J. Clark and A. Yuille, Data fusion for sensory information processing systems Springer, 1990.
DOI : 10.1007/978-1-4757-2076-1

M. Ernst and H. Bülthoff, Merging the senses into a robust percept, Trends in Cognitive Sciences, vol.8, issue.4, pp.162-169, 2004.
DOI : 10.1016/j.tics.2004.02.002

Y. Sato and T. Toyoizumi, Bayesian Inference Explains Perception of Unity and Ventriloquism Aftereffect: Identification of Common Sources of Audiovisual Stimuli, Neural Computation, vol.13, issue.1, pp.3335-3355, 2007.
DOI : 10.3758/BF03193329

K. Körding, U. Beierholm, W. Ma, S. Quartz, and J. Tenenbaum, Causal Inference in Multisensory Perception, PLoS ONE, vol.93, issue.9, p.943, 2007.
DOI : 10.1371/journal.pone.0000943.s003

M. Ernst and M. Banks, Humans integrate visual and haptic information in a statistically optimal fashion, Nature, vol.415, issue.6870, pp.429-433, 2002.
DOI : 10.1038/415429a

P. Battaglia, R. Jacobs, and R. Aslin, Bayesian integration of visual and auditory signals for spatial localization, Journal of the Optical Society of America A, vol.20, issue.7, pp.1391-1397, 2003.
DOI : 10.1364/JOSAA.20.001391

L. Shams, W. Ma, and U. Beierholm, Sound-induced flash illusion as an optimal percept, NeuroReport, vol.16, issue.17, pp.1923-1927, 2005.
DOI : 10.1097/01.wnr.0000187634.68504.bb

H. Bernstein, H. Clark, and A. Edelstein, Intermodal effects in choice reaction time., Journal of Experimental Psychology, vol.81, issue.2, pp.405-407, 1969.
DOI : 10.1037/h0027750

H. Bernstein, H. Clark, and A. Edelstein, Effects of an auditory signal on visual reaction time., Journal of Experimental Psychology, vol.80, issue.3, Pt.1, pp.567-569, 1969.
DOI : 10.1037/h0027444

D. Hecht, M. Reiner, and A. Karni, Multisensory enhancement: gains in choice and in simple response times, Experimental Brain Research, vol.84, issue.2, pp.133-143, 2008.
DOI : 10.3758/BF03193160

P. Besson, J. Richiardi, C. Bourdin, L. Bringoux, and D. Mestre, Bayesian networks and information theory for audio-visual perception modeling, Biological Cybernetics, vol.8, issue.3, p.213, 2010.
DOI : 10.1016/j.cub.2004.01.029
URL : https://hal.archives-ouvertes.fr/hal-01436027

P. Besson and J. Richiardi, A context-specific independence model of multisensory perception, 2009.

D. Knill and W. Richards, Perception as Bayesian Inference, 1996.
DOI : 10.1017/CBO9780511984037

N. Roach, J. Heron, and P. Mcgraw, Resolving multisensory conflict: a strategy for balancing the costs and benefits of audio-visual integration, Proceedings of the Royal Society B: Biological Sciences, vol.48, issue.4, pp.2159-2168, 2006.
DOI : 10.3758/BF03204939

D. Wozny, U. Beierholm, and L. Shams, Human trimodal perception follows optimal statistical inference, Journal of Vision, vol.8, issue.3, pp.1-11, 2008.
DOI : 10.1167/8.3.24
URL : http://jov.arvojournals.org/data/journals/jov/932853/jov-8-3-24.pdf

S. Molholm, W. Ritter, M. Murray, D. Javitt, and C. Schroeder, Multisensory auditory???visual interactions during early sensory processing in humans: a high-density electrical mapping study, Cognitive Brain Research, vol.14, issue.1, pp.115-128, 2002.
DOI : 10.1016/S0926-6410(02)00066-6

A. Diederich and H. Colonius, Bimodal and trimodal multisensory enhancement: Effects of stimulus onset and intensity on reaction time, Perception & Psychophysics, vol.69, issue.8, pp.1388-1404, 2004.
DOI : 10.1093/biomet/69.2.297

M. Jepma, E. Wagenmakers, G. Band, and S. Nieuwenhuis, The Effects of Accessory Stimuli on Information Processing: Evidence from Electrophysiology and a Diffusion Model Analysis, Journal of Cognitive Neuroscience, vol.57, issue.5, pp.847-864, 2009.
DOI : 10.1016/j.jml.2007.04.006

B. Rowland, S. Quessy, T. Stanford, and B. Stein, Multisensory Integration Shortens Physiological Response Latencies, Journal of Neuroscience, vol.27, issue.22, pp.5879-5884, 2007.
DOI : 10.1523/JNEUROSCI.4986-06.2007
URL : http://www.jneurosci.org/content/jneuro/27/22/5879.full.pdf

R. Nickerson, Intersensory facilitation of reaction time: Energy summation or preparation enhancement?, Psychological Review, vol.80, issue.6, pp.489-509, 1973.
DOI : 10.1037/h0035437

D. Braun, C. Mehring, and D. Wolpert, Structure learning in action, Behavioural Brain Research, vol.206, issue.2, pp.157-165, 2010.
DOI : 10.1016/j.bbr.2009.08.031

D. Warren, Spatial Localization under Conflict Conditions: Is There a Single Explanation?, Perception, vol.8, issue.3, pp.323-337, 1979.
DOI : 10.1037/h0031545

F. Sarlegna, N. Malfait, L. Bringoux, C. Bourdin, and J. Vercher, Force-field adaptation without proprioception: Can vision be used to model limb dynamics?, Neuropsychologia, vol.48, issue.1, pp.60-67, 2010.
DOI : 10.1016/j.neuropsychologia.2009.08.011
URL : https://hal.archives-ouvertes.fr/hal-01384815

R. Neapolitan, Learning Bayesian Networks, 2003.
DOI : 10.1016/B978-012370477-1.50021-9

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988.

T. Verma and J. Pearl, An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation, Proc. 8th Annual Conf. on Uncertainty in Artificial Intelligence (UAI-92), pp.323-333, 1992.
DOI : 10.1016/B978-1-4832-8287-9.50049-9

C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller, Context-specific independence in Bayesian networks, 1996.

D. Geiger and D. Heckerman, Advances in Probabilistic Reasoning, UAI. pp 118126, 1991.
DOI : 10.1016/B978-1-55860-203-8.50019-X

D. Geiger and D. Heckerman, Knowledge representation and inference in similarity networks and Bayesian multinets, Artificial Intelligence, vol.82, issue.1-2, pp.45-74, 1996.
DOI : 10.1016/0004-3702(95)00014-3

J. Bilmes, Dynamic Bayesian multinets, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI), 2000.

A. Cano, J. Castellano, A. Masegosa, and S. Moral, Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers, In: ECSQARU, pp.932-943, 2005.
DOI : 10.1007/11518655_78

N. Zhang and D. Poole, On the role of context-specific independence in probabilistic inference, Proceedings of the 16th international joint conference on Artificial intelligence, pp.1288-1293, 1999.

S. Theodoridis and K. Koutroumbas, Pattern Recognition, 2006.
DOI : 10.1016/B0-12-227240-4/00132-5

K. Murphy, Dynamic Bayesian Networks: Representation, Inference and Learning, 2002.

T. Hospedales and S. Vijayakumar, Multisensory Oddity Detection as Bayesian Inference, PLoS ONE, vol.38, issue.11, p.4205, 2009.
DOI : 10.1371/journal.pone.0004205.s002
URL : https://doi.org/10.1371/journal.pone.0004205

D. Talsma, D. Senkowski, S. Soto-faraco, and M. Woldorff, The multifaceted interplay between attention and multisensory integration, Trends in Cognitive Sciences, vol.14, issue.9, pp.400-410, 2010.
DOI : 10.1016/j.tics.2010.06.008
URL : http://europepmc.org/articles/pmc3306770?pdf=render

T. Koelewijn, A. Bronkhorst, and J. Theeuwes, Attention and the multiple stages of multisensory integration: A review of audiovisual studies, Acta Psychologica, vol.134, issue.3, pp.372-384, 2010.
DOI : 10.1016/j.actpsy.2010.03.010

T. Hospedales and S. Vijayakumar, Structure Inference for Bayesian Multisensory Scene Understanding, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.12, pp.2140-2157, 2008.
DOI : 10.1109/TPAMI.2008.25
URL : http://homepages.inf.ed.ac.uk/svijayak/publications/hospedales-PAMI2008.pdf

L. Shams and U. Beierholm, Causal inference in perception, Trends in Cognitive Sciences, vol.14, issue.9, pp.425-432, 2010.
DOI : 10.1016/j.tics.2010.07.001

C. Spence, Crossmodal spatial attention, Annals of the New York Academy of Sciences, vol.435, issue.Supp., pp.182-200, 2010.
DOI : 10.3758/BF03193639