Suboptimality in human categorization and identification

Neural dynamics underlying the acquisition of distinct auditory category structures

Despite the multidimensional and temporally fleeting nature of auditory signals we quickly learn to assign novel sounds to behaviorally relevant categories. The neural systems underlying the learning and representation of novel auditory categories are far from understood. Current models argue for a rigid specialization of hierarchically organized core regions that are fine-tuned to extracting and mapping relevant auditory dimensions to meaningful categories. Scaffolded within a dual-learning systems approach, we test a competing hypothesis: the spatial and temporal dynamics of emerging auditory-category representations are not driven by the underlying dimensions but are constrained by category structure and learning strategies. To test these competing models, we used functional Magnetic Resonance Imaging (fMRI) to assess representational dynamics during the feedback-based acquisition of novel non-speech auditory categories with identical dimensions but differing category structures: rule-based (RB) categories, hypothesized to involve an explicit sound-to-rule mapping network, and information integration (II) based categories, involving pre-decisional integration of dimensions via a procedural-based sound-to-reward mapping network. Adults were assigned to either the RB (n = 30, 19 females) or II (n = 30, 22 females) learning tasks. Despite similar behavioral learning accuracies, learning strategies derived from computational modeling and involvements of corticostriatal systems during feedback processing differed across tasks. Spatiotemporal multivariate representational similarity analysis revealed an emerging representation within an auditory sensory-motor pathway exclusively for the II learning task, prominently involving the superior temporal gyrus (STG), inferior frontal gyrus (IFG), and posterior precentral gyrus. In contrast, the RB learning task yielded distributed neural representations within regions involved in cognitive-control and attentional processes that emerged at different time points of learning. Our results unequivocally demonstrate that auditory learners' neural systems are highly flexible and show distinct spatial and temporal patterns that are not dimension-specific but reflect underlying category structures and learning strategies.

A computational account of the mechanisms underlying face perception biases in depression

Here, we take a computational approach to understand the mechanisms underlying face perception biases in depression. Thirty participants diagnosed with major depressive disorder and 30 healthy control participants took part in three studies involving recognition of identity and emotion in faces. We used signal detection theory to determine whether any perceptual biases exist in depression aside from decisional biases. We found lower sensitivity to happiness in general, and lower sensitivity to both happiness and sadness with ambiguous stimuli. Our use of highly-controlled face stimuli ensures that such asymmetry is truly perceptual in nature, rather than the result of studying expressions with inherently different discriminability. We found no systematic effect of depression on the perceptual interactions between face expression and identity. We also found that decisional strategies used in our task were different for people with depression and controls, but in a way that was highly specific to the stimulus set presented. We show through simulation that the observed perceptual effects, as well as other biases found in the literature, can be explained by a computational model in which channels encoding positive expressions are selectively suppressed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Learning and generalization of within-category representations in a rule-based category structure

The task requirements during the course of category learning are critical for promoting within-category representations (e.g., correlational structure of the categories). Recent data suggest that for unidimensional rule-based structures, only inference training promotes the learning of within-category representations, and generalization across tasks is limited. It is unclear if this is a general feature of rule-based structures, or a limitation of unidimensional rule-based structures. The present work reports the results of three experiments further investigating this issue using an exclusive-or rule-based structure where successful performance depends upon attending to two stimulus dimensions. Participants were trained using classification or inference and were tested using inference. For both the classification and inference training conditions, within-category representations were learned and could be generalized at test (i.e., from classification to inference) and this result was dependent upon a congruence between local and global regions of the stimulus space. These data further support the idea that the task requirements during learning (i.e., a need to attend to multiple stimulus dimensions) are critical determinants of the category representations that are learned and the utility of these representations for supporting generalization in novel situations.

Due Process in Dual Process: Model-Recovery Simulations of Decision-Bound Strategy Analysis in Category Learning

Behavioral evidence for the COVIS dual-process model of category learning has been widely reported in over a hundred publications (Ashby & Valentin, ). It is generally accepted that the validity of such evidence depends on the accurate identification of individual participants' categorization strategies, a task that usually falls to Decision Bound analysis (Maddox & Ashby, ). Here, we examine the accuracy of this analysis in a series of model-recovery simulations. In Simulation 1, over a third of simulated participants using an Explicit (conjunctive) strategy were misidentified as using a Procedural strategy. In Simulation 2, nearly all simulated participants using a Procedural strategy were misidentified as using an Explicit strategy. In Simulation 3, we re-examined a recently reported COVIS-supporting dissociation (Smith et al., ) and found that these misidentification errors permit an alternative, single-process, explanation of the results. Implications for due process in the future evaluation of dual-process theories, including recommendations for future practice, are discussed.

The impact of category structure and training methodology on learning and generalizing within-category representations

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.

Testing Separability and Independence of Perceptual Dimensions with General Recognition Theory: A Tutorial and New R Package (grtools)

Determining whether perceptual properties are processed independently is an important goal in perceptual science, and tools to test independence should be widely available to experimental researchers. The best analytical tools to test for perceptual independence are provided by General Recognition Theory (GRT), a multidimensional extension of signal detection theory. Unfortunately, there is currently a lack of software implementing GRT analyses that is ready-to-use by experimental psychologists and neuroscientists with little training in computational modeling. This paper presents grtools, an R package developed with the explicit aim of providing experimentalists with the ability to perform full GRT analyses using only a couple of command lines. We describe the software and provide a practical tutorial on how to perform each of the analyses available in grtools. We also provide advice to researchers on best practices for experimental design and interpretation of results when applying GRT and grtools.

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.

Is pressure stressful? The impact of pressure on the stress response and category learning

We examined the basic question of whether pressure is stressful. We proposed that when examining the role of stress or pressure in cognitive performance, it is important to consider the type of pressure, the stress response, and the aspect of cognition assessed. In Experiment 1, outcome pressure was not experienced as stressful but did lead to impaired performance on a rule-based (RB) category-learning task, but not on a more procedural information-integration (II) task. In Experiment 2, the addition of monitoring pressure resulted in a modest stress response to combined pressure and impairment on both tasks. Across experiments, higher stress appraisals were associated with decreased performance on the RB, but not on the II, task. In turn, higher stress reactivity (i.e., heart rate) was associated with enhanced performance on the II, but not on the RB, task. This work represents an initial step toward integrating the stress cognition and pressure cognition literatures and suggests that integrating these fields may require consideration of the type of pressure, the stress response, and the cognitive system mediating performance.

General recognition theory with individual differences: a new method for examining perceptual and decisional interactions with an application to face perception

A common question in perceptual science is to what extent different stimulus dimensions are processed independently. General recognition theory (GRT) offers a formal framework via which different notions of independence can be defined and tested rigorously, while also dissociating perceptual from decisional factors. This article presents a new GRT model that overcomes several shortcomings with previous approaches, including a clearer separation between perceptual and decisional processes and a more complete description of such processes. The model assumes that different individuals share similar perceptual representations, but vary in their attention to dimensions and in the decisional strategies they use. We apply the model to the analysis of interactions between identity and emotional expression during face recognition. The results of previous research aimed at this problem have been disparate. Participants identified four faces, which resulted from the combination of two identities and two expressions. An analysis using the new GRT model showed a complex pattern of dimensional interactions. The perception of emotional expression was not affected by changes in identity, but the perception of identity was affected by changes in emotional expression. There were violations of decisional separability of expression from identity and of identity from expression, with the former being more consistent across participants than the latter. One explanation for the disparate results in the literature is that decisional strategies may have varied across studies and influenced the results of tests of perceptual interactions, as previous studies lacked the ability to dissociate between perceptual and decisional interactions.

Context-dependent savings in procedural category learning

Environmental context can have a profound influence on the efficacy of intervention protocols designed to eliminate undesirable behaviors. This is clearly seen in drug rehabilitation clinics where patients often relapse soon after leaving the context of the treatment facility. A similar pattern is commonly observed in controlled laboratory studies of context-dependent savings in instrumental conditioning, where simply placing an animal back into the original conditioning chamber can renew an extinguished instrumental response. Surprisingly, context-dependent savings in human procedural learning has not been carefully examined in the laboratory. Here, we provide the first known empirical demonstration of context-dependent savings in a perceptual categorization task known to recruit procedural learning. We also present a computational account of these savings using a biologically detailed model in which a key role is played by cholinergic interneurons in the striatum.

Procedural-based category learning in patients with Parkinson's disease: impact of category number and category continuity

Previously we found that Parkinson's disease (PD) patients are impaired in procedural-based category learning when category membership is defined by a nonlinear relationship between stimulus dimensions, but these same patients are normal when the rule is defined by a linear relationship (Maddox and Filoteo, 2001; Filoteo et al., 2005a,b). We suggested that PD patients' impairment was due to a deficit in recruiting “striatal units” to represent complex nonlinear rules. In the present study, we further examined the nature of PD patients' procedural-based deficit in two experiments designed to examine the impact of (1) the number of categories, and (2) category discontinuity on learning. Results indicated that PD patients were impaired only under discontinuous category conditions but were normal when the number of categories was increased from two to four. The lack of impairment in the four-category condition suggests normal integrity of striatal medium spiny cells involved in procedural-based category learning. In contrast, and consistent with our previous observation of a nonlinear deficit, the finding that PD patients were impaired in the discontinuous condition suggests that these patients are impaired when they have to associate perceptually distinct exemplars with the same category. Theoretically, this deficit might be related to dysfunctional communication among medium spiny neurons within the striatum, particularly given that these are cholinergic neurons and a cholinergic deficiency could underlie some of PD patients' cognitive impairment.

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.

Analogical transfer in perceptual categorization

Analogical transfer is the ability to transfer knowledge despite significant changes in the surface features of a problem. In categorization, analogical transfer occurs if a classification strategy learned with one set of stimuli can be transferred to a set of novel, perceptually distinct stimuli. Three experiments investigated analogical transfer in rule-based and information-integration categorization tasks. In rule-based tasks, the optimal strategy is easy to describe verbally, whereas in information-integration tasks, accuracy is maximized only if information from two or more stimulus dimensions is integrated in a way that is difficult or impossible to describe verbally. In all three experiments, analogical transfer was nearly perfect in the rule-based conditions, but no evidence for analogical transfer was found in the information-integration conditions. These results were predicted a priori by the COVIS theory of categorization.

Development of implicit and explicit category learning

We present two studies that examined developmental differences in the implicit and explicit acquisition of category knowledge. College-attending adults consistently outperformed school-age children on two separate information-integration paradigms due to children's more frequent use of an explicit rule-based strategy. Accuracy rates were also higher for adults on a unidimensional rule-based task due to children's more frequent use of the irrelevant dimension to guide their behavior. Results across these two studies suggest that the ability to learn categorization structures may be dependent on a child's ability to inhibit output from the explicit system.

Rule-based and information-integration category learning in normal aging

The basal ganglia and prefrontal cortex play critical roles in category learning. Both regions evidence age-related structural and functional declines. The current study examined rule-based and information-integration category learning in a group of older and younger adults. Rule-based learning is thought to involve explicit, frontally mediated processes, whereas information-integration is thought to involve implicit, striatally mediated processes. As a group, older adults showed rule-based and information-integration deficits. A series of models were applied that provided insights onto the type of strategy used to solve the task. Interestingly, when the analyses focused only on participants who used the task appropriate strategy in the final block of trials, the age-related rule-based deficit disappeared whereas the information-integration deficit remained. For this group of individuals, the final block information-integration deficit was due to less consistent application of the task appropriate strategy by older adults, and over the course of learning these older adults shifted from an explicit hypothesis-testing strategy to the task appropriate strategy later in learning. In addition, the use of the task appropriate strategy was associated with less interference and better inhibitory control for rule-based and information-information learning, whereas use of the task appropriate strategy was associated with greater working memory and better new verbal learning only for the rule-based task. These results suggest that normal aging impacts both forms of category learning and that there are some important similarities and differences in the explanatory locus of these deficits. The data also support a two-component model of information-integration category learning that includes a striatal component that mediated procedural-based learning, and a prefrontal cortical component that mediates the transition from hypothesis-testing to procedural-based strategies. Implications for independent vs. interactive category learning systems are discussed.

Interactions between declarative and procedural-learning categorization systems

Two experiments tested whether declarative and procedural memory systems operate independently or inhibit each other during perceptual categorization. Both experiments used a hybrid category-learning task in which perfect accuracy could be achieved if a declarative strategy is used on some trials and a procedural strategy is used on others. In the two experiments, only 2 of 53 participants learned a strategy of this type. In Experiment 1, most participants appeared to use simple explicit rules, even though control participants reliably learned the procedural component of the hybrid task. In Experiment 2, participants pre-trained either with the declarative or procedural component and then transferred to the hybrid categories. Despite this extra training, no participants in either group learned to categorize the hybrid stimuli with a strategy of the optimal type. These results are inconsistent with the most prominent single- and multiple-system accounts of category learning. They also cannot be explained by knowledge partitioning, or by the hypothesis that the failure to learn was due to high switch costs. Instead, these results support the hypothesis that declarative and procedural memory systems interact during category learning.

Prefrontal contributions to rule-based and information-integration category learning

Previous research revealed that the basal ganglia play a critical role in category learning [Ell, S. W., Marchant, N. L., & Ivry, R. B. (2006). Focal putamen lesions impair learning in rule-based, but not information-integration categorization tasks. Neuropsychologia, 44(10), 1737-1751; Maddox, W. T. & Filoteo, J. V. (2007). Modeling visual attention and category learning in amnesiacs, striatal-damaged patients and normal aging. In Advances in Clinical-cognitive science: formal modeling and assessment of processes and symptoms (pp. 113-146). Washington DC: American Psychological Association] but less is known about the specific role of prefrontal cortical (PFC) regions in category learning. The current study examined rule-based (RB) and information-integration (II) category learning in 13 patients with damage primarily to ventral PFC regions. After 600 learning trials with feedback, patients were significantly less accurate than matched controls on both RB and II learning. Model-based analysis identified subgroups of patients whose impaired performance in each task was due to the use of sub-optimal learning strategies. Those patients impaired at either II or RB learning, performed significantly worse on the Wisconsin Card Sorting Test, a test of abstract rule formation and the ability to shift and maintain rules. Lesion analysis pointed to damage in a fairly circumscribed region of ventral medial prefrontal cortex as common to the impaired group of patients and those patients without ventral PFC damage mostly performed normally. These results provide further evidence that the ventromedial prefrontal cortex is critically important for the ability to monitor and integrate feedback in order to select and maintain optimal learning strategies.

Generalization and similarity in exemplar models of categorization: insights from machine learning

Exemplar theories of categorization depend on similarity for explaining subjects' ability to generalize to new stimuli. A major criticism of exemplar theories concerns their lack of abstraction mechanisms and thus, seemingly, of generalization ability. Here, we use insights from machine learning to demonstrate that exemplar models can actually generalize very well. Kernel methods in machine learning are akin to exemplar models and are very successful in real-world applications. Their generalization performance depends crucially on the chosen similarity measure. Although similarity plays an important role in describing generalization behavior, it is not the only factor that controls generalization performance. In machine learning, kernel methods are often combined with regularization techniques in order to ensure good generalization. These same techniques are easily incorporated in exemplar models. We show that the generalized context model (Nosofsky, 1986) and ALCOVE (Kruschke, 1992) are closely related to a statistical model called kernel logistic regression. We argue that generalization is central to the enterprise of understanding categorization behavior, and we suggest some ways in which insights from machine learning can offer guidance.

Evidence for disruption in prefrontal cortical functions in juvenile bipolar disorder

OBJECTIVES

Systematic parsing of executive function processes is critical for the development of more specific models of neurobiological processes mediating disturbed cognition in youth with bipolar disorder (BPD).

METHODS

A sample of 33 children and adolescents with bipolar I disorder (BPD I) (mean age 12.1 +/- 3.0 years, 39% female) and 44 demographically matched healthy participants (mean age 12.9 +/- 2.8 years, 50% female) completed a neurocognitive battery including measures aimed at detection of disruption in prefrontal cortical circuitry (i.e., working memory, set shifting, and rule attainment).

RESULTS

Compared to healthy controls, BPD I children exhibited significant deficits in spatial working memory, visual sequencing and scanning, verbal fluency and abstract problem solving, particularly when a memory component was involved. In our spatial delayed response task, memory set size was parametrically varied; the performance pattern in BPD I children suggested deficits in short-term memory encoding and/or storage, rather than capacity limitations in spatial working memory. Earlier age at onset of illness and antipsychotic medication usage were associated with poorer performance on speeded information-processing tasks; however, severity of mood symptomatology and comorbidity with disruptive behavior disorders were not associated with task performance.

CONCLUSIONS

These results suggest impairment in measures of prefrontal cortical function in juvenile BPD I that are similar to those seen in the adult form of the illness, and implicate both the ventral and dorsolateral prefrontal cortex as loci of pathology in juvenile BPD. As these deficits were not associated with clinical state or comorbidity with other disorders, they may reflect trait-related impairments, a hypothesis that will be pursued further in longitudinal studies.

The effects of category overlap on information-integration and rule-based category learning

In three experiments, we investigated whether the amount of category overlap constrains the decision strategies used in category learning, and whether such constraints depend on the type of category structures used. Experiments 1 and 2 used a category-learning task requiring perceptual integration of information from multiple dimensions (an information-integration task) and Experiment 3 used a task requiring the application of an explicit strategy (a rule-based task). In the information-integration task, participants used perceptual-integration strategies at moderate levels of category overlap, but explicit strategies at extreme levels of overlap--even when such strategies were suboptimal. In contrast, in the rule-based task, participants used explicit strategies, regardless of the level of category overlap. These data are consistent with a multiple systems view of category learning, and suggest that categorization strategy depends on the type of task that is used, and on the degree to which each stimulus is probabilistically associated with the contrasting categories.

Stimulus modality interacts with category structure in perceptual category learning

Two experiments were conducted that examined information integration and rule-based category learning, using stimuli that contained auditory and visual information. The results suggest that it is easier to perceptually integrate information within these sensory modalities than across modalities. Conversely, it is easier to perform a disjunctive rule-based task when information comes from different sensory modalities, rather than from the same modality. Quantitative model-based analyses suggested that the information integration deficit for across-modality stimulus dimensions was due to an increase in the use of hypothesis-testing strategies to solve the task and to an increase in random responding. The modeling also suggested that the across-modality advantage for disjunctive, rule-based category learning was due to a greater reliance on disjunctive hypothesis-testing strategies, as opposed to unidimensional hypothesis-testing strategies and random responding.

Explicit category learning in Parkinson's disease: deficits related to impaired rule generation and selection processes

The present study examined the source of explicit category learning deficits previously noted in patients with Parkinson's disease (PD). Task stimuli consisted of 4 binary-valued cues that together determined category assignment, although some cues were more important for the categorization decision. Participants verbalized the hypotheses being tested to provide several measures of the hypothesis testing. Analyses of these verbal protocols indicated that PD patients were impaired on rule generation and selection but not rule shifting. Patients had particular difficulty noting the relative importance of the cues. Specific aspects of performance were differently correlated with neuropsychological measures of working memory and hypothesis testing ability. Together, the results suggest that the cognitive processes required for explicit category learning are mediated by partially distinct neural mechanisms.

Focal putamen lesions impair learning in rule-based, but not information-integration categorization tasks

Previous research on the role of the basal ganglia in category learning has focused on patients with Parkinson's and Huntington's disease, neurodegenerative diseases frequently accompanied by additional cortical pathology. The goal of the present study was to extend this work to patients with basal ganglia lesions due to stroke, asking if similar changes in performance would be observed in patients with more focal pathology. Patients with basal ganglia lesions centered in the putamen (6 left side, 1 right side) were tested on rule-based and information-integration visual categorization tasks. In rule-based tasks, it is assumed that participants can learn the category structures through an explicit reasoning process. In information-integration tasks, optimal performance requires the integration of information from two or more stimulus components, and participants are typically unaware of the category rules. Consistent with previous studies involving patients with degenerative disorders of the basal ganglia, the stroke patients were impaired on the rule-based task, and quantitative, model-based analyses indicate that this deficit was due to the inefficient application of decision strategies. In contrast, the patients were unimpaired on the information-integration task. This pattern of results provides converging evidence supporting a role of the basal ganglia and, in particular, the putamen in rule-based category learning.

Categories and causality: the neglected direction

The standard approach guiding research on the relationship between categories and causality views categories as reflecting causal relations in the world. We provide evidence that the opposite direction also holds: categories that have been acquired in previous learning contexts may influence subsequent causal learning. In three experiments we show that identical causal learning input yields different attributions of causal capacity depending on the pre-existing categories to which the learning exemplars are assigned. There is a strong tendency to continue to use old conceptual schemes rather than switch to new ones even when the old categories are not optimal for predicting the new effect, and when they were motivated by goals that differed from the present context of causal discovery. However, we also found that the use of prior categories is dependent on the match between categories and causal effect. Whenever the category labels suggest natural kinds which can be plausibly related to the causal effects, transfer was observed. When the categories were arbitrary, or could not be plausibly related to the causal effect learners abandoned the categories, and used different categories to predict the causal effect.

Discontinuous categories affect information-integration but not rule-based category learning

Three experiments were conducted that provide a direct examination of within-category discontinuity manipulations on the implicit, procedural-based learning and the explicit, hypothesis-testing systems proposed in F. G. Ashby, L. A. Alfonso-Reese, A. U. Turken, and E. M. Waldron's (1998) competition between verbal and implicit systems model. Discontinuous categories adversely affected information-integration but not rule-based category learning. Increasing the magnitude of the discontinuity did not lead to a significant decline in performance. The distance to the bound provides a reasonable description of the generalization profile associated with the hypothesis-testing system, whereas the distance to the bound plus the distance to the trained response region provides a reasonable description of the generalization profile associated with the procedural-based learning system. These results suggest that within-category discontinuity differentially impacts information-integration but not rule-based category learning and provides information regarding the detailed processing characteristics of each category learning system.

Dissociating explicit and procedural-learning based systems of perceptual category learning

A fundamental question is whether people have available one category learning system, or many. Most multiple systems advocates postulate one explicit and one implicit system. Although there is much agreement about the nature of the explicit system, there is less agreement about the nature of the implicit system. In this article, we review a dual systems theory of category learning called competition between verbal and implicit systems (COVIS) developed by Ashby et al. The explicit system dominates the learning of verbalizable, rule-based category structures and is mediated by frontal brain areas such as the anterior cingulate, prefrontal cortex (PFC), and head of the caudate nucleus. The implicit system, which uses procedural learning, dominates the learning of non-verbalizable, information-integration category structures, and is mediated by the tail of the caudate nucleus and a dopamine-mediated reward signal. We review nine studies that test six a priori predictions from COVIS, each of which is supported by the data.

Human Category Learning

Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular-rule-based tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.

Cortico-striatal contributions to category learning: Dissociating the verbal and implicit systems

To better characterize the neuropsychological mechanisms of implicit and verbalizable category learning, the author studied weather prediction task (WPT) and information integration task (IIT) performance in patients with Parkinson's disease (PD) and healthy older and younger adults. Both older adults and patients with PD were impaired on the WPT, but only patients were impaired on the IIT, suggesting the 2 tasks rely on dissociable systems. Whereas the IIT appeared to rely on implicit processes, results suggest WPT classification depends on explicit processes. Awareness of underlying structure, hypothesis testing ability, and working memory capacity were all related to accuracy on the WPT but not the IIT. The variability commonly noted in WPT performance may reflect individual differences in hypothesis testing ability.

Information-Integration Category Learning in Patients With Striatal Dysfunction

Information-integration category learning was examined in patients with Parkinson's disease (PD) and in healthy control participants in 2 different conditions. In the linear condition, optimal categorization required a nonverbalizable linear integration of information from the 2 stimulus dimensions, whereas in the nonlinear condition, a nonlinear integration of information was required. Each participant completed 600 trials in each condition and was given corrective feedback following each trial. Results indicated that PD patients were not impaired in the linear condition across all trials, whereas the same patients were impaired in the nonlinear condition, but only later in training. The authors conducted model-based analyses to identify participants who used an information-integration approach, and a comparison of the accuracy rates of those individuals further revealed a specific deficit in information-integration category learning in patients with PD. These findings suggest that the striatum may be particularly involved in information-integration category learning when the rule is highly complex.

Category number impacts rule-based but not information-integration category learning: further evidence for dissociable category-learning systems

Category number effects on rule-based and information-integration category learning were investigated. Category number affected accuracy and the distribution of best-fitting models in the rule-based task but had no effect on accuracy and little effect on the distribution of best-fining models in the information-integration task. In the 2 category conditions, rule-based learning was better than information-integration learning, whereas in the 4 category conditions, unidimensional and conjunctive rule-based learning was worse than information-integration learning. Rule-based strategies were used in the 2-category/rule-based condition, but about half of the observers used rule-based strategies in the 4-category unidimensional and conjunctive rule-based conditions. Information-integration strategies were used in the 4-category/ information-integration condition and by the end of training were used in the 2-category/information-integration condition.

Procedural learning in perceptual categorization

In two experiments, observers learned two types of category structures: those in which perfect accuracy could be achieved via some explicit rule-based strategy and those in which perfect accuracy required integrating information from separate perceptual dimensions at some predecisional stage. At the end of training, some observers were required to switch their hands on the response keys, whereas the assignment of categories to response keys was switched for other observers. With the rule-based category structures, neither change in response instructions interfered with categorization accuracy. However, with the information-integration structures, switching response key assignments interfered with categorization performance, but switching hands did not. These results are consistent with the hypothesis that abstract category labels are learned in rule-based categorization, whereas response positions are learned in information-integration categorization. The association to response positions also supports the hypothesis of a procedural-learning-based component to information integration categorization.

A Quantitative Model-Based Approach to Examining Aging Effects on Information-Integration Category Learning

Information-integration category learning was examined in older and younger adults. Accuracy results indicated that older participants learned less well than younger participants in both linear and nonlinear conditions. Model-based analyses indicated that both groups in the linear condition tended to use information integration but that later in training younger participants were more likely to do so. In contrast, the 2 groups in the nonlinear condition were equally likely to use information integration. Further analysis indicated that younger adults were more accurate than older adults when an information-integration approach was adopted, whereas fewer age-related differences were observed when a rule-based approach was used, suggesting that age can have a negative impact on information-integration category learning processes but less impact on rule-based learning.

Human visual object categorization can be described by models with low memory capacity

Studies of high-level models of visual object categorization have left unresolved issues of neurobiological relevance, including how features are extracted from the image and the role played by memory capacity in categorization performance. We compared the ability of a comprehensive set of models to match the categorization performance of human observers while explicitly accounting for the models' numbers of free parameters. The most successful models did not require a large memory capacity, suggesting that a sparse, abstracted representation of category properties may underlie categorization performance. This type of representation--different from classical prototype abstraction--could also be extracted directly from two-dimensional images via a biologically plausible early-vision model, rather than relying on experimenter-imposed features.

Delayed feedback effects on rule-based and information-integration category learning

The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.

Separating perceptual and decisional attention processes in the identification and categorization of integral-dimension stimuli

Four observers performed matching, identification, and categorization with stimuli that varied along the integral dimensions: brightness and saturation. General recognition theory (F. G. Ashby & J. T. Townsend, 1986) was applied to quantify the separate influences of perceptual and decisional processes within and across tasks, with a focus on separating perceptual from decisional attention processes. Good accounts of the identification data were obtained from perceptual matching representation. This perceptual representation provided a good account of the categorization data, except when decisional selective attention to 1 stimulus dimension was required. Decisional selective attention reduced the attended-dimension perceptual variance relative to the unattended-dimension perceptual variance, with a larger reduction resulting when brightness, as opposed to saturation was attended. Implications for color vision research are discussed.

Human brain regions involved in visual categorization

Categorization of dot patterns is a frequently used paradigm in the behavioral study of natural categorization. To determine the human brain regions involved in categorization, we used Positron Emission Tomography to compare regional Cerebral Blood Flow patterns in two tasks employing patterns that consisted of nine dots. In the categorization task, subjects categorized novel exemplars of two categories, generated by distorting two prototypes, and other random dot patterns. In the control task, subjects judged the position of similarly distorted patterns. Each task was presented at two matched levels of difficulty. Fixation of the fixation target served as baseline condition. The categorization task differentially activated the orbitofrontal cortex and two dorsolateral prefrontal regions. These three prefrontal regions were equally weakly active in the position discrimination task and the baseline condition. The intraparietal sulcus was activated in both tasks, albeit significantly less in the position discrimination than in the categorization task. A similar activation pattern was present in the neostriatum. Task difficulty had no effect. These functional imaging results show that the dot-pattern categorization task strongly engages prefrontal and parietal cortical areas. The activation of prefrontal cortex during visual categorization in humans agrees with the recent finding of category-related responses in macaque prefrontal neurons.

Generalizing a neuropsychological model of visual categorization to auditory categorization of vowels

Twelve male listeners categorized 54 synthetic vowel stimuli that varied in second and third formant frequency on a Bark scale into the American English vowel categories [see text]. A neuropsychologically plausible model of categorization in the visual domain, the Striatal Pattern Classifier (SPC; Ashby & Waldron, 1999), is generalized to the auditory domain and applied separately to the data from each observer. Performance of the SPC is compared with that of the successful Normal A Posteriori Probability model (NAPP; Nearey, 1990; Nearey & Hogan, 1986) of auditory categorization. A version of the SPC that assumed piece-wise linear response region partitions provided a better account of the data than the SPC that assumed linear partitions, and was indistinguishable from a version that assumed quadratic response region partitions. A version of the NAPP model that assumed nonlinear response regions was superior to the NAPP model with linear partitions. The best fitting SPC provided a good account of each observer's data but was outperformed by the best fitting NAPP model. Implications for bridging the gap between the domains of visual and auditory categorization are discussed.

Separating perceptual processes from decisional processes in identification and categorization

Four observers completed perceptual matching, identification, and categorization tasks using separable-dimension stimuli. A unified quantitative approach relating perceptual matching, identification, and categorization was proposed and tested. The approach derives from general recognition theory (Ashby & Townsend, 1986) and provides a powerful method for quantifying the separate influences of perceptual processes and decisional processes within and across tasks. Good accounts of the identification data were obtained from an initial perceptual representation derived from perceptual matching. The same perceptual representation provided a good account of the categorization data, except when selective attention to one stimulus dimension was required. Selective attention altered the perceptual representation by decreasing the perceptual variance along the attended dimension. These findings suggest that a complete understanding of identification and categorization performance requires an understanding of perceptual and decisional processes. Implications for other psychological tasks are discussed.