Modeling the Perception of Audiovisual Distance: Bayesian Causal Inference and Other Models.

Area of Science:

• Psychology
• Neuroscience
• Sensory Perception

Background:

• Audiovisual perception of distance is under-researched.
• Existing models like sensory dominance and mandatory integration have limitations.

Purpose of the Study:

• To test various multisensory models for audiovisual distance perception.
• To evaluate a modified Bayesian causal inference model incorporating estimate distributions.

Main Methods:

• Comparison of audiovisual distance perception against traditional models and a modified causal inference model.
• Utilizing probability matching within the causal inference framework.
• Analysis of estimated sensory weights to determine interaction windows.

Main Results:

• Bayesian causal inference provided a superior explanation for audiovisual distance perception compared to other models.
• Causal inference resolved with probability matching demonstrated the best data fit.
• Visual stimulus consistently dominated visual distance perception (>80%).
• Visual stimulus significantly influenced auditory distance perception (>50%) within a 1-4m interaction window.

Conclusions:

• Bayesian causal inference is a robust framework for understanding audiovisual distance perception.
• Sensory weights derived from causal inference can define interaction dynamics between visual and auditory stimuli.
• The visual system plays a critical role in both visual and auditory distance perception, with context-dependent interaction windows.