1. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Bayesian Action-Perception loop modeling: Application to trajectory generation and recognition using internal motor simulation E. Gilet (1), J. Diard (2), R. Palluel-Germain (2), P. Bessière (1) (1) Laboratoire d’Informatique de Grenoble – CNRS, France (2) Laboratoire de Psychologie et NeuroCognition – CNRS, France July, 5, 2010 http://diard.wordpress.com/http://diard.wordpress.com/Julien.Diard@upmf-grenoble.fr

2. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Perception of actions 2 (Calvo-Merino et al., 2004)

3. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Reading and writing letters 3 (Longcamp, 2003) Writing Reading pseudo letters Reading letters

4. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Interpretation Motor simulation of actions during perception Articulation between perception and action processes 4

5. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Modeling both reading and writing Modeling internal simulation of movements 5

6. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Bayesian Action-Perception (BAP) model 6

7. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Summary BAP model –architecture and definition: overview Experimental results –simulation of cognitive tasks Experimental prediction 7

8. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model BAP model structure 8 internal letter representation perception model action model simulated perception model coherence variables

9. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Cartesian and effector spaces Common space for perceptive and motor internal representations –Cartesian space 9

10. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Letter representation: sequences of via-points 10

11. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model 11 Letter representation « Laplace succession laws »

12. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Parameter indentification 12

13. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model 13

14. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model 14

15. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model 15

16. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model 16

17. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Perception model 17 Deterministic via-point extraction

18. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Action model 18

19. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Trajectory generation model Minimum-acceleration model: –Cost function –Boundary conditions Polynomial solution 19

20. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Simulated perception model Identical to the perception model 20

21. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Coherence variables Allow to activate or deactivate submodels –« Bayesian switch » 21

22. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Coherence variable for controlling submodel activation Model –λ binary variable –Joint – Inference –P(A) = P(A): value of B does not influence A – 22 AB λ AB AB

23. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Summary BAP model –architecture and definition: overview Experimental results –simulation of cognitive tasks Experimental prediction 23

24. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Perception: reading letters 24 Correct recognition: 93.36%

25. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Perception: writer recognition 25 Correct recognition: 79.5%

26. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Action: writing letters 26 Variability between writers Variability between trials

27. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Motor equivalence 27

28. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Motor equivalence Writer “style” –(Wright, 1990) Common activated motor areas –(Wing, 2000) 28 (Serratrice. 1993)

29. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Action: Motor equivalence 29

30. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model 30 Action: Motor equivalence

31. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Perception and Action: Copy 31 Trajectory copyLetter copy

32. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Perception and Action: Reading letters with motor simulation 32 Recall: reading letters without simulation

33. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model 33 Perception and Action: Reading letters with motor simulation

34. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Perception and Action: Reading letters with motor simulation Complete trajectories –Correct recognition score with simulation 93.36% –Correct recognition score without simulation 90.2% Incomplete trajectories 34

35. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Summary BAP model –architecture and definition: overview Experimental results –simulation of cognitive tasks Experimental prediction 35

36. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Experimental prediction 36

37. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Preliminary data 37 F(1,23) = 3.06, p = 0.093

38. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Summary BAP model –Bayesian model of perception and action –Includes an internal simulation loop Cognitive tasks –Reading without and with motor simulation –Writer recognition –Writing with different effectors –Copying letters and trajectories Basis for experimental predictions 38

39. Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRS Bayesian Action-Perception model Thank you for your attention ! Questions ?