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• W4312803853 abstract "Interaction portrays the process of exchanging emotions and states between individuals in a specific temporal and spatial context. Interactions are layered on a temporal scale, including social signals at an instant, intrinsic structures of a process, and enduring relationships between the participants. Existing studies of affective computing focus on the identification of social signals at specific moments, which are precise but limited to the microscopic level; while psychological studies focus on the process and relationship analysis, which are semantic but qualitative. In this paper, we focused on interaction process analysis. By combining psychological knowledge and time series analysis techniques, we proposed quantitative methods for features of personal status, dyad correlation, synchrony coefficients, and ‘lead-follow’ structures. We conducted experiments on the mother-infant interaction dataset. We first compared the differences in interaction patterns between social signals. We found that gaze direction in the free-play phase, emotion synchrony in the recov-ery phase, and engagement in both phases, play an important role in differentiating attachment styles. We then explored the differences in interaction patterns. We found the ‘infant dropping while mother rising’ asynchrony coefficient and the ‘infant leads, mother follows’ structure related to secure attachment styles. Finally, we successfully predicted infant attachment formed one year later based on machine learning methods, confirming the effectiveness of our feature extraction method." @default.
• W4312803853 created "2023-01-05" @default.
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• W4312803853 date "2022-08-21" @default.
• W4312803853 modified "2023-09-22" @default.
• W4312803853 title "Psychology-Inspired Interaction Process Analysis based on Time Series" @default.
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• W4312803853 doi "https://doi.org/10.1109/icpr56361.2022.9956367" @default.
• W4312803853 hasPublicationYear "2022" @default.
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