Optimal compensation for changes in effective movement variability

1. Optimal compensation for changes in effective movement variability in planning movement under risk Julia Trommershäuser 1, Sergei Gepshtein 2, Larry Maloney 1, Mike Landy 1, Marty Banks 2 1: Dept. of Psychology and Center for Neural Science NYU, New York, USA 2: School of Optometry, UC Berkeley, Berkeley, USA Sarasota, May 3, 2004

2. Motor responses have consequences. Trommershäuser, Maloney, Landy (2003). JOSA A, 20,1419. Trommershäuser, Maloney, Landy (2003). Spat. Vis., 16, 255.

3. Experimental Task Target display (700 ms) L

4. Experimental Task L

5. The green target is hit: +100 points 100 Experimental Task L 100

6. Experimental Task L

7. The red target is hit: -500 points -500 Experimental Task L -500

8. Experimental Task L

9. Scores add if both targets are hit: -500 100 Experimental Task L -500 100

10. Experimental Task L

11. Experimental Task The screen is hit later than 700 ms after target display: -700 points. You are too slow: -700

12. Experimental Task End of trial Current score: 500

13. Outline • Optimal Performance: • A Maximum Expected Gain Model • ofMovement under Risk (MEGaMove) • Human vs. Optimal Performance: • Compensation for Changes in • Effective Movement Variability • Conclusion

14. L Optimal visuo-motor strategy -500 The optimal mover chooses the motor strategy that maximizes the expected gain. 100 Trommershäuser, Maloney, Landy (2003). JOSA A, 20,1419. Trommershäuser, Maloney, Landy (2003). Spat. Vis., 16, 255.

15. 20 10 0 -10 -20 -20 -10 0 10 20 Distribution of movement endpoints: Bivariate Gaussian, width : yhit-ymean (mm)  = 3.62 mm, 72x15 = 1080 end points xhit-xmean (mm)

16. Optimal visuo-motor strategy optimal mean end point = 3.48 mm

17. Optimal visuo-motor strategy optimal mean end point = 3.48 mm

18. What if we change your movement variability?

19. Optimal visuo-motor strategy optimal mean end point optimal mean end point, increased noise = 3.48 mm = 6.19 mm

20. Optimal visuo-motor strategy • Parameters of the model: • reward structure of experiment: • experimenter-imposed • subject’s movement variability : • measured -500 100  = 3.23 mm  = 4.17 mm

21. Optimal visuo-motor strategy • Parameters of the model: • reward structure of experiment: • experimenter-imposed • subject’s movement variability : • measured -500 100 All parameters estimated. Parameter-free predictions !

22. Outline • Optimal Performance: • A Maximum Expected Gain Model • ofMovement under Risk (MEGaMove) • Human vs. Optimal Performance: • Compensation for Changes in • Effective Movement Variability • Conclusion

23. Experiment Manipulation of effective movement variability: perturbation of visual feedback

24. Experiment Perturbation of visual feedback:  increase in effective variability

25. Experiment Visually-imposed changes in effective movement variability. • Idea: • finger visually represented by red point • on each trial: unpredictable perturbation • of the visual feedback of the finger tip • Points are scored based on the perturbed • finger position

26. Experiment Visually-imposed changes in effective movement variability. Perturbation of the visual feedback of the finger tip by Medium increase in noise: High increase in noise:

27. Experiment Visually-imposed changes in effective movement variability. Experimental set-up:

28. varied between blocks Reward: 100 Penalties: 0 -200 -500 3 Penalties varied between sessions (retraining) Added noise 3 : 0 mm 4.5 mm 6 mm Design near middle 4 varied within blocks Configurations

29. Design • Six subjects • 1 practice session: 300 trials, • decreasing time limit • per noise condition: • 1 learning session: 300 trials • 2 sessions of data collection: 360 trials each • (40 repetitions per condition) • Payment: 1000 points = 25¢

30. Results Additivity of Variances

31. Optimal visuo-motor strategy optimal mean end point, no added noise optimal mean end point, = 3.48 mm = 6.19 mm

32. Results Scores: average subject data near middle

33. Results Scores: actual vs. optimal performance near middle middle, -200 near, -200 near, -500 middle, -500

34. Results Shift in end points: average subject data near middle

35. Results Shift in end points: actual vs. optimal shifts near middle middle, -200 near, -200 near, -500 middle, -500

36. Conclusions Movement planning takes extrinsic costs and the subject’s own motor uncertainty into account. Subjects combine visual and motor variability to compensate for changes in effective movement variability. Subjects do not differ significantly from ideal movement planners that maximize gain. Thank you!

38. Results Learning of “new” effective variability learning session actual finger position

39. Results: trial-by-trail analysis

40. Results: trial-by-trail analysis

41. Movement variability remains constant. Relative to stimulus size is larger for the small configuration.  Experiment 2 Movement endpoints in response to changes in relative movement variability Stimulus configurations: small: large: 9 mm 6.3 mm

42. 9 mm 6.3 mm 1 1 Experiment 2 Movement endpoints in response to changes in relative movement variability Stimulus configurations: small: large:   /R /R larger relative variability smaller relative variability

43. R 2R Experiment 2 Movement endpoints in response to changes in relative movement variability 4 stimulus configurations in 2 sizes: small: R = 6.3 mm large: R = 9 mm (varied within blocks) 2 penalty conditions: 0 and -500 points (varied between blocks) 1 practice session: 300 trials, decreasing time limit 1 session: 16 warm-up trials, 6x2x32 trials

44. x/R = 1.1 x/R = 0.7 large, penalty = 0 large, penalty = 500 small, penalty = 0 small, penalty = 500 Experiment 2: Results Subject 1: = 3.16 mm x model

45. Experiment 2: Results (Data corrected for constant pointing bias)

46. Experiment 2: Results (Data corrected for constant pointing bias)

47. Experiment 2: Results (Data corrected for constant pointing bias)

48. Experiment 2: Results (Data corrected for constant pointing bias)

49. Experiment 2: Results (Data corrected for constant pointing bias)

50. Experiment 2: Results (Data corrected for constant pointing bias)