title: Structured statistical models of inductive reasoning.
abstract: Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.
authors.affiliation: Department of Psychology, Carnegie Mellon University. ckemp@cmu.edu
authors.forename: Charles
authors.initials: C
authors.lastname: Kemp
authors.forename: Joshua B
authors.initials: JB
authors.lastname: Tenenbaum
mesh_terms: D001245:Association Learning; D001499:Bayes Theorem; D003210:Concept Formation; D003657:Decision Making; D006801:Humans; D007600:Judgment; D019359:Knowledge; D011939:Mental Recall; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011340:Problem Solving; D011582:Psychological Theory