title: Towards a neural implementation of causal inference in cue combination.
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.
authors.affiliation: Baylor College of Medicine, 1 Baylor Plaza, Houston TX 77030, Texas, USA. wjma@bcm.edu
authors.forename: Wei Ji
authors.initials: WJ
authors.lastname: Ma
authors.forename: Masih
authors.initials: M
authors.lastname: Rahmati
mesh_terms: D001307:Auditory Perception / Q000502:physiology*; D001499:Bayes Theorem; D001519:Behavior / Q000502:physiology; D003463:Cues; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D011601:Psychophysics / Q000379:methods; D014796:Visual Perception / Q000502:physiology*