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Erschienen in: Journal of Computational Neuroscience 2/2016

01.10.2016

Parallel linear dynamic models can mimic the McGurk effect in clinical populations

verfasst von: Nicholas Altieri, Cheng-Ta Yang

Erschienen in: Journal of Computational Neuroscience | Ausgabe 2/2016

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Abstract

One of the most common examples of audiovisual speech integration is the McGurk effect. As an example, an auditory syllable /ba/ recorded over incongruent lip movements that produce “ga” typically causes listeners to hear “da”. This report hypothesizes reasons why certain clinical and listeners who are hard of hearing might be more susceptible to visual influence. Conversely, we also examine why other listeners appear less susceptible to the McGurk effect (i.e., they report hearing just the auditory stimulus without being influenced by the visual). Such explanations are accompanied by a mechanistic explanation of integration phenomena including visual inhibition of auditory information, or slower rate of accumulation of inputs. First, simulations of a linear dynamic parallel interactive model were instantiated using inhibition and facilitation to examine potential mechanisms underlying integration. In a second set of simulations, we systematically manipulated the inhibition parameter values to model data obtained from listeners with autism spectrum disorder. In summary, we argue that cross-modal inhibition parameter values explain individual variability in McGurk perceptibility. Nonetheless, different mechanisms should continue to be explored in an effort to better understand current data patterns in the audiovisual integration literature.

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Metadaten
Titel
Parallel linear dynamic models can mimic the McGurk effect in clinical populations
verfasst von
Nicholas Altieri
Cheng-Ta Yang
Publikationsdatum
01.10.2016
Verlag
Springer US
Erschienen in
Journal of Computational Neuroscience / Ausgabe 2/2016
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI
https://doi.org/10.1007/s10827-016-0610-z

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