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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(More)
According to various influential formal models of cognition, perceptual categorization and old-new recognition recruit the same memory system. By contrast, the prevailing view in the cognitive neuroscience literature is that separate neural systems mediate perceptual categorization and recognition. A direct form of evidence is that separate brain regions(More)
A recent resurgence in logical-rule theories of categorization has motivated the development of a class of models that predict not only choice probabilities but also categorization response times (RTs; Fifić, Little, & Nosofsky, 2010). The new models combine mental-architecture and random-walk approaches within an integrated framework and predict detailed(More)
Raven's Progressive Matrices (Raven, Raven, & Court, 1998) is one of the most prevalent assays of fluid intelligence; however , most theoretical accounts of Raven's focus on producing models which can generate the correct answer but do not fit human performance data. We provide a computational-level theory which interprets rule induction in Raven's as(More)
Exemplar-similarity models such as the exemplar-based random walk (EBRW) model (Nosofsky & Palmeri, 1997b) were designed to provide a formal account of multidimensional classification choice probabilities and response times (RTs). At the same time, a recurring theme has been to use exemplar models to account for old-new item recognition and to explain(More)
A classic distinction in perceptual information processing is whether stimuli are composed of separable dimensions, which are highly analyzable, or integral dimensions, which are processed holistically. Previous tests of a set of logical-rule models of classification have shown that separable-dimension stimuli are processed serially if the dimensions are(More)
Experiments were conducted to contrast the predictions from exemplar models and rule-based decision-boundary models of perceptual classification. Observers classified multidimensional stimuli into categories that could be described in terms of easily verbalized logical rules. The critical manipulation was that some pairs of stimuli received probabilistic(More)
Despite the fact that categories are often composed of correlated features, the evidence that people detect and use these correlations during intentional category learning has been overwhelmingly negative to date. Nonetheless, on other categorization tasks, such as feature prediction, people show evidence of correlational sensitivity. A conventional(More)
The relationship between fluid intelligence and working memory is of fundamental importance to understanding how capacity-limited structures such as working memory interact with inference abilities to determine intelligent behavior. Recent evidence has suggested that the relationship between a fluid abilities test, Raven's Progressive Matrices, and working(More)