We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli along a set of component dimensions. Those independent decisions are then combined via logical rules to determine the overall categorization response. The time course of the independent decisions is modeled via random-walk processes operating along individual dimensions. Alternative mental architectures are used as mechanisms for combining the independent decisions to implement the logical rules. We derive fundamental qualitative contrasts for distinguishing among the predictions of the rule models and major alternative models of classification RT. We also use the models to predict detailed RT distribution data associated with individual stimuli in tasks of speeded perceptual classification.

A growing methodology, known as the systems factorial technology (SFT), is being developed to diagnose the types of information-processing architectures (serial, parallel, or coactive) and stopping rules (exhaustive or self-terminating) that operate in tasks of multidimensional perception. Whereas most previous applications of SFT have been in domains of simple detection and visual–memory search, this research extends the applications to foundational issues in multidimensional classification. Experiments are conducted in which subjects are required to classify objects into a conjunctive-rule category structure. In one case the stimuli vary along highly separable dimensions, whereas in another case they vary along integral dimensions. For the separable-dimension stimuli, the SFT methodology revealed a serial or parallel architecture with an exhaustive stopping rule. By contrast, for the integral-dimension stimuli, the SFT methodology provided clear evidence of coactivation. The research provides a validation of the SFT in the domain of classification and adds to the list of converging operations for distinguishing between separable-dimension and integral-dimension interactions.

on feedback. Context (skewed stimulus presentation probabilities) was manipulated between and feedback within subjects in two experiments with diverse stimulus sets. Prototype- and exemplar-based scaling models are contrasted on the basis of their diverging predictions in this paradigm. The critical factor is that prototype-based categories cannot increase their coverage on the continuum without decreasing their coverage on the opposite side. The range of qualitative behavioral patterns consistent with each model class is shown using computer simulations with two representative members: ANCHOR and an instance-based modification thereof. ANCHOR can exhibit context effects in either assimilative or compensatory direction depending on feedback. The instance-based model always exhibits assimilative context effects. The human data show a significant context-by-feedback interaction. The main context effect is assimilative in one data set and compensatory in the other. This pattern is consistent with ANCHOR but rules out the instance-based variant, which fails to account for the compensatory effect and the interaction. This suggests that human category rating is based on unitary representations.

Observers made speeded old–new recognition judgments of color stimuli embedded in a multidimensional similarity space. The paradigm used multiple lists but with the underlying similarity structures repeated across lists, to allow for quantitative modeling of the data at the individual-participant and individual-item levels. Correct-rejection response times (RTs) got systematically faster as the similarity of foils to the old study items decreased. There were also intricate patterns of speed–accuracy trade-offs that varied across individual items and participants. An exemplar-based random-walk model provided a good overall quantitative account of the recognition choice probabilities, mean correct RTs, and mean error RTs associated with the individual items on the basis of their positions in multidimensional similarity space. However, the model failed to predict the very long RTs associated with correct rejections of a prototype foil.

Experiments were conducted to contrast the predictions from exemplar models and rulebased decision-bound models of perceptual classification. Observers classified multidimensional stimuli into categories that could be described in terms of easily verbalizable logical rules. The critical manipulation was that some pairs of stimuli received probabilistic feedback, whereas other control pairs received deterministic feedback. Despite the probabilistic feedback, the probabilistic pairs and the deterministic pairs were the same distance from idealobserver, rule-based decision boundaries. Across two experiments with varying category structures, observers classified the probabilistic pairs with slower response times (RTs) and lower accuracies than the comparison deterministic pairs. The effects were relatively long-term, extending into test blocks in which all feedback was withheld. The results were as predicted by exemplar models, but challenged models that posit that RT is a function solely of the distance of a stimulus from rule-based boundaries. The studies add considerable generality to previous ones and suggest that, even in domains involving rule-based category structures, exemplarretrieval processes play a significant role. Supplementary material related to this article may be downloaded from www.psychonomic.org/archive.

We use the Exemplar-Based Random-Walk model (EBRW) to extend the Varying Abstraction Model (VAM). Unlike the VAM which is designed to account for categorization proportions, this Varying Abstraction-Based Random-Walk (VABRW) model is able to predict categorization response times. The extension is especially useful in situations where response accuracies are not very informative for distinguishing between models. Application of the VABRW to data previously gathered by Nosofsky and Palmeri (1997) provides additional evidence for the view that people use