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

This study presents a Bayesian Action-Perception (BAP) model that integrates perception and action processes through internal motor simulation. The model is applied to trajectory generation and recognition tasks. Experimental results and predictions are discussed.

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

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  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

  13. Perception model • Deterministicvia-point extraction

  14. Action model

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

  16. Simulated perception model • Identical to the perception model

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

  18. 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

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

  20. Perception: reading letters Correct recognition: 93.36%

  21. Perception: writer recognition Correct recognition: 79.5%

  22. Action: writing letters Variabilitybetween trials Variabilitybetweenwriters

  23. Motor equivalence

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

  25. Action: Motor equivalence

  26. Action: Motor equivalence

  27. Perception and Action: Copy Trajectory copy Letter copy

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

  29. Perception and Action: Reading letters with motor simulation

  30. 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

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

  32. Experimentalprediction

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

  34. 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

  35. Thank you for your attention ! Questions ?

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