A-Situ: a computational framework for affective labeling from psychological behaviors in real-life situations

This paper presents a computational framework for providing affective labels to real-life situations, called A-Situ. We first define an affective situation, as a specific arrangement of affective entities relevant to emotion elicitation in a situation. Then, the affective situation is represented as...

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Bibliographic Details
Published in:Scientific reports 2020-09, Vol.10 (1), p.15916-15916, Article 15916
Main Authors: Kim, Byung Hyung, Jo, Sungho, Choi, Sunghee
Format: Article
Language:English
Subjects:
631/378/1457
639/705/117
692/700
Arousal - physiology
Brain - physiology
Electroencephalography
Emotions - physiology
Humanities and Social Sciences
Humans
Models, Psychological
multidisciplinary
Science
Science (multidisciplinary)
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Summary:This paper presents a computational framework for providing affective labels to real-life situations, called A-Situ. We first define an affective situation, as a specific arrangement of affective entities relevant to emotion elicitation in a situation. Then, the affective situation is represented as a set of labels in the valence-arousal emotion space. Based on psychological behaviors in response to a situation, the proposed framework quantifies the expected emotion evoked by the interaction with a stimulus event. The accumulated result in a spatiotemporal situation is represented as a polynomial curve called the affective curve, which bridges the semantic gap between cognitive and affective perception in real-world situations. We show the efficacy of the curve for reliable emotion labeling in real-world experiments, respectively concerning (1) a comparison between the results from our system and existing explicit assessments for measuring emotion, (2) physiological distinctiveness in emotional states, and (3) physiological characteristics correlated to continuous labels. The efficiency of affective curves to discriminate emotional states is evaluated through subject-dependent classification performance using bicoherence features to represent discrete affective states in the valence-arousal space. Furthermore, electroencephalography-based statistical analysis revealed the physiological correlates of the affective curves.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-72829-3