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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
• 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
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)
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)
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