Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

Summary

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.

Key Points

• 1The study identifies brain regions responsible for different stages of Bayesian Causal Inference in perception
• 2Signals from independent sources are processed separately in primary sensory areas
• 3The brain combines information optimally about the causal structure of the environment
• 4Hierarchical processing influences how we perceive spatial locations from audiovisual signals
• 5Bayesian models explain how observers integrate sensory inputs based on reliability and spatial disparities

Related articles

• Adolescent Development: Phases, Domains, and Challenges

  This document outlines the stages of adolescent development, focusing on physical, cognitive, socio-emotional, and spiritual changes across different age groups. It also discusses the developmental tasks and challenges adolescents face, along with coping strategies for managing stress and promoting mental health.

• The Lacanian Concept of Paranoia: An Historical Perspective

  This article examines the Lacanian perspective on paranoia within the context of modern psychiatric classification systems, arguing for the importance of Lacan's formulations in understanding psychosis.

• Annual Review of Neuroscience Planning in the Brain: It's Not What You Think It Is

  This document reviews the neuroscience of planning, arguing that it encompasses a broader range of computations than the traditional analogy to search algorithms in artificial intelligence. It explores how mental simulations can facilitate learning and decision making well before a choice is needed.

• Difference Between Psychological Test and Psychological Assessment

  A presentation exploring the distinctions between psychological tests and psychological assessments, focusing on their purposes, processes, and the roles of evaluators.

• Research Methods in Psychology

  This textbook provides a comprehensive overview of research methods used in psychology, covering topics such as experimental design, research ethics, and analytical techniques. It serves as a resource for students and educators in the field of psychology.