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Using a Kalman Filter to predict visuomotor adaptation behavior


Ernst,  MO
Research Group Multisensory Perception and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Burge,  J
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Banks,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Ernst, M., Burge, J., & Banks, M. (2005). Using a Kalman Filter to predict visuomotor adaptation behavior. Poster presented at 28th European Conference on Visual Perception, A Coruña, Spain.

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The sensorimotor system recalibrates when the visual and motor maps are in conflict, bringing the maps back into correspondence. We investigated the rate at which this recalibration occurs. The Kalman filter is a reasonable statistical model for describing visuomotor adaptation. It predicts that the rate of adaptation is dependent on the reliability of the feedback signal. It also predicts that random trial-to-trial perturbation of the feedback signal should have little or no effect on the adaptation rate. We tested these predictions using a pointing task. Subjects pointed with the unseen hand to a brief visual target. Visual feedback was then provided to indicate where the pointing movement had landed. During the experiment, we introduced a constant conflict between the pointing and feedback locations, and we examined the changes in pointing as the subject adapted. From the change in pointing position over trials we determined the adaptation rate. In experiment 1, we tested whether the reliability of the feedback affected adaptation rate by blurring the visual feedback and thereby reducing its localisability. Six levels of blur were used and spatial discrimination measurements confirmed that the blur was effective in altering stimulus localisability. We also constructed a Kalman filter model of the task. We found that adaptation rates of the filter and of the subjects decreased when blur was increased (ie with less reliable feedback). In experiment 2, the reliability of the visual feedback signal was manipulated by randomly perturbing the feedback signal on a trial-by-trial basis. Again, in good agreement with the prediction of the Kalman filter, we found no significant effect on adaptation rate as we manipulated the amount of perturbation. Taken together, these results provide evidence that human visuomotor adaptation behaviour is well modeled by a Kalman filter that uses weighted information from previous trials, including the reliability of the information, to update the visuomotor map.