Interactions between declarative and procedural-learning categorization systems

Cognitive processing stages in mental rotation - How can cognitive modelling inform HsMM-EEG models?

The aspiration for insight into human cognitive processing has traditionally driven research in cognitive science. With methods such as the Hidden semi-Markov Model-Electroencephalography (HsMM-EEG) method, new approaches have been developed that help to understand the temporal structure of cognition by identifying temporally discrete processing stages. However, it remains challenging to assign concrete functional contributions by specific processing stages to the overall cognitive process. In this paper, we address this challenge by linking HsMM-EEG3 with cognitive modelling, with the aim of further validating the HsMM-EEG3 method and demonstrating the potential of cognitive models to facilitate functional interpretation of processing stages. For this purpose, we applied HsMM-EEG3 to data from a mental rotation task and developed an ACT-R cognitive model that is able to closely replicate human performance in this task. Applying HsMM-EEG3 to the mental rotation experiment data revealed a strong likelihood for 6 distinct stages of cognitive processing during trials, with an additional stage for non-rotated conditions. The cognitive model predicted intra-trial mental activity patterns that project well onto the processing stages, while explaining the additional stage as a marker of non-spatial shortcut use. Thereby, this combined methodology provided substantially more information than either method by itself and suggests conclusions for cognitive processing in general.

A computational model of prefrontal and striatal interactions in perceptual category learning

Work on multiple-system theories of cognition mostly focused on the systems themselves, while limited work has been devoted to understanding the interactions between systems. Generally, multiple-system theories include a model-based decision system supported by the prefrontal cortex and a model-free decision system supported by the striatum. Here we propose a neurobiological model to describe the interactions between model-based and model-free decision systems in category learning. The proposed model used spiking neurons to simulate activity of the hyperdirect pathway of the basal ganglia. The hyperdirect pathway acts as a gate for the response signal from the model-free system located in the striatum. We propose that the model-free system's response is inhibited when the model-based system is in control of the response. The new model was used to simulate published data from young adults, people with Parkinson's disease, and aged-matched older adults. The simulation results further suggest that system-switching ability may be related to individual differences in executive function. A new behavioral experiment tested this model prediction. The results show that an updating score predicts the ability to switch system in a categorization task. The article concludes with new model predictions and implications of the results for research on system interactions.

Delaying feedback compensates for impaired reinforcement learning in developmental dyslexia

A theoretical framework suggests that developmental dyslexia is characterized by abnormalities in brain structures underlying the procedural learning and memory systems while the declarative learning and memory systems are presumed to remain intact or even enhanced (Procedural Deficit Hypothesis). This notion has been supported by a substantial body of research, which focused on each system independently. However, less attention has been paid to interactions between these memory systems which may provide insights as to learning situations and conditions in which learning in dyslexia can be improved. The current study was undertaken to examine these important but unresolved issues. To this end, probabilistic reinforcement learning and episodic memory tasks were examined in participants with dyslexia and neurotypicals simultaneously within a single task. Feedback timing presentation was manipulated, building on prior research indicating that delaying feedback timing shifts striatal-based probabilistic learning, to become more hippocampal-dependent. It was hypothesized that if the procedural learning and memory systems are impaired in dyslexia, performance will be impaired under conditions that encourage procedural memory engagement (immediate feedback trials) but not under conditions that promote declarative memory processing (long delayed feedback trials). It was also predicted that the ability to incidentally acquire episodic information would be preserved in dyslexia. The results supported these predictions. Participants with dyslexia were impaired in probabilistic learning of cue-outcome associations compared to neurotypicals in an immediate feedback condition, but not when feedback on choices was presented after a long delay. Furthermore, participants with dyslexia demonstrated similar performance to neurotypicals in a task requiring incidental episodic memory formation. These findings attest to a dissociation between procedural-based and declarative-based learning in developmental dyslexia within a single task, a finding that adds discriminative validity to the Procedural Deficit Hypothesis. Just as important, the present findings suggest that training conditions designed to shift the load from midbrain/striatal systems to declarative memory mechanisms have the potential to compensate for impaired learning in developmental dyslexia.

Competitive and cooperative interactions between medial temporal and striatal learning systems

The striatum and medial temporal lobes (MTL) exhibit dissociable roles during learning. Whereas the striatum and its network of thalamic relays and cortical nodes are necessary for nondeclarative learning, the MTL and associated network are required for declarative learning. Several studies have suggested that these networks are functionally competitive during learning. Since these discoveries, however, evidence has accumulated that they can operate in a cooperative fashion. In this review, we discuss evidence for both competition and cooperation between these systems during learning, with the aim of reconciling these seemingly contradictory findings. Examples of cooperation between the striatum and MTL have been provided, especially during consolidation and generalization of knowledge, and do not appear to be precluded by differences in functional specialization. However, whether these systems cooperate or compete does seem to depend on the phase of learning and cognitive or motor aspects of the task. The involvement of other regions, such as midbrain dopaminergic nuclei and the prefrontal cortex, may promote and mediate cooperation between the striatum and the MTL during learning. Building on this body of research, we propose a model for striatum-MTL interactions in learning and memory and attempt to predict, in general terms, when cooperation or competition will occur.

Auditory information-integration category learning in young children and adults

Adults outperform children on category learning that requires selective attention to individual dimensions (rule-based categories) due to their more highly developed working memory abilities, but much less is known about developmental differences in learning categories that require integration across multiple dimensions (information-integration categories). The current study investigated auditory information-integration category learning in 5- to 7-year-old children (n = 34) and 18- to 25-year-old adults (n = 35). Adults generally outperformed children during learning. However, some children learned the categories well and used strategies similar to those of adults, as assessed through decision-bound computational models. The results demonstrate that information-integration learning ability continues to develop throughout at least middle childhood. These results have implications for the development of mechanisms that contribute to speech category learning.

Categorization system-switching deficits in typical aging and Parkinson's disease

OBJECTIVE

Numerous studies documenting cognitive deficits in Parkinson's disease (PD) revealed impairment in a variety of tasks related to memory, learning, and attention. One ubiquitous task that has not received much attention, is categorization system-switching. Categorization system-switching is a form of task-switching requiring participants to switch between different categorization systems. In this article, we explore whether older adults and people with PD show deficits in categorization system-switching.

METHOD

Twenty older adults diagnosed with PD, 20 neurologically intact older adults, and 67 young adults participated in this study. Participants were first trained in rule-based (RB) and later information-integration (II) categorization separately. After training on the tasks, participants performed a block of trial-by-trial switching where the RB and II trials were randomly intermixed. Finally, the last block of trials also intermixed RB and II trials were randomly but additionally changed the location of the response buttons.

RESULTS

Contrary to our hypothesis, the results show no difference in accuracy between older adults and people with PD during the intermixed trial block, as well as no difference in response time (RT) switch cost. However, both groups were less accurate during intermixed trial blocks and had a higher RT switch cost when compared with young adults. In addition, the proportion of participants able to switch systems was smaller in people with PD than in young adults.

CONCLUSIONS

The results suggest that older adults and people with PD have impaired categorization system-switching ability, and that this ability may be related to a decrease in tonic dopamine (DA) levels associated with normal aging and PD. (PsycINFO Database Record

Trial-by-trial identification of categorization strategy using iterative decision-bound modeling

Identifying the strategy that participants use in laboratory experiments is crucial in interpreting the results of behavioral experiments. This article introduces a new modeling procedure called iterative decision-bound modeling (iDBM), which iteratively fits decision-bound models to the trial-by-trial responses generated from single participants in perceptual categorization experiments. The goals of iDBM are to identify: (1) all response strategies used by a participant, (2) changes in response strategy, and (3) the trial number at which each change occurs. The new method is validated by testing its ability to identify the response strategies used in noisy simulated data. The benchmark simulation results show that iDBM is able to detect and identify strategy switches during an experiment and accurately estimate the trial number at which the strategy change occurs in low to moderate noise conditions. The new method is then used to reanalyze data from Ell and Ashby (2006). Applying iDBM revealed that increasing category overlap in an information-integration category learning task increased the proportion of participants who abandoned explicit rules, and reduced the number of training trials needed to abandon rules in favor of a procedural strategy. Finally, we discuss new research questions made possible through iDBM.

Practice and Preparation Time Facilitate System-Switching in Perceptual Categorization

Mounting evidence suggests that category learning is achieved using different psychological and biological systems. While existing multiple-system theories and models of categorization may disagree about the number or nature of the different systems, all assume that people can switch between systems seamlessly. However, little empirical data has been collected to test this assumption, and recent available data suggest that system-switching is difficult. The main goal of this article is to identify factors influencing the proportion of participants who successfully learn to switch between procedural and declarative systems on a trial-by-trial basis. Specifically, we tested the effects of preparation time and practice, two factors that have been useful in task-switching, in a system-switching experiment. The results suggest that practice and preparation time can be beneficial to system-switching (as calculated by a higher proportion of switchers and lower switch costs), especially when they are jointly present. However, this improved system-switching comes at the cost of a larger button-switch interference when changing the location of the response buttons. The article concludes with a discussion of the implications of these findings for empirical research on system-switching and theoretical work on multiple-systems of category learning.

Hierarchical control of procedural and declarative category-learning systems

Substantial evidence suggests that human category learning is governed by the interaction of multiple qualitatively distinct neural systems. In this view, procedural memory is used to learn stimulus-response associations, and declarative memory is used to apply explicit rules and test hypotheses about category membership. However, much less is known about the interaction between these systems: how is control passed between systems as they interact to influence motor resources? Here, we used fMRI to elucidate the neural correlates of switching between procedural and declarative categorization systems. We identified a key region of the cerebellum (left Crus I) whose activity was bidirectionally modulated depending on switch direction. We also identified regions of the default mode network (DMN) that were selectively connected to left Crus I during switching. We propose that the cerebellum-in coordination with the DMN-serves a critical role in passing control between procedural and declarative memory systems.

Trial-by-trial switching between procedural and declarative categorization systems

Considerable evidence suggests that human category learning recruits multiple memory systems. A popular assumption is that procedural memory is used to form stimulus-to-response mappings, whereas declarative memory is used to form and test explicit rules about category membership. The multiple systems framework has been successful in motivating and accounting for a broad array of empirical observations over the past 20 years. Even so, only a couple of studies have examined how the different categorization systems interact. Both previous studies suggest that switching between explicit and procedural responding is extremely difficult. But they leave unanswered the critical questions of whether trial-by-trial system switching is possible, and if so, whether it is qualitatively different than trial-by-trial switching between two explicit tasks. The experiment described in this article addressed these questions. The results (1) confirm that effective trial-by-trial system switching, although difficult, is possible; (2) suggest that switching between tasks mediated by different memory systems is more difficult than switching between two declarative memory tasks; and (3) point to a serious shortcoming of current category-learning theories.

The time course of explicit and implicit categorization

Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit categorization.

One Giant Leap for Categorizers: One Small Step for Categorization Theory

We explore humans’ rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans’ rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans’ psychology in these tasks. An important next step will be to build a new generation of models that can do so.

Declarative strategies persist under increased cognitive load

When humans simultaneously execute multiple tasks, performance on individual tasks suffers. Complementing existing theories, this article poses a novel question to investigate interactions between memory systems supporting multi-tasking performance: When a primary and dual task both recruit declarative learning and memory systems, does simultaneous performance of both tasks impair primary task performance because learning in the declarative system is reduced, or because control of the primary task is passed to slower procedural systems? To address this question, participants were trained on either a perceptual categorization task believed to rely on procedural learning or one of three different categorization tasks believed to rely on declarative learning. Task performance was examined with and without a simultaneous dual task thought to recruit working memory and executive attention. To test whether the categories were learned procedurally or declaratively, the response keys were switched after a learning criterion had been reached. Large impairments in performance after switching the response keys are taken to indicate procedural learning, and small impairments are taken to indicate declarative learning. Our results suggest that the declarative memory categorization tasks (regardless of task difficulty) were learned by declarative systems, regardless of whether they were learned under dual-task conditions.

Is state-trace analysis an appropriate tool for assessing the number of cognitive systems?

There is now much evidence that humans have multiple memory systems, and evidence is also building that other cognitive processes are mediated by multiple systems. Even so, several recent articles have questioned the existence of multiple cognitive systems, and a number of these have based their arguments on results from state-trace analysis. State-trace analysis was not developed for this purpose but, rather, to identify data sets that are consistent with variation in a single parameter. All previous applications have assumed that state-trace plots in which the data fall on separate curves rule out any model in which only a single parameter varies across the two tasks under study. Unfortunately, this assumption is incorrect. Models in which only one parameter varies can generate any type of state-trace plot, as can models in which two or more parameters vary. In addition, it is straightforward to show that both single-system and multiple-systems models can generate state-trace plots that are considered in the literature to be consistent with either one or multiple cognitive systems. Thus, without additional information, there is no empirical state-trace plot that supports any inferences about the number of underlying parameters or systems.

Continuous executive function disruption interferes with application of an information integration categorization strategy

Category learning is often characterized as being supported by two separate learning systems. A verbal system learns rule-defined (RD) categories that can be described using a verbal rule and relies on executive functions (EFs) to learn via hypothesis testing. A nonverbal system learns non-rule-defined (NRD) categories that cannot be described by a verbal rule and uses automatic, procedural learning. The verbal system is dominant in that adults tend to use it during initial learning but may switch to the nonverbal system when the verbal system is unsuccessful. The nonverbal system has traditionally been thought to operate independently of EFs, but recent studies suggest that EFs may play a role in the nonverbal system-specifically, to facilitate the transition away from the verbal system. Accordingly, continuously interfering with EFs during the categorization process, so that EFs are never fully available to facilitate the transition, may be more detrimental to the nonverbal system than is temporary EF interference. Participants learned an NRD or an RD category while EFs were untaxed, taxed temporarily, or taxed continuously. When EFs were continuously taxed during NRD categorization, participants were less likely to use a nonverbal categorization strategy than when EFs were temporarily taxed, suggesting that when EFs were unavailable, the transition to the nonverbal system was hindered. For the verbal system, temporary and continuous interference had similar effects on categorization performance and on strategy use, illustrating that EFs play an important but different role in each of the category-learning systems.

Intention understanding over T: a neuroimaging study on shared representations and tennis return predictions

Studying the way athletes predict actions of their peers during fast-ball sports, such as a tennis, has proved to be a valuable tool for increasing our knowledge of intention understanding. The working model in this area is that the anticipatory representations of others' behaviors require internal predictive models of actions formed from pre-established and shared representations between the observer and the actor. This model also predicts that observers would not be able to read accurately the intentions of a competitor if the competitor were to perform the action without prior knowledge of their intention until moments before the action. To test this hypothesis, we recorded brain activity from 25 male tennis players while they performed a novel behavioral tennis intention inference task, which included two conditions: (i) one condition in which they viewed video clips of a tennis athlete who knew in advance where he was about to act/serve (initially intended serves) and (ii) one condition in which they viewed video clips of that same athlete when he did not know where he was to act/serve until the target was specified after he had tossed the ball into the air to complete his serve (non-initially intended serves). Our results demonstrated that (i) tennis expertise is related to the accuracy in predicting where another server intends to serve when that server knows where he intends to serve before (but not after) he tosses the ball in the air; and (ii) accurate predictions are characterized by the recruitment of both cortical areas within the human mirror neuron system (that is known to be involved in higher-order (top-down) processes of embodied cognition and shared representation) and subcortical areas within brain regions involved in procedural memory (caudate nucleus). Interestingly, inaccurate predictions instead recruit areas known to be involved in low-level (bottom-up) computational processes associated with the sense of agency and self-other distinction.

Prior experience with negative spectral correlations promotes information integration during auditory category learning

Complex sounds vary along a number of acoustic dimensions. These dimensions may exhibit correlations that are familiar to listeners due to their frequent occurrence in natural sounds-namely, speech. However, the precise mechanisms that enable the integration of these dimensions are not well understood. In this study, we examined the categorization of novel auditory stimuli that differed in the correlations of their acoustic dimensions, using decision bound theory. Decision bound theory assumes that stimuli are categorized on the basis of either a single dimension (rule based) or the combination of more than one dimension (information integration) and provides tools for assessing successful integration across multiple acoustic dimensions. In two experiments, we manipulated the stimulus distributions such that in Experiment 1, optimal categorization could be accomplished by either a rule-based or an information integration strategy, while in Experiment 2, optimal categorization was possible only by using an information integration strategy. In both experiments, the pattern of results demonstrated that unidimensional strategies were strongly preferred. Listeners focused on the acoustic dimension most closely related to pitch, suggesting that pitch-based categorization was given preference over timbre-based categorization. Importantly, in Experiment 2, listeners also relied on a two-dimensional information integration strategy, if there was immediate feedback. Furthermore, this strategy was used more often for distributions defined by a negative spectral correlation between stimulus dimensions, as compared with distributions with a positive correlation. These results suggest that prior experience with such correlations might shape short-term auditory category learning.

Costs and benefits of automatization in category learning of ill-defined rules

Learning ill-defined categories (such as the structure of Medin & Schaffer, 1978) involves multiple learning systems and different corresponding category representations, which are difficult to detect. Application of latent Markov analysis allows detection and investigation of such multiple latent category representations in a statistically robust way, isolating low performers and quantifying shifts between latent strategies. We reanalyzed data from three experiments presented in Johansen and Palmeri (2002), which comprised prolonged training of ill-defined categories, with the aim of studying the changing interactions between underlying learning systems. Our results broadly confirm the original conclusion that, in most participants, learning involved a shift from a rule-based to an exemplar-based strategy. Separate analyses of latent strategies revealed that (a) shifts from a rule-based to an exemplar-based strategy resulted in an initial decrease of speed and an increase of accuracy; (b) exemplar-based strategies followed a power law of learning, indicating automatization once an exemplar-based strategy was used; (c) rule-based strategies changed from using pure rules to rules-plus-exceptions, which appeared as a dual processes as indicated by the accuracy and response-time profiles. Results suggest an additional pathway of learning ill-defined categories, namely involving a shift from a simple rule to a complex rule after which this complex rule is automatized as an exemplar-based strategy.

A neurocomputational theory of how explicit learning bootstraps early procedural learning

It is widely accepted that human learning and memory is mediated by multiple memory systems that are each best suited to different requirements and demands. Within the domain of categorization, at least two systems are thought to facilitate learning: an explicit (declarative) system depending largely on the prefrontal cortex, and a procedural (non-declarative) system depending on the basal ganglia. Substantial evidence suggests that each system is optimally suited to learn particular categorization tasks. However, it remains unknown precisely how these systems interact to produce optimal learning and behavior. In order to investigate this issue, the present research evaluated the progression of learning through simulation of categorization tasks using COVIS, a well-known model of human category learning that includes both explicit and procedural learning systems. Specifically, the model's parameter space was thoroughly explored in procedurally learned categorization tasks across a variety of conditions and architectures to identify plausible interaction architectures. The simulation results support the hypothesis that one-way interaction between the systems occurs such that the explicit system "bootstraps" learning early on in the procedural system. Thus, the procedural system initially learns a suboptimal strategy employed by the explicit system and later refines its strategy. This bootstrapping could be from cortical-striatal projections that originate in premotor or motor regions of cortex, or possibly by the explicit system's control of motor responses through basal ganglia-mediated loops.

A matched filter hypothesis for cognitive control

The prefrontal cortex exerts top-down influences on several aspects of higher-order cognition by functioning as a filtering mechanism that biases bottom-up sensory information toward a response that is optimal in context. However, research also indicates that not all aspects of complex cognition benefit from prefrontal regulation. Here we review and synthesize this research with an emphasis on the domains of learning and creative cognition, and outline how the appropriate level of cognitive control in a given situation can vary depending on the organism's goals and the characteristics of the given task. We offer a matched filter hypothesis for cognitive control, which proposes that the optimal level of cognitive control is task-dependent, with high levels of cognitive control best suited to tasks that are explicit, rule-based, verbal or abstract, and can be accomplished given the capacity limits of working memory and with low levels of cognitive control best suited to tasks that are implicit, reward-based, non-verbal or intuitive, and which can be accomplished irrespective of working memory limitations. Our approach promotes a view of cognitive control as a tool adapted to a subset of common challenges, rather than an all-purpose optimization system suited to every problem the organism might encounter.

Differential impact of relevant and irrelevant dimension primes on rule-based and information-integration category learning

Research has identified multiple category-learning systems with each being "tuned" for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while information-integration (II) classification requires the implicit learning of stimulus-response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning.

Erasing the engram: the unlearning of procedural skills

Huge amounts of money are spent every year on unlearning programs--in drug-treatment facilities, prisons, psychotherapy clinics, and schools. Yet almost all of these programs fail, since recidivism rates are high in each of these fields. Progress on this problem requires a better understanding of the mechanisms that make unlearning so difficult. Much cognitive neuroscience evidence suggests that an important component of these mechanisms also dictates success on categorization tasks that recruit procedural learning and depend on synaptic plasticity within the striatum. A biologically detailed computational model of this striatal-dependent learning is described (based on Ashby & Crossley, 2011). The model assumes that a key component of striatal-dependent learning is provided by interneurons in the striatum called the tonically active neurons (TANs), which act as a gate for the learning and expression of striatal-dependent behaviors. In their tonically active state, the TANs prevent the expression of any striatal-dependent behavior. However, they learn to pause in rewarding environments and thereby permit the learning and expression of striatal-dependent behaviors. The model predicts that when rewards are no longer contingent on behavior, the TANs cease to pause, which protects striatal learning from decay and prevents unlearning. In addition, the model predicts that when rewards are partially contingent on behavior, the TANs remain partially paused, leaving the striatum available for unlearning. The results from 3 human behavioral studies support the model predictions and suggest a novel unlearning protocol that shows promising initial signs of success.

Categorization = decision making + generalization

We rarely, if ever, repeatedly encounter exactly the same situation. This makes generalization crucial for real world decision making. We argue that categorization, the study of generalizable representations, is a type of decision making, and that categorization learning research would benefit from approaches developed to study the neuroscience of decision making. Similarly, methods developed to examine generalization and learning within the field of categorization may enhance decision making research. We first discuss perceptual information processing and integration, with an emphasis on accumulator models. We then examine learning the value of different decision making choices via experience, emphasizing reinforcement learning modeling approaches. Next we discuss how value is combined with other factors in decision making, emphasizing the effects of uncertainty. Finally, we describe how a final decision is selected via thresholding processes implemented by the basal ganglia and related regions. We also consider how memory related functions in the hippocampus may be integrated with decision making mechanisms and contribute to categorization.

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.

Theory of Mind Deficit versus Faulty Procedural Memory in Autism Spectrum Disorders

Individuals with autism spectrum disorders (ASD) have impairments in social interaction, communicative capacity, and behavioral flexibility (core triad). Three major cognitive theories (theory of mind deficit, weak central coherence, and executive dysfunction) seem to explain many of these impairments. Currently, however, the empathizing-systemizing (a newer version of the theory of mind deficit account) and mnesic imbalance theories are the only ones that attempt to explain all these core triadic symptoms of ASD On the other hand, theory of mind deficit in empathizing-systemizing theory is the most influential account for ASD, but its counterpart in the mnesic imbalance theory, faulty procedural memory, seems to occur earlier in development; consequently, this might be a better solution to the problem of the etiology of ASD, if it truly meets the precedence criterion. Hence, in the present paper I review the reasoning in favor of the theory of mind deficit but with a new interpretation based on the mnesic imbalance theory, which posits that faulty procedural memory causes deficits in several cognitive skills, resulting in poor performance in theory of mind tasks.

Nonlinguistic learning in individuals with aphasia: effects of training method and stimulus characteristics

PURPOSE

The purpose of the current study was to explore nonlinguistic learning ability in individuals with aphasia, examining the impact of stimulus typicality and feedback on success with learning.

METHOD

Eighteen individuals with aphasia and 8 nonaphasic controls participated in this study. All participants completed 4 computerized, nonlinguistic category-learning tasks. Learning ability was probed under 2 methods of instruction: feedback-based (FB) and paired-associate (PA). The impact of task complexity on learning ability was also examined, comparing 2 stimulus conditions: typical and atypical. Performance was compared between groups and across conditions.

RESULTS

The controls were able to successfully learn categories under all conditions. For the individuals with aphasia, 2 patterns of performance arose: One subgroup of individuals was able to maintain learning across task manipulations and conditions; the other subgroup demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions.

CONCLUSION

Results support the hypothesis that impairments of general learning are present in individuals with aphasia. Some individuals demonstrated the ability to extract category information under complex training conditions; others learned only under conditions that were simplified and that emphasized salient category features. Overall, the typical training condition facilitated learning for all of the participants. Findings have implications for treatment, which are discussed.

Posterror slowing predicts rule-based but not information-integration category learning

We examined whether error monitoring, operationalized as the degree to which individuals slow down after committing an error (i.e., posterror slowing), is differentially important in the learning of rule-based versus information-integration category structures. Rule-based categories are most efficiently solved through the application of an explicit verbal strategy (e.g., "sort by color"). In contrast, information-integration categories are believed to be learned in a trial-by-trial, associative manner. Our results indicated that posterror slowing predicts enhanced rule-based but not information-integration category learning. Implications for multiple category-learning systems are discussed.

Brain activity across the development of automatic categorization: a comparison of categorization tasks using multi-voxel pattern analysis

Previous evidence suggests that relatively separate neural networks underlie initial learning of rule-based and information-integration categorization tasks. With the development of automaticity, categorization behavior in both tasks becomes increasingly similar and exclusively related to activity in cortical regions. The present study uses multi-voxel pattern analysis to directly compare the development of automaticity in different categorization tasks. Each of the three groups of participants received extensive training in a different categorization task: either an information-integration task, or one of two rule-based tasks. Four training sessions were performed inside an MRI scanner. Three different analyses were performed on the imaging data from a number of regions of interest (ROIs). The common patterns analysis had the goal of revealing ROIs with similar patterns of activation across tasks. The unique patterns analysis had the goal of revealing ROIs with dissimilar patterns of activation across tasks. The representational similarity analysis aimed at exploring (1) the similarity of category representations across ROIs and (2) how those patterns of similarities compared across tasks. The results showed that common patterns of activation were present in motor areas and basal ganglia early in training, but only in the former later on. Unique patterns were found in a variety of cortical and subcortical areas early in training, but they were dramatically reduced with training. Finally, patterns of representational similarity between brain regions became increasingly similar across tasks with the development of automaticity.

Feedback and stimulus-offset timing effects in perceptual category learning

We examined how feedback delay and stimulus offset timing affected declarative, rule-based and procedural, information-integration category-learning. We predicted that small feedback delays of several hundred milliseconds would lead to the best information-integration learning based on a highly regarded neurobiological model of learning in the striatum. In Experiment 1 information-integration learning was best with feedback delays of 500ms compared to delays of 0 and 1000ms. This effect was only obtained if the stimulus offset following the response. Rule-based learning was unaffected by the length of feedback delay, but was better when the stimulus was present throughout feedback than when it offset following the response. In Experiment 2 we found that a large variance (SD=150ms) in feedback delay times around a mean delay of 500ms attenuated information-integration learning, but a small variance (SD=75ms) did not. In Experiment 3 we found that the delay between stimulus offset and feedback is more critical to information-integration learning than the delay between the response and feedback. These results demonstrate the importance of feedback timing in category-learning situations where a declarative, verbalizable rule cannot easily be used as a heuristic to classify members into their correct category.

The basal ganglia

Through its connections with widespread cortical areas and with dopaminergic midbrain areas, the basal ganglia are well situated to integrate patterns of cortical input with the dopaminergic reward signal originating in the midbrain. In this review, we consider the functions of the basal ganglia in relation to its gross and cellular anatomy, and discuss how these mechanisms subserve the thresholding and selection of motor and cognitive processes. We also discuss how the dopaminergic reward signal enables flexible task learning through modulation of striatal plasticity, and how reinforcement learning models have been used to account for various aspects of basal ganglia activity. Specifically, we will discuss the important role of the basal ganglia in instrumental learning, cognitive control, sequence learning, and categorization tasks. Finally, we will discuss the neurobiological and cognitive characteristics of Parkinson's disease, Huntington's disease and addiction to illustrate the relationship between the basal ganglia and cognitive function. WIREs Cogn Sci 2013, 4:135-148. doi: 10.1002/wcs.1217 For further resources related to this article, please visit the WIREs website.

Combining computational modeling and neuroimaging to examine multiple category learning systems in the brain

Considerable evidence has argued in favor of multiple neural systems supporting human category learning, one based on conscious rule inference and one based on implicit information integration. However, there have been few attempts to study potential system interactions during category learning. The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli. Incorporating competing systems requires inclusion of cognitive mechanisms associated with resolving this competition and creates a potential credit assignment problem in handling feedback. The hypothesized mechanisms make predictions about internal mental states that are not always reflected in choice behavior, but may be reflected in neural activity. Two prior functional magnetic resonance imaging (fMRI) studies of category learning were re-analyzed using PINNACLE to identify neural correlates of internal cognitive states on each trial. These analyses identified additional brain regions supporting the two types of category learning, regions particularly active when the systems are hypothesized to be in maximal competition, and found evidence of covert learning activity in the “off system” (the category learning system not currently driving behavior). These results suggest that PINNACLE provides a plausible framework for how competing multiple category learning systems are organized in the brain and shows how computational modeling approaches and fMRI can be used synergistically to gain access to cognitive processes that support complex decision-making machinery.

Information-integration category learning and the human uncertainty response

The human response to uncertainty has been well studied in tasks requiring attention and declarative memory systems. However, uncertainty monitoring and control have not been studied in multi-dimensional, information-integration categorization tasks that rely on non-declarative procedural memory. Three experiments are described that investigated the human uncertainty response in such tasks. Experiment 1 showed that following standard categorization training, uncertainty responding was similar in information-integration tasks and rule-based tasks requiring declarative memory. In Experiment 2, however, uncertainty responding in untrained information-integration tasks impaired the ability of many participants to master those tasks. Finally, Experiment 3 showed that the deficit observed in Experiment 2 was not because of the uncertainty response option per se, but rather because the uncertainty response provided participants a mechanism via which to eliminate stimuli that were inconsistent with a simple declarative response strategy. These results are considered in the light of recent models of category learning and metacognition.

Cortical and striatal contributions to automaticity in information-integration categorization

In information-integration categorization, accuracy is maximized only if information from two or more stimulus components is integrated at some pre-decisional stage. In many cases the optimal strategy is difficult or impossible to describe verbally. Evidence suggests that success in information-integration tasks depends on procedural learning that is mediated largely within the striatum. Although many studies have examined initial information-integration learning, little is known about how automaticity develops in information-integration tasks. To address this issue, each of ten human participants received feedback training on the same information-integration categories for more than 11,000 trials spread over 20 different training sessions. Sessions 2, 4, 10, and 20 were performed inside an MRI scanner. The following results stood out. 1) Automaticity developed between sessions 10 and 20. 2) Pre-automatic performance depended on the putamen, but not on the body and tail of the caudate nucleus. 3) Automatic performance depended only on cortical regions, particularly the supplementary and pre-supplementary motor areas. 4) Feedback processing was mainly associated with deactivations in motor and premotor regions of cortex, and in the ventral lateral prefrontal cortex. 5) The overall effects of practice were consistent with the existing literature on the development of automaticity.

Human category learning 2.0

During the 1990s and early 2000s, cognitive neuroscience investigations of human category learning focused on the primary goal of showing that humans have multiple category-learning systems and on the secondary goals of identifying key qualitative properties of each system and of roughly mapping out the neural networks that mediate each system. Many researchers now accept the strength of the evidence supporting multiple systems, and as a result, during the past few years, work has begun on the second generation of research questions-that is, on questions that begin with the assumption that humans have multiple category-learning systems. This article reviews much of this second generation of research. Topics covered include (1) How do the various systems interact? (2) Are there different neural systems for categorization and category representation? (3) How does automaticity develop in each system? and (4) Exactly how does each system learn?