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

Overview of attention for article published in Journal of Computational Neuroscience, June 2016
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Title
Parallel linear dynamic models can mimic the McGurk effect in clinical populations
Published in
Journal of Computational Neuroscience, June 2016
DOI 10.1007/s10827-016-0610-z
Pubmed ID
Authors

Nicholas Altieri, Cheng-Ta Yang

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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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 6%
Unknown 17 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 22%
Student > Master 4 22%
Researcher 3 17%
Student > Bachelor 2 11%
Student > Doctoral Student 1 6%
Other 4 22%
Readers by discipline Count As %
Psychology 7 39%
Computer Science 3 17%
Nursing and Health Professions 2 11%
Neuroscience 2 11%
Arts and Humanities 1 6%
Other 3 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 June 2016.
All research outputs
#15,377,214
of 22,876,619 outputs
Outputs from Journal of Computational Neuroscience
#169
of 307 outputs
Outputs of similar age
#213,143
of 341,017 outputs
Outputs of similar age from Journal of Computational Neuroscience
#2
of 5 outputs
Altmetric has tracked 22,876,619 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 307 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.