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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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 December 2011.
All research outputs
#17,302,400
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#7,481
of 8,964 outputs
Outputs of similar age
#83,521
of 97,408 outputs
Outputs of similar age from PLoS Computational Biology
#29
of 39 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 97,408 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.