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A Bayesian account of two-factor theory of emotion process

Mr. Lance Ying
University of Michigan - Ann Arbor ~ Computer Science and Engineering
Prof. Jun Zhang
University of Michigan ~ Psychology and Mathematics

Bayesian inference has been used in the past to model visual perception (Kerson et al., 2004), accounting for the Helmholtz principle of perception of “unconscious inference.” In this paper, we adapt the Bayesian framework to model emotion in accordance with Schachter-Singer’s Two-Factor theory, which argued that emotion is the outcome of cognitive labeling or attribution of a diffuse pattern of autonomic arousal (Schachter & Singer, 1962). In analogous to visual perception, we conceptualize the emotion process, in which emotional labels are constructed, as an instance of unconscious Bayesian inference combining the contextual information with a person’s physiological arousal patterns. We develop a drift-diffusion model to simulate Schachter-Singer’s experimental findings. There, participants who were physiologically aroused (via drug injection but were not informed of arousal) later reported different emotions (i.e., labeled their arousal pattern differently) based on the nature of their interaction with a experimental confederate they encountered post-injection. In our drift-diffusion modeling, the decision boundaries correspond to the euphoric and anger state experienced by the participants in the experiment, and boundary-crossing constitutes “labeling” in Schachter-Singer’s sense. Response time (RT) in the drift-diffusion model is used as a surrogate measure of the self-rated intensity of the emotional state, where high intensity corresponds to a shorter response time. We propose two model scenarios (versions). In the first version, arousal pattern is used as the prior and the likelihood function for evidence accumulation is models the interaction with the confederate (context). We adopt an unbiased prior, while allowing the drift-rate (and its sign) to capture the nature of interaction with the confederate. In the second setup, we use the context as the prior and physiological arousal patterns as the likelihood function. We expect an initial bias depending on the polarity of the interactive experience with the confederate, but the drift-rate is of zero-mean (diffuse but polarity-neutral arousal pattern). The comparison between the simulations of the two versions of the Bayesian drift-diffusion models and the original Schachter & Singer (1962) experimental data will be reported.



Bayesian inference
Emotion modeling
Drift-diffusion modeling

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Cite this as:

Ying, L., & Zhang, J. (2021, July). A Bayesian account of two-factor theory of emotion process. Paper presented at Virtual MathPsych/ICCM 2021. Via