Two connectionist models of attention in associative learning, previously used to model human category learning, are shown to have special cases that are essentially equivalent to N. J. Mackintosh's (1975, Psychological Review, 82, 276 298) classic model of attention in animal learning. The models unify formulas for associative weight change with formulas for attentional change, under a common goal of error reduction. Error-driven attentional shifting accelerates learning of new associations but also protects previously learned associations from retroactive interference. The models are fit to data from a recent experiment in human associative learning (J. K. Kruschke 6 N. J. Blair, 2000, Psychonomic Bulletin 6 Review, 7, 636 645), which shows that blocking of learning involves learned inattention. The approach also provides a novel and unifying theory of latent inhibition (the preexposure effect) in terms of blocking. The discussion summarizes how the approach accounts for a variety of other ``irrational' ' phenomena in associative learning, including base rate effects, perseveration of attention through relevance

The authors compared the exemplar-based random-walk (EBRW) model of Nosofsky and Palmeri

Recent studies of visual statistical learning (VSL) have demonstrated that statistical regularities in sequences of visual stimuli can be automatically extracted, even without intent or awareness. Despite much work on this topic, however, several fundamental questions remain about the nature of VSL. In particular, previous experiments have not explored the underlying units over which VSL operates. In a sequence of colored shapes, for example, does VSL operate over each feature dimension independently, or over multidimensional objects in which color and shape are bound together? The studies reported here demonstrate that VSL can be both object-based and feature-based, in systematic ways based on how different feature dimensions covary. For example, when each shape covaried perfectly with a particular color, VSL was object-based: Observers expressed robust VSL for colored-shape sub-sequences at test but failed when the test items consisted of monochromatic shapes or color patches. When shape and color pairs were partially decoupled during learning, however, VSL operated over features: Observers expressed robust VSL when the feature dimensions were tested separately. These results suggest that VSL is object-based, but that sensitivity to feature correlations in multidimensional sequences (possibly another form of VSL) may in turn help define what counts as an object.

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Maddox and Ashby's current stance represents a marked departure from their previously published claims about the unimportance of selective attention in categorization, (b) they are inconsistent with their own work when they criticize S. C. McKinley and R. M. Nosofsky's (1996) tests of the linear-boundary models, (c) their arguments about modeling averaged data have no bearing on the central conclusions reached by McKinley and Nosofsky, and (d) they make incorrect assertions regarding the application and predictions of the exemplar model. Finally, the author defends the theoretical progress that has been made in recent years with the exemplar model and argues instead that it is the decision-bound theory of Ashby and Maddox that is in need of greater constraints. In this reply to Maddox and Ashby's (1998) commentary, I make five main points. First, I argue that Maddox and Ashby's discussion of the role of selective attention in classification represents a major departure from all their previously published claims that motivated the McKinley and Nosofsky (1996) article. Indeed, Maddox and Ashby's

Multidimensional scaling (MDS) techniques provide a promising measurement strategy for characterizing individual differences in cognitive processing, which many clinical theories associate with the development, maintenance, and treatment of psychopathology. The authors describe the use of deterministic and probabilistic MDS techniques for investigating numerous aspects of perceptual organization, such as dimensional attention, perceptual correlation, within-attribute organization, and perceptual variability. Additionally, they discuss how formal quantitative models can be used, in conjunction with MDS-derived representations of individual differences in perceptual organization, to test theories about the role of cognitive processing in clinically relevant phenomena. They include applied examples from their work in the areas of eating disorders and sexual coercion. Cognitive theorists implicate individual differences in social information processing, particularly construal processes, in the development, maintenance, and treatment of various forms of psychopathology (Beck, 1976; Ellis, 1994; Kelly, 1955; McFall, 1982). Clinical scientists have had difficulty finding valid methods to assess social information-processing constructs, however. One

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.

Minda and Ross (2004) described two experiments where subjects simultaneously learned both a category and a function. They showed that when both tasks were performed in parallel on the same stimuli, the inductive bias on the categorization task–to focus on a single attribute–spread to the function learning task. Here, we present a new computational model of this phenomenon, using the ALCOVE model of categorization, a new model of function learning, and a hypothesis for their interaction: shared selective attention. The model parsimoniously succeeds in learning the category and function, then in accounting for human generalization patterns on conflicting transfer stimuli. The novel function-learning component of the model, extending previous work in mixture-of-experts approaches (Kalish, Lewandowsky, & Kruschke, 2004; Harris & Minda, 2005), is also introduced.

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We consider the task of human collaborative category learning, where two people work together to classify test items into appropriate categories based on what they learn from a training set. We propose a novel collaboration policy based on the Co-Training algorithm in machine learning, in which the two people play the role of the base learners. The policy restricts each learner’s view of the data and limits their communication to only the exchange of their labelings on test items. In a series of empirical studies, we show that the Co-Training policy leads collaborators to jointly produce unique and potentially valuable classification outcomes that are not generated under other collaboration policies. We further demonstrate that these observations can be explained with appropriate machine learning models.