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Motor planning under temporal uncertainty is suboptimal when the gain function is asymmetric

Overview of attention for article published in Frontiers in Computational Neuroscience, July 2015
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Title
Motor planning under temporal uncertainty is suboptimal when the gain function is asymmetric
Published in
Frontiers in Computational Neuroscience, July 2015
DOI 10.3389/fncom.2015.00088
Pubmed ID
Authors

Keiji Ota, Masahiro Shinya, Kazutoshi Kudo

Abstract

For optimal action planning, the gain/loss associated with actions and the variability in motor output should both be considered. A number of studies make conflicting claims about the optimality of human action planning but cannot be reconciled due to their use of different movements and gain/loss functions. The disagreement is possibly because of differences in the experimental design and differences in the energetic cost of participant motor effort. We used a coincident timing task, which requires decision making with constant energetic cost, to test the optimality of participant's timing strategies under four configurations of the gain function. We compared participant strategies to an optimal timing strategy calculated from a Bayesian model that maximizes the expected gain. We found suboptimal timing strategies under two configurations of the gain function characterized by asymmetry, in which higher gain is associated with higher risk of zero gain. Participants showed a risk-seeking strategy by responding closer than optimal to the time of onset/offset of zero gain. Meanwhile, there was good agreement of the model with actual performance under two configurations of the gain function characterized by symmetry. Our findings show that human ability to make decisions that must reflect uncertainty in one's own motor output has limits that depend on the configuration of the gain function.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
United States 1 2%
Romania 1 2%
Unknown 43 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 28%
Student > Master 9 20%
Student > Bachelor 7 15%
Student > Doctoral Student 3 7%
Researcher 3 7%
Other 4 9%
Unknown 7 15%
Readers by discipline Count As %
Neuroscience 10 22%
Psychology 8 17%
Sports and Recreations 7 15%
Computer Science 2 4%
Agricultural and Biological Sciences 2 4%
Other 7 15%
Unknown 10 22%
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 16 July 2015.
All research outputs
#18,418,919
of 22,817,213 outputs
Outputs from Frontiers in Computational Neuroscience
#1,053
of 1,343 outputs
Outputs of similar age
#189,024
of 262,607 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#37
of 44 outputs
Altmetric has tracked 22,817,213 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,343 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.