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The noisy encoding of disparity model of the McGurk effect

Overview of attention for article published in Psychonomic Bulletin & Review, September 2014
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Title
The noisy encoding of disparity model of the McGurk effect
Published in
Psychonomic Bulletin & Review, September 2014
DOI 10.3758/s13423-014-0722-2
Pubmed ID
Authors

John F. Magnotti, Michael S. Beauchamp

Abstract

In the McGurk effect, incongruent auditory and visual syllables are perceived as a third, completely different syllable. This striking illusion has become a popular assay of multisensory integration for individuals and clinical populations. However, there is enormous variability in how often the illusion is evoked by different stimuli and how often the illusion is perceived by different individuals. Most studies of the McGurk effect have used only one stimulus, making it impossible to separate stimulus and individual differences. We created a probabilistic model to separately estimate stimulus and individual differences in behavioral data from 165 individuals viewing up to 14 different McGurk stimuli. The noisy encoding of disparity (NED) model characterizes stimuli by their audiovisual disparity and characterizes individuals by how noisily they encode the stimulus disparity and by their disparity threshold for perceiving the illusion. The model accurately described perception of the McGurk effect in our sample, suggesting that differences between individuals are stable across stimulus differences. The most important benefit of the NED model is that it provides a method to compare multisensory integration across individuals and groups without the confound of stimulus differences. An added benefit is the ability to predict frequency of the McGurk effect for stimuli never before seen by an individual.

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The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Spain 2 4%
United States 2 4%
Japan 1 2%
United Kingdom 1 2%
Unknown 49 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 32%
Researcher 12 21%
Student > Master 9 16%
Student > Bachelor 3 5%
Student > Doctoral Student 2 4%
Other 6 11%
Unknown 7 12%
Readers by discipline Count As %
Psychology 22 39%
Neuroscience 6 11%
Agricultural and Biological Sciences 5 9%
Computer Science 3 5%
Linguistics 2 4%
Other 8 14%
Unknown 11 19%