Perceptual Decisions in the Face of Explicit Costs and Perceptual Variability

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 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 EGp1G1p2G2
• 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 ? 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
? 500, s 2.6 deg
20
Stimulus Orientation Variability
? 50, s 8 deg
21
Stimulus Orientation Variability
? 5, s 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 arent 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.)
51
Expt. 2 Results (contd.)
52
Expt. 2 Results (contd.)
53
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.