ArticleProceedings of the National Academy of Sciences of the United States of America2021
How multisensory neurons solve causal inference.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 71 citations in OpenAlex.
- Late Cross-Modal Biases in Neural Spatial Representations Revealed by EEG Decoding.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- From Multisensory Integration to Decision Execution: Computational Roles of the Posterior Parietal Cortex in Mice.Neuroscience bulletin · 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
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- A deep neural network model of audiovisual speech recognition reports the McGurk effect.Psychonomic bulletin & review · 2026Article
- Decoding self-motion: insights into visual-vestibular causal inference.Frontiers in human neuroscience · 2026Review
- A Deep Neural Network Model of Audiovisual Speech Recognition Reports the McGurk Effect.bioRxiv : the preprint server for biology · 2025Article
- Environmental motion presented ahead of self-motion modulates heading direction estimation.PLoS computational biology · 2025Article
- Scikit-NeuroMSI: A Generalized Framework for Modeling Multisensory Integration.Neuroinformatics · 2025Article
- Humans use underestimates of auditory spatial and temporal uncertainty for perceptual inference.Proceedings. Biological sciences · 2025Article
- Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli.Journal of the Royal Society, Interface · 2025Article
- Multimodal evidence challenges the effectiveness of probabilistic cueing for establishing sensory expectations.Imaging neuroscience (Cambridge, Mass.) · 2025Article
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- Bio-Plausible Multimodal Learning with Emerging Neuromorphic Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- The effect of posture on virtual walking experience using foot vibrations.Scientific reports · 2024Article
- Nonlinear fusion is optimal for a wide class of multisensory tasks.PLoS computational biology · 2024Article
- Innovative approaches for managing patients with chronic vestibular disorders: follow-up indicators and predictive markers for studying the vestibular error signal.Frontiers in rehabilitation sciences · 2024Article
- Multisensory Conflict Impairs Cortico-Muscular Network Connectivity and Postural Stability: Insights from Partial Directed Coherence Analysis.Neuroscience bulletin · 2024Article
- Causal Inference Meets Deep Learning: A Comprehensive Survey.Research (Washington, D.C.) · 2024Review
- Vahagn: VisuAl Haptic Attention Gate Net for slip detection.Frontiers in neurorobotics · 2024Article
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction ("congruent" neurons), while others prefer opposing directions ("opposite" neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference.
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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.