期刊
PLOS COMPUTATIONAL BIOLOGY
卷 11, 期 11, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1004567
关键词
-
资金
- Harvard University
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据