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感知声音情绪的表象动态从类别演变为维度。

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

机构信息

Institute of Neuroscience of la Timone UMR 7289 Centre National de la Recherche Scientifique and Aix-Marseille University, Marseille, France.

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

出版信息

Nat Hum Behav. 2021 Sep;5(9):1203-1213. doi: 10.1038/s41562-021-01073-0. Epub 2021 Mar 11.

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.

摘要

长期以来,情感科学理论将情感刺激的感知理解为离散类别(例如,愤怒的声音)或连续维度属性(例如,强烈而消极的声音情绪)。哪种观点能提供更好的解释仍存在广泛争议。在这里,我们对比了这两种观点,以解释感知声音情绪的与听觉无关的知觉和大脑代表性几何结构。我们结合了对听到的声音刺激的大脑反应的多模态成像(使用功能磁共振成像和脑磁图),以及对声音情绪感知的扫描后行为评估。通过使用代表性相似性分析,我们发现类别在知觉和早期(小于 200 毫秒)额颞叶大脑代表性几何结构中占主导地位,而维度主要影响后期边缘颞叶网络(在 240 毫秒和 500 毫秒之后)。这些结果通过重新构建强调类别或维度的具有不同代表性动态的大脑网络之间的相互作用,调和了这两种对立观点,从而解释了情绪感知。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99c5/7611700/203236deec61/EMS116179-f001.jpg

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