Perceptual Decisions in the Face of Explicit Costs and Perceptual Variability

1. Perceptual Decisions in the Face of Explicit Costs and Perceptual Variability Deepali Gupta Michael S. Landy Also: Larry Maloney, Julia Trommershäuser, Ross Goutcher, Pascal Mamassian

2. Statistical/Optimal Modelsin Vision & Action • MEGaMove – Maximum Expected Gain model for Movement planning (Trommershäuser, Maloney & Landy) • A choice of movement plan fixes the probabilities pi of each possible outcome i with gain Gi • The resulting expected gain EG=p1G1+p2G2+… • A movement plan is chosen to maximize EG • Uncertainty of outcome is due to both perceptual and motor variability • Subjects are typically optimal for pointing tasks

3. Statistical/Optimal Modelsin Vision & Action • MEGaMove – Maximum Expected Gain model for Movement planning • MEGaVis – Maximum Expected Gain model for Visual estimation • Task: Orientation estimation, method of adjustment • Do subjects remain optimal when motor variability is minimized? • Do subjects remain optimal when visual reliability is manipulated?

4. Task – Orientation Estimation

5. Task – Orientation Estimation Payoff (100 points) Penalty (0, -100 or -500 points, in separate blocks)

6. Task – Orientation Estimation Payoff (100 points) Penalty (0, -100 or -500 points, in separate blocks)

7. Task – Orientation Estimation

8. Task – Orientation Estimation

9. Task – Orientation Estimation

10. Task – Orientation Estimation

11. Task – Orientation Estimation

12. Task – Orientation Estimation Done!

13. Task – Orientation Estimation

14. Task – Orientation Estimation

15. Task – Orientation Estimation 100

16. Task – Orientation Estimation -500

17. Task – Orientation Estimation -400

18. Experiment 1 – Three Variabilities • Three levels of orientation variability • Von Mises κ values of 500, 50 and 5 • Corresponding standard deviations of 2.6, 8 and 27 deg • Two spatial configurations of white target arc and black penalty arc (abutting or half overlapped) • Three penalty levels: 0, 100 and 500 points • One payoff level: 100 points

19. Stimulus – Orientation Variability κ = 500, σ = 2.6 deg

20. Stimulus – Orientation Variability κ = 50, σ = 8 deg

21. Stimulus – Orientation Variability κ = 5, σ = 27 deg

22. Payoff/Penalty Configurations

23. Payoff/Penalty Configurations

24. Payoff/Penalty Configurations

25. Payoff/Penalty Configurations

26. Where should you “aim”?Penalty = 0 case Payoff (100 points) Penalty (0 points)

27. Where should you “aim”?Penalty = -100 case Payoff (100 points) Penalty (-100 points)

28. Where should you “aim”?Penalty = -500 case Payoff (100 points) Penalty (-500 points)

29. Where should you “aim”?Penalty = -500, overlapped penalty case Payoff (100 points) Penalty (-500 points)

30. Where should you “aim”?Penalty = -500, overlapped penalty,high image noise case Payoff (100 points) Penalty (-500 points)

31. Expt. 1 – Variability

32. Expt. 1 – Setting Shifts

33. Expt. 1 – Score

34. Expt. 1 – Efficiency

35. Expt. 1 – Discussion • Subjects are by and large near-optimal in this task • That means they take into account their own variability in each condition as well as the penalty level and payoff/penalty configuration • They respond to changing variability on a trial-by-trial basis.

36. Expt. 1 – Discussion However: • A hint that naïve subjects aren’t that good at the task • Concerns about obvious stimulus variability categories • → Re-run using variability chosen from a continuum and more naïve subjects

37. Expt. 2 – Results

38. Expt. 2 – Results

39. Expt. 2 – Results (contd.)

40. Expt. 2 – Results (contd.)

41. Expt. 2 – Results (contd.)

42. Expt. 2 – Results (contd.)

43. Expt. 2 – Results (contd.)

44. Expt. 2 – Results (contd.)

45. Expt. 2 – Results (contd.)

46. Expt. 2 – Results (contd.)

47. Expt. 2 – Results, so far • Subjects MSL (non-naïve) and MMC (naïve) shift away from the penalty with increasing stimulus variability. • These subjects appear to estimate variability on a trial-by-trial basis and respond appropriately • Their shifts are near-optimal • However, …

48. Expt. 2 – Results (contd.)

49. Expt. 2 – Results (contd.)

50. Expt. 2 – Results (contd.)