Working memory does not dissociate between different perceptual categorization tasks

Working memory is crucial for many higher level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship between working memory and categorization. This article reports 2 studies that related people's working memory capacity (WMC) to their learning performance on multiple rule-based and information-integration perceptual categorization tasks. In both studies, structural equation modeling revealed a strong relationship between WMC and category learning irrespective of the requirement to integrate information across multiple perceptual dimensions. WMC was also uniformly related to people's ability to focus on the most task-appropriate strategy, regardless of whether or not that strategy involved information integration. Contrary to the predictions of the multiple systems view of categorization, working memory thus appears to underpin performance in both major classes of perceptual category-learning tasks.     

Rule-based extrapolation in perceptual categorization

Erickson and Kruschke (1998) provided a demonstration that in certain situations people will classify novel stimuli according to an extrapolated rule, even when the most similar training exemplar is an exception to the rule. This result challenged exemplar models. Nosofsky and Johansen (2000) have called this finding into question by offering an exemplar-based explanation for those data based on the perceptual features of the stimuli. Here, we describe the results of a new experiment that yields results similar to those found previouslywithout the questionable perceptual features:Participantswho learn to classify all the training stimuli have patterns of generalization that indicate a combination of rule and exemplar representation. ATRIUM, a hybrid rule and exemplar model (Erickson & Kruschke, 1998), is shown to account for these data much better than ALCOVE, an exemplar model (Kruschke, 1992).Moreover, four alternate exemplar explanations, including one suggested by Nosofsky and Johansen, cannot account for our new findings.     

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.     

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.     

Comparison and contrast in perceptual categorization

People categorized pairs of perceptual stimuli that varied in both category membership and pairwise similarity. Experiments 1 and 2 showed categorization of 1 color of a pair to be reliably contrasted from that of the other. This similarity-based contrast effect occurred only when the context stimulus was relevant for the categorization of the target (Experiment 3). The effect was not simply owing to perceptual color contrast (Experiment 4), and it extended to pictures from common semantic categories (Experiment 5). Results were consistent with a sign-and-magnitude version of N. Stewart and G. D. A. Brown's (2005) similarity-dissimilarity generalized context model, in which categorization is affected by both similarity to and difference from target categories. The data are also modeled with criterion setting theory (M. Treisman & T. C. Williams, 1984), in which the decision criterion is systematically shifted toward the mean of the current stimuli.     

Multiple attention systems in perceptual categorization

Five observers categorized inverted L-shaped stimuli according to the length of the horizontal line segment. A centrally located spatial cue preceded the stimulus on each trial. On 80% of the trials, the (relevant) horizontal line segment fell within the cued location, and on 20% of the trials the (irrelevant) vertical line segment fell within the cued location. The empirical results provide support for the hypothesis that perceptual attention can focus on the stimulus attribute inside the spatially cued location at the same time that decisional attention is focused on the (relevant) horizontal attribute--that is, the results suggest that perceptual and decisional attention can function independently during categorization. Decision bound models and extended generalized context models that assume separate perceptual and decisional attention systems were fitted to the data. Versions of the models that assume that the spatial cue affected perceptual attention were superior to versions that assume no effect on perceptual attention. These theoretical analyses support the functional independence hypothesis and suggest that formal theories of categorization should model the effects of perceptual and decisional attention separately.     

Costs and benefits in perceptual categorization

Observers categorized perceptual stimuli when the category costs and benefits were manipulated across conditions, and costs were either zero or nonzero. The cost-benefit structures were selected so that performance across conditions was equivalent with respect to the optimal classifier. Each observer completed several blocks of trials in each of the experimental conditions, and a series of nested models was applied to the individual observer data from all conditions. In general, performance became more nearly optimal as observers gained experience with the cost-benefit structures, but performance reached asymptote at a suboptimal level. Observers behaved differently in the zero- and nonzero-cost conditions, performing consistently worse when costs were nonzero. A test of the hypothesis that observers weight costs more heavily than benefits was inconclusive. Some aspects of the data supported this differential weighting hypothesis, but others did not. Implications for current theories of cost-benefit learning are discussed.     

The prioritization of perceptual processing in categorization

In three experiments, the effects of selective attention on perceptual processes in a complex multidimensional object categorization task were investigated. In each experiment, participants completed a perceptual-matching task to gain estimates of the perceptual salience of each stimulus dimension, then a categorization task using the same stimuli. In Experiments 1 and 2, the perceptual processing of stimulus dimensions was faster when dimensions were more diagnostic of category membership, regardless of their perceptual salience. Experiment 3 demonstrated that this prioritization of perceptual processing was evident even when stimuli were presented in unpredictable locations during categorization, indicating that the physical characteristics of the stimulus guide selective attention to diagnostic stimulus dimensions.     

Response time distributions in multidimensional perceptual categorization

Three speeded categorization experiments were conducted using separable dimension stimuli. The form of the category boundary was manipulated across experiments, and the distance from category exemplars to the category boundary was manipulated within each experiment. Observers completed several sessions in each experiment, yielding 300–400 repetitions of each stimulus. The large sample sizes permitted accurate estimates of the response time (RT) distributions and RT hazard functions. Analyses of these data indicated: (1) RT was faster for stimuli farther from the category boundary, and this stochastic dominance held at the level of the RT distributions; (2) RT was invariant for all stimuli the same distance from the category boundary; (3) when task difficulty was high, errors were slower than correct responses, whereas this difference disappeared when difficulty was low; (4) small, consistent response biases appeared to have a large effect on the relation between correct and error RT; (5) the shape of the RT hazard function was qualitatively affected by distance to the category boundary. These data establish a rich set of empirical constraints for testing developing models of categorization RT.     

Orthographic neighborhood effects in perceptual identification and semantic categorization tasks: a test of the multiple read-out model

How should a word's orthographic neighborhood affect perceptual identification and semantic categorization, both of which require a word to be uniquely identified? According to the multiple read-out model (Grainger & Jacobs, 1996), inhibitory neighborhood frequency effects should be observed in these types of tasks, and facilitatory neighborhood size effects should not be. In Experiments 1 and 2 (perceptual identification), these effects were examined as a function of stimulus visibility (i.e., high vs. low visibility) to provide as full a test as possible of the model's predictions. In the high-visibility conditions, words with large neighborhoods were reported less accurately than words with small neighborhoods, but there was no effect of neighborhood frequency (i.e., whether the word had a higher frequency neighbor). In the low-visibility conditions, low-frequency words with large neighborhoods and low-frequency words with higher frequency neighbors showed superior identification performance. In the semantic categorization task (Experiment 3), words with large neighborhoods were responded to more rapidly than words with small neighborhoods, but there was no effect of neighborhood frequency. These results are inconsistent with two of the basic premises of the multiple read-out model--namely, that facilitatory neighborhood size effects are due to a variable response criterion (the sigma criterion), rather than to lexical selection processes, and that the lexical selection processes themselves produce an inhibitory neighborhood frequency effect (via the M criterion). Instead, the present results, in conjunction with previous findings, suggest that large neighborhoods (and perhaps higher frequency neighbors) do aid lexical selection.     

Measuring category intuitiveness in unconstrained categorization tasks

What makes a category seem natural or intuitive? In this paper, an unsupervised categorization task was employed to examine observer agreement concerning the categorization of nine different stimulus sets. The stimulus sets were designed to capture different intuitions about classification structure. The main empirical index of category intuitiveness was the frequency of the preferred classification, for different stimulus sets. With 169 participants, and a within participants design, with some stimulus sets the most frequent classification was produced over 50 times and with others not more than two or three times. The main empirical finding was that cluster tightness was more important in determining category intuitiveness, than cluster separation. The results were considered in relation to the following models of unsupervised categorization: DIVA, the rational model, the simplicity model, SUSTAIN, an Unsupervised version of the Generalized Context Model (UGCM), and a simple geometric model based on similarity. DIVA, the geometric approach, SUSTAIN, and the UGCM provided good, though not perfect, fits. Overall, the present work highlights several theoretical and practical issues regarding unsupervised categorization and reveals weaknesses in some of the corresponding formal models.     

A neurobiological theory of automaticity in perceptual categorization

A biologically detailed computational model is described of how categorization judgments become automatic in tasks that depend on procedural learning. The model assumes 2 neural pathways from sensory association cortex to the premotor area that mediates response selection. A longer and slower path projects to the premotor area via the striatum, globus pallidus, and thalamus. A faster, purely cortical path projects directly to the premotor area. The model assumes that the subcortical path has greater neural plasticity because of a dopamine-mediated learning signal from the substantia nigra. In contrast, the cortical-cortical path learns more slowly via (dopamine independent) Hebbian learning. Because of its greater plasticity, early performance is dominated by the subcortical path, but the development of automaticity is characterized by a transfer of control to the faster cortical-cortical projection. The model, called SPEED (Subcortical Pathways Enable Expertise Development), includes differential equations that describe activation in the relevant brain areas and difference equations that describe the 2- and 3-factor learning. A variety of simulations are described, showing that the model accounts for some classic single-cell recording and behavioral results.     

Similarity and dissimilarity as evidence in perceptual categorization

In exemplar models the similarities between a new stimulus and each category exemplar constitute positive evidence for category membership. In contrast, other models assume that, if the new stimulus is sufficiently dissimilar to a category member, then that dissimilarity constitutes evidence against category membership. We propose a new similarity-dissimilarity exemplar model that provides a framework for integrating these two types of accounts. The evidence for a category is assumed to be the summed similarity to members of that category plus the summed dissimilarity to members of competing categories. The similarity-dissimilarity exemplar model is shown to mimic the standard exemplar model very closely in the unidimensional domain.     

The effect of category variability in perceptual categorization

Exemplar and distributional accounts of categorization make differing predictions for the classification of a critical exemplar precisely halfway between the nearest exemplars of 2 categories differing in variability. Under standard conditions of sequential presentation, the critical exemplar was classified into the most similar, least variable category, consistent with an exemplar account. However, if the difference in variability is made more salient, then the same exemplar is classified into the more variable, most likely category, consistent with a distributional account. This suggests that participants may be strategic in their use of either strategy. However, when the relative variability of 2 categories was manipulated, participants showed changes in the classification of intermediate exemplars that neither approach could account for.     

Sequence effects in categorization of simple perceptual stimuli

Categorization research typically assumes that the cognitive system has access to a (more or less noisy) representation of the absolute magnitudes of the properties of stimuli and that this information is used in reaching a categorization decision. However, research on identification of simple perceptual stimuli suggests that people have very poor representations of absolute magnitude information and that judgments about absolute magnitude are strongly influenced by preceding material. The experiments presented here investigate such sequence effects in categorization tasks. Strong sequence effects were found. Classification of a borderline stimulus was more accurate when preceded by a distant member of the opposite category than by a distant member of the same category. It is argued that this category contrast effect cannot be accounted for by extant exemplar or decision-bound models of categorization. The effect suggests the use of relative magnitude information in categorization. A memory and contrast model illustrates how relative magnitude information may be used in categorization.     

On the relation between decision rules and perceptual representation in multidimensional perceptual categorization

This article examines the relation between changing categorization decision rules and the nature of the underlying perceptual representation. Observers completed a matching task that required them to adjust the length and orientation of a single line stimulus until they perceived it to "match" a second line stimulus (Alfonso-Reese, 1996, 1997). The same observers then completed four categorization tasks with the same stimuli. Data from the matching task were used to estimate a perceptual representation for each stimulus and observer. Three hypotheses regarding potential interactions between categorization decision rules and perceptual representation were examined. One assumed that there was no interaction between decision rules and perceptual representation. The second assumed that linear categorization rules affect the perceptual representation differently from nonlinear categorization rules. The third assumed that dimensional integration rules affected the perceptual representation differently from decision rules that require the observer to set a criterion along one stimulus dimension while ignoring the other; this is referred to as decisional selective attention. The results suggested that (1) the matching task perceptual representation provided a good account of the categorization data, (2) decisional selective attention affected the perceptual representation differently from decisional integration, and (3) decisional selective attention generally decreased the perceptual variability along the attended dimension.     

The role of background knowledge in speeded perceptual categorization

We examined the time-course of the influence of background knowledge on perceptual categorization by manipulating the meaningfulness of labels associated with categories and by manipulating the amount of time provided to subjects for making a categorization decision. Extending a paradigm originally reported by Wisniewski and Medin (1994) (Cog. Sci. 18 (1994) 221), subjects learned two categories of children's drawings that were given either meaningless labels (drawings by children from 'group 1' or 'group 2') or meaningful labels (drawings by 'creative' or 'non-creative' children); the meaningfulness of the label had a significant effect on how new drawings were classified. In addition, half of the subjects were provided unlimited time to respond, while the other half of the subjects were forced to respond quickly; speeded response conditions had a relatively large effect on categorization decisions by subjects given the meaningless labels but had relatively little effect on categorization decisions by subjects given the meaningful labels. These results suggest that some forms of background knowledge can show an influence at relatively early stages in the time-course of a categorization decision.     

A feedback model of perceptual learning and categorization

Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This paper employs a computational model of the cortical region inter-actions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorized and could also facilitate the learning of subordinate level representations suitable for object identification and perceptual expertise.