ABSTRACT Long-standing affective science theories conceive the perception of emotional stimuli either as discrete categories (for example, an angry voice) or continuous dimensional

attributes (for example, an intense and negative vocal emotion). Which position provides a better account is still widely debated. Here we contrast the positions to account for

acoustics-independent perceptual and cerebral representational geometry of perceived voice emotions. We combined multimodal imaging of the cerebral response to heard vocal stimuli (using

functional magnetic resonance imaging and magneto-encephalography) with post-scanning behavioural assessment of voice emotion perception. By using representational similarity analysis, we

different representational dynamics that emphasize either categories or dimensions. Access through your institution Buy or subscribe This is a preview of subscription content, access via

your institution ACCESS OPTIONS Access through your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $29.99 / 30 days

cancel any time Learn more Subscribe to this journal Receive 12 digital issues and online access to articles $119.00 per year only $9.92 per issue Learn more Buy this article * Purchase on

SpringerLink * Instant access to full article PDF Buy now Prices may be subject to local taxes which are calculated during checkout ADDITIONAL ACCESS OPTIONS: * Log in * Learn about

institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS CORRELATES OF INDIVIDUAL VOICE AND FACE PREFERENTIAL RESPONSES DURING RESTING

STATE Article Open access 03 May 2022 BASAL GANGLIA AND CEREBELLUM CONTRIBUTIONS TO VOCAL EMOTION PROCESSING AS REVEALED BY HIGH-RESOLUTION FMRI Article Open access 20 May 2021 THE

PARADOXICAL ROLE OF EMOTIONAL INTENSITY IN THE PERCEPTION OF VOCAL AFFECT Article Open access 06 May 2021 DATA AVAILABILITY The following materials are available from a Dryad repository

(https://datadryad.org/stash/dataset/doi:10.5061/dryad.m905qfv0k): single-trial behavioural data, single-cross-validation fold fMRI data, and single-trial MEG data for all participants;

anonymized anatomical information required to reconstruct the MEG sources and deform native-space statistical maps to DARTEL and MNI space; and sound stimuli and MTF representations. CODE

AVAILABILITY The Matlab code for reconstructing the MEG sources, carrying out a group-level RSA analysis of the fMRI and MEG representation of perceived emotions, and generating MNI-space

statistical maps is available at the following Dryad repository: https://datadryad.org/stash/dataset/doi:10.5061/dryad.m905qfv0k. REFERENCES * Ekman, P. in _The Science of Facial Expression_

(eds Fernandez-Dols, J. M. & Russell, J. A.), 39–56 (Oxford Univ. Press, 2017). * Sauter, D. A. & Eimer, M. Rapid detection of emotion from human vocalizations. _J. Cogn. Neurosci._

22, 474–481 (2010). Article  PubMed  Google Scholar  * Russell, J. A. Core affect and the psychological construction of emotion. _Psychol. Rev._ 110, 145–172 (2003). Article  PubMed  Google

Scholar  * Barrett, L. F. The theory of constructed emotion: an active inference account of interoception and categorization. _Soc. Cogn. Affect. Neurosci._ 12, 1–23 (2017). Article  PubMed

  Google Scholar  * Hamann, S. Mapping discrete and dimensional emotions onto the brain: controversies and consensus. _Trends Cogn. Sci._ 16, 458–466 (2012). Article  PubMed  Google Scholar

  * Vytal, K. & Hamann, S. Neuroimaging support for discrete neural correlates of basic emotions: a voxel-based meta-analysis. _J. Cogn. Neurosci._ 22, 2864–2885 (2010). Article  PubMed

  Google Scholar  * Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E. & Barrett, L. F. The brain basis of emotion: a meta-analytic review. _Behav. Brain Sci._ 35, 121–143

(2012). Article  PubMed  PubMed Central  Google Scholar  * Kober, H. et al. Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies.

_Neuroimage_ 42, 998–1031 (2008). Article  Google Scholar  * Rolls, E. T., Grabenhorst, F. & Franco, L. Prediction of subjective affective state from brain activations. _J.

Neurophysiol._ 101, 1294–1308 (2009). Article  PubMed  Google Scholar  * Kotz, S. A., Kalberlah, C., Bahlmann, J., Friederici, A. D. & Haynes, J. D. Predicting vocal emotion expressions

from the human brain. _Hum. Brain Mapp._ 34, 1971–1981 (2013). Article  PubMed  Google Scholar  * Skerry, A. E. & Saxe, R. Neural representations of emotion are organized around abstract

event features. _Curr. Biol._ 25, 1945–1954 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Saarimaki, H. et al. Discrete neural signatures of basic emotions. _Cereb.

Cortex_ 26, 2563–2573 (2016). Article  PubMed  Google Scholar  * Kragel, P. A. & LaBar, K. S. Decoding the nature of emotion in the brain. _Trends Cogn. Sci._ 20, 444–455 (2016). Article

  PubMed  PubMed Central  Google Scholar  * Briesemeister, B. B., Kuchinke, L. & Jacobs, A. M. Emotion word recognition: discrete information effects first, continuous later? _Brain

Res._ 1564, 62–71 (2014). Article  CAS  PubMed  Google Scholar  * Grootswagers, T. & Kennedy, B. L. & Most, S. B. & Carlson, T. A. Neural signatures of dynamic emotion constructs

in the human brain. _Neuropsychologia_ 145, 106535 (2020). Article  PubMed  Google Scholar  * Belin, P., Fillion-Bilodeau, S. & Gosselin, F. The ‘Montreal Affective Voices’: a validated

set of nonverbal affect bursts for research on auditory affective processing. _Behav. Brain Res._ 40, 531–539 (2008). Google Scholar  * Kriegeskorte, N., Mur, M. & Bandettini, P.

Representational similarity analysis—connecting the branches of systems neuroscience. _Front. Syst. Neurosci._ 2, 1–28 (2009). Google Scholar  * Chi, T., Ru, P. & Shamma, S. A.

Multiresolution spectrotemporal analysis of complex sounds. _J. Acoust. Soc. Am._ 118, 887–906 (2005). Article  PubMed  Google Scholar  * Belyk, M., Brown, S., Lim, J. & Kotz, S. A.

Convergence of semantics and emotional expression within the IFG pars orbitalis. _Neuroimage_ 156, 240–248 (2017). Article  PubMed  Google Scholar  * Touroutoglou, A. et al. A ventral

salience network in the macaque brain. _Neuroimage_ 132, 190–197 (2016). Article  PubMed  Google Scholar  * Anderson, D. J. & Adolphs, R. A framework for studying emotions across

species. _Cell_ 157, 187–200 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Cowen, A. S. & Keltner, D. Self-report captures 27 distinct categories of emotion bridged by

continuous gradients. _Proc. Natl Acad. Sci. USA_ 114, E7900–E7909 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Cowen, A. S., Laukka, P., Elfenbein, H. A., Liu, R. &

Keltner, D. The primacy of categories in the recognition of 12 emotions in speech prosody across two cultures. _Nat. Hum. Behav._ 3, 369–382 (2019). Article  PubMed  PubMed Central  Google

Scholar  * Giordano, B. L. et al. Contributions of local speech encoding and functional connectivity to audio-visual speech perception. _eLife_ 6, e24763 (2017). Article  PubMed  PubMed

Central  Google Scholar  * Pessoa, L. Understanding emotion with brain networks. _Curr. Opin. Behav. Sci._ 19, 19–25 (2018). Article  PubMed  Google Scholar  * Vaux, D. L., Fidler, F. &

Cumming, G. Replicates and repeats-what is the difference and is it significant? A brief discussion of statistics and experimental design. _EMBO Rep._ 13, 291–296 (2012). Article  CAS 

PubMed  PubMed Central  Google Scholar  * Kawahara, H. & Matsui, H. Auditory morphing based on an elastic perceptual distance metric in an interference-free time-frequency

representation. In _Proc. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing_ 256–259 (2003). * Hutton, C. et al. Image distortion correction in fMRI: A

quantitative evaluation. _Neuroimage_ 16, 217–240 (2002). Article  PubMed  Google Scholar  * Santoro, R. et al. Encoding of natural sounds at multiple spectral and temporal resolutions in

the human auditory cortex. _PLoS Comput. Biol._ 10, e1003412 (2014). Article  PubMed  PubMed Central  CAS  Google Scholar  * Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere,

S. V. & McDermott, J. H. A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. _Neuron_ 98, 630–644

(2018). Article  CAS  PubMed  Google Scholar  * Cao, Y., Summerfield, C., Park, H., Giordano, B. L. & Kayser, C. Causal inference in the multisensory brain. _Neuron_ 102, 1076–1087

(2019). Article  CAS  PubMed  Google Scholar  * Mantel, N. The detection of disease clustering and a generalized regression approach. _Cancer Res._ 27, 209–220 (1967). CAS  PubMed  Google

Scholar  * Oostenveld, R. & Fries, P. & Maris, E. & Schoffelen, J. M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.

_Comput. Intell. Neurosci._ 2011, 156869 (2011). Article  PubMed  Google Scholar  * Kay, K. N., Rokem, A., Winawer, J., Dougherty, R. F. & Wandell, B. A. GLMdenoise: a fast, automated

technique for denoising task-based fMRI data. _Front. Neurosci._ 7, 247 (2013). Article  PubMed  PubMed Central  Google Scholar  * Ashburner, J. A fast diffeomorphic image registration

algorithm. _Neuroimage_ 38, 95–113 (2007). Article  PubMed  Google Scholar  * Hipp, J. F. & Siegel, M. Dissociating neuronal gamma-band activity from cranial and ocular muscle activity

in EEG. _Front. Hum. Neurosci._ 7, 338 (2013). Article  PubMed  PubMed Central  Google Scholar  * Cichy, R. M., Pantazis, D. & Oliva, A. Resolving human object recognition in space and

time. _Nat. Neurosci._ 17, 455–462 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Cichy, R. M. & Pantazis, D. Multivariate pattern analysis of MEG and EEG: a comparison

of representational structure in time and space. _Neuroimage_ 158, 441–454 (2017). Article  PubMed  Google Scholar  * Walther, A. et al. Reliability of dissimilarity measures for multi-voxel

pattern analysis. _Neuroimage_ 137, 188–200 (2016). Article  PubMed  Google Scholar  * Diedrichsen, J. & Kriegeskorte, N. Representational models: a common framework for understanding

encoding, pattern-component, and representational-similarity analysis. _PLoS Comput. Biol._ 13, e1005508 (2017). Article  PubMed  PubMed Central  CAS  Google Scholar  * Maris, E. &

Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. _J. Neurosci. Methods_ 164, 177–190 (2007). Article  PubMed  Google Scholar  * Rolls, E. T., Joliot, M. &

Tzourio-Mazoyer, N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. _NeuroImage_ 122, 1–5 (2015). Article  PubMed  Google Scholar

  * De Leeuw, J. & Mair, P. Multidimensional scaling using majorization: SMACOF in R. _J. Stat. Softw._ 31, 1–30 (2009). Article  Google Scholar  * Ashby, F. G., Boynton, G. & Lee,

W. W. Categorization response time with multidimensional stimuli. _Percept. Psychophys._ 55, 11–27 (1994). Article  CAS  PubMed  Google Scholar  * Fonov, V. et al. Unbiased average

age-appropriate atlases for pediatric studies. _Neuroimage_ 54, 313–327 (2011). Article  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS This work was supported by the UK

Biotechnology and Biological Sciences Research Council (grants BB/M009742/1 to J.G., B.L.G., S.A.K. and P.B., and BB/L023288/1 to P.B. and J.G.), by the French Fondation pour la Recherche

Médicale (grant AJE201214 to P.B.), and by Research supported by grants ANR-16-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI), and the Excellence Initiative of Aix-Marseille University (A*MIDEX).

The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank O. Coulon and O. Garrod for help with the development

of the 3D glass brain, as well as Y. Cao, I. Charest, C. Crivelli, B. De Gelder, G. Masson, R. A. A. Ince, F. Kusnir, S. McAdams and R. J. Zatorre for useful comments on previous versions of

the manuscript. AUTHOR INFORMATION Author notes * These authors jointly supervised this work: Joachim Gross, Pascal Belin. AUTHORS AND AFFILIATIONS * Institute of Neuroscience of la Timone

UMR 7289 Centre National de la Recherche Scientifique and Aix-Marseille University, Marseille, France Bruno L. Giordano & Pascal Belin * Institute of Neuroscience and Psychology,

University of Glasgow, Glasgow, UK Bruno L. Giordano, Caroline Whiting & Joachim Gross * Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA Nikolaus

Kriegeskorte * Faculty of Psychology and Neuroscience, Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, The Netherlands Sonja A. Kotz * Department of

Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany Sonja A. Kotz * Institute for Biomagnetism and Biosignalanalysis, University of Münster,

Münster, Germany Joachim Gross * Department of Psychology, University of Montréal, Montreal, Canada Pascal Belin Authors * Bruno L. Giordano View author publications You can also search for

this author inPubMed Google Scholar * Caroline Whiting View author publications You can also search for this author inPubMed Google Scholar * Nikolaus Kriegeskorte View author publications

You can also search for this author inPubMed Google Scholar * Sonja A. Kotz View author publications You can also search for this author inPubMed Google Scholar * Joachim Gross View author

publications You can also search for this author inPubMed Google Scholar * Pascal Belin View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS

Conceptualization: B.L.G. and P.B.; methodology: B.L.G., C.W., N.K., S.A.K., P.B. and J.G.; software: B.L.G.; validation: B.L.G.; formal analysis: B.L.G., C.W. and J.G.; investigation:

B.L.G. and C.W.; resources: B.L.G. and P.B.; data curation: B.L.G. and C.W.; writing, original draft: B.L.G., C.W., S.A.K., P.B. and J.G.; writing, review and editing: B.L.G., C.W., N.K.,

S.A.K., P.B. and J.G.; visualization: B.L.G.; supervision: B.L.G., P.B. and J.G.; project administration: J.G.; and funding acquisition: B.L.G., S.A.K., P.B. and J.G. CORRESPONDING AUTHORS

Correspondence to Bruno L. Giordano, Joachim Gross or Pascal Belin. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PEER REVIEW

INFORMATION _Nature Human Behaviour_ thanks Behtash Babadi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jamie

Horder; Marike Schiffer. PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION Supplementary Figs. 1–4 and Supplementary Table 1. REPORTING SUMMARY PEER REVIEW INFORMATION SUPPLEMENTARY AUDIO 1 Sound stimuli RIGHTS AND PERMISSIONS Reprints and

permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Giordano, B.L., Whiting, C., Kriegeskorte, N. _et al._ The representational dynamics of perceived voice emotions evolve from categories to

dimensions. _Nat Hum Behav_ 5, 1203–1213 (2021). https://doi.org/10.1038/s41562-021-01073-0 Download citation * Received: 06 February 2020 * Accepted: 08 February 2021 * Published: 11 March

2021 * Issue Date: September 2021 * DOI: https://doi.org/10.1038/s41562-021-01073-0 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get

shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative