Literature DB >> 33606815

Bayesian binding and fusion models explain illusion and enhancement effects in audiovisual speech perception.

Alma Lindborg1,2, Tobias S Andersen2.   

Abstract

Speech is perceived with both the ears and the eyes. Adding congruent visual speech improves the perception of a faint auditory speech stimulus, whereas adding incongruent visual speech can alter the perception of the utterance. The latter phenomenon is the case of the McGurk illusion, where an auditory stimulus such as e.g. "ba" dubbed onto a visual stimulus such as "ga" produces the illusion of hearing "da". Bayesian models of multisensory perception suggest that both the enhancement and the illusion case can be described as a two-step process of binding (informed by prior knowledge) and fusion (informed by the information reliability of each sensory cue). However, there is to date no study which has accounted for how they each contribute to audiovisual speech perception. In this study, we expose subjects to both congruent and incongruent audiovisual speech, manipulating the binding and the fusion stages simultaneously. This is done by varying both temporal offset (binding) and auditory and visual signal-to-noise ratio (fusion). We fit two Bayesian models to the behavioural data and show that they can both account for the enhancement effect in congruent audiovisual speech, as well as the McGurk illusion. This modelling approach allows us to disentangle the effects of binding and fusion on behavioural responses. Moreover, we find that these models have greater predictive power than a forced fusion model. This study provides a systematic and quantitative approach to measuring audiovisual integration in the perception of the McGurk illusion as well as congruent audiovisual speech, which we hope will inform future work on audiovisual speech perception.

Entities:  

Year:  2021        PMID: 33606815      PMCID: PMC7895372          DOI: 10.1371/journal.pone.0246986

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  39 in total

1.  Visual speech speeds up the neural processing of auditory speech.

Authors:  Virginie van Wassenhove; Ken W Grant; David Poeppel
Journal:  Proc Natl Acad Sci U S A       Date:  2005-01-12       Impact factor: 11.205

2.  Learning to integrate arbitrary signals from vision and touch.

Authors:  Marc O Ernst
Journal:  J Vis       Date:  2007-06-25       Impact factor: 2.240

3.  A reanalysis of McGurk data suggests that audiovisual fusion in speech perception is subject-dependent.

Authors:  Jean-Luc Schwartz
Journal:  J Acoust Soc Am       Date:  2010-03       Impact factor: 1.840

4.  Dual neural routing of visual facilitation in speech processing.

Authors:  Luc H Arnal; Benjamin Morillon; Christian A Kell; Anne-Lise Giraud
Journal:  J Neurosci       Date:  2009-10-28       Impact factor: 6.167

5.  Neurophysiology underlying influence of stimulus reliability on audiovisual integration.

Authors:  Hannah Shatzer; Stanley Shen; Jess R Kerlin; Mark A Pitt; Antoine J Shahin
Journal:  Eur J Neurosci       Date:  2018-02-09       Impact factor: 3.386

6.  The visual filter mediating letter identification.

Authors:  J A Solomon; D G Pelli
Journal:  Nature       Date:  1994-06-02       Impact factor: 49.962

7.  Causal inference in multisensory perception.

Authors:  Konrad P Körding; Ulrik Beierholm; Wei Ji Ma; Steven Quartz; Joshua B Tenenbaum; Ladan Shams
Journal:  PLoS One       Date:  2007-09-26       Impact factor: 3.240

8.  A possible neurophysiological correlate of audiovisual binding and unbinding in speech perception.

Authors:  Attigodu C Ganesh; Frédéric Berthommier; Coriandre Vilain; Marc Sato; Jean-Luc Schwartz
Journal:  Front Psychol       Date:  2014-11-26

9.  A Causal Inference Model Explains Perception of the McGurk Effect and Other Incongruent Audiovisual Speech.

Authors:  John F Magnotti; Michael S Beauchamp
Journal:  PLoS Comput Biol       Date:  2017-02-16       Impact factor: 4.475

10.  To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.

Authors:  Máté Aller; Uta Noppeney
Journal:  PLoS Biol       Date:  2019-04-02       Impact factor: 8.029

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