1. bookVolume 15 (2016): Issue 1 (April 2016)
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eISSN
1647-659X
First Published
01 Mar 2016
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3 times per year
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English
access type Open Access

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

Published Online: 30 Apr 2016
Volume & Issue: Volume 15 (2016) - Issue 1 (April 2016)
Page range: 19 - 45
Journal Details
License
Format
Journal
eISSN
1647-659X
First Published
01 Mar 2016
Publication timeframe
3 times per year
Languages
English
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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