TY - JOUR
T1 - Bayesian theories of conditioning in a changing world
AU - Courville, Aaron C.
AU - Daw, Nathaniel D.
AU - Touretzky, David S.
N1 - Funding Information:
A.C. and N.D. contributed equally to the manuscript and the ordering of authorship was determined arbitrarily. We thank Peter Dayan, Sham Kakade and Geoff Gordon for many helpful and influential discussions surrounding the issues discussed here. We also thank Alan Yuille and Josh Tenenbaum for organizing the IPAM workshop on ‘Probabilistic models of cognition: The mathematics of mind’ that led to this article. This work was supported in part by National Science Foundation grant IIS-9978403. N.D. is funded by a Royal Society USA Research Fellowship, the Gatsby Foundation, and the EU BIBA project.
PY - 2006/7
Y1 - 2006/7
N2 - The recent flowering of Bayesian approaches invites the re-examination of classic issues in behavior, even in areas as venerable as Pavlovian conditioning. A statistical account can offer a new, principled interpretation of behavior, and previous experiments and theories can inform many unexplored aspects of the Bayesian enterprise. Here we consider one such issue: the finding that surprising events provoke animals to learn faster. We suggest that, in a statistical account of conditioning, surprise signals change and therefore uncertainty and the need for new learning. We discuss inference in a world that changes and show how experimental results involving surprise can be interpreted from this perspective, and also how, thus understood, these phenomena help constrain statistical theories of animal and human learning.
AB - The recent flowering of Bayesian approaches invites the re-examination of classic issues in behavior, even in areas as venerable as Pavlovian conditioning. A statistical account can offer a new, principled interpretation of behavior, and previous experiments and theories can inform many unexplored aspects of the Bayesian enterprise. Here we consider one such issue: the finding that surprising events provoke animals to learn faster. We suggest that, in a statistical account of conditioning, surprise signals change and therefore uncertainty and the need for new learning. We discuss inference in a world that changes and show how experimental results involving surprise can be interpreted from this perspective, and also how, thus understood, these phenomena help constrain statistical theories of animal and human learning.
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U2 - 10.1016/j.tics.2006.05.004
DO - 10.1016/j.tics.2006.05.004
M3 - Article
C2 - 16793323
AN - SCOPUS:33746365099
SN - 1364-6613
VL - 10
SP - 294
EP - 300
JO - Trends in Cognitive Sciences
JF - Trends in Cognitive Sciences
IS - 7
ER -