when uncertainty matters: the selection of rapid, goal-directed movements
DESCRIPTION
: -500. : -500. : 100 points (2.5 ¢). : 100 points (2.5 ¢). exp., penalty = 0. 1. 2. 3. 4. model, penalty = 500. x. 10. 5. -0. -5. -10. -10. -5. 0. 5. 10. 15. 20. R = 9 mm. When Uncertainty Matters: The Selection of Rapid, Goal-Directed Movements - PowerPoint PPT PresentationTRANSCRIPT
When Uncertainty Matters: The Selection of Rapid, Goal-Directed Movements
J. Trommershäuser, L. T. Maloney , M. S. Landy, Psychology and Neural Science, New York University
Supported by NIH EY08266 and HFSP RG0109/1999-B, J.T. funded by the DFG (Emmy-Noether Programm)
*: Maximum Expected Gain Model of Movement planning
VSS 2003 TALKSarasota, FL
Movement under Risk
Kassi Price, 2001 US Nationals
The green target is hit: +100 points
100100
The red target is hit: -500 points
-500
x (mm)
y (m
m)
100 points100 points
-32 points
100 points-400 points. . . .
= 4.83 mm
: -500 : 100 points (2.5 ¢)
x (mm)
y (m
m)
-32 points
3070 points
= 4.83 mm
: -500 : 100 points (2.5 ¢)
Expected Gain Surface
90
0
60
<-60-30
30
points per trial
x (mm)
y (m
m)
= 4.83 mm
-10 -5 0 5 10 15 20
-10
-5
-0
5
10
target: 100penalty: -500
x [mm] x [mm] x [mm]
y [m
m]
y [m
m]
y [m
m]
90
0
60
<-60
-30
30
poin
ts p
er tr
ial
xy y yx x
penalty: 0 penalty: 500penalty: 100
x, y: mean movement end point [mm]
= 4.83 mm
Key assumption:
The mover chooses the motor strategy that maximizes the expected gain , taking into account motor uncertainty.
100-500
Consequence:
The choice of motor strategy depends on
• the reward structure of the environment
• the mover's own motor variability.
Trommershäuser, Maloney, Landy (2003) JOSA A, in press.Trommershäuser, Maloney, Landy (2003) Spatial Vision, 16, 255-275.Maloney, Trommershäuser, Landy (2003) VSS
Mean movement end points withstimulus configurations at differentorientations.
5 “practiced movers”1 session: 12 warm-
up trials, 6x2x16 trials per session,24 data points per condition
2 penalty conditions:0 and -500 points
Experiment 1
1 2 3 4
R = 9 mm
x (mm)
y (m
m)
S1
exp., penalty = 500model, penalty = 500x
exp., penalty = 0
Results:
Mean movement end points with more complex configurations.
5 “practiced movers”1 session: 12 warm-
up trials, 6x2x16 trials per session,24 data points per condition
2 penalty conditions:0 and -500 points
Experiment 2
x (mm)
exp., penalty = 500
model, penalty = 500x
exp., penalty = 0
x (mm)
x (mm)
y (m
m)
y (m
m)
S1 S2
S3 S4
S5
x (mm)
Results:
x (mm) x (mm)
y (m
m)
y (m
m)
MEGaMove* Model for Movement under Risk
MEGaMove Model: Effect of Motor Uncertainty
MEGaMove Model: The Expected Gain Surface
The Experimental Task
Speeded movement:
Hit targets, withFingertip, avoidpenalties.
700 ms time limit.
S2
S3
S5
S4
The MEGaMove* Model predicts that subjects will take account of their own motor uncertainty in planning movements. Here we report the outcomes of two experimental tests of the model insimple environments where there are explicit gains and losses associated with the outcomes of movements.
Our results indicate that humans take both costs and their own movement uncertainty into account in planning movement.
In this simple task, a motor strategy is characterized bythe distribution of end pointsaround a mean end point.Each mean end point corresponds to an expected gain. This expected gain depends only on the subject’smotor uncertainty and the rewards and penalties present in the environment.We can compute, for any subject, an expected gain surface.