A Bayesian view on multimodal cue integration

Ernst MO (2006)
Perception 131(Chapter 6): 105-131.

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Abstract
We perceive our own body and the world surrounding us via multiple sources of sensory information derived from several modalities, including vision, touch and audition. To enable interactions with the environment this information has to converge into a coherent and unambiguous multimodal percept of the body and the world. But how does the brain come up with such a unique percept? In this chapter I review a model that in the statistical sense describes an optimal integration mechanism. The benefit of integrating sensory information comes from a reduction in variance of the final perceptual estimate. Furthermore, I point out how this integration scheme can be incorporated in a larger framework using Bayesian decision theory (BDT).
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Ernst MO. A Bayesian view on multimodal cue integration. Perception. 2006;131(Chapter 6):105-131.
Ernst, M. O. (2006). A Bayesian view on multimodal cue integration. Perception, 131(Chapter 6), 105-131.
Ernst, M. O. (2006). A Bayesian view on multimodal cue integration. Perception 131, 105-131.
Ernst, M.O., 2006. A Bayesian view on multimodal cue integration. Perception, 131(Chapter 6), p 105-131.
M.O. Ernst, “A Bayesian view on multimodal cue integration”, Perception, vol. 131, 2006, pp. 105-131.
Ernst, M.O.: A Bayesian view on multimodal cue integration. Perception. 131, 105-131 (2006).
Ernst, Marc O. “A Bayesian view on multimodal cue integration”. Perception 131.Chapter 6 (2006): 105-131.
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