Coupling Causal Inference and Cross-Modal Recalibration: A Unified Framework for Adaptive Multisensory Perception

Area of Science:

• Neuroscience
• Cognitive Science
• Sensory Processing

Background:

• Natural perception integrates multisensory information for accuracy.
• Sensory signals can be noisy and lack common sources, requiring the brain to dynamically integrate or segregate inputs.
• Multisensory causal inference explains how the brain infers causal structures and arbitrates between integration and segregation.

Purpose of the Study:

• To clarify the relationship between multisensory causal inference and multisensory recalibration.
• To propose a unified framework for understanding how the brain adapts to multisensory information across different timescales.

Main Methods:

• Synthesis of behavioral, computational, and neurophysiological evidence.
• Review of existing literature on multisensory integration, segregation, causal inference, and recalibration.

Main Results:

• Causal inference and recalibration operate as a coupled adaptive system across distinct timescales.
• Causal inference determines if sensory discrepancies trigger recalibration.
• Recalibration modifies sensory representations and expectations, influencing future causal judgments.

Conclusions:

• Multisensory causal inference and recalibration are interconnected mechanisms for adaptive perception.
• This coupled system allows the brain to flexibly adjust to noisy and conflicting sensory inputs.
• Understanding this relationship offers insights into neural plasticity and perceptual adaptation.