Causal Inference in Multisensory Perception

Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study caus...

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Published in	PloS one Vol. 2; no. 9; p. e943
Main Authors	Körding, Konrad P., Beierholm, Ulrik, Ma, Wei Ji, Quartz, Steven, Tenenbaum, Joshua B., Shams, Ladan
Format	Journal Article
Language	English
Published	United States Public Library of Science 26.09.2007 Public Library of Science (PLoS)
Subjects	Analysis Auditory Perception - physiology Auditory tasks Bayes Theorem Brain Brain - physiology Cognition Cognition & reasoning Computational Biology/Computational Neuroscience Cues Humans Inference Localization Nervous system Neurons, Afferent - physiology Neurophysiology Neuroscience Neuroscience/Behavioral Neuroscience Neuroscience/Cognitive Neuroscience Neuroscience/Sensory Systems Neuroscience/Theoretical Neuroscience Perception Population Senescence Sensory integration Space Perception - physiology Studies Task Performance and Analysis Trends Variables Visual perception Visual Perception - physiology Visual tasks Los Angeles California United States > US California
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Abstract	Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.
AbstractList	Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception. Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.
Audience	Academic
Author	Beierholm, Ulrik Ma, Wei Ji Quartz, Steven Körding, Konrad P. Tenenbaum, Joshua B. Shams, Ladan
AuthorAffiliation	5 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America 4 Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America Indiana University, United States of America 1 Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America 3 Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America 6 Department of Psychology, University of California at Los Angeles, Los Angeles, California, United States of America 2 Computation and Neural Systems, California Institute of Technology, Pasadena, California, United States of America
AuthorAffiliation_xml	– name: 2 Computation and Neural Systems, California Institute of Technology, Pasadena, California, United States of America – name: 5 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America – name: 4 Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America – name: 3 Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America – name: Indiana University, United States of America – name: 6 Department of Psychology, University of California at Los Angeles, Los Angeles, California, United States of America – name: 1 Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America
Author_xml	– sequence: 1 givenname: Konrad P. surname: Körding fullname: Körding, Konrad P. – sequence: 2 givenname: Ulrik surname: Beierholm fullname: Beierholm, Ulrik – sequence: 3 givenname: Wei Ji surname: Ma fullname: Ma, Wei Ji – sequence: 4 givenname: Steven surname: Quartz fullname: Quartz, Steven – sequence: 5 givenname: Joshua B. surname: Tenenbaum fullname: Tenenbaum, Joshua B. – sequence: 6 givenname: Ladan surname: Shams fullname: Shams, Ladan
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/17895984$$D View this record in MEDLINE/PubMed
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Copyright	COPYRIGHT 2007 Public Library of Science 2007 Körding et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Körding et al. 2007
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 Conceived and designed the experiments: LS SQ JT. Performed the experiments: UB. Analyzed the data: UB KK WM. Wrote the paper: UB KK LS WM.
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Snippet	Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The...
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SubjectTerms	Analysis Auditory Perception - physiology Auditory tasks Bayes Theorem Brain Brain - physiology Cognition Cognition & reasoning Computational Biology/Computational Neuroscience Cues Humans Inference Localization Nervous system Neurons, Afferent - physiology Neurophysiology Neuroscience Neuroscience/Behavioral Neuroscience Neuroscience/Cognitive Neuroscience Neuroscience/Sensory Systems Neuroscience/Theoretical Neuroscience Perception Population Senescence Sensory integration Space Perception - physiology Studies Task Performance and Analysis Trends Variables Visual perception Visual Perception - physiology Visual tasks
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Title	Causal Inference in Multisensory Perception
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