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Predicting vocal emotion expressions from the human brain

Authors

  • Sonja A. Kotz,

    Corresponding author
    1. Minerva Research Group “Neurocognition of Rhythm in Communication”, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    • Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraβe 1A, 04103 Leipzig, Germany
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    • Sonja A. Kotz, Christian Kalberlah, and Jörg Bahlmann contributed equally to this work.

  • Christian Kalberlah,

    1. Max Planck Fellow Group “Attention and Awareness”, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    2. Bernstein Center for Computational Neuroscience, Charité – Universitätsmedizin Berlin, Berlin, Germany
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    • Sonja A. Kotz, Christian Kalberlah, and Jörg Bahlmann contributed equally to this work.

  • Jörg Bahlmann,

    1. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    2. Helen Wills Neuroscience Institute, University of California, Berkeley, California
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    • Sonja A. Kotz, Christian Kalberlah, and Jörg Bahlmann contributed equally to this work.

  • Angela D. Friederici,

    1. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    2. Graduate School of Mind and Brain, Humboldt-University, Berlin, Germany
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  • John-D. Haynes

    1. Max Planck Fellow Group “Attention and Awareness”, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    2. Bernstein Center for Computational Neuroscience, Charité – Universitätsmedizin Berlin, Berlin, Germany
    3. Graduate School of Mind and Brain, Humboldt-University, Berlin, Germany
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Abstract

Speech is an important carrier of emotional information. However, little is known about how different vocal emotion expressions are recognized in a receiver's brain. We used multivariate pattern analysis of functional magnetic resonance imaging data to investigate to which degree distinct vocal emotion expressions are represented in the receiver's local brain activity patterns. Specific vocal emotion expressions are encoded in a right fronto-operculo-temporal network involving temporal regions known to subserve suprasegmental acoustic processes and a fronto-opercular region known to support emotional evaluation, and, moreover, in left temporo-cerebellar regions covering sequential processes. The right inferior frontal region, in particular, was found to differentiate distinct emotional expressions. The present analysis reveals vocal emotion to be encoded in a shared cortical network reflected by distinct brain activity patterns. These results shed new light on theoretical and empirical controversies about the perception of distinct vocal emotion expressions at the level of large-scale human brain signals. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.

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