The effects of concurrent task interference on category learning: evidence for multiple category learning systems.

Participants learned simple and complex category structures under typical single-task conditions and when performing a simultaneous numerical Stroop task. In the simple categorization tasks, each set of contrasting categories was separated by a unidimensional explicit rule, whereas the complex tasks required integrating information from three stimulus dimensions and resulted in implicit rules that were difficult to verbalize. The concurrent Stroop task dramatically impaired learning of the simple explicit rules, but did not significantly delay learning of the complex implicit rules. These results support the hypothesis that category learning is mediated by multiple learning systems.

Suboptimality in human categorization and identification.

Categorization and identification decision processes were examined and compared in 4 separate experiments. In all tasks, the critical stimulus component was a line that varied across trials in length and orientation, and the optimal decision rules were always complex piecewise quadratic functions. Evidence was found that identification is mediated by separate explicit and implicit systems. In addition, a common type of suboptimality was found in both categorization and identification. In particular, observers apparently approximated the piecewise quadratic functions of the optimal decision rules with simpler piecewise linear functions. A computational model, which was motivated by a recent neuropsychological theory of category learning, successfully accounted for this suboptimal performance in both categorization and identification. The model assigns a key role to the striatum and assumes the observed suboptimality was largely due to massive convergence of visual cortical cells onto single striatal units.

The neurobiology of human category learning.

Categorization is among the most important skills that any organism can possess. Recent advances in cognitive neuroscience have led to new insights about the neural basis of category learning. Perhaps most important is the finding that many different, widely separated neural structures appear to participate in category learning, but to varying degrees that depend on category structure. In particular, different brain regions are implicated according to whether the category-learning task involves explicit rules, prototype distortion or information integration.

A neuropsychological theory of positive affect and its influence on cognition.

Positive affect systematically influences performance on many cognitive tasks. A new neuropsychological theory is proposed that accounts for many of these effects by assuming that positive affect is associated with increased brain dopamine levels. The theory predicts or accounts for influences of positive affect on olfaction, the consolidation of long-term (i.e., episodic) memories, working memory, and creative problem solving. For example, the theory assumes that creative problem solving is improved, in part, because increased dopamine release in the anterior cingulate improves cognitive flexibility and facilitates the selection of cognitive perspective.

On the dominance of unidimensional rules in unsupervised categorization.

In several experiments, observers tried to categorize stimuli constructed from two separable stimulus dimensions in the absence of any trial-by-trial feedback. In all of the experiments, the observers were told the number of categories (i.e., two), they were told that perfect accuracy was possible, and they were given extensive experience in the task (i.e., 800 trials). When the boundary separating the contrasting categories was unidimensional, the accuracy of all observers improved significantly over blocks (i.e., learning occurred), and all observers eventually responded optimally. When the optimal boundary was diagonal, none of the observers responded optimally. Instead they all used some sort of suboptimal unidimensional rule. In a separate feedback experiment, all observers responded optimally in the diagonal condition. These results contrast with those for supervised category learning; they support the hypothesis that in the absence of feedback, people are constrained to use unidimensional rules.

On the nature of implicit categorization.

Current categorization models disagree about whether people make a priori assumptions about the structure of unfamiliar categories. Data from two experiments provided strong evidence that people do not make such assumptions. These results rule out prototype models and many decision bound models of categorization. We review previously published neuropsychological results that favor the assumption that category learning relies on a procedural-memory-based system, rather than on an instance-based system (as is assumed by exemplar models). On the basis of these results, a new category-learning model is proposed that makes no a priori assumptions about category structure and that relies on procedural learning and memory.

A neuropsychological theory of multiple systems in category learning.

A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.

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.

Selective attention and the formation of linear decision boundaries: comment on McKinley and Nosofsky (1996).

S. C. McKinley and R. M. Nosofsky (1996) compared a linear decision-bound model with the generalized context model (GCM) in their ability to account for categorization data from experiments that used integral- or separable-dimension stimuli and required selective attention or attention to both dimensions. McKinley and Nosofsky (1996) found support for the GCM and concluded that decision-bound theory needs to incorporate assumptions about selective attention. In this commentary it is argued that (a) unlike the GCM, decision-bound theory provides a framework for independently investigating perceptual and decisional forms of selective attention; (b) the effect of stimulus integrality on the form of the optimal decision bound is misinterpreted; (c) averaged data is biased against decision-bound theory and toward the GCM; (d) many a priori predictions of the GCM are violated empirically; and (e) exemplar theory has lost much of its initial theoretical structure.

Perceptual separability, decisional separability, and the identification-speeded classification relationship.

Three observers participated in several sessions of identification and speeded classification. Predictions from F. G. Ashby and W. T. Maddox's (1994a) response time (RT) theory of speeded classification were tested. A key theoretical concept is decisional separability (DS), that is, that the decision about the level of 1 component does not depend on the perceived value of the other component. The theory predicted that DS would hold in the filtering task but would be violated in the redundancy task, resulting in the redundancy gains. To summarize the results, (a) DS held in the filtering conditions, but not in the redundancy conditions; (b) redundancy gains occurred; (c) despite the redundancy gains, strong evidence was obtained that the stimulus components are perceptually separable; (d) 2 new models that each assumed RT decreases with the distance between the percept and the decision bound provided good accounts of the RT distributions and accuracy rates; and (e) the shift from identification to speeded classification influenced both perceptual and decisional processes.

A formal theory of feature binding in object perception.

Visual objects are perceived correctly only if their features are identified and then bound together. Illusory conjunctions result when feature identification is correct but an error occurs during feature binding. A new model is proposed that assumes feature binding errors occur because of uncertainty about the location of visual features. This model accounted for data from 2 new experiments better than a model derived from A. M. Treisman and H. Schmidt's (1982) feature integration theory. The traditional method for detecting the occurrence of true illusory conjunctions is shown to be fundamentally flawed. A reexamination of 2 previous studies provided new insights into the role of attention and location information in object perception and a reinterpretation of the deficits in patients who exhibit attentional disorders.

Categorization response time with multidimensional stimuli.

Categorization response time (RT) was examined in three separate experiments, in each of which exemplars varied on two physical dimensions. Three different types of stimuli were used: (1) horizontal and vertical line segments of varying length that were joined at an upper left corner, (2) rectangles of varying width and height, and (3) circles or semicircles of varying size with a radial arm of varying orientation. No evidence was found that stimulus familiarity or the category prototypes played any special role in determining categorization RT. Instead, RT decreased with distance from the stimulus to the categorization decision bound.

A probabilistic multidimensional model of location information.

A probabilistic multidimensional model of location discrimination is developed and applied to data from an experiment in which subjects are required to determine whether a briefly presented horizontal and vertical bar are touching. The proposed gap-detection model assumes that errors in perception are due to variability in the perceived location and/or in the perceived length of the bars. A series of gap-detection models that allow variability only in perceived location were rejected on the basis of likelihood-ratio tests of overall goodness of fit. However, when the models were modified to account for: (a) a compression of the distance perceived between the bars (Wolford, 1975), or (b) the bisection illusion (Künnapas, 1955), excellent absolute fits to the data were obtained. A pair of models that suggests that the horizontal/vertical illusion or a response bias was operative failed. Applications of the model to more conventional object-perception experiments (e.g., the illusory-conjunction experiment) are discussed.

Comparing decision bound and exemplar models of categorization.

The performance of a decision bound model of categorization (Ashby, 1992a; Ashby & Maddox, in press) is compared with the performance of two exemplar models. The first is the generalized context model (e.g., Nosofsky, 1986, 1992) and the second is a recently proposed deterministic exemplar model (Ashby & Maddox, in press), which contains the generalized context model as a special case. When the exemplars from each category were normally distributed and the optimal decision bound was linear, the deterministic exemplar model and the decision bound model provided roughly equivalent accounts of the data. When the optimal decision bound was non-linear, the decision bound model provided a more accurate account of the data than did either exemplar model. When applied to categorization data collected by Nosofsky (1986, 1989), in which the category exemplars are not normally distributed, the decision bound model provided excellent accounts of the data, in many cases significantly outperforming the exemplar models. The decision bound model was found to be especially successful when (1) single subject analyses were performed, (2) each subject was given relatively extensive training, and (3) the subject's performance was characterized by complex suboptimalities. These results support the hypothesis that the decision bound is of fundamental importance in predicting asymptotic categorization performance and that the decision bound models provide a viable alternative to the currently popular exemplar models of categorization.

Subitizing: magical numbers or mere superstition?

It is widely believed that humans are endowed with a specialized numerical process, called subitizing, which enables them to apprehend rapidly and accurately the numerosity of small sets of objects. A major part of the evidence for this process is a purported discontinuity in the mean response time (RT) versus numerosity curves at about 4 elements, when subjects enumerate up to 7 or more elements in a visual display. In this article, RT data collected in a speeded enumeration experiment are subjected to a variety of statistical analyses, including several tests on the RT distributions. None of these tests reveals a significant discontinuity as numerosity increases. The data do suggest a strong stochastic dominance in RT by display numerosity, indicating that the mental effort required to enumerate does increase with each additional element in the display, both within and beyond the putative subitizing range.

Is subitizing a unique numerical ability?

Two models that predict the relation between mean enumeration time and numerosity in a speeded enumeration experiment are tested. The first is a bilinear two-process model, and the second is a log-linear single-process model. Previously, support for the bilinear model has provided evidence for the existence of a unique numerical ability called "subitizing." Both models are shown to yield close approximations to the empirical data, but at the same time to consistently violate the robust shape of these data. Two fundamental discrepancies exist: (1) Enumeration of single-element displays is unpredictably fast, both in the data reported here and elsewhere, and (2) the response-time function for multiple elements is continuously convex upward, with a significant log-quadratic component. The findings support the contention that enumeration is a capacity-limited process, but not statistically reliable change in processing character, that is, from subitizing to some other process, is evident in enumeration of displays of up to six elements.

Predicting similarity and categorization from identification.

In this article, the relation between the identification, similarity judgment, and categorization of multidimensional perceptual stimuli is studied. The theoretical analysis focused on general recognition theory (GRT), which is a multidimensional generalization of signal detection theory. In one application, 2 Ss first identified a set of confusable stimuli and then made judgments of their pairwise similarity. The second application was to Nosofsky's (1985b, 1986) identification-categorization experiment. In both applications, a GRT model accounted for the identification data better than Luce's (1963) biased-choice model. The identification results were then used to predict performance in the similarity judgment and categorization conditions. The GRT identification model accurately predicted the similarity judgments under the assumption that Ss allocated attention to the 2 stimulus dimensions differently in the 2 tasks. The categorization data were predicted successfully without appealing to the notion of selective attention. Instead, a simpler GRT model that emphasized the different decision rules used in identification and categorization was adequate.

Integrating information from separable psychological dimensions.

This article examines decision processes in the perception and categorization of stimuli composed of the separable psychological dimensions, orientation and size. The randomization technique (Ashby & Gott, 1988) of general recognition theory, which allows accurate estimation of a subject's decision boundary in a categorization task, is used in 4 experiments. Even though the stimulus components are clearly separable, it was found that Ss were not constrained to use separable response strategies, nor were they constrained to attend to distance to the prototypes. Instead, they used decision rules that were nearly optimal, even if this required information integration or for the Ss to attend to higher level category properties such as component correlation.

Influence of positive affect on the subjective utility of gains and losses: it is just not worth the risk.

A modification of the procedure originally used by Davidson, Suppes, and Siegel (1956) to measure subjective utility was used to study the influence of positive affect on individuals' perceived value (utility) functions. Results indicated, as expected, that persons in whom positive affect had been induced showed a more negative subjective utility for losses than did controls. This indicates that losses seem worse to people who are feeling happy than to those in a control condition. The subjective utility functions of the two groups did not differ as much, however, when people were considering potential gain. Thus, at least in the situation tested in this study, potential gains did not seem to be more appealing (nor less so) for affect subjects than they did for controls. These findings are discussed in relation to theoretical issues in decision making and work suggesting that positive affect can promote increased sensitivity to losses in situations of potential meaningful loss.