Analogical and Category-Based Inference A Theoretical Integration With Bayesian Causal Models

A fundamental issue for theories of human induction is to specify constraints on potential inferences For inferences based on shared category membership an analogy and/or a relational schema it appears that the basic goal of induction is to make accurate and goal relevant inferences that are sensitive to uncertainty People can use source information at various levels of abstraction (including both specific instances and more general categories) coupled with prior causal knowledge to build a causal model for a target situation which in turn constrains inferences about the target We propose a computational theory in the framework of Bayesian Inference and test its predictions (parameter free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences including evidence that analogical inferences are partially dissociable from overall mapping quality