title: Representing the task in Bayesian reasoning: comment on Lovett and Schunn (1999).
abstract: The RCCL model (M. C. Lovett & C. D. Schunn, 1999) produces predictions that are non-novel or that do not truly spring from its principles. However, it offers the valuable insight that learning processes may affect the selection of both representations and strategies within those representations, and points the way to possible theoretical progress on implicit and explicit control. The authors' account of base-rate neglect under direct experience is compared with RCCL, and it is concluded that learning-based models allow for tests that are not fostered by representation-based models.
authors.affiliation: Department of Psychology, University of Georgia, Athens, Georgia 30602-3013, USA. goodie@egon.psy.uga.edu
authors.forename: A S
authors.initials: AS
authors.lastname: Goodie
authors.forename: E
authors.initials: E
authors.lastname: Fantino
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D007858:Learning; D008960:Models, Psychological