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Predicting vocal emotion expressions from the human brain

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Kotz,  Sonja A.
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Kalberlah,  Christian
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Bernstein Center for Computational Neuroscience, Berlin, Germany;

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Bahlmann,  Jörg
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA;

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Friederici,  Angela D.
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;

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Haynes,  John-Dylan
Max Planck Fellow Research Group Attention and Awareness, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Bernstein Center for Computational Neuroscience, Berlin, Germany;
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;

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Citation

Kotz, S. A., Kalberlah, C., Bahlmann, J., Friederici, A. D., & Haynes, J.-D. (2013). Predicting vocal emotion expressions from the human brain. Human Brain Mapping, 34(8), 1971-1981. doi:10.1002/hbm.22041.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-10E2-2
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.