Linked Life Data

20502912

Source:http://linkedlifedata.com/resource/pubmed/id/20502912

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-8-10
pubmed:abstractText
Thanks to their different senses, human observers acquire multiple information coming from their environment. Complex cross-modal interactions occur during this perceptual process. This article proposes a framework to analyze and model these interactions through a rigorous and systematic data-driven process. This requires considering the general relationships between the physical events or factors involved in the process, not only in quantitative terms, but also in term of the influence of one factor on another. We use tools from information theory and probabilistic reasoning to derive relationships between the random variables of interest, where the central notion is that of conditional independence. Using mutual information analysis to guide the model elicitation process, a probabilistic causal model encoded as a Bayesian network is obtained. We exemplify the method by using data collected in an audio-visual localization task for human subjects, and we show that it yields a well-motivated model with good predictive ability. The model elicitation process offers new prospects for the investigation of the cognitive mechanisms of multisensory perception.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1432-0770
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
103
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
213-26
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Bayesian networks and information theory for audio-visual perception modeling.
pubmed:affiliation
Institute of Movement Sciences, CNRS & Université de la Méditerranée, Marseille, France. patricia.besson@univmed.fr
pubmed:publicationType
Journal Article