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Dynamics of sensorimotor adaptation

Dynamics of sensorimotor adaptation. Sen Cheng, Philip N Sabes University of California, San Francisco Annual Swartz-Sloan Centers Meeting, 26 th July 2005. A simple sensorimotor task. Motivation and outline. block design. trial-by-trial dynamics What is the learning rule of adaptation?

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Dynamics of sensorimotor adaptation

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  1. Dynamics of sensorimotor adaptation Sen Cheng, Philip N Sabes University of California, San Francisco Annual Swartz-Sloan Centers Meeting, 26th July 2005

  2. A simple sensorimotor task

  3. Motivation and outline block design trial-by-trial dynamics • What is the learning rule of adaptation? • What signals drive learning? • Noise in the learning process? • Spatial anisotropies? • More powerful correlation between behavior and neural activity. • Steady-state of adaptation • Compare average behavior pre- and post-exposure

  4. Virtual reality setup

  5. Concurrent test and exposure

  6. Linear dynamical system (LDS) Model for dynamics of adaptation general state space model ut : inputs (?) xt : internal state, planned/expected reach error yt : actual reach error qt : learning noise rt : motor noise

  7. Questions 1. What signals drive learning? 2. Noise in the learning process? 3. Spatial anisotropies?

  8. Two candidate learning signals et : visual error dt : perturbation/ discrepancy betw. vision and proprioception Learning equation with two input signals System identification with expectation-maximization (EM) algorithm, Cheng and Sabes, 2005, submitted

  9. Sample data and vis-model fit perturbation reach error model prediction

  10. Residual autocorrelations Portmanteau statistic (Hosking, 1980) Portmanteau test for serial autocorrelations Is the sequence of residuals a white noise sequence? Portmanteau test for vis-model

  11. pert-model fit to sample data perturbation reach error vis-model pert-model

  12. Portmanteau test cannot distinguish models for pert-model for vis-model

  13. p < 10-4 (n=18) p < 10-4 (n=18) p=0.006 (n=1), p>0.067 (n=17) p>0.22 (n=18) Likelihood ratio test (LRT) for nested models M1: no input M2: pert M3: vis error M4: pert and vis

  14. Questions 1. What signals drive learning? 2. Noise in the learning process? 3. Spatial anisotropies?

  15. The signal that drives learning pert-model Estimated models pert-model vis-model apply to no feedback (noFB) reaches: vis-model

  16. Questions 1. What signals drive learning? 2. Noise in the learning process? 3. Spatial anisotropies?

  17. Learning noise x stochastic pert LRT (n=18) p < 10-4 noFB LRT (n=18) p < 0.0003

  18. Questions 1. What signals drive learning? 2. Noise in the learning process?  3. Spatial anisotropies?

  19. Anisotropy in learning and noise *

  20. Conclusions • LDS are good models for adaptation dynamics • New insights into adaptation • Visual error drives adaptation predominantly • There is learning noise • Dynamics are anisotropic • Can now correlate trial-by-trial changes of behavior with neural activity. supported by the Swartz foundation

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