1. Perceptual Learning, Roving and the Unsupervised Bias By Aaron Clarke, Henning Sprekeler, Wolfram Gerstner and Michael Herzog Brain Mind Institute École Polytechnique Fédérale De Lausanne Switzerland

2. Talk Outline Perceptual Learning & Roving The Unsupervised Bias Critical Experiment

3. Perceptual Learning

4. 05101520253035404550 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Block Number d'

5. Talk Outline Perceptual Learning & Roving The Unsupervised Bias Critical Experiment

6. Roving 1200” Learning Task 1

7. Roving 1200” 1800” Learning Task 1 Learning Task 2

13. Roving Adapted from Tartaglia, Bamert, Mast & H. Herzog (2009)

14. Hypotheses Roving may disrupt memory-trace-buildup for the roved stimuli (Yu et al., 2004). Roving may diminish the stimuli’s predictability (Adini et al., 2004). Roving may prevent the participants from conceptually tagging each stimulus type in order to switch their attention to the appropriate perceptual template (Zhang et al., 2008).

15. Roving 1200” 1800” Learning Task 1 Learning Task 2

16. Hypotheses Roving may disrupt memory-trace-buildup for the roved stimuli (Yu et al., 2004). Roving may diminish the stimuli’s predictability (Adini et al., 2004). Roving may prevent the participants from conceptually tagging each stimulus type in order to switch their attention to the appropriate perceptual template (Zhang et al., 2008).

17. Talk Outline Perceptual Learning & Roving The Unsupervised Bias Critical Experiment

18. Talk Outline Perceptual Learning & Roving The Unsupervised Bias Critical Experiment

19. Model Predictions SupervisedUnsupervisedReward-Based No feedback Trial by trial feedback Error feedback Teacher signal Output Desired Output Error Input Output Desired Output Error Reward Feedback after many trials Error feedback Teacher signal i j Input Δw ij = pre i × e ij Δw ij = Cov(R,w ij ) + ‹R› ‹w ij ›Δw ij = pre i × post j

20. Δw ij = pre i × e ij Model Predictions UnsupervisedReward-Based No feedback Input Output Desired Output Error Reward Feedback after many trials Error feedback Teacher signal i j Supervised Trial by trial feedback Error feedback Teacher signal Input Output Desired Output Error Herzog & Fahle (1998) Feedback improves performance. Learning is possible without feedback Δw ij = Cov(R,w ij ) + ‹R› ‹w ij ›Δw ij = pre i × post j

21. Reward-Based Learning Δw ij = Cov(R,w ij ) + ‹R› ‹w ij › weight change Covariation between reward weight change Average reward Averages of past trialsReward & current activations

22. Reward-Based Learning Δw ij = Cov(R,w ij ) + ‹R› ‹w ij › weight change Covariation between reward weight change Average reward = 0 Averages of past trialsReward & current activations

23. Reward-Based Learning Δw ij = Cov(R 1 +R 2,w ij ) + ‹R 1 +R 2 › ‹w ij › weight change Covariation between reward weight change Average reward Averages of past trials Learning is impossible with two stimuli. Reward & current activations

24. Roving Adapted from Tartaglia, Bamert, Mast & H. Herzog (2009)

25. Talk Outline Perceptual Learning & Roving The Unsupervised Bias Critical Experiment

26. Hypothesis Roving impairs perceptual learning when the average reward for the two learned stimuli differs significantly. – This kind of situation occurs when the two roved tasks differ in their difficulty levels.

27. Roving 1200” 1800” Learning Task 1 Learning Task 2

28. Results H 0 : Mean Hard Slopes = 0: t(7) = -1.115, p = 0.151 1200” 1800” H 0 : Mean Easy Slopes = 0: t(7) = -0.222, p = 0.415 05101520 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Block Number d' Easy Hard

29. Results 05101520 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Block Number d' Easy Hard 05101520 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Block Number d' H 0 : Mean Non-Roved Slopes = 0: t(7) = 2.144, p = 0.035

30. Summary There are three types of learning models: supervised, unsupervised and reward-based. Only reward-based learning withstands empirical falsification, and it suffers from the unsupervised bias. When roving two tasks, easy and hard, learning fails, as can be shown mathematically. And that is why roving occurs empirically. A strange prediction from this is that roving a hard and a very easy task should deteriorate performance. Roving two hard tasks might make learning easier than roving a hard and an easy task, and this has actually been shown in other studies.

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32. When is Learning During Roving Successful? Vs. 150 ms500 ms Vs.

33. Experiment Used two stimuli: 1800” and 1200”. Measured pre-training thresholds for both stimuli in isolation. Trained subjects with fixed offsets (easy = 1.5 × pre- training threshold, hard = 0.9 × pre-training threshold). In 20 blocks of 80 trials. Roved stimuli. 1800” Easy 1200” Hard Easy 1200”

34. Other Hypotheses Roving may interact with the participants’ initial performance levels where worse initial performers learn more than high initial performers. Roving might cause low-level interference between stimulus types (Tartaglia et al., 2009; Zhaoping, Herzog, & Dayan, 2003).