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Cross-Modal Multivariate Pattern Analysis

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A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect.

Simon Forest^1,2, Jean-Charles Quinton³, Mathieu Lefort⁴

¹Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, UMR 5224, F-38000 Grenoble, France.

Neural Computation

|July 7, 2022

Summary

This study introduces a novel dynamic neural field model for multimodal sensory integration, simulating trial-by-trial localization decisions. The model offers insights into perceptual variations and future applications in behavioral dynamics.

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Area of Science:

Computational neuroscience
Sensory integration
Psychophysics

Background:

Multimodal merging integrates imprecise sensory information (e.g., sight, hearing) for localization.
Bayesian models traditionally describe merging decisions based on aggregated behavioral data.
Existing models lack trial-by-trial dynamic simulation capabilities.

Purpose of the Study:

Introduce a novel computational model using dynamic neural fields for multimodal merging.
Simulate trial-by-trial localization decisions adapting to audio-visual discrepancies.
Compare model performance and dynamics against established Bayesian models.

Main Methods:

Developed a dynamic neural field model simulating mesoscopic neural processes.
Fit the model to human psychophysical data, specifically the ventriloquist effect.
Investigated the influence of retinotopic projection on the superior colliculus.

Main Results:

The dynamic neural field model simulates localization decisions trial-by-trial.
Model performance is comparable to Bayesian models on average.
Qualitative analysis revealed insights into decision dynamics and noise-induced perceptual variations.

Conclusions:

Dynamic neural fields offer a viable alternative to Bayesian models for simulating multimodal merging dynamics.
The model's parameter space allows flexibility for fitting specific criteria or enhancing performance.
This approach has potential applications in simulating behavioral dynamics and visually guided saccades.