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Updated: Jul 3, 2026
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Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
Linking neurons to behavior in multisensory perception: a computational review.
1Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA. weijima@gmail.com
Brain Research
|July 8, 2008
Summary
Humans and animals integrate multisensory information using Bayesian strategies. This approach optimizes how signals are combined and when they are attributed to a single source, paving the way for a neural theory of perception.
Area of Science:
- Neuroscience
- Cognitive Science
- Computational Biology
Background:
- Multisensory integration is crucial for perception.
- Key questions involve how information is combined and source attribution.
- Bayesian strategies are increasingly recognized as a framework for understanding these processes.
Purpose of the Study:
- To review psychophysical, physiological, and computational findings on multisensory integration.
- To explore the neural basis of Bayes-optimal multisensory computations.
- To outline a path towards a comprehensive neural theory of multisensory perception.
Main Methods:
- Review of existing psychophysical and physiological studies.
- Analysis of computational models of multisensory integration.
- Examination of the neural underpinnings of Bayesian inference in sensory processing.
Main Results:
- Evidence supports the use of Bayesian strategies for both information combination and source attribution.
- Computational studies suggest that linear combinations of neural activity can achieve Bayes-optimality under specific conditions (Poisson-like variability).
Conclusions:
- Multisensory perception can be largely explained by Bayesian principles.
- Understanding the neural implementation of these principles is key to a complete theory.
- Future research should bridge computational and neural findings to advance multisensory neuroscience.
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