Linked Life Data

19033240

Source:http://linkedlifedata.com/resource/pubmed/id/19033240

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2008-11-26
pubmed:abstractText
Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1530-7026
pubmed:author
pubmed:issnType
Print
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
429-53
pubmed:dateRevised
2011-2-24
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Decision theory, reinforcement learning, and the brain.
pubmed:affiliation
University College London, London, England. dayan@gatsby.ucl.ac.uk
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't