Julien Diard
Julien Diard
Laboratoire de Psychologie et NeuroCognition - CNRS
Verified email at univ-grenoble-alpes.fr - Homepage
Cited by
Cited by
Bayesian robot programming
O Lebeltel, P Bessière, J Diard, E Mazer
Autonomous Robots 16 (1), 49-79, 2004
Common Bayesian models for common cognitive issues
F Colas, J Diard, P Bessiere
Acta biotheoretica 58 (2-3), 191-216, 2010
Adverse conditions improve distinguishability of auditory, motor, and perceptuo-motor theories of speech perception: An exploratory Bayesian modelling study
C Moulin-Frier, R Laurent, P Bessière, JL Schwartz, J Diard
Language and Cognitive Processes 27 (7-8), 1240-1263, 2012
COSMO (“Communicating about Objects using Sensory–Motor Operations”): A Bayesian modeling framework for studying speech communication and the emergence of phonological systems
C Moulin-Frier, J Diard, JL Schwartz, P Bessière
Journal of Phonetics 53, 5-41, 2015
Bayesian action–perception computational model: interaction of production and recognition of cursive letters
E Gilet, J Diard, P Bessière
PloS one 6 (6), e20387, 2011
Enhancing reading performance through action video games: the role of visual attention span
A Antzaka, M Lallier, S Meyer, J Diard, M Carreiras, S Valdois
Scientific reports 7 (1), 1-10, 2017
La carte bayésienne: un modèle probabiliste hiérarchique pour la navigation en robotique mobile
J Diard
Institut National Polytechnique de Grenoble-INPG, 2003
Optimal speech motor control and token-to-token variability: a Bayesian modeling approach
JF Patri, J Diard, P Perrier
Biological cybernetics 109 (6), 611-626, 2015
The complementary roles of auditory and motor information evaluated in a Bayesian perceptuo-motor model of speech perception.
R Laurent, ML Barnaud, JL Schwartz, P Bessière, J Diard
Psychological review 124 (5), 572, 2017
What drives the perceptual change resulting from speech motor adaptation? Evaluation of hypotheses in a Bayesian modeling framework
JF Patri, P Perrier, JL Schwartz, J Diard
PLoS computational biology 14 (1), e1005942, 2018
Bayesian programming and hierarchical learning in robotics
J Diard, O Lebeltel
Bayesian learning experiments with a khepera robot
J Diard, O Lebeltel
Emergence of articulatory-acoustic systems from deictic interaction games in a" Vocalize to Localize" framework
C Moulin-Frier, JL Schwartz, J Diard, P Bessière
Primate Communication and Human Language-Vocalisation, gestures, imitation …, 2011
Hierarchies of probabilistic models of navigation: the bayesian map and the abstraction operator
J Diard, P Bessière, E Mazer
IEEE International Conference on Robotics and Automation, 2004. Proceedings …, 2004
Proxemics models for human-aware navigation in robotics: Grounding interaction and personal space models in experimental data from psychology
ML Barnaud, N Morgado, R Palluel-Germain, J Diard, A Spalanzani
A bayesian framework for robotic programming
O Lebeltel, J Diard, P Bessiere, E Mazer
AIP Conference Proceedings 568 (1), 625-637, 2001
Bayesian modeling in speech motor control: a principled structure for the integration of various constraints
JF Patri, P Perrier, J Diard
17th Annual Conference of the International Speech Communication Association …, 2016
A theoretical comparison of probabilistic and biomimetic models of mobile robot navigation
J Diard, P Bessière, E Mazer
IEEE International Conference on Robotics and Automation, 2004. Proceedings …, 2004
Combining probabilistic models of space for mobile robots: the Bayesian Map and the Superposition operator
J Diard, P Bessiere, E Mazer
Modeling the length effect for words in lexical decision: The role of visual attention
E Ginestet, T Phénix, J Diard, S Valdois
Vision research 159, 10-20, 2019
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