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      Causal Inference in Multisensory Perception

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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.

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          Most cited references41

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          The ventriloquist effect results from near-optimal bimodal integration.

          Ventriloquism is the ancient art of making one's voice appear to come from elsewhere, an art exploited by the Greek and Roman oracles, and possibly earlier. We regularly experience the effect when watching television and movies, where the voices seem to emanate from the actors' lips rather than from the actual sound source. Originally, ventriloquism was explained by performers projecting sound to their puppets by special techniques, but more recently it is assumed that ventriloquism results from vision "capturing" sound. In this study we investigate spatial localization of audio-visual stimuli. When visual localization is good, vision does indeed dominate and capture sound. However, for severely blurred visual stimuli (that are poorly localized), the reverse holds: sound captures vision. For less blurred stimuli, neither sense dominates and perception follows the mean position. Precision of bimodal localization is usually better than either the visual or the auditory unimodal presentation. All the results are well explained not by one sense capturing the other, but by a simple model of optimal combination of visual and auditory information.
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            Integration of proprioceptive and visual position-information: An experimentally supported model.

            To localize one's hand, i.e., to find out its position with respect to the body, humans may use proprioceptive information or visual information or both. It is still not known how the CNS combines simultaneous proprioceptive and visual information. In this study, we investigate in what position in a horizontal plane a hand is localized on the basis of simultaneous proprioceptive and visual information and compare this to the positions in which it is localized on the basis of proprioception only and vision only. Seated at a table, subjects matched target positions on the table top with their unseen left hand under the table. The experiment consisted of three series. In each of these series, the target positions were presented in three conditions: by vision only, by proprioception only, or by both vision and proprioception. In one of the three series, the visual information was veridical. In the other two, it was modified by prisms that displaced the visual field to the left and to the right, respectively. The results show that the mean of the positions indicated in the condition with both vision and proprioception generally lies off the straight line through the means of the other two conditions. In most cases the mean lies on the side predicted by a model describing the integration of multisensory information. According to this model, the visual information and the proprioceptive information are weighted with direction-dependent weights, the weights being related to the direction-dependent precision of the information in such a way that the available information is used very efficiently. Because the proposed model also can explain the unexpectedly small sizes of the variable errors in the localization of a seen hand that were reported earlier, there is strong evidence to support this model. The results imply that the CNS has knowledge about the direction-dependent precision of the proprioceptive and visual information.
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              Theory-based Bayesian models of inductive learning and reasoning.

              Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS ONE
                plos
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2007
                26 September 2007
                : 2
                : 9
                : e943
                Affiliations
                [1 ]Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America
                [2 ]Computation and Neural Systems, California Institute of Technology, Pasadena, California, United States of America
                [3 ]Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
                [4 ]Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America
                [5 ]Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
                [6 ]Department of Psychology, University of California at Los Angeles, Los Angeles, California, United States of America
                Indiana University, United States of America
                Author notes
                * To whom correspondence should be addressed. E-mail: beierh@ 123456caltech.edu

                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.

                Article
                07-PONE-RA-01534R1
                10.1371/journal.pone.0000943
                1978520
                17895984
                820c24b4-c1d5-4a91-8d0c-f955963827c6
                Körding et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 15 June 2007
                : 3 September 2007
                Page count
                Pages: 10
                Categories
                Research Article
                Neuroscience
                Computational Biology/Computational Neuroscience
                Neuroscience/Behavioral Neuroscience
                Neuroscience/Cognitive Neuroscience
                Neuroscience/Sensory Systems
                Neuroscience/Theoretical Neuroscience

                Uncategorized
                Uncategorized

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