Temporal Influence on Subject Reaching Strategies

1. Temporal Influence on Subject Reaching Strategies CoSMo 2012 Matt Balcarras Irene Tamagnone Leonie OostwoudWijdenes Andrew Brennan Deborah Barany YasharZeighami “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 Temporal Structure • Three approaches • Regression analysis • Prior evolution • Kalman Filter Regression Analysis Prior Evolution Kalman Filter

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

5. Regression Analysis • Predictors • Mid-point hand position • Mid-point cursor position • Five-trial running mean • Cumulative mean

6. Regression Coefficients

8. Estimating the Prior Berniker, Voss, & Körding, 2010

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

10. Likelihood Variance ∞ 0.29 0.43 0.60 σL σ∞ σ0 σM

11. Implementation of Kalman Filter • Our state vector : Current perturbation Pt and perturbation mean μtcomputed 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 Typical Subject Kalman Filter Subject Data End Hand Position (cm) α (across subjects)=0.35 R2 = 0.18 Trial

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