MEGaVis: Perceptual Decisions in the Face of Explicit Costs and Benefits
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Transcript of MEGaVis: Perceptual Decisions in the Face of Explicit Costs and Benefits
MEGaVis: Perceptual Decisions in the Face of Explicit Costs and
Benefits
Michael S. Landy
Julia Trommershäuser
Laurence T. Maloney
Ross Goutcher
Pascal Mamassian
Statistical/Optimal Modelsin Vision & Action
• Sequential Ideal Observer Analysis• Statistical Models of Cue Combination• Statistical Models of Movement Planning
and Control– Minimum variance movement planning/control– MEGaMove – Maximum Expected Gain model
for Movement planning
Statistical/Optimal Modelsin Vision & Action
• MEGaMove – Maximum Expected Gain model for Movement planning– 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
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?
Task – Orientation Estimation
Task – Orientation Estimation
Task – Orientation Estimation
Payoff(100 points)
Penalty(0, -100 or-500 points, in separate blocks)
Task – Orientation Estimation
Payoff(100 points)
Penalty(0, -100 or-500 points, in separate blocks)
Task – Orientation Estimation
Task – Orientation Estimation
Task – Orientation Estimation
Task – Orientation Estimation
Task – Orientation Estimation
Task – Orientation Estimation
Task – Orientation Estimation
Task – Orientation Estimation
Task – Orientation Estimation
Done!
Task – Orientation Estimation
Task – Orientation Estimation
Task – Orientation Estimation
100
Task – Orientation Estimation
-400
Task – Orientation Estimation
-500
Task – Orientation Estimation
• Align the white arcs with the remembered mean orientation to earn points
• Avoid alignment with the black arcs to avoid the penalty
• Feedback provided as to whether the payoff, penalty, both or neither were awarded
Task – Orientation Estimation• 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
Stimulus – Orientation Variability
κ = 500, σ = 2.6 deg
Stimulus – Orientation Variability
κ = 50, σ = 8 deg
Stimulus – Orientation Variability
κ = 5, σ = 27 deg
Payoff/Penalty Configurations
Payoff/Penalty Configurations
Payoff/Penalty Configurations
Payoff/Penalty Configurations
Where should you “aim”?Penalty = 0 case
Payoff(100 points)
Penalty(0 points)
Where should you “aim”?Penalty = -100 case
Payoff(100 points)
Penalty(-100 points)
Where should you “aim”?Penalty = -500 case
Payoff(100 points)
Penalty(-500 points)
Where should you “aim”?Penalty = -500, overlapped penalty case
Payoff(100 points)
Penalty(-500 points)
Where should you “aim”?Penalty = -500, overlapped penalty,
high image noise case
Payoff(100 points)
Penalty(-500 points)
Experiment 1 – Variability
Experiment 1 – Setting Shifts (HB)
Experiment 1 – Score (HB)
Experiment 1 – Setting Shifts (MSL)
Experiment 1 – Score (MSL)
Experiment 1 – Setting Shifts(3 more subjects)
Experiment 1 – Score(3 more subjects)
Experiment 1 - Efficiency
Intermediate Conclusions
• 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
• Can they respond to changing variability on a trial-by-trial basis?
• → Re-run using a mixed-list design (all noise levels mixed together in a block; only penalty level is blocked)
Experiment 2 – Setting Shifts (HB)
Experiment 2 – Score (HB)
Experiment 2 – Setting Shifts (MSL)
Experiment 2 – Score (MSL)
Experiment 2 – Setting Shifts(2 more subjects)
Experiment 2 – Score(2 more subjects)
Experiment 2 - Efficiency
Conclusion• Subjects are nearly optimal in all conditions• Thus, effectively they are able to calculate
and maximize effective gain across a variety of target/penalty configurations, penalty values and stimulus uncertainties
• The main sub-optimality is an unwillingness to “aim” outside of the target
• This is “risk-seeking” behavior, unlike what is seen in paper-and-pencil decision tasks