Journal article
Structured Statistical Models of Inductive Reasoning
C Kemp, JB Tenenbaum
Psychological Review | AMER PSYCHOLOGICAL ASSOC | Published : 2009
DOI: 10.1037/a0014282
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 describe 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 categorie..
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Awarded by Air Force Office of Scientific Research
Awarded by Direct For Computer & Info Scie & Enginr; Div Of Information & Intelligent Systems
Funding Acknowledgements
This work was supported in part by the Air Force Office of Scientific Research under contracts FA9550-05-1-0321 and FA9550-07-2-0351. Charles Kemp was supported by the William Asbjornsen Albert Memorial Fellowship, and Joshua B. Tenenbaum was supported by the Paul E. Newton Chair. Many of the ideas in this article were developed in collaborations with Neville Sanjana, Patrick Shafto, Elizabeth Baraff-Bonawitz, and John Coley. We thank Erik Sudderth and Bob Rehder for valuable discussions, Sergey Blok and collaborators for sharing data described in Blok et al. (2007), and Steven Sloman for sharing his copy of the feature data described in Osherson et al. ( 1991).