1. Temporal Influence on Subject Reaching Strategies Matt Balcarras Irene Tamagnone Leonie Oostwoud Wijdenes Andrew Brennan Deborah Barany Yashar Zeighami CoSMo 2012 “Kalman, maybe?”

2. Outline How do reaching strategies depend on recent experience and current sensory information? Analyzed Körding & Wolpert (2004) data from the ★ DREAM ★ database Original paper did not consider recent trial effects on performance

3. Outline Three approaches 1.Regression analysis 2.Prior evolution 3.Kalman Filter Temporal Structure Regression Analysis Kalman Filter Prior Evolution

4. Körding & Wolpert (2004) End point hand position Deviation from midpoint and endpoint positions End point cursor position

5. Regression Analysis Predictors 1.Mid-point hand position 2.Mid-point cursor position 3.Five-trial running mean 4.Cumulative mean

6. Regression Coefficients

8. Estimating the Prior Berniker, Voss, & K ö rding, 2010

9. Prior Estimate Over Time Trial Bin Prior (cm)

10. Likelihood Variance 0.290.430.60 ∞ σ0σ0 σMσM σLσL σ∞σ∞

11. Implementation of Kalman Filter Our state vector : Current perturbation P t and perturbation mean μ t computed on all the previous trials Our observer : Midpoint Cursor Position where: Parameter α Weights the contributions of the previous perturbation and of the whole history of perturbations in the current prediction Is optimized on the training data (first 1000 trials)

12. Kalman Filter Fit Kalman Filter Subject Data Trial End Hand Position (cm) α (across subjects)=0.35 R 2 = 0.18 Typical Subject

13. Conclusions Recent trial history and online cursor feedback play a significant role in predicting end position The prior is learned quickly and is stable over time Likelihood variance increases with uncertainty The Kalman filter reveals that subjects trust the estimation of the previous trial Under uncertain conditions, temporal factors influence subject strategy suboptimally

14. M I L A D Y