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Shanks (1991) reported experiments that show selective-learning effects in a categorization task, and presented simulations of his data using a connectionist network model implementing the Rescorla-Wagner (R-W) theory of animal conditioning. He concluded that his results (a) support the application of the R-W theory to account for human categorization, and (b) contradict a particular variant of contingency-based theories of categorization. We examine these conclusions. We show that the asymptotic weights produced by the R-W model actually predict systematic deviations from the observed human learning data. Shanks claimed that his simulations provided good qualitative fits to the observed data when the weights in the networks were allowed to reach their asymptotic values. However, analytic derivations of the asymptotic weights reveal that the final weights obtained in Shanks' Simulations 1 and 2 do not correspond to the actual asymptotic weights, apparently because the networks were not in fact run to asymptote. We show that a contingency-based theory that incorporates the notion of focal sets can provide a more adequate explanation of cue competition than does the R-W model.' Because the critical cues in Shanks's (1991) noncontingent conditions were contingently related to the respective diseases by the conventional definition, the labels for his stimulus sets in Experiments 1 and 2-contingent condition and noncontingent condition-do not conform to conventional usage. 2 In the animal conditioning literature the term blocking is reserved for a paradigm in which the animal is first conditioned to a single cue presented alone, which then blocks subsequent learning to a second cue that is always paired with the first cue when reinforcement is given. In this article we use the term blocking in a more general sense to refer to reduction in associative learning to 1398 This document is copyrighted by the American Psychological Association or one of its allied publishers.This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

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Link discovery is a new challenge in data mining whose primary concerns are to identify strong links and discover hidden relationships among entities and organizations based on low-level, incomplete and noisy evidence data. To address this challenge, we are developing a hybrid link discovery system called KOJAK that combines state-of-theart knowledge representation and reasoning (KR&R) technology with statistical clustering and analysis techniques from the area of data mining. In this paper we report on the architecture and technology of its first fully completed module called the KOJAK Group Finder. The Group Finder is capable of finding hidden groups and group members in large evidence databases. Our group finding approach addresses a variety of important LD challenges, such as being able to exploit heterogeneous and structurally rich evidence, handling the connectivity curse, noise and corruption as well as the capability to scale up to very large, realistic data sets. The first version of the KOJAK Group Finder has been successfully tested and evaluated on a variety of synthetic datasets.

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