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The representational dynamics of perceived voice emotions evolve from categories to dimensions

Nature Human Behaviour volume 5, pages 1203–1213 (2021)Cite this article

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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 find that categories prevail in perceptual and early (less than 200 ms) frontotemporal cerebral representational geometries and that dimensions impinge predominantly on a later limbic–temporal network (at 240 ms and after 500 ms). These results reconcile the two opposing views by reframing the perception of emotions as the interplay of cerebral networks with different representational dynamics that emphasize either categories or dimensions.

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Fig. 1: Emotional voice stimuli.
Fig. 2: Spatiotemporal RSA of the representation of perceived emotion attributes in cerebral representational geometries.
Fig. 3: Categories better account for perceived dissimilarity than valence and arousal.
Fig. 4: Perceptual correlates and cerebral representational geometry of acoustics.
Fig. 5: Cerebral representational geometries initially dominated by categories subsequently emphasize dimensions.

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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

  1. Ekman, P. in The Science of Facial Expression (eds Fernandez-Dols, J. M. & Russell, J. A.), 39–56 (Oxford Univ. Press, 2017).

  2. Sauter, D. A. & Eimer, M. Rapid detection of emotion from human vocalizations. J. Cogn. Neurosci. 22, 474–481 (2010).

    Article  PubMed  Google Scholar 

  3. Russell, J. A. Core affect and the psychological construction of emotion. Psychol. Rev. 110, 145–172 (2003).

    Article  PubMed  Google Scholar 

  4. 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 

  5. Hamann, S. Mapping discrete and dimensional emotions onto the brain: controversies and consensus. Trends Cogn. Sci. 16, 458–466 (2012).

    Article  PubMed  Google Scholar 

  6. 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 

  7. 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 

  8. 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 

  9. 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 

  10. 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 

  11. 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 

  12. Saarimaki, H. et al. Discrete neural signatures of basic emotions. Cereb. Cortex 26, 2563–2573 (2016).

    Article  PubMed  Google Scholar 

  13. 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 

  14. 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 

  15. 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 

  16. 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 

  17. Kriegeskorte, N., Mur, M. & Bandettini, P. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 1–28 (2009).

    Google Scholar 

  18. 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 

  19. 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 

  20. Touroutoglou, A. et al. A ventral salience network in the macaque brain. Neuroimage 132, 190–197 (2016).

    Article  PubMed  Google Scholar 

  21. Anderson, D. J. & Adolphs, R. A framework for studying emotions across species. Cell 157, 187–200 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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 

  23. 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 

  24. 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 

  25. Pessoa, L. Understanding emotion with brain networks. Curr. Opin. Behav. Sci. 19, 19–25 (2018).

    Article  PubMed  Google Scholar 

  26. 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 

  27. 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).

  28. Hutton, C. et al. Image distortion correction in fMRI: A quantitative evaluation. Neuroimage 16, 217–240 (2002).

    Article  PubMed  Google Scholar 

  29. 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 

  30. 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 

  31. 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 

  32. Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220 (1967).

    CAS  PubMed  Google Scholar 

  33. 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 

  34. 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 

  35. Ashburner, J. A fast diffeomorphic image registration algorithm. Neuroimage 38, 95–113 (2007).

    Article  PubMed  Google Scholar 

  36. 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 

  37. 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 

  38. 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 

  39. Walther, A. et al. Reliability of dissimilarity measures for multi-voxel pattern analysis. Neuroimage 137, 188–200 (2016).

    Article  PubMed  Google Scholar 

  40. 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 

  41. Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).

    Article  PubMed  Google Scholar 

  42. 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 

  43. De Leeuw, J. & Mair, P. Multidimensional scaling using majorization: SMACOF in R. J. Stat. Softw. 31, 1–30 (2009).

    Article  Google Scholar 

  44. 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 

  45. 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

  1. These authors jointly supervised this work: Joachim Gross, Pascal Belin.

Authors and Affiliations

  1. 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

  2. Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK

    Bruno L. Giordano, Caroline Whiting & Joachim Gross

  3. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA

    Nikolaus Kriegeskorte

  4. Faculty of Psychology and Neuroscience, Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, The Netherlands

    Sonja A. Kotz

  5. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

    Sonja A. Kotz

  6. Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany

    Joachim Gross

  7. Department of Psychology, University of Montréal, Montreal, Canada

    Pascal Belin

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  1. Bruno L. Giordano
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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.

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The authors declare no competing interests.

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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.

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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

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