PowerPoint-Pr

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Optimal compensation for changes in effective
movement variability in planning movement under
risk
Julia Trommershäuser 1, Sergei Gepshtein 2, Larry
Maloney 1, Mike Landy 1, Marty Banks 2 1 Dept.
of Psychology and Center for Neural Science NYU,
New York, USA 2 School of Optometry, UC
Berkeley, Berkeley, USA
Sarasota, May 3, 2004
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Motor responses have consequences.
Trommershäuser, Maloney, Landy (2003). JOSA A,
20,1419. Trommershäuser, Maloney, Landy (2003).
Spat. Vis., 16, 255.
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Experimental Task
Target display (700 ms)
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Experimental Task
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Experimental Task
L
100
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Experimental Task
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Experimental Task
L
-500
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Experimental Task
L
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Experimental Task
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-500 100
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Experimental Task
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Experimental Task
The screen is hit later than 700 ms after
target display -700 points.
You are too slow -700
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Experimental Task
End of trial
Current score 500
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Outline

• Optimal Performance
• A Maximum Expected Gain Model
• of Movement under Risk (MEGaMove)
• Human vs. Optimal Performance
• Compensation for Changes in
• Effective Movement Variability
• Conclusion

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Optimal visuo-motor strategy
-500
The optimal mover chooses the motor strategy
that maximizes the expected gain.
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Trommershäuser, Maloney, Landy (2003). JOSA A,
20,1419. Trommershäuser, Maloney, Landy (2003).
Spat. Vis., 16, 255.
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Distribution of movement endpoints
Bivariate Gaussian, width ?
yhit-ymean (mm)
? 3.62 mm, 72x15 1080 end points
xhit-xmean (mm)
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Optimal visuo-motor strategy
optimal mean end point
3.48 mm
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Optimal visuo-motor strategy
optimal mean end point
3.48 mm
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What if we change your movement variability?
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Optimal visuo-motor strategy
optimal mean end point optimal mean end
point, increased noise
3.48 mm
6.19 mm
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Optimal visuo-motor strategy

• Parameters of the model
• reward structure of experiment
• experimenter-imposed
• subjects movement variability ?
• measured

-500
100
? 3.23 mm
? 4.17 mm
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Optimal visuo-motor strategy

• Parameters of the model
• reward structure of experiment
• experimenter-imposed
• subjects movement variability ?
• measured

-500
100
All parameters estimated. Parameter-free
predictions !
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Outline

• Optimal Performance
• A Maximum Expected Gain Model
• of Movement under Risk (MEGaMove)
• Human vs. Optimal Performance
• Compensation for Changes in
• Effective Movement Variability
• Conclusion

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Experiment
Manipulation of effective movement variability
perturbation of visual feedback
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Experiment
Perturbation of visual feedback
? increase in effective variability
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Experiment
Visually-imposed changes in effective movement
variability.

• Idea
• finger visually represented by red point
• on each trial unpredictable perturbation
• of the visual feedback of the finger tip
• Points are scored based on the perturbed
• finger position

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Experiment
Visually-imposed changes in effective movement
variability.
Perturbation of the visual feedback of the
finger tip by
Medium increase in noise
High increase in noise
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Experiment
Visually-imposed changes in effective movement
variability.
Experimental set-up
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Design
middle
near
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varied within blocks
Configurations
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Design

• Six subjects
• 1 practice session 300 trials,
• decreasing time
  limit
• per noise condition
• 1 learning session 300 trials
• 2 sessions of data collection 360 trials each
• (40 repetitions per condition)
• Payment 1000 points 25

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Results
Additivity of Variances
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Optimal visuo-motor strategy
optimal mean end point, no added noise
optimal mean end point,
3.48 mm
6.19 mm
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Results
Scores average subject data
near
middle
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Results
Scores actual vs. optimal performance
near
middle
middle, -200
near, -200
near, -500
middle, -500
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Results
Shift in end points average subject data
near
middle
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Results
Shift in end points actual vs. optimal shifts
near
middle
middle, -200
near, -200
near, -500
middle, -500
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Conclusions
Movement planning takes extrinsic costs and the
subjects own motor uncertainty into
account. Subjects combine visual and motor
variability to compensate for changes in
effective movement variability. Subjects do not
differ significantly from ideal movement
planners that maximize gain.
Thank you!
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Results
Learning of new effective variability
learning session
actual finger position
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Results trial-by-trail analysis
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Results trial-by-trail analysis
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Experiment 2
Movement endpoints in response to changes in
relative movement variability
Stimulus configurations
small
large
9 mm
6.3 mm
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Experiment 2
Movement endpoints in response to changes in
relative movement variability
Stimulus configurations
small
large
?
?
/R
/R
larger relative variability ?
smaller relative variability ?
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Experiment 2
Movement endpoints in response to changes in
relative movement variability
4 stimulus configurations in 2 sizes small R
6.3 mm large R 9 mm (varied within blocks)
2 penalty conditions 0 and -500 points (varied
between blocks)
1 practice session 300 trials, decreasing time
limit 1 session 16 warm-up trials, 6x2x32
trials
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Experiment 2 Results
Subject 1 ? 3.16 mm
x model
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Experiment 2 Results
(Data corrected for constant pointing bias)
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Experiment 2 Results
(Data corrected for constant pointing bias)
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Experiment 2 Results
(Data corrected for constant pointing bias)
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Experiment 2 Results
(Data corrected for constant pointing bias)
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Experiment 2 Results
(Data corrected for constant pointing bias)
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Experiment 2 Results
(Data corrected for constant pointing bias)
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Experiment 2 Results
Subjects shift their relative mean movement
endpoints farther when their relative movement
variability increases. Subjects win less money
in conditions with higher relative motor
variability. In most conditions subjects are
around 95 of optimal performance.
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Distribution of movement end points
left, near
left, middle
right, near
right, middle
penalty
0
yhit-ymean (mm)
-200
-400
xhit-xmean (mm)
? 3.62 mm, 72 data points per condition
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Experimental task
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Acknowledgements
Berkeley
NYU
Marty Banks Sergei Gepshtein
Mike Landy Larry Maloney
Thank you!
Support Deutsche Forschungsgemeinschaft
(Emmy-Noether-Programme) Grant EY08266 from the
National Institute of Health Grant
RG0109/1999-B from the Human Frontiers Science
Program.
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A Maximum Expected Gain Model of Movement Planning
Key assumption The mover chooses the visuo-motor
strategy that maximizes the expected gain .
-500
100
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