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Causal Inference in Multisensory Perception

Körding, Konrad and Beierholm, Ulrik and Ma, Wei Ji and Quartz, Steven and Tenenbaum, Joshua B. and Shams, Ladan (2007) Causal Inference in Multisensory Perception. PLOS ONE, 2 (9). Art. No. e943. ISSN 1932-6203. PMCID PMC1978520. https://resolver.caltech.edu/CaltechAUTHORS:KORplosone07

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1371/journal.pone.0000943DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1978520/PubMed CentralArticle
Additional Information:© 2007 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. Received: June 15, 2007; Accepted: September 3, 2007; Published: September 26, 2007. We would like to thank David Knill and Philip Sabes for inspiring discussions and Jose L. Pena for help with the setup of the experiment in figure 2. K.P.K., U.B., and W.J.M. contributed equally to this paper. [1] After completion of this work, we became aware of a paper by Yoshiyuki Sato, Taro Toyoizumi and Kazuyuki Aihara, who independently developed a similar model (Neural Computation, in press). KPK was supported by a DFG Heisenberg Stipend. UB and SQ were supported by the David and Lucille Packard Foundation as well as by the Moore Foundation. JBT was supported by the P. E. Newton Career Development Chair. LS was supported by UCLA Academic senate and Career development grants. Author Contributions: 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. Competing interests: The authors have declared that no competing interests exist. Supporting Information: Text S1. Supporting Information for “Causal inference in multisensory perception” (0.11 MB DOC) Figure S1. The interaction priors when fit to our dataset are shown for the causal inference model, the Roach et al. [1] and the Bresciani et al. priors[3]. (1.15 MB EPS) Figure S2. The average auditory bias, i.e. the relative influence of the visual position on the perceived auditory position, is shown as a function of the absolute spatial disparity (solid line, as in Fig. 2 main text) along with the model predictions (dashed lines). Red: causal inference model. Green: behavior derived from using the Roach et al prior. Purple: behaviour derived from using the Bresciani et al prior. (0.94 MB EPS)
Issue or Number:9
PubMed Central ID:PMC1978520
Record Number:CaltechAUTHORS:KORplosone07
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:KORplosone07
Official Citation:Körding KP, Beierholm U, Ma WJ, Quartz S, Tenenbaum JB, et al. (2007) Causal Inference in Multisensory Perception. PLoS ONE 2(9): e943. doi:10.1371/journal.pone.0000943
Usage Policy:© 2007 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.
ID Code:9673
Collection:CaltechAUTHORS
Deposited By: Archive Administrator
Deposited On:27 Feb 2008
Last Modified:12 Feb 2020 21:54

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