Our perception of the word is the result of combining information
between several senses, such as vision, audition and proprioception.
These sensory modalities use widely different frames of reference
to represent the properties and locations of object. Moreover, multisensory
cues come with different degrees of reliability, and the reliability
of a given cue can change in different contexts. The Bayesian frameworkwhich
we describe in this reviewprovides an optimal solution to deal
with this issue of combining cues that are not equally reliable.
However, this approach does not address the issue of frames of references.
We show that this problem can be solved by creating cross-modal spatial
links in basis function networks. Finally, we show how the basis
function approach can be combined with the Bayesian framework to
yield networks that can perform optimal multisensory combination.
On the basis of this theory, we argue that multisensory integration
is a dialogue between sensory modalities rather that the convergence
of all sensory information onto a supra-modal area.
%0 Journal Article
%1 Deneve:2004
%A Deneve, Sophie
%A Pouget, Alexandre
%D 2004
%J Journal of Physiology - Paris
%K Basis Bayesian; Frames Gain Multisensory Partially Recurrent fields functions; integration; modulation; networks; of receptive reference; shifting
%P 249-258
%T Bayesian multisensory integration and cross-modal spatial links
%V 98
%X Our perception of the word is the result of combining information
between several senses, such as vision, audition and proprioception.
These sensory modalities use widely different frames of reference
to represent the properties and locations of object. Moreover, multisensory
cues come with different degrees of reliability, and the reliability
of a given cue can change in different contexts. The Bayesian frameworkwhich
we describe in this reviewprovides an optimal solution to deal
with this issue of combining cues that are not equally reliable.
However, this approach does not address the issue of frames of references.
We show that this problem can be solved by creating cross-modal spatial
links in basis function networks. Finally, we show how the basis
function approach can be combined with the Bayesian framework to
yield networks that can perform optimal multisensory combination.
On the basis of this theory, we argue that multisensory integration
is a dialogue between sensory modalities rather that the convergence
of all sensory information onto a supra-modal area.
@article{Deneve:2004,
abstract = {Our perception of the word is the result of combining information
between several senses, such as vision, audition and proprioception.
These sensory modalities use widely different frames of reference
to represent the properties and locations of object. Moreover, multisensory
cues come with different degrees of reliability, and the reliability
of a given cue can change in different contexts. The Bayesian frameworkwhich
we describe in this reviewprovides an optimal solution to deal
with this issue of combining cues that are not equally reliable.
However, this approach does not address the issue of frames of references.
We show that this problem can be solved by creating cross-modal spatial
links in basis function networks. Finally, we show how the basis
function approach can be combined with the Bayesian framework to
yield networks that can perform optimal multisensory combination.
On the basis of this theory, we argue that multisensory integration
is a dialogue between sensory modalities rather that the convergence
of all sensory information onto a supra-modal area.},
added-at = {2009-06-26T15:25:19.000+0200},
author = {Deneve, Sophie and Pouget, Alexandre},
biburl = {https://www.bibsonomy.org/bibtex/2313dce2789d641e600f5e26ebf0116bf/butz},
description = {diverse cognitive systems bib},
interhash = {adeacf221bab5841ad6badabecf6a2c7},
intrahash = {313dce2789d641e600f5e26ebf0116bf},
journal = {Journal of Physiology - Paris},
keywords = {Basis Bayesian; Frames Gain Multisensory Partially Recurrent fields functions; integration; modulation; networks; of receptive reference; shifting},
owner = {martin},
pages = {249-258},
review = {short Review of multisensory integration},
timestamp = {2009-06-26T15:25:27.000+0200},
title = {Bayesian multisensory integration and cross-modal spatial links},
volume = 98,
year = 2004
}