1. JJ Orban de Xivry jj.orban@kuleuven.be
Hands on session
JJ Orban de Xivry

2. Visuomotor rotation
Changing the direction of cursor motion with respect to actual hand motion

3. Saving in visuomotor rotation

4. Interference Error (deg) Cycles (8 trials each)
Krakauer et al. Journal of Neuroscience 2005

5. Model-free memory
Huang, V. S., Haith, A. M., Mazzoni, P., & Krakauer, J. W. (2011). Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models. Neuron, 70(4), 787–801.

6. Model-free memory
Huang, V. S., Haith, A. M., Mazzoni, P., & Krakauer, J. W. (2011). Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models. Neuron, 70(4), 787–801.

7. Effect of reinforcement learning
Shmuelof, L., Huang, V. S., Haith, A. M., Delnicki, R. J., Mazzoni, P., & Krakauer, J. W. (2012). Overcoming motor “forgetting” through reinforcement of learned actions. Journal of Neuroscience, 32(42), 14617–21.

8. Effect of reinforcement learning
Shmuelof, L., Huang, V. S., Haith, A. M., Delnicki, R. J., Mazzoni, P., & Krakauer, J. W. (2012). Overcoming motor “forgetting” through reinforcement of learned actions. Journal of Neuroscience, 32(42), 14617–21.

9. Shmuelof: to do list Fit exponential to first learning phase
Compute average during the last 20 trials of reinforcement
Compare the two groups
Look at error at the end of “error-clamp” measure. Did you reproduce the results from original paper?
Fit a state-space model on the average data (from initial learning to end of error-clamp period). Any differences between the two groups?

10. Huang: to do list Fit exponential to first learning phase
Fit exponential to second learning phase
Compute average error at the end of washout
Compare the two groups
Fit a state-space model on the average data. Any differences between the two groups?
What is the variability of the learning rate? How many subjects are needed to detect a change of learning rate of 50% (effect size = 0.5)?
s= estimated standard deviation (from sample)
d = effect size
2 is parameter for 95% confidence interval

11. Design of an experiment
You are nothing without a good control group
Inter-subject variability: need of a power analysis to compute require sample size
Choosing the right variable (mid-movement, end of movement)

12. Richness of behavior Reaction time
Feedforward (planning) vs feedback (online control of movement)
Different measures of motor memory (faster relearning, interference, error-clamp measure, )
Many factors can influence motor memories (errors, binary reinforcement, reward, etc)

13. Further ideas
How to obtain a motor memory (following learning, sequence learning, motor adaptation, bimanual coordination learning, etc.)
How can you measure it, improve it, disrupt it, etc.
Which patients are likely to be impaired at this memory?
Motor behavior is really rich if it is adequately measured.
Models can make predictions that are testable