Context, Learning, and Extinction

Autor/inn/en	Gershman, Samuel J.; Blei, David M.; Niv, Yael
Titel	Context, Learning, and Extinction
Quelle	In: Psychological Review, 117 (2010) 1, S.197-209 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-295X
DOI	10.1037/a0017808
Schlagwörter	Conditioning; Statistical Inference; Inferences; Bayesian Statistics; Models; Experiments; Psychology; Reinforcement; Classical Conditioning; Stimuli; Cues; Probability; Cluster Grouping; Animals; Learning Processes; Markov Processes; Monte Carlo Methods + Suchen Sie Ihr Suchwort? Inferential statistics; Schließende Statistik; Inference; Inferenz; Analogiemodell; Erprobung; Psychologie; Positive Verstärkung; Anreizsystem; Stichwort; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Eingruppierung; Animal; Tier; Tiere; Learning process; Lernprozess; Markowscher Prozess; Monte-Carlo-Methode
Abstract	A. Redish et al. (2007) proposed a reinforcement learning model of context-dependent learning and extinction in conditioning experiments, using the idea of "state classification" to categorize new observations into states. In the current article, the authors propose an interpretation of this idea in terms of normative statistical inference. They focus on renewal and latent inhibition, 2 conditioning paradigms in which contextual manipulations have been studied extensively, and show that online Bayesian inference within a model that assumes an unbounded number of latent causes can characterize a diverse set of behavioral results from such manipulations, some of which pose problems for the model of Redish et al. Moreover, in both paradigms, context dependence is absent in younger animals, or if hippocampal lesions are made prior to training. The authors suggest an explanation in terms of a restricted capacity to infer new causes. (Contains 6 figures and 10 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10