August 2012
Volume 12, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2012
When correlation implies causation in multisensory integration
Author Affiliations
  • Cesare Parise
    Max Planck Institute for Biological Cybernetics & Bernstein Center for Computational Neuroscience, Tuebingen\nDepartment of Experimental Psychology, University of Oxford
  • Charles Spence
    Department of Experimental Psychology, University of Oxford
  • Marc Ernst
    Max Planck Institute for Biological Cybernetics & Bernstein Center for Computational Neuroscience, Tuebingen\nDepartment of Cognitive Neuroscience & Cognitive Interaction Technology-Center of Excellence, University of Bielefeld
Journal of Vision August 2012, Vol.12, 611. doi:10.1167/12.9.611
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      Cesare Parise, Charles Spence, Marc Ernst; When correlation implies causation in multisensory integration. Journal of Vision 2012;12(9):611. doi: 10.1167/12.9.611.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Inferring which signals have a common underlying cause, and hence should be integrated, represents a primary challenge for a perceptual system dealing with multiple sensory inputs. This challenge is often referred to as the correspondence problem or causal inference. Previous research has demonstrated that spatiotemporal cues, along with prior knowledge, are exploited by the human brain to solve this problem. Here we explore the role of correlation between the fine temporal structure of auditory and visual signals in causal inference. Specifically, we investigated whether correlated signals are inferred to originate from the same distal event and hence are integrated optimally. In a localization task with visual, auditory, and combined audiovisual targets, the improvement in precision for combined relative to unimodal targets was statistically optimal only when audiovisual signals were correlated. This result demonstrates that humans use the similarity in the temporal structure of multisensory signals to solve the correspondence problem, hence inferring causation from correlation.

Meeting abstract presented at VSS 2012

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