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Learning the Optimal Control of Coordinated Eye and Head Movements

Overview of attention for article published in PLoS Computational Biology, November 2011
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Title
Learning the Optimal Control of Coordinated Eye and Head Movements
Published in
PLoS Computational Biology, November 2011
DOI 10.1371/journal.pcbi.1002253
Pubmed ID
Authors

Sohrab Saeb, Cornelius Weber, Jochen Triesch

Abstract

Various optimality principles have been proposed to explain the characteristics of coordinated eye and head movements during visual orienting behavior. At the same time, researchers have suggested several neural models to underly the generation of saccades, but these do not include online learning as a mechanism of optimization. Here, we suggest an open-loop neural controller with a local adaptation mechanism that minimizes a proposed cost function. Simulations show that the characteristics of coordinated eye and head movements generated by this model match the experimental data in many aspects, including the relationship between amplitude, duration and peak velocity in head-restrained and the relative contribution of eye and head to the total gaze shift in head-free conditions. Our model is a first step towards bringing together an optimality principle and an incremental local learning mechanism into a unified control scheme for coordinated eye and head movements.

X Demographics

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 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 8 8%
United States 2 2%
Australia 2 2%
France 1 1%
Japan 1 1%
United Kingdom 1 1%
Unknown 85 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 28%
Student > Ph. D. Student 22 22%
Student > Master 9 9%
Student > Postgraduate 6 6%
Professor > Associate Professor 6 6%
Other 18 18%
Unknown 11 11%
Readers by discipline Count As %
Computer Science 16 16%
Engineering 14 14%
Neuroscience 13 13%
Agricultural and Biological Sciences 13 13%
Psychology 10 10%
Other 21 21%
Unknown 13 13%
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 04 November 2011.
All research outputs
#17,285,668
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#7,480
of 8,960 outputs
Outputs of similar age
#106,477
of 153,748 outputs
Outputs of similar age from PLoS Computational Biology
#93
of 136 outputs
Altmetric has tracked 25,373,627 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 8,960 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 153,748 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.