Data from: The representational dynamics of perceived voice emotions evolve from categories to dimensions
Giordano, Bruno L. et al. (2021), Data from: The representational dynamics of perceived voice emotions evolve from categories to dimensions, Dryad, Dataset, https://doi.org/10.5061/dryad.m905qfv0k
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) fronto-temporal 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.
This dataset includes functional resonance imaging (fMRI) and magnetoencephalography (MEG) data collected while participants (N = 10) heard emotionally expressive human vocalizations. Also included are behavioural data collected while the same participants heard the experimental stimuli (tasks = emotion categorization; emotion category intensity ratings; valence and arousal ratings; pairwise dissimilarity ratings).
See readme.txt and contents.txt
1. Matlab >= R2015
2. SPM12 installed and added to matlab path.
SPM12 available at:
3. compiled mtimesx added to matlab path.
mtimesx source code available at:
1. extract archive with path
2. >> run(install.m)
3. run example fMRI/MEG analyses with example_code.m
Biological Sciences Research Council, Award: BB/M009742/1
Biological Sciences Research Council, Award: BB/L023288/1
French Fondation pour la Recherche Médicale, Award: AJE201214
Agence Nationale de la Recherche, Award: ANR-16-CONV-0002
Aix-Marseille Université, Award: A*MIDEX: Excellence Initiative