title: A probabilistic contrast model of causal induction.
abstract: Deviations from the predictions of covariational models of causal attribution have often been reported in the literature. These include a bias against using consensus information, a bias toward attributing effects to a person, and a tendency to make a variety of unpredicted conjunctive attributions. It is contended that these deviations, rather than representing irrational biases, could be due to (a) unspecified information over which causal inferences are computed and (b) the questionable normativeness of the models against which these deviations have been measured. A probabilistic extension of Kelley's analysis-of-variance analogy is proposed. An experiment was performed to assess the above biases and evaluate the proposed model against competing ones. The results indicate that the inference process is unbiased.
authors.affiliation: Department of Psychology, University of California, Los Angeles 90024-1563.
authors.forename: P W
authors.initials: PW
authors.lastname: Cheng
authors.forename: L R
authors.initials: LR
authors.lastname: Novick
mesh_terms: D000328:Adult; D003210:Concept Formation; D006801:Humans; D007206:Individuality; D007257:Information Theory; D015233:Models, Statistical; D011337:Probability Learning; D011340:Problem Solving