W. Todd Maddox

Comparing decision bound and exemplar models of categorization

The performance of a decision bound model of categorization (Ashby, J992a; 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 nonlinear, 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 subjects 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.

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.

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.

Dual-task interference in perceptual category learning

The effect of a working-memory—demanding dual task on perceptual category learning was investigated. In Experiment 1, participants learned unidimensional rule-based or information integration category structures. In Experiment 2, participants learned a conjunctive rule-based category structure. In Experiment 1, unidimensional rule-based category learning was disrupted more by the dual working memory task than was information integration category learning. In addition, rule-based category learning differed qualitatively from information integration category learning in yielding a bimodal, rather than a normal, distribution of scores. Experiment 2 showed that rule-based learning can be disrupted by a dual working memory task even when both dimensions are relevant for optimal categorization. The results support the notion of at least two systems of category learning: a hypothesis-testing system that seeks verbalizable rules and relies on working memory and selective attention, and an implicit system that is procedural-learning based and is essentially automatic.

Complex Decision Rules in Categorization: Contrasting Novice and Experienced Performance

The ability of novice and experienced Ss to learn complex decision rules was tested with 3 categorization tasks. Each task involved 2 categories with exemplars that were normally distributed on 2 stimulus dimensions. 3 separate sets of stimuli were used, and in each task the decision rule that maximized categorization accuracy was a highly nonlinear function of the stimulus dimension values. In the 3 tasks, all experienced Ss used highly nonlinear decision rules. Quadratic rules were supported over bilinear rules, and in many cases, Ss used nearly optimal decision rules. These findings did not depend on whether the stimulus components were integral or separable. Novice Ss also did not use simple linear rules. A model that assumed Ss tried a succession of different linear rules was also rejected. Instead, novices appeared to use quadratic rules, although less consistently than experienced Ss.

On the Dangers of Averaging Across Subjects When Using Multidimensional Scaling or the Similarity-Choice Model

When ratings of judged similarity or frequencies of stimulus identification are averaged across subjects, the psychological structure of the data is fundamentally changed. Regardless of the structure of the individual-subject data, the averaged similarity data will likely be well fit by a standard multidimensional scaling model, and the averaged identification data will likely be well fit by the similarity-choice model. In fact, both models often provide excellent fits to averaged data, even if they fail to fit the data of each individual subject. Thus, a good fit of either model to averaged data cannot be taken as evidence that the model describes the psychological structure that characterizes individual subjects. We hypothesize that these effects are due to the increased symmetry that is a mathematical consequence of the averaging operation.

Observational versus feedback training in rule-based and information-integration category learning

The effects of two different kinds of categorization training were investigated. In observational training, observers are presented with a category label and then shown an exemplar from that category. In feedback training, they are shown an exemplar, asked to assign it to a category, and then given feedback about the accuracy of their response. These two types of training were compared as observers learned two types of category structures—those in which optimal accuracy could be achieved via some explicit rule-based strategy, and those in which optimal accuracy required integrating information from separate perceptual dimensions at some predecisional stage. There was an overall advantage for feedback training over observational training, but most importantly, type of training interacted strongly with type of category structure. With rule-based structures, the effects of training type were small, but with information-integration structures, accuracy was substantially higher with feedback training, and people were less likely to use suboptimal rule-based strategies. The implications of these results for current theories of category learning are discussed.

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?

Disrupting feedback processing interferes with rule-based but not information-integration category learning.

The effect of a sequentially presented memory scanning task on rule-based and informationintegration category learning was investigated. On each trial in the short feedback-processing time condition, memory scanning immediately followed categorization. On each trial in the long feedbackprocessing time condition, categorization was followed by a 2.5-sec delay and then memory scanning. In the control condition, no memory scanning was required. Rule-based category learning was significantly worse in the short feedback-processing time condition than in the long feedback-processing time condition or control condition, whereas information-integration category learning was equivalent across conditions. In the rule-based condition, a smaller proportion of observers learned the task in the short feedback-processing time condition, and those who learned took longer to reach the performance criterion than did those in the long feedback-processing time or control condition. No differences were observed in 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.

Delayed Feedback Disrupts the Procedural-Learning System but Not the Hypothesis-Testing System in Perceptual Category Learning.

W. T. Maddox, F. G. Ashby, and C. J. Bohil (2003) found that delayed feedback adversely affects information-integration but not rule-based category learning in support of a multiple-systems approach to category learning. However, differences in the number of stimulus dimensions relevant to solving the task and perceptual similarity failed to rule out 2 single-system interpretations. The authors conducted an experiment that remedied these problems and replicated W. T. Maddox et al.s findings. The experiment revealed a strong performance decrement for information-integration but not rule-based category learning under delayed feedback that was due to an increase in the number of observers using hypothesis-testing strategies to solve the information-integration task, and lower accuracy rates for the few observers using information-integration strategies.