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Unfolding and dynamics of affect bursts decoding in humans
Simon Schaerlaeken1, Didier Grandjean1
1Neuroscience of Emotion and Affective Dynamics Lab, Faculty of Psychology and Educational Sciences and Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland.
Plos One
|October 31, 2018
Summary
Decoding vocally expressed emotions requires understanding how much information is presented. Threatening emotions like anger and fear are recognized faster than joy or sadness, highlighting distinct acoustic feature contributions.
Area of Science:
- Psychology
- Acoustics
- Speech Science
Background:
- Vocal emotion expression is key to understanding emotional states.
- Decoding emotions from speech requires analyzing dynamic vocal cues.
Purpose of the Study:
- To determine the information needed to decode vocally expressed emotions.
- To investigate how emotion recognition changes with stimulus duration.
Main Methods:
- Utilized affect bursts and a gating paradigm to control stimulus presentation.
- Employed linear mixed models to analyze recognition accuracy.
- Performed principal component analysis on acoustic features.
Main Results:
- Fear, anger, and disgust were recognized better at full duration than joy, sadness, or neutral.
- Recognition accuracy increased with a larger proportion of stimulus presented.
- Specific acoustic features (e.g., fundamental frequency, pitch range) predicted recognition accuracy for certain emotions.
Conclusions:
- The unfolding dynamics of vocal emotions are crucial for accurate decoding.
- Threat-related emotions show distinct recognition patterns compared to others.
- Acoustic feature sets differentially contribute to the recognition of specific vocal emotions.
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