Chapter

Humans' Multisensory Perception, from Integration to Segregation, Follows Bayesian Inference

Ladan Shams and Ulrik Beierholm

in Sensory Cue Integration

Published in print September 2011 | ISBN: 9780195387247
Published online September 2012 | e-ISBN: 9780199918379 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780195387247.003.0013

Series: Computational Neuroscience Series

Humans' Multisensory Perception, from Integration to Segregation, Follows Bayesian Inference

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This chapter first discusses experimental findings showing that multisensory perception encompasses a spectrum of phenomena ranging from full integration (or fusion), to partial integration, to complete segregation. Next, it describes two Bayesian causal-inference models that can account for the entire range of combinations of two or more sensory cues. It shows that one of these models, which is a hierarchical Bayesian model, is a special form of the other one (which is a nonhierarchical model). It then compares the predictions of these models with human data in multiple experiments and shows that Bayesian causal-inference models can account for the human data remarkably well. Finally, a study is presented that investigates the stability of priors in the face of drastic change in sensory conditions.

Keywords: multisensory perception; cue integration; Bayesian causal-inference models; sensory cues

Chapter.  5811 words.  Illustrated.

Subjects: Neuropsychology

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