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

Gilet, Diard, Palluel-Germain & Bessière — LIG & LPNC-CNRSBayesian Action-Perception model

Bayesian Action-Perception loop modeling: Application to trajectory generation and recognition using internal motor simulationE. 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, 2010http://diard.wordpress.com/ Julien.Diard@upmf-grenoble.fr

Perception of actions

(Calvo-Merino et al., 2004)

Reading and writing letters

(Longcamp, 2003)

Writing

Reading pseudo letters

Reading letters

Interpretation

• Motor simulation of actions during perception

• Articulation between perception and action processes

Modeling both reading and writingModeling internal simulation of

movements

Bayesian Action-Perception (BAP) model

Summary

• BAP model – architecture and definition: overview

• Experimental results– simulation of cognitive tasks

• Experimental prediction

BAP model structure

internal letter representation

perception model

action model

simulated perception model

coherence variables

Cartesian and effector spaces

• Common space for perceptive and motor internal representations– Cartesian space

Letter representation: sequences of via-points

Letter representation

« Laplace succession laws »

Parameter indentification

Perception model

• Deterministic via-point extraction

Action model

Trajectory generation model

• Minimum-acceleration model:– Cost function– Boundary conditions

• Polynomial solution

Simulated perception model

• Identical to the perception model

Coherence variables

• Allow to activate or deactivate submodels– « Bayesian switch »

Coherence variable for controlling submodel activation

• Model– λ binary variable– Joint–

• Inference– P(A) = P(A): value of B does not influence A–

Summary

Perception: reading letters

Correct recognition: 93.36%

Perception: writer recognition

Correct recognition: 79.5%

Action: writing letters

Variability between writers Variability between trials

Motor equivalence

• Writer “style”– (Wright, 1990)

• Common activated motor areas– (Wing, 2000)

(Serratrice. 1993)

Action: Motor equivalence

Perception and Action: Copy

Trajectory copy Letter copy

Perception and Action: Reading letters with motor simulation

Recall: reading letters without simulation

• Complete trajectories– Correct recognition score with simulation 93.36%– Correct recognition score without simulation 90.2%

• Incomplete trajectories

Summary

Experimental prediction

Preliminary data

Control Group (motor simulation unaffected)

Motor interference Group (motor simulation affected)

Complete letters

Truncated letters

Recognition Performance (%)

F(1,23) = 3.06, p = 0.093

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

predictions38

Thank you for your attention !

Questions ?

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

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