High-level reasoning and base-rate use: do we need cue-competition to explain the inverse base-rate effect?

Previous accounts of the inverse base-rate effect (D. L. Medin & S. M. Edelson, 1988) have revolved around the concept of cue-competition. In this article, the authors propose that high-level reasoning in the form of an eliminative inference mechanism may contribute to the effect. A quantitative implementation of this idea demonstrates that it has the power by itself to produce the pattern of base-rate effects in the Medin and Edelson (1988) design. Four predictions are derived that contradict the predictions by attention to distinctive input (ADIT; J. K. Kruschke, 1996), up to date the most successful account of the inverse base-rate effect. Results from 3 experiments disconfirm the predictions by ADIT and demonstrate the importance of high-level reasoning in designs of the Medin and Edelson kind. Implications for the interpretation of the inverse base-rate effect and the attention-shifting mechanisms presumed by ADIT are discussed.

[1]  D. Medin,et al.  Sensitivity to changes in base-rate information , 1991 .

[2]  J. Koehler.  The base rate fallacy reconsidered: Descriptive, normative, and methodological challenges , 1996, Behavioral and Brain Sciences.

[3]  D. Medin,et al.  Problem structure and the use of base-rate information from experience. , 1988, Journal of experimental psychology. General.

[4]  A. Markman.  LMS rules and the inverse base-rate effect: Comment on Gluck and Bower (1988). , 1989 .

[5]  John K. Kruschke,et al.  Associative learning in baboons (Papio papio) and humans (Homo sapiens): species differences in learned attention to visual features , 1998, Animal Cognition.

[6]  S. Stoness,et al.  Proceedings of the Twenty First Annual Conference of the Cognitive Science Society , 1999 .

[7]  R. Nosofsky,et al.  Rule-plus-exception model of classification learning. , 1994, Psychological review.

[8]  R. Nosofsky,et al.  An exemplar-based random walk model of speeded classification. , 1997, Psychological review.

[9]  J. Kruschke,et al.  Rules and exemplars in category learning. , 1998, Journal of experimental psychology. General.

[10]  R. Nosofsky.  Attention and learning processes in the identification and categorization of integral stimuli. , 1987, Journal of experimental psychology. Learning, memory, and cognition.

[11]  J. Kruschke,et al.  A model of probabilistic category learning. , 1999, Journal of experimental psychology. Learning, memory, and cognition.

[12]  John R. Anderson.  The Adaptive Character of Thought , 1990 .

[13]  Jay J.J. Christensen-Szalanski,et al.  Physicians' use of probabilistic information in a real clinical setting. , 1981 .

[14]  David R. Shanks.  Connectionist Accounts of the Inverse Base-rate Effect in Categorization , 1992 .

[15]  Douglas L. Medin,et al.  Context theory of classification learning. , 1978 .

[16]  L. Beach,et al.  Experience and the base-rate fallacy. , 1982, Organizational behavior and human performance.

[17]  J. Kruschke.  Base rates in category learning. , 1996, Journal of experimental psychology. Learning, memory, and cognition.

[18]  G. Bower,et al.  From conditioning to category learning: an adaptive network model. , 1988 .

[19]  J. Kruschke,et al.  ALCOVE: an exemplar-based connectionist model of category learning. , 1992, Psychological review.

[20]  R. Nosofsky.  Similarity Scaling and Cognitive Process Models , 1992 .

[21]  R. Nosofsky,et al.  Combining exemplar-based category representations and connectionist learning rules. , 1992, Journal of experimental psychology. Learning, memory, and cognition.