Abstract
The fundamental cognitive problem for active organisms is to decide what to do next in a changing environment. In this article, we analyze motor and action control in computational models that utilize reinforcement learning (RL) algorithms. In reinforcement learning, action control is governed by an action selection policy that maximizes the expected future reward in light of a predictive world model. In this paper we argue that RL provides a way to explicate the so-called action-oriented views of cognitive systems in representational terms.
Highlights
The fundamental cognitive problem for active organisms is to decide what to do in changing environments
We have illustrated how reinforcement learning (RL) algorithms are used to study the cognitive dynamics of action control
When action control is characterized in this way, it is understood as the control system of a cognitive agent who can learn, anticipate and adapt
Summary
The fundamental cognitive problem for active organisms is to decide what to do in changing environments. Neurocognitive systems rely on representations when solving this problem These internal model-like states allow organisms to plan and control sequences of behavior.. The representationalist framework is a result of a misinterpretation concerning what the experimental research entails Some, such as recent radical enactivists, argue there is no satisfactory account of how contentful representational states drive action (Hutto & Myin, 2020). Enactivists demand, we should explain action in terms of reactivations and re-enactments (Hutto & Myin, 2020).2 In their arguments, sensorimotor action control is typically taken as a paradigmatic example of a non-representational phenomenon. The algorithm’s computational objective is to learn the best possible action policy, in light of reward maximization For doing so, it utilizes representational states. This framework challenges the enactivist presuppositions on what action control representations are, and what they are used for
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