A Neural Implementation of Optimal Cue Integration

Wei Ji Ma, Jeff Beck, Alexandre Pouget

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter lays out a theoretical framework for how optimal cue integration can be implemented by neural populations. The main significance of this framework does not merely lie in understanding multisensory perception in a principled manner, but in the fact that it provides a blueprint for finding neural implementations of other forms of Bayes-optimal computation. Evidence for Bayesian optimality of human behavior has been found in many perceptual tasks, including decision making, visual search, oddity detection, and multiple-trajectory tracking. Probabilistic population coding provides a roadmap for identifying a neural implementation of each of these computations: First the Bayesian model at the behavioral level needs to be worked out, then it needs to be assumed that probability distributions in this model are encoded in neural populations with Poisson-like variability, and finally the neural operations that map onto the desired operations on probability distributions should be identified.

Original languageEnglish (US)
Title of host publicationSensory Cue Integration
PublisherOxford University Press
ISBN (Electronic)9780199918379
ISBN (Print)9780195387247
StatePublished - Sep 20 2012


  • Bayesian cue combination
  • Cue integration
  • Multisensory perception
  • Neural populations
  • Probabilistic population coding

ASJC Scopus subject areas

  • General Psychology


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