A Unifying Probabilistic View of Associative Learning.
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 t...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4633133?pdf=render |
| Summary: | 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. |
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| ISSN: | 1553-734X 1553-7358 |