Bayesian Action-Perception Loop Modeling: Application to Trajectory Generation and Recognition using Internal Motor Simu

1. 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/Julien.Diard@upmf-grenoble.fr BayesianAction-Perceptionloopmodeling: Application to trajectorygeneration and recognition usinginternalmotor simulation

2. Perception of actions (Calvo-Merino et al., 2004)

3. Reading and writing letters Writing Reading letters Reading pseudo letters (Longcamp, 2003)

4. Interpretation • Motor simulation of actions during perception • Articulation between perception and action processes

5. Modelingbothreading and writingModelinginternal simulation of movements

6. BayesianAction-Perception (BAP) model

7. Summary • BAP model • architecture and definition: overview • Experimentalresults • simulation of cognitive tasks • Experimentalprediction

8. BAP model structure internalletterrepresentation coherence variables perception model simulated perception model action model

9. Cartesian and effector spaces • Common space for perceptive and motor internal representations • Cartesianspace

10. Letter representation: sequences of via-points

11. Letterrepresentation « Laplace succession laws »

12. Parameter indentification

17. Perception model • Deterministicvia-point extraction

18. Action model

19. Trajectorygeneration model • Minimum-acceleration model: • Costfunction • Boundary conditions • Polynomial solution

20. Simulated perception model • Identical to the perception model

21. Coherence variables • Allow to activate or deactivatesubmodels • « Bayesianswitch »

22. Coherence variable for controllingsubmodel activation • Model • λbinary variable • Joint • Inference • P(A) = P(A): value of Bdoes not influence A λ A B A B A B

23. Summary • BAP model • architecture and definition: overview • Experimentalresults • simulation of cognitive tasks • Experimentalprediction

24. Perception: reading letters Correct recognition: 93.36%

25. Perception: writer recognition Correct recognition: 79.5%

26. Action: writing letters Variabilitybetween trials Variabilitybetweenwriters

27. Motor equivalence

28. Motor equivalence • Writer “style” • (Wright, 1990) • Common activated motor areas • (Wing, 2000) (Serratrice. 1993)

29. Action: Motor equivalence

30. Action: Motor equivalence

31. Perception and Action: Copy Trajectory copy Letter copy

32. Perception and Action: Reading letters with motor simulation Recall: readingletterswithout simulation

33. Perception and Action: Reading letters with motor simulation

34. Perception and Action: Reading letters with motor simulation • Complete trajectories • Correct recognition score with simulation 93.36% • Correct recognition score without simulation 90.2% • Incompletetrajectories

35. Summary • BAP model • architecture and definition: overview • Experimentalresults • simulation of cognitive tasks • Experimentalprediction

36. Experimentalprediction

37. Preliminary data F(1,23) = 3.06, p = 0.093

38. 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

39. Thank you for your attention ! Questions ?