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A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control
Author(s) -
Lionel Rigoux,
Emmanuel Guigon
Publication year - 2012
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1002716
Subject(s) - normative , control (management) , maximization , computer science , motor control , optimal control , function (biology) , decision model , neuroeconomics , action (physics) , artificial intelligence , machine learning , psychology , economics , cognitive psychology , neuroscience , mathematical optimization , microeconomics , mathematics , biology , philosophy , physics , epistemology , quantum mechanics , evolutionary biology
Costs (e.g. energetic expenditure) and benefits (e.g. food) are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories). Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control.

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