Courville Aaron C, Daw Nathaniel D, Touretzky David S
Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, QC H3C 3J7, Canada.
Trends Cogn Sci. 2006 Jul;10(7):294-300. doi: 10.1016/j.tics.2006.05.004. Epub 2006 Jun 21.
The recent flowering of Bayesian approaches invites the re-examination of classic issues in behavior, even in areas as venerable as Pavlovian conditioning. A statistical account can offer a new, principled interpretation of behavior, and previous experiments and theories can inform many unexplored aspects of the Bayesian enterprise. Here we consider one such issue: the finding that surprising events provoke animals to learn faster. We suggest that, in a statistical account of conditioning, surprise signals change and therefore uncertainty and the need for new learning. We discuss inference in a world that changes and show how experimental results involving surprise can be interpreted from this perspective, and also how, thus understood, these phenomena help constrain statistical theories of animal and human learning.
贝叶斯方法最近的兴起促使人们重新审视行为学中的经典问题，即使是在像巴甫洛夫条件反射这样古老的领域。一种统计学解释能够为行为提供全新的、有原则的解读，而先前的实验和理论也能为贝叶斯研究中许多未被探索的方面提供参考。在此，我们探讨这样一个问题：令人惊讶的事件会促使动物学习得更快这一发现。我们认为，在对条件反射的统计学解释中，惊讶信号意味着变化，进而意味着不确定性以及对新学习的需求。我们讨论在一个不断变化的世界中的推理，并展示如何从这一视角解读涉及惊讶的实验结果，以及如此理解这些现象如何有助于约束动物和人类学习的统计理论。
