and incremental and it requires immediate and consistent feedback [39,40]. Much evidence suggests that procedural learning is mediated largely within the basal ganglia [39,41,42]. Because procedural learning requires many repetitions, it is not likely to influence performance when the categories have a simple structure that can be discovered via logical reasoning or when the categories have only a few exemplars and are unstructured. Thus, it seems that if procedural memory ever mediated learning in a categorization task, it would be with structured categories containing many exemplars that could not be easily learned via a logical reasoning process. In fact, such categories are common in everyday life. For example, the set of all x-rays displaying a tumor forms a perceptual category, but deciding whether a particular x-ray shows a tumor requires years of training and expert radiologists are only partially successful at describing their categorization strategies. (See Box 3 for an example of an experimental task with such properties.) Several studies have provided direct evidence that learning in information-integration tasks is mediated primarily by procedural memory. The quintessential paradigm for studying procedural learning is the serial reaction time (SRT) task [43], in which subjects press keys as quickly as possible in response to stimuli that appear in various locations on the screen. large response time improvement is observed when the stimulus sequence is repeated, even when subjects are unaware that a sequence exists. In addition, changing the location of the response keys interferes with SRT learning, but changing the fingers that push the keys does not [44]. Thus, if procedural learning is used in information-integration tasks then switching the locations of the response keys should interfere with learning, but switching the fingers that depress the keys should not. In fact, shby et al. [45] reported evidence that directly supported this prediction. They also reported that neither manipulation had any effect on rule-based category learning. Maddox et al. [46] reported a similar sensitivity of information-integration category learning to response location. Together, these results provide the first direct evidence of procedural learning in perceptual categorization. There is also evidence that as in traditional procedurallearning tasks, information-integration category learning is most effective when the feedback signal is delivered immediately after the categorization response. First, although some rule-based categories can be learned without feedback of any kind, there is no evidence that information-integration categories can be learned without feedback [47]. Second, delaying the feedback by as little as 2.5 s after the response significantly interferes with information-integration category learning, but delays as long as 10 s have no effect on rule-based learning [48]. Third, observational training is equally effective to traditional feedback training with rule-based categories, but with information-integration categories a distinct advantage occurs for feedback training [49]. During observational training, subjects are informed of the category membership of each stimulus just before it appears, whereas during feedback training the stimulus is presented and then the category label is shown immediately after the subject responds. Several studies with neuropsychological patient groups also support the hypothesis that information-integration category learning is mediated primarily by procedural memory. s procedural memory is thought to depend crucially on the basal ganglia, an obvious prediction is that patients with basal ganglia disease should be impaired in information-integration tasks. Two diseases in which the initial effects are primarily within the basal

5 Review TRENDS in Cognitive Sciences Vol.9 No.2 February Table 1. Memory systems utilized in categorization tasks Memory systems Brain regions Categorization task Working memory Prefrontal cortex, basal ganglia Rule-based Episodic/Semantic memory Medial temporal lobe Unstructured Procedural memory Basal ganglia Information-integration Perceptual Representation System (PRS) Sensory cortex (, not ) prototype distortion ganglia are Parkinson s and Huntington s disease. Both groups are impaired in information-integration category learning, although several studies have shown that Parkinson s patients are impaired only if the category structures are complex [16,50 52]. The perceptual representation memory system The perceptual representation memory system (PRS) was proposed to account for various repetition-priming effects in which exposure to a degraded stimulus improves performance on subsequent presentations of that stimulus, even when subjects have no conscious recollection of the degraded stimulus, and even in patients with amnesia [29,53]. Cognitive neuroscience theories postulate that the learning that underlies the PRS occurs within visual cortex [1,54,55]. The idea is that repeated presentations of the same stimulus during some relatively brief time interval enhance the firing of the visual cortical unit that is maximally sensitive to that stimulus [56]. Such enhanced sensitivity could facilitate category learning only if many category exemplars are highly similar to some central prototype. This way, the presentation of many stimuli belonging to the same category will stimulate the same visual cortical unit, thereby enhancing its response. nother important property is that only one category prototype elicits an enhanced visual response. If so, then the mere existence of an enhanced response signals to the subject which response is correct. However, if there are two categories, and B, both with exemplars that are highly similar to their own prototype, then after training an enhanced visual response will be likely on all trials. ccurate responding is still possible, but it will require some other memory system besides the PRS, which associates one enhanced response with category and the other enhanced response with category B. popular experimental paradigm that meets all these criteria is the (, not ) prototypedistortion task (Box 3). t present there are no data from traditional cognitive experiments that speak to the question of whether the Box 4. Questions for future research Is it possible to observe behavioral evidence that episodic/semantic and PRS memory systems sometimes mediate category learning? If category learning uses multiple memory systems, then how do these systems interact? Do they operate independently? Or do they compete or cooperate? Does the categorization behavior of experts depend on the same memory systems that are used during early category learning? Or is there a consolidation of learning into some common long-term (e.g. cortical) category representation as expertise develops? If multiple memory systems are recruited during category learning, then how can we exploit the unique properties of the different memory systems to facilitate the teaching of complex new categories? PRS mediates performance in (, not ) prototypedistortion tasks. Even so, there are neuropsychological and neuroimaging data that support this position. In fact, the neuropsychological patient data are profoundly different for the (, not ) prototype distortion task than for rule-based or information-integration tasks. In particular, a variety of patients groups that are known to have deficits in other categorization tasks show apparently normal (, not ) prototype-distortion learning. This includes patients with Parkinson s disease [57], schizophrenia [58], lzheimer s disease [33,59], or amnesia [29,30]. t least two studies have compared (, not ) and (, B) prototypedistortion learning on the same patients and both studies report normal (, not ) performance but impaired (, B) performance [33,34]. ll published neuroimaging studies that used (, not ) prototype distortion tasks have reported learning-related changes in occipital cortex [60 62]. The two known studies that used (, B) prototype distortion tasks reported quite different results [63,64]. Both found prefrontal and parietal activation but only one reported categorizationrelated activation in occipital cortex [63]. One possibility is that the PRS is ineffective in (, B) tasks, and instead other memory systems must be used. Conclusions We have reviewed evidence that each of the major memory systems contributes to category learning, and we described some categorization tasks in which each might control performance. Table 1 summarizes our conclusions. It should be noted however, that there is little or no direct evidence from traditional cognitive experiments that implicate episodic/semantic or PRS memory systems in category learning (see also Box 4). Instead the direct evidence for a role of these systems comes exclusively from neuropsychological and neuroimaging experiments, so much more experimental work is needed. Even if the multiple memory systems hypothesis were true however, we would expect several memory systems to contribute in most real-life category-learning situations, especially when the categories are complex and extended experience is required to achieve expertise. For example, although it seems plausible that procedural learning is required to become an expert radiologist, it is also undeniable that extensive explicit learning is required, which probably depends on all available declarative memory systems. By exploring the relationship between category learning and memory, two large and old literatures in cognitive science are brought together. This has already proved valuable to our understanding of categorization, as several of the new discoveries described above were motivated by well-known findings from classic memory

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