Towards a neural implementation of causal inference in cue combination

Wei Ji Ma, Masih Rahmati

Research output: Contribution to journalArticlepeer-review


Causal inference in sensory cue combination is the process of determining whether multiple sensory cues have the same cause or different causes. Psychophysical evidence indicates that humans closely follow the predictions of a Bayesian causal inference model. Here, we explore how Bayesian causal inference could be implemented using probabilistic population coding and plausible neural operations, but conclude that the resulting architecture is unrealistic.

Original languageEnglish (US)
Pages (from-to)159-176
Number of pages18
JournalMultisensory Research
Issue number1-2
StatePublished - 2013


  • Bayesian inference
  • Multisensory perception
  • causal inference
  • cue combination
  • modeling
  • neural networks
  • population coding

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Ophthalmology
  • Sensory Systems
  • Computer Vision and Pattern Recognition
  • Cognitive Neuroscience


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