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

2. Statistical/Optimal Models in Vision & Action MEGaMove – Maximum Expected Gain model for Movement planning (Trommershäuser, Maloney & Landy) –A choice of movement plan fixes the probabilities p i of each possible outcome i with gain G i –The resulting expected gain EG=p 1 G 1 +p 2 G 2 +… –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 Models in 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. 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

12. Done!

13. Task – Orientation Estimation

15. 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

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

39. 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.)

54. Expt. 2 – Summary Subjects MSL (non-naïve) and MMC (naïve) are near-optimal. Other subjects use a variety of sub-optimal strategies, including –Increased setting variability with higher penalty due to avoiding the penalty/target when task gets difficult –Aiming at the target center regardless of the penalty

55. Conclusion Subjects can estimate their setting variability and attain near-optimal performance in this task. In Expt. 1, the main sub-optimality is an unwillingness to “aim” outside of the target. In Expt. 2, naïve subjects do not generally use anything like an optimal strategy, although in some cases efficiency remains high.