Context, Learning, and Extinction
Context, Learning, and Extinction
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DOI：
10.1037/a0017808
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发表时间：
2010-01-01
影响因子：
5.4
通讯作者：
Niv, Yael
中科院分区：
文献类型：
--
作者：
Gershman, Samuel J.;Blei, David M.;Niv, Yael
A.Reish等人。(2007)在条件化实验中提出了一种上下文相关学习与灭绝的强化学习模型，利用状态分类的思想将新观察数据分类为状态。在这篇文章中，作者从规范统计推断的角度对这一观点进行了解释。他们专注于更新和潜在抑制，其中语境操纵已被广泛研究的2个条件范式，并表明在假设无限数量的潜在原因的模型中的在线贝叶斯推理可以表征这种操纵的不同行为结果集，其中一些给Reish等人的模型带来了问题。此外，在这两种模式中，年轻动物或如果在训练前对海马区造成损害，都不存在上下文依赖。作者提出了一种解释，即推断新原因的能力有限。
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.