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- W2166644652 endingPage "e1004567" @default.
- W2166644652 startingPage "e1004567" @default.
- W2166644652 abstract "Two important ideas about associative learning have emerged in recent decades: (1) Animals are Bayesian learners, tracking their uncertainty about associations; and (2) animals acquire long-term reward predictions through reinforcement learning. Both of these ideas are normative, in the sense that they are derived from rational design principles. They are also descriptive, capturing a wide range of empirical phenomena that troubled earlier theories. This article describes a unifying framework encompassing Bayesian and reinforcement learning theories of associative learning. Each perspective captures a different aspect of associative learning, and their synthesis offers insight into phenomena that neither perspective can explain on its own." @default.
- W2166644652 created "2016-06-24" @default.
- W2166644652 creator A5031715686 @default.
- W2166644652 date "2015-11-04" @default.
- W2166644652 modified "2023-10-12" @default.
- W2166644652 title "A Unifying Probabilistic View of Associative Learning" @default.
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- W2166644652 doi "https://doi.org/10.1371/journal.pcbi.1004567" @default.
- W2166644652 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4633133" @default.
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