title: Covariation in natural causal induction.
abstract: The covariation component of everyday causal inference has been depicted, in both cognitive and social psychology as well as in philosophy, as heterogeneous and prone to biases. The models and biases discussed in these domains are analyzed with respect to focal sets: contextually determined sets of events over which covariation is computed. Moreover, these models are compared to our probabilistic contrast model, which specifies causes as first and higher order contrasts computed over events in a focal set. Contrary to the previous depiction of covariation computation, the present assessment indicates that a single normative mechanism--the computation of probabilistic contrasts--underlies this essential component of natural causal induction both in everyday and in scientific situations.
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: D003071:Cognition; D006801:Humans; D007858:Learning; D015233:Models, Statistical; D009042:Motivation; D011340:Problem Solving