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A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002716
Pubmed ID
Authors

Lionel Rigoux, Emmanuel Guigon

Abstract

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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 258 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 5 2%
United Kingdom 5 2%
Switzerland 3 1%
France 2 <1%
Italy 1 <1%
Slovenia 1 <1%
Belgium 1 <1%
United States 1 <1%
Unknown 239 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 63 24%
Researcher 44 17%
Student > Master 37 14%
Professor 15 6%
Student > Bachelor 14 5%
Other 39 15%
Unknown 46 18%
Readers by discipline Count As %
Psychology 49 19%
Neuroscience 41 16%
Engineering 34 13%
Agricultural and Biological Sciences 29 11%
Medicine and Dentistry 15 6%
Other 32 12%
Unknown 58 22%