ArticlePLoS biology2015
Cortical hierarchies perform Bayesian causal inference in multisensory perception.
Article in PLoS biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers, 1 of them a synthesis that pooled it.
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Who cites it
161 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis.Human brain mapping · 2018Pooled it
- Trial
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- Did you see the sound? A Bayesian assessment on cross-modal perception in low vision.Journal of vision · 2026Article
- Perceptual decision-making in the sound-induced flash illusion: a trial-level hierarchical Bayesian integration of EEG and behavior.Behavioral and brain functions : BBF · 2026Article
- Frontal cortex organization supporting audiovisual processing during naturalistic viewing.Nature communications · 2026Article
- Learning new perceptual skills: Individual differences in the computations that integrate novel sensory cues into depth perception.iScience · 2026Article
- Observed hand-movement reversals postdictively bias ambiguous motion judgements.Scientific reports · 2026Article
- Distilling noise characteristics and prior expectations in multisensory causal inference.PLoS computational biology · 2026Article
- Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal regions.Nature communications · 2026Article
- Coupling Causal Inference and Cross-Modal Recalibration: A Unified Framework for Adaptive Multisensory Perception.Biology · 2026Review
- Causal inference shapes crossmodal postdiction in multisensory integration.Scientific reports · 2026Article
- Crossmodal interaction of flashes and beeps across time and number follows Bayesian causal inference.Psychonomic bulletin & review · 2026Article
- Space Impacts Temporal Processing via a Visual-Dependent Spatially Organized Neural Architecture.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Cortical vestibular-visual interactions and cross-modal plasticity-adaptive neural processes for stable perception.Frontiers in neural circuits · 2026Review
- Decoding self-motion: insights into visual-vestibular causal inference.Frontiers in human neuroscience · 2026Review
- Sensory reliability takes priority over the central tendency effect in temporal and spatial estimation.Scientific reports · 2025Article
- Article
- Environmental motion presented ahead of self-motion modulates heading direction estimation.PLoS computational biology · 2025Article
- Variations in unisensory speech perception explain interindividual differences in McGurk illusion susceptibility.Psychonomic bulletin & review · 2025Article
101 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.