A Bayesian Drift-Diffusion Model of Schachter-Singer's Two Factor Theory of Emotion

TL;DR

This paper models the process of emotion attribution using a Bayesian drift-diffusion framework, integrating physiological arousal and contextual information to simulate emotional labeling, and tests it against classic experimental data.

Contribution

It introduces a novel Bayesian drift-diffusion model of emotion based on Schachter-Singer's theory, linking cognitive labeling to evidence accumulation in a formal computational framework.

Findings

Model fits well with Schachter & Singer (1962) data when arousal is prior and context is likelihood.

Model fits better with Ross et al. (1969) data when context is prior and arousal is likelihood.

Demonstrates Bayesian inference as a plausible mechanism for emotional decision-making.

Abstract

Bayesian inference has been used in the past to model visual perception (Kersten, Mamassian, & Yuille, 2004), accounting for the Helmholtz principle of perception as "unconscious inference" that is constrained by bottom-up sensory evidence (likelihood) while subject to top-down expectation, priming, or other contextual influences (prior bias); here "unconsciousness" merely relates to the "directness" of perception in the sense of Gibson. Here, we adopt the same Bayesian framework to model emotion process in accordance with Schachter-Singer's Two-Factor theory, which argues 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 as an instance of Bayesian inference, combining the contextual information with a person's physiological arousal patterns. Drift-diffusion models were constructed to simulate emotional processes, where the decision boundaries correspond to the emotional state experienced by the participants, and boundary-crossing constitutes "labeling" in Schachter-Singer's sense. Our model is tested against experimental data from the Schachter & Singer's study (1962) and the Ross et al. study (1969). Two model scenarios are investigated, in which arousal pattern as one factor is pitted against contextual interaction with an confederate (in Schachter-Singer case) or explicitly instructed mis-attribution (in Ross et al. case) as another factor, mapping onto the Bayesian prior (initial position of the drift) and the likelihood function (evidence accumulation or drift rate). We find that the first scenario (arousal as the prior and context as the likelihood) has a better fit with Schachter & Singer (1962) whereas the second scenario (context as the prior and arousal as the likelihood) has a better fit with Ross et al. (1969).