How and why actions are selected: action selection and the dark room problem

E. Venter
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Abstract

Abstract In this paper, I examine an evolutionary approach to the action selection problem and illustrate how it helps raise an objection to the predictive processing account. Clark examines the predictive processing account as a theory of brain function that aims to unify perception, action, and cognition, but - despite this aim - fails to consider action selection overtly. He off ers an account of action control with the implication that minimizing prediction error is an imperative of living organisms because, according to the predictive processing account, action is employed to fulfill expectations and reduce prediction error. One way in which this can be achieved is by seeking out the least stimulating environment and staying there (Friston et al. 2012: 2). Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently, the maximization of expectation. But, most living organisms do not find, and stay in, surprise free environments. This paper explores this objection, also called the “dark room problem”, and examines Clark’s response to the problem. Finally, I recommend that if supplemented with an account of action selection, Clark’s account will avoid the dark room problem.
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如何以及为什么选择行动:行动选择和暗室问题
在本文中,我研究了行动选择问题的进化方法,并说明它如何有助于提出对预测处理帐户的反对意见。克拉克将预测处理理论作为一种旨在统一感知、行动和认知的脑功能理论来研究,但尽管有这个目的,却没有公开地考虑行动选择。他提出了一个行动控制的解释,暗示最小化预测误差是生物体的当务之急,因为根据预测处理的解释,行动是用来实现期望和减少预测误差的。实现这一目标的一种方法是寻找最不刺激的环境并停留在那里(弗里斯顿等人,2012:2)。贝叶斯、神经科学和机器学习方法进入一个单一的框架,其总体原则是最小化惊喜(或等效地,最大化期望)。但是,大多数生物并没有发现并生活在令人惊讶的自由环境中。本文探讨了这一反对意见,也被称为“暗室问题”,并考察了克拉克对这一问题的回应。最后,我建议如果补充一个行动选择的帐户,Clark的帐户将避免暗室问题。
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