Discovering Optimal Training Policies: A New Experimental Paradigm

1. Discovering Optimal Training Policies:A New Experimental Paradigm Robert V. Lindsey, Michael C. Mozer Institute of Cognitive ScienceDepartment of Computer ScienceUniversity of Colorado, Boulder Harold Pashler Department of PsychologyUC San Diego

2. Common Experimental Paradigm In Human Learning Research • Propose several instructional conditions to compare based on intuition or theory • E.g., spacing of study sessions in fact learning • Equal: 1 – 1 – 1 • Increasing: 1 – 2 – 4 • Run many participants in each condition • Perform statistical analyses to establish reliable differencebetween conditions

3. What Most Researchers Interested In Improving Instruction Really Want To Do • Find the best training policy (study schedule) • Abscissa: space of all training policies • Performance function definedover policy space

4. Approach • Perform single-participant experiments at selected points in policy space (o) • Use function approximationtechniques to estimate shapeof the performance function • Given current estimate,select promising policiesto evaluate next. • promising = has potentialto be the optimum policy Gaussian processregression linear regression

5. Gaussian Process Regression • Assumes only that functions are smooth • Uses data efficiently • Accommodates noisy data • Produces estimates of both function shape and uncertainty

6. Simulated Experiment

8. Embellishments On Off-The-ShelfGP Regression • Active selection heuristic: upper confidence bound • GP is embedded in generative task model • GP represents skill level (-∞ +∞) • Mapped to population mean accuracy on test (0  1) • Mapped to individual’s mean accuracy, allowing for interparticipant variability • Mapped to # correct responses via binomial sampling • Hierarchical Bayesian approach to parameter selection • Interparticipant variability • GP smoothness (covariance function)

9. Concept Learning Task

15. GLOPNOR = Graspability • Ease of picking up & manipulating object with one hand • Based on norms from Salmon, McMullen, & Filliter (2010)

16. Two-Dimensional Policy Space • Fading policy • Repetition/alternationpolicy

17. Two-Dimensional Policy Space

18. Policy Space fading policy repetition/ alternation policy

19. Experiment Training • 25 trial sequence generated by chosen policy • Balanced positive / negative • Testing • 24 test trials, ordered randomly, balanced • No feedback, forced choice • Amazon Mechanical Turk • $0.25 / participant

20. Results # correct of 25

21. Best Policy • Fade from easy to semi-difficulty • Repetitions initially, alternations later *

22. Results

23. Final Evaluation 65.7% 60.9% N=49 N=53 68.6% N=48 66.6% N=50

24. Novel Experimental Paradigm • Instead of running a few conditions each with many participants, … • …run many conditions each with a different participant. • Although individual participants provide a very noisy estimate of the population mean, optimization techniques allow us to determine the shape of the policy space.

25. What Next? • Plea for more interesting policy spaces! • Other optimization problems • Abstract concepts from examples • E.g., irony • Motivation • Manipulations • Rewards/points, trial pace, task difficulty, time pressure • Measure • Voluntary time on task

26. Leftovers

27. Optimization • E.g., time-varying repetition/alternation policy

28. How To Do Optimization? • Reinforcement Learning • POMDPs • Function Approximation • Gaussian Process Surrogate-Based Optimization

29. Approach • (1) Using the current policy function estimate, choose a promising next policy to evaluate • (2) Conduct a small experiment using that policy to obtain a (noisy) estimate of population mean performance for that policy • (3) Use data collected so far to reestimate the shape of the policy function • (4) Go to step 1