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Optimal Compensation for Temporal Uncertainty in Movement Planning

Overview of attention for article published in PLoS Computational Biology, July 2008
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Title
Optimal Compensation for Temporal Uncertainty in Movement Planning
Published in
PLoS Computational Biology, July 2008
DOI 10.1371/journal.pcbi.1000130
Pubmed ID
Authors

Todd E. Hudson, Laurence T. Maloney, Michael S. Landy

Abstract

Motor control requires the generation of a precise temporal sequence of control signals sent to the skeletal musculature. We describe an experiment that, for good performance, requires human subjects to plan movements taking into account uncertainty in their movement duration and the increase in that uncertainty with increasing movement duration. We do this by rewarding movements performed within a specified time window, and penalizing slower movements in some conditions and faster movements in others. Our results indicate that subjects compensated for their natural duration-dependent temporal uncertainty as well as an overall increase in temporal uncertainty that was imposed experimentally. Their compensation for temporal uncertainty, both the natural duration-dependent and imposed overall components, was nearly optimal in the sense of maximizing expected gain in the task. The motor system is able to model its temporal uncertainty and compensate for that uncertainty so as to optimize the consequences of movement.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
France 3 2%
United Kingdom 3 2%
Brazil 2 1%
Netherlands 2 1%
Germany 1 <1%
Argentina 1 <1%
Slovenia 1 <1%
China 1 <1%
Other 1 <1%
Unknown 117 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 25%
Researcher 30 22%
Student > Master 14 10%
Student > Bachelor 10 7%
Student > Postgraduate 9 7%
Other 24 18%
Unknown 16 12%
Readers by discipline Count As %
Psychology 51 37%
Agricultural and Biological Sciences 25 18%
Neuroscience 15 11%
Engineering 8 6%
Computer Science 6 4%
Other 15 11%
Unknown 17 12%