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/ [email protected]

http://diard.wordpress.com/


Perception of actions

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(Calvo-Merino et al., 2004)


Reading and writing letters

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(Longcamp, 2003)

Writing

Reading pseudo letters

Reading letters


Interpretation

• Motor simulation of actions during perception

• Articulation between perception and action processes

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Modeling both reading and writingModeling internal simulation of

movements

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Bayesian Action-Perception (BAP) model

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Summary

• BAP model – architecture and definition: overview

• Experimental results– simulation of cognitive tasks

• Experimental prediction

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BAP model structure

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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

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Letter representation: sequences of via-points

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Letter representation

« Laplace succession laws »


Parameter indentification

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Perception model

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• Deterministic via-point extraction


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Action model


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Trajectory generation model

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

• Polynomial solution


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Simulated perception model

• Identical to the perception model


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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–

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A B

λ

A B

A B


Summary




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Perception: reading letters

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Correct recognition: 93.36%


Perception: writer recognition

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Correct recognition: 79.5%


Action: writing letters

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Variability between writers Variability between trials


Motor equivalence

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Motor equivalence

• Writer “style”– (Wright, 1990)

• Common activated motor areas– (Wing, 2000)

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(Serratrice. 1993)


Action: Motor equivalence

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Action: Motor equivalence


Perception and Action: Copy

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Trajectory copy Letter copy


Perception and Action: Reading letters with motor simulation

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Recall: reading letters without simulation


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

• Incomplete trajectories


Summary




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Experimental prediction

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Preliminary data

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60

70

80

90

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


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gilet, diard, palluel-germain & bessière — lig & lpnc-cnrs bayesian action-perception...

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perception articulation

action processes

grenoble cnrs

upm slide

extraction slide

internal motor simulation

writing reading pseudo

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