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Probabilistic models of state estimation predict visuomotor transformations during prism adaptation

Published online by Cambridge University Press:  01 March 2012

MASAKI YAMAMOTO  and
HIROSHI ANDO
MASAKI YAMAMOTO*
Affiliation:
Division of Human Biology, Department of Rehabilitation Science, Graduate School of Health Sciences, Kobe University, Kobe, Japan
HIROSHI ANDO
Affiliation:
Division of Human Biology, Department of Rehabilitation Science, Faculty of Health Sciences, School of Medicine, Kobe University, Kobe, Japan
*
*Address correspondence and reprint requests to: Masaki Yamamoto, Graduate School of Health Sciences, Kobe University, 10-2, 7, Tomogaoka, Suma, Kobe 654-0142, Japan. E-mail: yamamotoma@kawasakigakuen.ac.jp
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Abstract

This study aims to create a prediction model for state-space estimation and to elucidate the required information processing for identifying an external space in prism adaptation. Subjects were 57 healthy students. The subjects were instructed to rapidly perform reaching movements to one of the randomly illuminating light-emitting diode lights. Their movements were measured while wearing prism glasses and after removing that. We provided the following four conditions and control. In target condition, reaching error distance was visually fed back to the subject. In trajectory condition, the trajectory of fingertip movement could be seen, and the final reaching error was not fed back. Two restricted visual feedback conditions were prepared based on a different presentation timing (on-time and late-time conditions). We set up a linear parametric model and an estimation model using Kalman filtering. The goodness of fit between the estimated and observed values in each model was examined using Akaike information criterion (AIC). AIC would be one way to evaluate two models with different number of parameters. In the control, the value of AIC was 179.0 and 154.0 for the linear model and Kalman filtering, respectively, while these values were 173.6 and 161.1 for the target condition, 202.8 and 159.7 for the trajectory condition, 192.7 and 180.8 for the on-time condition, and 206.9 and 174.0 for the late-time condition. Kalman gain in the control was 0.07–0.26. Kalman gain relies on the prior estimation distribution when its value is below 0.5. Kalman gain in the trajectory and late-time conditions was 0.03–0.60 and 0.08–0.95, respectively. The Kalman filter, a state estimation model based on Bayesian theory, expressed the dynamics of the internal model under uncertain feedback information better than the linear parametric model. The probabilistic estimation model can clearly simulate state estimation according to the reliability of the visual feedback.

Keywords

Sensorimotor transformationMotor adaptationProbabilistic prediction modelSystem identificationKalman filtering
Type
Research Articles
Information
Visual Neuroscience , Volume 29 , Issue 2 , March 2012 , pp. 119 - 129
Copyright
Copyright © Cambridge University Press 2012

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