Affective Dynamics: Causality Modeling of Temporally Evolving Perceptual and Affective Responses

Human perceptual and affective responses change dynamically when stimuli are experienced. In this study, we developed a method for modeling the causal structures of affective dynamics using time-series data. Using the temporal dominance of sensations method, perceptual and affective data were collec...

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Bibliographic Details
Published in:IEEE transactions on affective computing 2022-04, Vol.13 (2), p.628-639
Main Authors: Okada, Takumu, Okamoto, Shogo, Yamada, Yoji
Format: Article
Language:English
Subjects:
Brain modeling
Causality
Causality modeling
Data models
Granger causality
Mathematical model
Modelling
Reactive power
Regression models
Semantics
Series (mathematics)
Stimuli
temporal dominance of sensations
Time series analysis
VAR model
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Summary:Human perceptual and affective responses change dynamically when stimuli are experienced. In this study, we developed a method for modeling the causal structures of affective dynamics using time-series data. Using the temporal dominance of sensations method, perceptual and affective data were collected from individuals eating strawberries, and the resulting time-series data were mathematically represented using a vector auto-regression model. Multihierarchical and multidimensional causality structures that explain the temporal evolution of perceptual and affective responses were then established based on Granger causality and the information criterion. The established model suggests how affective and preferential responses are triggered following exposure to stimuli. We also assessed the quantitative and semantic validity of the model.
ISSN:1949-3045
1949-3045
DOI:10.1109/TAFFC.2019.2942931