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

Perception in natural environments relies on the integration of information from multiple sensory modalities. Although combining sensory cues can improve perceptual accuracy, sensory signals are inherently noisy and do not always arise from a common source. As a result, the brain must continuously determine whether multisensory signals should be integrated or segregated, while also adapting sensory representations when discrepancies persist over time. Multisensory causal inference provides a principled framework of how the brain infers the causal structure underlying sensory inputs and flexibly arbitrates between integration and segregation. In parallel, extensive behavioral work has shown that prolonged exposure to cross-modal conflicts induces multisensory recalibration, leading to persistent changes in unisensory and multisensory perception. Despite progress in both areas, the relationship between causal inference and recalibration has remained unclear. In this review, we synthesize behavioral, computational, and neurophysiological evidence to argue that causal inference and recalibration form a coupled adaptive system operating across distinct time scales. We propose that causal inference constrains whether sensory discrepancies drive recalibration, whereas recalibration reshapes sensory representations and expectations, thereby influencing future causal judgments.