TY - JOUR
T1 - Theory-based Bayesian models of inductive learning and reasoning
AU - Tenenbaum, Joshua B.
AU - Griffiths, Thomas L.
AU - Kemp, Charles
N1 - Funding Information:
We thank the current and past members of the Computational Cognitive Science group at MIT for innumerable discussions of the work described here. We owe particular debts to Fei Xu, Patrick Shafto, and Sourabh Niyogi, who have collaborated closely on the projects described here. We acknowledge support from NTT Communication Sciences Laboratories, Mitsubishi Electric Research Labs, the National Science Foundation, the James S. McDonnell Foundation Causal Learning Collaborative, the Paul E. Newton Career Development Chair (J.B.T.), the Stanford Graduate Fellowships (T.L.G.), and the William Asbjornsen Albert Memorial Graduate Fellowship (C.K.).
PY - 2006/7
Y1 - 2006/7
N2 - Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.
AB - Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.
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U2 - 10.1016/j.tics.2006.05.009
DO - 10.1016/j.tics.2006.05.009
M3 - Article
C2 - 16797219
AN - SCOPUS:33746260413
SN - 1364-6613
VL - 10
SP - 309
EP - 318
JO - Trends in Cognitive Sciences
JF - Trends in Cognitive Sciences
IS - 7
ER -