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Updated: Feb 5, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
Published on: May 10, 2019
Published estimates of group differences in multisensory integration are inflated
John F Magnotti1, Michael S Beauchamp1
1Department of Neurosurgery and Core for Advanced MRI, Baylor College of Medicine, Houston, Texas, United States of America.
Published studies often overestimate group differences in the McGurk effect due to selective reporting. This leads to failed replications and inaccurate findings in sensory processing research.
Area of Science:
- Cognitive psychology
- Neuroscience
- Psycholinguistics
Background:
- The McGurk effect, a multisensory integration illusion, is frequently used to study differences in sensory processing across various populations.
- Published research indicates variations in McGurk effect susceptibility based on age, gender, culture, language, and neurological or psychiatric conditions.
Purpose of the Study:
- To investigate the impact of publication bias on reported group differences in McGurk effect susceptibility.
- To demonstrate how selective reporting inflates effect size estimates and hinders replication.
Main Methods:
- Analysis of empirical data on the McGurk effect.
- Statistical simulations under diverse conditions to model the effects of publication bias.
- Comparison of reported effect sizes with simulated, unbiased estimates.
Main Results:
- Published estimates of group differences in the McGurk effect are significantly inflated due to the common practice of only publishing statistically significant results (p < 0.05).
- A 10% group difference, typical in many studies, can be reported as 31% when using standard sample sizes and selective reporting.
- This inflation contributes to frequent failures in replicating findings of large between-group differences.
Conclusions:
- Publication bias inflates effect sizes for the McGurk effect, leading to unreliable conclusions about sensory processing differences.
- Replication failures in studies, particularly those involving clinical populations and interventions, are exacerbated by these inflated estimates.
- To improve accuracy and replicability, research on the McGurk effect necessitates a tenfold increase in sample size compared to current practices.
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