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MEGaVis: Perceptual Decisions in the Face of Explicit Costs and Benefits. Ross Goutcher Pascal Mamassian. Michael S. Landy Julia Trommersh ä user Laurence T. Maloney. Statistical/Optimal Models in Vision & Action. Sequential Ideal Observer Analysis Statistical Models of Cue Combination
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MEGaVis: Perceptual Decisions in the Face of Explicit Costs and Benefits Ross Goutcher Pascal Mamassian Michael S. Landy Julia Trommershäuser Laurence T. Maloney
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 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 • 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
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)
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)
