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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.

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Mendeley readers

The data shown below were compiled from readership statistics for 101 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 86 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 20 20%
Unknown 10 10%
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 23 23%
Unknown 12 12%