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Analysis of the Ventriloquism Aftereffect Using Network Theory Techniques

Sayan Saha

TL;DR

This work investigates the neural basis of the ventriloquism after-effect by applying network-theory analyses to EEG during auditory localization tasks, revealing that recalibration occurs early in the auditory processing pathway and decays with time post-exposure. The authors combine time-resolved functional connectivity (using Phase Locking Value and Imaginary Coherence), modularity analysis via Louvain detection, and nonstationary decomposition (Empirical Mode Decomposition) with pattern classification to characterize the recalibration timeline. Key findings include early differentiation between veridical and ventriloquized sounds within approximately the first 100–150 ms and evidence that the after-effect weakens across subsequent trials, supporting a rapid, transient recalibration mechanism. The study advances multisensory integration research by linking network dynamics to perceptual recalibration and offers avenues for predictive-coding interpretations and future multimodal imaging approaches.

Abstract

Ventriloquism After-Effect is the phenomenon where sustained exposure to the ventriloquist illusion causes a change in unisensory auditory localization towards the location where the visual stimulus was present. We investigate the recalibration in EEG networks that causes this change and the track the timeline of changes in the auditory processing pathway. Our results obtained using network analysis, non-stationary time series analysis and multivariate pattern classification show that recalibration takes place early in the auditory processing pathway and the after-effect decays with time after exposure to the illusion.

Analysis of the Ventriloquism Aftereffect Using Network Theory Techniques

TL;DR

This work investigates the neural basis of the ventriloquism after-effect by applying network-theory analyses to EEG during auditory localization tasks, revealing that recalibration occurs early in the auditory processing pathway and decays with time post-exposure. The authors combine time-resolved functional connectivity (using Phase Locking Value and Imaginary Coherence), modularity analysis via Louvain detection, and nonstationary decomposition (Empirical Mode Decomposition) with pattern classification to characterize the recalibration timeline. Key findings include early differentiation between veridical and ventriloquized sounds within approximately the first 100–150 ms and evidence that the after-effect weakens across subsequent trials, supporting a rapid, transient recalibration mechanism. The study advances multisensory integration research by linking network dynamics to perceptual recalibration and offers avenues for predictive-coding interpretations and future multimodal imaging approaches.

Abstract

Ventriloquism After-Effect is the phenomenon where sustained exposure to the ventriloquist illusion causes a change in unisensory auditory localization towards the location where the visual stimulus was present. We investigate the recalibration in EEG networks that causes this change and the track the timeline of changes in the auditory processing pathway. Our results obtained using network analysis, non-stationary time series analysis and multivariate pattern classification show that recalibration takes place early in the auditory processing pathway and the after-effect decays with time after exposure to the illusion.
Paper Structure (17 sections, 25 equations, 7 figures)

This paper contains 17 sections, 25 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Methods
  3. Network Analysis
  4. Key Network Measures
  5. Louvain Community Detection
  6. Metrics
  7. Phase Locking Value
  8. Imaginary Part of Coherence
  9. Empirical Mode Decomposition
  10. Experiment and Results
  11. Experimental Paradigm
  12. EEG Modalilties
  13. Spectral Analysis
  14. Functional Connectivity Estimation
  15. Network Analysis
  16. ...and 2 more sections

Figures (7)

  • Figure 1: Electrodes where nodal clustering coefficient is significanty greater ($p<0.05)$ in right adpated audio left conditions in the beta band using Imcoh
  • Figure 2: No. of modules vs time graph averaged across left-adpated subjects in the audio right condition in alpha band networks using Imcoh
  • Figure 3: Modularity vs time graph averaged across left-adpated subjects in the audio right condition in alpha band networks using Imcoh
  • Figure 4: Classification Performance in Right Adapted Subjects
  • Figure 5: Classification Performance in Left Adapted Subjects
  • ...and 2 more figures