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BioMed Research International
Volume 2014, Article ID 162423, 13 pages
http://dx.doi.org/10.1155/2014/162423
Research Article

Evaluating the Influence of Motor Control on Selective Attention through a Stochastic Model: The Paradigm of Motor Control Dysfunction in Cerebellar Patient

1Eye Tracking & Visual Application Lab, University of Siena, Viale Bracci 2, 53100 Siena, Italy
2Department of Neurological Sciences and Behavior, University of Siena, Viale Bracci 2, 53100 Siena, Italy

Received 29 April 2013; Revised 3 November 2013; Accepted 7 November 2013; Published 9 February 2014

Academic Editor: Hitoshi Mochizuki

Copyright © 2014 Giacomo Veneri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Attention allows us to selectively process the vast amount of information with which we are confronted, prioritizing some aspects of information and ignoring others by focusing on a certain location or aspect of the visual scene. Selective attention is guided by two cognitive mechanisms: saliency of the image (bottom up) and endogenous mechanisms (top down). These two mechanisms interact to direct attention and plan eye movements; then, the movement profile is sent to the motor system, which must constantly update the command needed to produce the desired eye movement. A new approach is described here to study how the eye motor control could influence this selection mechanism in clinical behavior: two groups of patients (SCA2 and late onset cerebellar ataxia LOCA) with well-known problems of motor control were studied; patients performed a cognitively demanding task; the results were compared to a stochastic model based on Monte Carlo simulations and a group of healthy subjects. The analytical procedure evaluated some energy functions for understanding the process. The implemented model suggested that patients performed an optimal visual search, reducing intrinsic noise sources. Our findings theorize a strict correlation between the “optimal motor system” and the “optimal stimulus encoders.”