Learning and Adaptation of Sensorimotor Contingencies: Prism-Adaptation, a Case Study
This paper focuses on learning and adaptation of sensorimotor contingencies. As a specific case, we investigate the application of prism glasses, which change visual-motor contingencies. After an initial disruption of sensorimotor coordination, humans quickly adapt. However, scope and generalization of that adaptation is highly dependent on the type of feedback and exhibits markedly different degrees of generalization. We apply a model with a specific interaction of forward and inverse models to a robotic setup and subject it to the identical experiments that have been used on previous human psychophysical studies. Our model demonstrates both locally specific adaptation and global generalization in accordance with the psychophysical experiments. These results emphasize the role of the motor system for sensory processes and open an avenue to improve on sensorimotor processing.
KeywordsSensorimotor contingencies prism-adaptation motor learning/ adaptation body maps inverse kinematics
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- 1.Baraduc, P., Wolpert, D.: Adaptation to a visuomotor shift depends on the starting posture. Journal of Neurophysiology 88(2), 973–981 (2002)Google Scholar
- 2.D’Souza, A., Vijayakumar, S., Schaal, S.: Learning inverse kinematics. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2001)Google Scholar
- 7.Martin, T., Keating, J., Goodkin, H., Bastian, A., Thach, W.: Throwing while looking through prisms. ii. Specificity and storage of multiple gaze-throw calibrations. Brain 119(4), 1199–1212 (1996)Google Scholar
- 8.Nagel, S., Carl, C., Kringe, T., Märtin, R., König, P.: Beyond sensory substitution – learning the sixth sense. Journal of Neural Engineering 2, R13 (2005)Google Scholar
- 12.Popović, M., Kootstra, G., Jørgensen, J.A., Kragic, D., Krüger, N.: Grasping unknown objects using an early cognitive vision system for general scene understanding. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 987–994. IEEE, San Francisco (2011)Google Scholar
- 13.Rasmussen, C.E., Williams, C.: Gaussian Processes for Machine Learning. The MIT Press (2006)Google Scholar