Elmarie Venter
Published Online: 30 Apr 2016
Page range: 19 – 45
Abstract
In this paper, I examine an evolutionary approach to the action selectionproblem and illustrate how it helps raise an objection to the predictive processingaccount. Clark examines the predictive processing account as a theory of brain func-tion that aims to unify perception, action, and cognition, but – despite this aim – failsto consider action selection overtly. He offers an account of action control with theimplication that minimizing prediction error is an imperative of living organismsbecause, according to the predictive processing account, action is employed to ful”llexpectations and reduce prediction error. One way in which this can be achieved isby seeking out the least stimulating environment and staying there (Friston et al.2012: 2). Bayesian, neuroscienti”c, and machine learning approaches into a singleframework whose overarching principle is the minimization of surprise (or, equiva-lently, the maximization of expectation. But, most living organisms do not “nd, andstay in, surprise free environments. This paper explores this objection, also calledthe “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 ac-count will avoid the dark room problem.
Cite
Venter, Elmarie. “How and why actions are selected: action selection and the dark room problem” Kairos. Journal of Philosophy & Science, vol.15, no.1, 2016, pp.19-45. https://doi.org/10.1515/kjps-2016-0002