Can patients with Alzheimer's disease learn a category implicitly?

An adaptive linear filter model of procedural category learning

We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.

Strategy Development and Feedback Processing During Complex Category Learning

In this study, 38 young adults participated in a probabilistic A/B prototype category learning task under observational and feedback-based conditions. The study compared learning success (testing accuracy) and strategy use (multi-cue vs. single feature vs. random pattern) between training conditions. The feedback-related negativity (FRN) and P3a event related potentials were measured to explore the relationships between feedback processing and strategy use under a probabilistic paradigm. A greater number of participants were found to utilize an optimal, multi-cue strategy following feedback-based training than observational training, adding to the body of research suggesting that feedback can influence learning approach. There was a significant interaction between training phase and strategy on FRN amplitude. Specifically, participants who used a strategy in which category membership was determined by a single feature (single feature strategy) exhibited a significant decrease in FRN amplitude from early training to late training, perhaps due to reduced utilization of feedback or reduced prediction error. There were no significant main or interaction effects between valence, training phase, or strategy on P3a amplitude. Findings are consistent with prior research suggesting that learners vary in their approach to learning and that training method influences learning. Findings also suggest that measures of feedback processing during probabilistic category learning may reflect changes in feedback utilization and may further illuminate differences among individual learners.

RUBubbles as a novel tool to study categorization learning

Grouping objects into discrete categories affects how we perceive the world and represents a crucial element of cognition. Categorization is a widespread phenomenon that has been thoroughly studied. However, investigating categorization learning poses several requirements on the stimulus set in order to control which stimulus feature is used and to prevent mere stimulus-response associations or rote learning. Previous studies have used a wide variety of both naturalistic and artificial categories, the latter having several advantages such as better control and more direct manipulation of stimulus features. We developed a novel stimulus type to study categorization learning, which allows a high degree of customization at low computational costs and can thus be used to generate large stimulus sets very quickly. 'RUBubbles' are designed as visual artificial category stimuli that consist of an arbitrary number of colored spheres arranged in 3D space. They are generated using custom MATLAB code in which several stimulus parameters can be adjusted and controlled separately, such as number of spheres, position in 3D-space, sphere size, and color. Various algorithms for RUBubble generation can be combined with distinct behavioral training protocols to investigate different characteristics and strategies of categorization learning, such as prototype- vs. exemplar-based learning, different abstraction levels, or the categorization of a sensory continuum and category exceptions. All necessary MATLAB code is freely available as open-source code and can be customized or expanded depending on individual needs. RUBubble stimuli can be controlled purely programmatically or via a graphical user interface without MATLAB license or programming experience.

The role of working memory and visual processing in prototype category learning

To investigate whetherworking memory and visual processing havethe same role or differentrolesin A/B and A/not A prototype category learning,the present study adoptedan A/Bor A/not A category learningtask in control and dual conditions. The results of Experiment 1 showed that an additional dual visual working memory taskrather thanadualverbal working memory task reduced accuracy of the A/B task, whereasnodual tasksinfluencedaccuracy of the A/not A task. The results of Experiment 2 revealed that an additionaldual visual processing task impairedaccuracy of the A/B task, whereas the dual visual processing task did not influence accuracy of the A/not Atask. These results indicate that visual working memory and visual processing play different roles in A/B and A/not A prototype category learning, andsupport that thesetwo types of prototype category learning are mediated by differentmemory systems.

Prototype-based category learning in autism: A review

Similarity-based categorization, as an important cognitive skill, can be performed by abstracting a categories' central tendency, the so-called prototype, or by memorizing individual exemplars of a category. The flexible selection of an appropriate strategy is crucial for effective cognitive functioning. The detail-focused cognitive style in individuals with autism spectrum disorders (ASD) has been hypothesized to specifically impair prototype-based categorization but to leave exemplar-based categorization unimpaired. We first give an overview of approaches to investigate prototype-based abstraction in the prototype-distortion task, with an emphasis on model-based approaches suitable to discern the two strategies on the individual level. The second part summarizes literature speaking to prototype-based categorization in ASD using that task. Despite considerable inconsistencies, most studies appear to confirm that autistic individuals have more difficulties to perform prototype-distortion tasks than non-autistic individuals. We highlight how inconsistencies in literature can be resolved by taking the differences in task designs into account. The current review illustrates the need for sensitive computational approaches, suitable to detect hidden individual differences and potential compensatory strategies.

Perceived similarity ratings predict generalization success after traditional category learning and a new paired-associate learning task

The current study investigated category learning across two experiments using face-blend stimuli that formed face families controlled for within- and between-category similarity. Experiment 1 was a traditional feedback-based category-learning task, with three family names serving as category labels. In Experiment 2, the shared family name was encountered in the context of a face-full name paired-associate learning task, with a unique first name for each face. A subsequent test that required participants to categorize new faces from each family showed successful generalization in both experiments. Furthermore, perceived similarity ratings for pairs of faces were collected before and after learning, prior to generalization test. In Experiment 1, similarity ratings increased for faces within a family and decreased for faces that were physically similar but belonged to different families. In Experiment 2, overall similarity ratings decreased after learning, driven primarily by decreases for physically similar faces from different families. The post-learning category bias in similarity ratings was predictive of subsequent generalization success in both experiments. The results indicate that individuals formed generalizable category knowledge prior to an explicit demand to generalize and did so both when attention was directed towards category-relevant features (Experiment 1) and when attention was directed towards individuating faces within a family (Experiment 2). The results tie together research on category learning and categorical perception and extend them beyond a traditional category-learning task.

Tracking prototype and exemplar representations in the brain across learning

There is a long-standing debate about whether categories are represented by individual category members (exemplars) or by the central tendency abstracted from individual members (prototypes). Neuroimaging studies have shown neural evidence for either exemplar representations or prototype representations, but not both. Presently, we asked whether it is possible for multiple types of category representations to exist within a single task. We designed a categorization task to promote both exemplar and prototype representations and tracked their formation across learning. We found only prototype correlates during the final test. However, interim tests interspersed throughout learning showed prototype and exemplar representations across distinct brain regions that aligned with previous studies: prototypes in ventromedial prefrontal cortex and anterior hippocampus and exemplars in inferior frontal gyrus and lateral parietal cortex. These findings indicate that, under the right circumstances, individuals may form representations at multiple levels of specificity, potentially facilitating a broad range of future decisions.

Generalization and the hippocampus: More than one story?

Memory-based cognition depends on both the ability to remember specific details of individual experiences and the ability to combine information across experiences to generalize and derive new knowledge. A hippocampal role in rapid encoding of specific events is long established. More recent research also demonstrates hippocampal contributions to generalization, but their nature is still debated. The current review provides an overview of hippocampal-based generalization in two lines of research-episodic inference and categorization-and discusses evidence for four candidate mechanisms and representational schemes that may underpin such generalization. We highlight evidence showing that the hippocampus contributes specific memories to generalization decisions, but also forms generalized representations that integrate information across experiences. Multiple views are currently plausible of how such generalized representations form and relate to specific memories. Future research that uses behavioral and neural indices of both generalization and specificity may help resolve between the candidate generalization mechanisms, with the possibility that more than one view of hippocampal-based generalization may be valid. Importantly, all views share the emphasis on the broader role of the hippocampus in cognition that goes beyond remembering the past.

The Role of Edge-Based and Surface-Based Information in Incidental Category Learning: Evidence From Behavior and Event-Related Potentials

Although it has been demonstrated that edge-based information is more important than surface-based information in incidental category learning, it remains unclear how the two types of information play different roles in incidental category learning. To address this issue, the present study combined behavioral and event-related potential (ERP) techniques in an incidental category learning task in which the categories were defined by either edge- or surface-based features. The results from Experiment 1 showed that participants could simultaneously learn both edge- and surface-based information in incidental category learning, and importantly, there was a larger learning effect for the edge-based category than for the surface-based category. The behavioral results from Experiment 2 replicated those from Experiment 1, and the ERP results further revealed that the stimuli from the edge-based category elicited larger anterior and posterior P2 components than those from the surface-based category, whereas the stimuli from the surface-based category elicited larger anterior N1 and P3 components than those from the edge-based category. Taken together, the results suggest that, although surface-based information might attract more attention during feature detection, edge-based information plays more important roles in evaluating the relevance of information in making a decision in categorization.

Stimulus modality influences the acquisition and use of the rule-based strategy and the similarity-based strategy in category learning

This study aimed to investigate whether stimulus modality influenced the acquisition and use of the rule-based strategy and the similarity-based strategy in category learning and whether the use of the two strategies was supported by shared or separate neural substrates. To address these issues, we combined behavioral and fNIRS methods in a modified prototype distortion task in which each category member has one rule feature and ten similarity features, and each type of feature can be presented in either the visual modality or the auditory modality. The results in Experiment 1 revealed that the learning effect in the "auditory rule-visual similarity" condition was the highest among all four conditions; further analysis revealed that in the "auditory rule-visual similarity" condition, the number of participants who used the rule-based strategy was more than the number of participants who used the similarity-based strategy, and the learning effect was always much higher for the rule-based strategy than for the similarity-based strategy. The behavioral results in Experiment 2 replicated the main findings in Experiment 1, and the fNIRS results showed that the use of the visual rule-based strategy was mediated by the dorsolateral prefrontal cortex, whereas the use of the auditory similarity-based strategy mainly engaged in the superior temporal gyrus, and the use of the visual similarity-based strategy mainly engaged in the inferior temporal gyrus. The results in Experiment 3 revealed that when the stimuli had only one type of feature, the visual rule rather than the auditory rule was learned more easily. The results provide new evidence that the stimulus modality can influence the acquisition and use of the rule-based strategy and the similarity-based strategy in category learning and that the use of the two types of strategies is supported by separate neural substrates both in the auditory modality and the visual modality.

Which Matters More in Incidental Category Learning: Edge-Based Versus Surface-Based Features

Although many researches have shown that edge-based information is more important than surface-based information in object recognition, it remains unclear whether edge-based features play a more crucial role than surface-based features in category learning. To address this issue, a modified prototype distortion task was adopted in the present study, in which each category was defined by a rule or a similarity about either the edge-based features (i.e., contours or shapes) or the corresponding surface-based features (i.e., color and textures). The results of Experiments 1 and 2 showed that when the category was defined by a rule, the performance was significantly better in the edge-based condition than in the surface-based condition in the testing phase, and increasing the defined dimensions enhanced rather than reduced performance in the edge-based condition but not in the surface-based condition. The results of Experiment 3 showed that when each category was defined by a similarity, there was also a larger learning effect when the category was defined by edge-based dimensions than by surface-based dimensions in the testing phase. The current study is the first to provide convergent evidence that the edge-based information matters more than surface-based information in incidental category learning.

The effect of encoding conditions on learning in the prototype distortion task

The prototype distortion task demonstrates that it is possible to learn about a category of physically similar stimuli through mere observation. However, there have been few attempts to test whether different encoding conditions affect learning in this task. This study compared prototypicality gradients produced under incidental learning conditions in which participants performed a visual search task, with those produced under intentional learning conditions in which participants were required to memorize the stimuli. Experiment 1 showed that similar prototypicality gradients could be obtained for category endorsement and familiarity ratings, but also found (weaker) prototypicality gradients in the absence of exposure. In Experiments 2 and 3, memorization was found to strengthen prototypicality gradients in familiarity ratings in comparison to visual search, but there were no group differences in participants' ability to discriminate between novel and presented exemplars. Although the Search groups in Experiments 2 and 3 produced prototypicality gradients, they were no different in magnitude to those produced in the absence of stimulus exposure in Experiment 1, suggesting that incidental learning during visual search was not conducive to producing prototypicality gradients. This study suggests that learning in the prototype distortion task is not implicit in the sense of resulting automatically from exposure, is affected by the nature of encoding, and should be considered in light of potential learning-at-test effects.

Abstract Memory Representations in the Ventromedial Prefrontal Cortex and Hippocampus Support Concept Generalization

Memory function involves both the ability to remember details of individual experiences and the ability to link information across events to create new knowledge. Prior research has identified the ventromedial prefrontal cortex (VMPFC) and the hippocampus as important for integrating across events in the service of generalization in episodic memory. The degree to which these memory integration mechanisms contribute to other forms of generalization, such as concept learning, is unclear. The present study used a concept-learning task in humans (both sexes) coupled with model-based fMRI to test whether VMPFC and hippocampus contribute to concept generalization, and whether they do so by maintaining specific category exemplars or abstract category representations. Two formal categorization models were fit to individual subject data: a prototype model that posits abstract category representations and an exemplar model that posits category representations based on individual category members. Latent variables from each of these models were entered into neuroimaging analyses to determine whether VMPFC and the hippocampus track prototype or exemplar information during concept generalization. Behavioral model fits indicated that almost three-quarters of the subjects relied on prototype information when making judgments about new category members. Paralleling prototype dominance in behavior, correlates of the prototype model were identified in VMPFC and the anterior hippocampus with no significant exemplar correlates. These results indicate that the VMPFC and portions of the hippocampus play a broad role in memory generalization and that they do so by representing abstract information integrated from multiple events.SIGNIFICANCE STATEMENT Whether people represent concepts as a set of individual category members or by deriving generalized concept representations abstracted across exemplars has been debated. In episodic memory, generalized memory representations have been shown to arise through integration across events supported by the ventromedial prefrontal cortex (VMPFC) and hippocampus. The current study combined formal categorization models with fMRI data analysis to show that the VMPFC and anterior hippocampus represent abstract prototype information during concept generalization, contributing novel evidence of generalized concept representations in the brain. Results indicate that VMPFC-hippocampal memory integration mechanisms contribute to knowledge generalization across multiple cognitive domains, with the degree of abstraction of memory representations varying along the long axis of the hippocampus.

Occipitotemporal Category Representations Are Sensitive to Abstract Category Boundaries Defined by Generalization Demands

Categorization involves organizing perceptual information so as to maximize differences along dimensions that predict class membership while minimizing differences along dimensions that do not. In the current experiment, we investigated how neural representations reflecting learned category structure vary according to generalization demands. We asked male and female human participants to switch between two rules when determining whether stimuli should be considered members of a single known category. When categorizing according to the "strict" rule, participants were required to limit generalization to make fine-grained distinctions between stimuli and the category prototype. When categorizing according to the "lax" rule, participants were required to generalize category knowledge to highly atypical category members. As expected, frontoparietal regions were primarily sensitive to decisional demands (i.e., the distance of each stimulus from the active category boundary), whereas occipitotemporal representations were primarily sensitive to stimulus typicality (i.e., the similarity between each exemplar and the category prototype). Interestingly, occipitotemporal representations of stimulus typicality differed between rules. While decoding models were able to predict unseen data when trained and tested on the same rule, they were unable to do so when trained and tested on different rules. We additionally found that the discriminability of the multivariate signal negatively covaried with distance from the active category boundary. Thus, whereas many accounts of occipitotemporal cortex emphasize its important role in transforming visual information to accentuate learned category structure, our results highlight the flexible nature of these representations with regards to transient decisional demands.SIGNIFICANCE STATEMENT Occipitotemporal representations are known to reflect category structure and are often assumed to be largely invariant with regards to transient decisional demands. We found that representations of equivalent stimuli differed between strict and lax generalization rules, and that the discriminability of these representations increased as distance from abstract category boundaries decreased. Our results therefore indicate that occipitotemporal representations are flexibly modulated by abstract decisional factors.

Longitudinal Assessment of Verbal Learning and Memory in Amnestic Mild Cognitive Impairment: Practice Effects and Meaningful Changes

Objectives: To identify learning effects and meaningful changes in amnestic mild cognitive impairment (aMCI) at a follow-up assessment.

Method: The Spanish version of the California Verbal Learning Test (CVLT) was administered to a sample of 274 adults of age over 50 years with subjective memory complains (SMC), including single and multiple domain aMCI groups and participants with SMC but without cognitive impairment (SMC group). The Wilcoxon test was used to compare results at baseline and after 18 months in short and long recall, and standardized regression-based (SRB) methods were used to study meaningful changes.

Results: Scores were significantly higher at follow-up for short and long-delayed recall in all groups indicating generalized practice effect. SRB scores indicated a significant decline in recall in a higher proportion of participants with aMCI than in SMC group.

Discussion: Patients with multiple and single domain aMCI benefit from practice in a verbal learning memory test. The SRB approach revealed a higher incidence of meaningful decline in short and long-delay recall and recognition in the aMCI groups than in the SMC group. Specifically, compared to SMC participants, single-domain aMCI individuals declined in a higher proportion in all measures, and multiple-domain aMCI individuals in long delay free recall.

Category learning in Alzheimer's disease and normal cognitive aging depends on initial experience of feature variability

Semantic category learning is dependent upon several factors, including the nature of the learning task, as well as individual differences in the quality and heterogeneity of exemplars that an individual encounters during learning. We trained healthy older adults (n=39) and individuals with a diagnosis of Alzheimer's disease or Mild Cognitive Impairment (n=44) to recognize instances of a fictitious animal, a "crutter". Each stimulus item contained 10 visual features (e.g., color, tail shape) which took one of two values for each feature (e.g., yellow/red, curly/straight tails). Participants were presented with a series of items (learning phase) and were either told the items belonged to a semantic category (explicit condition) or were told to think about the appearance of the items (implicit condition). Half of participants saw learning items with higher similarity to an unseen prototype (high typicality learning set), and thus lower between-item variability in their constituent features; the other half learned from items with lower typicality (low typicality learning set) and higher between-item feature variability. After the learning phase, participants were presented with test items one at a time that varied in the number of typical features from 0 (antitype) to 10 (prototype). We examined between-subjects factors of learning set (lower or higher typicality), instruction type (explicit or implicit), and group (patients vs. elderly control). Learning in controls was aided by higher learning set typicality: while controls in both learning set groups demonstrated significant learning, those exposed to a high-typicality learning set appeared to develop a prototype that helped guide their category membership judgments. Overall, patients demonstrated more difficulty with category learning than elderly controls. Patients exposed to the higher-typicality learning set were sensitive to the typical features of the category and discriminated between the most and least typical test items, although less reliably than controls. In contrast, patients exposed to the low-typicality learning set showed no evidence of learning. Analysis of structural imaging data indicated a positive association between left hippocampal grey matter density in elderly controls but a negative association in the patient group, suggesting differential reliance on hippocampal-mediated learning. Contrary to hypotheses, learning did not differ between explicit and implicit conditions for either group. Results demonstrate that category learning is improved when learning materials are highly similar to the prototype.

An interplay of fusiform gyrus and hippocampus enables prototype- and exemplar-based category learning

The aim of the present study was to examine the contributions of different brain structures to prototype- and exemplar-based category learning using functional magnetic resonance imaging (fMRI). Twenty-eight subjects performed a categorization task in which they had to assign prototypes and exceptions to two different families. This test procedure usually produces different learning curves for prototype and exception stimuli. Our behavioral data replicated these previous findings by showing an initially superior performance for prototypes and typical stimuli and a switch from a prototype-based to an exemplar-based categorization for exceptions in the later learning phases. Since performance varied, we divided participants into learners and non-learners. Analysis of the functional imaging data revealed that the interaction of group (learners vs. non-learners) and block (Block 5 vs. Block 1) yielded an activation of the left fusiform gyrus for the processing of prototypes, and an activation of the right hippocampus for exceptions after learning the categories. Thus, successful prototype- and exemplar-based category learning is associated with activations of complementary neural substrates that constitute object-based processes of the ventral visual stream and their interaction with unique-cue representations, possibly based on sparse coding within the hippocampus.

Prototype learning and dissociable categorization systems in Alzheimer's disease

Recent neuroimaging studies suggest that prototype learning may be mediated by at least two dissociable memory systems depending on the mode of acquisition, with A/Not-A prototype learning dependent upon a perceptual representation system located within posterior visual cortex and A/B prototype learning dependent upon a declarative memory system associated with medial temporal and frontal regions. The degree to which patients with Alzheimer's disease (AD) can acquire new categorical information may therefore critically depend upon the mode of acquisition. The present study examined A/Not-A and A/B prototype learning in AD patients using procedures that allowed direct comparison of learning across tasks. Despite impaired explicit recall of category features in all tasks, patients showed differential patterns of category acquisition across tasks. First, AD patients demonstrated impaired prototype induction along with intact exemplar classification under incidental A/Not-A conditions, suggesting that the loss of functional connectivity within visual cortical areas disrupted the integration processes supporting prototype induction within the perceptual representation system. Second, AD patients demonstrated intact prototype induction but impaired exemplar classification during A/B learning under observational conditions, suggesting that this form of prototype learning is dependent on a declarative memory system that is disrupted in AD. Third, the surprisingly intact classification of both prototypes and exemplars during A/B learning under trial-and-error feedback conditions suggests that AD patients shifted control from their deficient declarative memory system to a feedback-dependent procedural memory system when training conditions allowed. Taken together, these findings serve to not only increase our understanding of category learning in AD, but to also provide new insights into the ways in which different memory systems interact to support the acquisition of categorical knowledge.

Priming for performance: valence of emotional primes interact with dissociable prototype learning systems

Arousal Biased Competition theory suggests that arousal enhances competitive attentional processes, but makes no strong claims about valence effects. Research suggests that the scope of enhanced attention depends on valence with negative arousal narrowing and positive arousal broadening attention. Attentional scope likely affects declarative-memory-mediated and perceptual-representation-mediated learning systems differently, with declarative-memory-mediated learning depending on narrow attention to develop targeted verbalizable rules, and perceptual-representation-mediated learning depending on broad attention to develop a perceptual representation. We hypothesize that negative arousal accentuates declarative-memory-mediated learning and attenuates perceptual-representation-mediated learning, while positive arousal reverses this pattern. Prototype learning provides an ideal test bed as dissociable declarative-memory and perceptual-representation systems mediate two-prototype (AB) and one-prototype (AN) prototype learning, respectively, and computational models are available that provide powerful insights on cognitive processing. As predicted, we found that negative arousal narrows attentional focus facilitating AB learning and impairing AN learning, while positive arousal broadens attentional focus facilitating AN learning and impairing AB learning.

Performing prototype distortion tasks requires no contribution from the explicit memory systems: evidence from amnesic MCI patients in a new experimental paradigm

Evidence shows that amnesic patients are able to categorize new exemplars drawn from the same prototype as in previously encountered items. It is still unclear, however, whether this ability is due to a spared implicit learning system or residual explicit memory and/or working memory resources. In this study, we used a new paradigm devised expressly to rule out any possible contribution of episodic and working memory in performing a prototype distortion task. We enrolled patients with amnesic MCI and Normal Controls. Our paradigm consisted of a study phase and a test phase; two-thirds of the participants performed the study phase and all participants performed the test phase. In the study phase, participants had to judge how pleasant morphed faces, drawn from a single prototype, seemed to them. Half of the participants were shown faces drawn from the A-prototype and half from the B-prototype. A- and B-faces were opposite in a morphing space with a neutral human face at the center. In the test phase, participants had to judge the regularity of faces they had never seen before. Three different types of faces were shown in the test phase, that is, A-, B-, or neutral-faces. We expected that implicit learning of the category boundaries would lead to a category-specific increase in perceived regularity. The results confirmed our predictions. In fact, trained subjects (compared with subjects who did not undergo the study phase) assigned higher regularity scores to new faces drawn from the same prototype as the faces seen during training, and they gave lower regularity scores to new faces drawn from the opposite prototype. This effect was super imposable across subjects' groups.

Normal aging and the dissociable prototype learning systems

Dissociable prototype learning systems have been demonstrated behaviorally and with neuroimaging in younger adults as well as with patient populations. In A/not-A (AN) prototype learning, participants are shown members of category A during training, and during test are asked to decide whether novel items are in category A or are not in category A. Research suggests that AN learning is mediated by a perceptual learning system. In A/B (AB) prototype learning, participants are shown members of category A and B during training, and during test are asked to decide whether novel items are in category A or category B. In contrast to AN, research suggests that AB learning is mediated by a declarative memory system. The current study examined the effects of normal aging on AN and AB prototype learning. We observed an age-related deficit in AB learning, but an age-related advantage in AN learning. Computational modeling supports one possible interpretation based on narrower selective attentional focus in older adults in the AB task and broader selective attention in the AN task. Neuropsychological testing in older participants suggested that executive functioning and attentional control were associated with better performance in both tasks. However, nonverbal memory was associated with better AN performance, while visual attention was associated with worse AB performance. The results support an interactive memory systems approach and suggest that age-related declines in one memory system can lead to deficits in some tasks, but to enhanced performance in others.

Activation in the neural network responsible for categorization and recognition reflects parameter changes

According to various influential formal models of cognition, perceptual categorization and old-new recognition recruit the same memory system. By contrast, the prevailing view in the cognitive neuroscience literature is that separate neural systems mediate perceptual categorization and recognition. A direct form of evidence is that separate brain regions are activated when observers engage in categorization and recognition tasks involving the same types of stimuli. However, even if the same memory-based comparison processes underlie categorization and recognition, one would not expect to see identical patterns of brain activity across the tasks; the reason is that observers would adjust parameter settings (e.g., vary criterion settings) across the tasks to satisfy the different task goals. In this fMRI study, we conducted categorization and recognition tasks in which stimulus conditions were held constant, and in which observers were induced to vary hypothesized parameter settings across conditions. A formal exemplar model was fitted to the data to track the changes in parameters to help interpret the fMRI results. We observed systematic effects of changes in parameters on patterns of brain activity, which were interpretable in terms of differing forms of evidence accumulation that resulted from the changed parameter settings. After controlling for stimulus and parameter-related differences, we found little evidence that categorization and recognition recruit separate memory systems.

The effects of sleep deprivation on dissociable prototype learning systems

BACKGROUND

The cognitive neural underpinnings of prototype learning are becoming clear. Evidence points to 2 different neural systems, depending on the learning parameters. A/not-A (AN) prototype learning is mediated by posterior brain regions that are involved in early perceptual learning, whereas A/B (AB) is mediated by frontal and medial temporal lobe regions.

STUDY OBJECTIVES

To investigate the effects of sleep deprivation on AN and AB prototype learning and to use established prototype models to provide insights into the cognitive-processing locus of sleep-deprivation deficits.

DESIGN

Participants performed an AN and an AB prototype learning task twice, separated by a 24-hour period, with or without sleep between testing sessions.

PARTICIPANTS

Eighteen West Point cadets participated in the sleep-deprivation group, and 17 West Point cadets participated in a control group.

MEASUREMENTS AND RESULTS

Sleep deprivation led to an AN, but not an AB, performance deficit. Prototype model analyses indicated that the AN deficit was due to changes in attentional focus and a decrease in confidence that is reflected in an increased bias to respond non-A.

CONCLUSIONS

The findings suggest that AN, but not AB, prototype learning is affected by sleep deprivation. Prototype model analyses support the notion that the effect of sleep deprivation on AN is consistent with lapses in attentional focus that are more detrimental to AN than to AB. This finding adds to a growing body of work that suggests that different performance changes associated with sleep deprivation can be attributed to a common mechanism of changes in simple attention and vigilance.

Dissociable prototype learning systems: evidence from brain imaging and behavior

The neural underpinnings of prototype learning are not well understood. A major source of confusion is that two versions of the prototype learning task have been used interchangeably in the literature; one where participants learn to categorize exemplars derived from two prototypes (A/B task), and one where participants learn to categorize exemplars derived from one prototype and noncategorical exemplars (A/non-A). We report results from an fMRI study of A/B and A/non-A prototype learning that allows for a direct contrast of the two learning methods. Accuracy in the two tasks did not correlate within subject despite equivalent average difficulty. The fMRI results revealed neural activation in a network of regions consistent with episodic memory retrieval for the A/B task while greater activation of a nondeclarative learning network was observed for the A/non-A task. The results demonstrate that learning in these two tasks is mediated by different neural systems and that recruitment of each system is dictated by the context of learning rather than the actual category structure.

Medial temporal lobe involvement in an implicit memory task: evidence of collaborating implicit and explicit memory systems from FMRI and Alzheimer's disease

We used a prototype extraction task to assess implicit learning of a meaningful novel visual category. Cortical activation was monitored in young adults with functional magnetic resonance imaging. We observed occipital deactivation at test consistent with perceptually based implicit learning, and lateral temporal cortex deactivation reflecting implicit acquisition of the category's semantic nature. Medial temporal lobe (MTL) activation during exposure and test suggested involvement of explicit memory as well. Behavioral performance of Alzheimer's disease (AD) patients and healthy seniors was also assessed, and AD performance was correlated with gray matter volume using voxel-based morphometry. AD patients showed learning, consistent with preserved implicit memory, and confirming that AD patients' implicit memory is not limited to abstract patterns. However, patients were somewhat impaired relative to healthy seniors. Occipital and lateral temporal cortical volume correlated with successful AD patient performance, and thus overlapped with young adults' areas of deactivation. Patients' severe MTL atrophy precluded involvement of this region. AD patients thus appear to engage a cortically based implicit memory mechanism, whereas their relative deficit on this task may reflect their MTL disease. These findings suggest that implicit and explicit memory systems collaborate in neurologically intact individuals performing an ostensibly implicit memory task.

The case for implicit category learning

This article evaluates the evidence regarding the claim that people can learn a novel category implicitly--that is, by an implicit memory system that is qualitatively different from an explicit system. The evidence that is considered is based on the prototype extraction task, in which participants are first exposed to a set of category exemplars under incidental learning instructions and are then required to categorize novel test items. Knowlton and Squire (1993) first reported that memory-impaired patients performed normally on the prototype extraction task while being impaired on a comparable recognition task. Several studies have replicated these results, but other articles have criticized the evidence for implicit category learning on both methodological and theoretical grounds. In this article, we consider five of these criticisms-for example, that the normal performance of the patients is due to intact working memory mechanisms (see, e.g., Palmeri & Flannery, 1999) or to the lesser cognitive demands of prototype extraction rather than recognition (e.g., Nosofsky & Zaki, 1998). For each of the five criticisms, we offer counterevidence that supports implicit category learning.

Multiple systems of category learning

We review neuropsychological and neuroimaging evidence for the existence of three qualitatively different categorization systems. These categorization systems are themselves based on three distinct memory systems: working memory (WM), explicit long-term memory (explicit LTM), and implicit long-term memory (implicit LTM). We first contrast categorization based on WM with that based on explicit LTM, where the former typically involves applying rules to a test item and the latter involves determining the similarity between stored exemplars or prototypes and a test item. Neuroimaging studies show differences between brain activity in normal participants as a function of whether they are instructed to categorize novel test items by rule or by similarity to known category members. Rule instructions typically lead to more activation in frontal or parietal areas, associated with WM and selective attention, whereas similarity instructions may activate parietal areas associated with the integration of perceptual features. Studies with neurological patients in the same paradigms provide converging evidence, e.g., patients with Alzheimer's disease, who have damage in prefrontal regions, are more impaired with rule than similarity instructions. Our second contrast is between categorization based on explicit LTM with that based on implicit LTM. Neuropsychological studies with patients with medial-temporal lobe damage show that patients are impaired on tasks requiring explicit LTM, but perform relatively normally on an implicit categorization task. Neuroimaging studies provide converging evidence: whereas explicit categorization is mediated by activation in numerous frontal and parietal areas, implicit categorization is mediated by a deactivation in posterior cortex.

Category learning and the memory systems debate

A substantial and growing body of evidence from cognitive neuroscience supports the concept of multiple memory systems (MMS). However, the existence of multiple systems has been questioned by theorists who instead propose that dissociations can be accounted for within a single memory system. We present convergent evidence from neuroimaging and neuropsychological studies of category learning in favor of the existence of MMS for category learning and declarative knowledge. Whereas single-system theorists have argued that their approach is more parsimonious because it only postulates a single form of memory representation, we show that the MMS approach is superior in its ability to account for a broad range of data from psychology and neuroscience.

Basal ganglia and dopamine contributions to probabilistic category learning

Studies of the medial temporal lobe and basal ganglia memory systems have recently been extended towards understanding the neural systems contributing to category learning. The basal ganglia, in particular, have been linked to probabilistic category learning in humans. A separate parallel literature in systems neuroscience has emerged, indicating a role for the basal ganglia and related dopamine inputs in reward prediction and feedback processing. Here, we review behavioral, neuropsychological, functional neuroimaging, and computational studies of basal ganglia and dopamine contributions to learning in humans. Collectively, these studies implicate the basal ganglia in incremental, feedback-based learning that involves integrating information across multiple experiences. The medial temporal lobes, by contrast, contribute to rapid encoding of relations between stimuli and support flexible generalization of learning to novel contexts and stimuli. By breaking down our understanding of the cognitive and neural mechanisms contributing to different aspects of learning, recent studies are providing insight into how, and when, these different processes support learning, how they may interact with each other, and the consequence of different forms of learning for the representation of knowledge.