Towards a neural implementation of causal inference in cue combination

Wei Ji Ma, Masih Rahmati

Research output: Contribution to journalArticle

Abstract

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
Volume26
Issue number1-2
DOIs
StatePublished - 2013

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Cues
Bayes Theorem
Population

Keywords

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

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Ophthalmology
  • Cognitive Neuroscience
  • Sensory Systems
  • Experimental and Cognitive Psychology
  • Medicine(all)

Cite this

Towards a neural implementation of causal inference in cue combination. / Ma, Wei Ji; Rahmati, Masih.

In: Multisensory research, Vol. 26, No. 1-2, 2013, p. 159-176.

Research output: Contribution to journalArticle

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