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


• 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


• 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)


Done!


100


-400


-500


• 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


κ = 50, σ = 8 deg


κ = 5, σ = 27 deg

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)


Where should you “aim”?Penalty = -500, overlapped penalty case

Payoff(100 points)


Where should you “aim”?Penalty = -500, overlapped penalty,

high image noise case

Payoff(100 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

megavis: perceptual decisions in the face of explicit costs and benefits

Documents