1. Study on Learning Effect in Visuomotor Tracking
Yi Su and Xiangmin Zhang
May 5, 2010
Visuomotor definition: relating to movement involving visual cues

2. Outline Experimental Design: Complicated! Data: Incomplete!
Initial Checking
Model Building:
Model 1: Split-Plot Design
Model 2: Split-Split-Plot Design
Discussion

3. Experimental Design Client’s Goal:
Is there procedural learning for different groups, different tracking methods and different types of sine wave?
Two Response Variables measuring subjects tracking performance:
Delay: related to phase, good if close to 1
Gain: related to amplitude, good if close to 0

4. Experimental Design: Factors
1. Group (4 levels)
People with Parkinson’s Disease
People who Stutter
Control Group Matched to Parkinson’s Disease
Control Group Matched to Stutter
2. Type of tracking method (2 levels)
Hand Tracking
Jaw Tracking
3. Trial (levels vary from 1 to 3)

5. Experimental Design: Factors
4. Types of sine wave (13 levels)
Simple Repetitive
3 levels with large amplitude
Complex Repetitive
3 levels with SML amplitude pattern
Complex Non-repetitive
3 levels with no amplitude pattern
Tracking by Memory
3 levels with target disappearing after 30 seconds
Random frequency, random amplitude
1 level

6. Data
Only jaw tracking
Only one type of sine wave: 0.3 Hz simple repetitive
Three Groups:
Group 1: Parkinson’s Disease (6 subjects)
Group 2: Stutter (8 subjects)
Group 3: Control matched to stutter (11 subjects)
Two Trials
Both delay and gain data

7. Data (continued) Data file from client
Model absolute value of delay away from zero
Model absolute value of gain away from one
Data for model 1 data file
Data for model 2 data file

8. Initial Checking: Absolute Delay

9. Initial Checking: Absolute Gain

10. Model Building: Model 1 The Initial Full Model: SAS Code:
Response (delay or gain)
= group + trial + group*trial + SUBJECT (group) + error
The response is absolute delay or gain averaged over 60 seconds (51 data points)
SAS Code:
proc mixed data=summary1;
class group subject trial;
model delay = group trial group*trial/ddfm=satterth;
random subject(group);
run;

11. Model Building: Model 1 For Delay Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
group <.0001
trial
group*trial
For Gain
Effect DF DF F Value Pr > F
group <.0001
trial
group*trial

12. Model Building: Model 1

13. Model Building: Model 1

14. Model Building: Model 2 The Initial Full Model:
Response (delay or gain)
= group+ trial + time
+ group*trial + group*time +trial*time
+ group*trial*time
+ SUBJECT (group) + trial* SUBJECT (group) + error
The response is absolute delay or gain averaged over 17 data points of 3 consecutive time intervals within a trial.
Time treated as continuous in the model
In SAS:
proc mixed data=summary2;
class group subject trial;
model delay = group trial time group*trial time*trial time*group time*group*trial/ddfm=satterth;
random subject(group) trial*subject(group);
run;

15. Type 3 Tests of Fixed Effects
Model Building: Model 2
For Delay
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
group
trial
time
group*trial
time*trial
time*group*trial

16. Model Building: Model 2 For Gain Type 3 Tests of Fixed Effects Num Den
Effect DF DF F Value Pr > F
group
trial
time
group*trial
time*trial
time*group
time*group*trial

17. Model Building: Model 2 For Delay For Gain
After throwing away all 2-way and 3-way interactions,
For Delay
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
group <.0001
trial
time
For Gain
group <.0001
trial
time

18. Discussion
There is no trial and time effect in any model we built, indicating no procedural learning over time.
Model raw data instead of averaged data
Learning may be confounded with fatigue. This may be a reason for no improvement in tracking performance

19. Thank you! Any questions?