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

Lance Ying, Audrey Michal, Jun Zhang

TL;DR

This work casts first-person emotion labeling as Bayesian inference, linking Schachter–Singer's Two-Factor theory to a prior–likelihood decomposition and implementing it with drift-diffusion dynamics where boundary crossing encodes emotional labeling. It compares two mappings: (i) arousal as the prior with context as the likelihood, and (ii) context as the prior with arousal as the likelihood, evaluating them against Schachter–Singer (1962) and Ross et al. (1969) data. The findings indicate Model 1 best fits Schachter–Singer data, while Model 2 aligns better with Ross data, illustrating how attribution processes and experimental design shape the neural and cognitive underpinnings of emotion. By integrating Bayesian inference with sequential-sampling models, the paper provides a quantitative account of arousal-context interactions, misattribution effects, and time-pressured emotion labeling with potential implications for affective neuroscience and cognitive modeling of emotion.

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).

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

TL;DR

This work casts first-person emotion labeling as Bayesian inference, linking Schachter–Singer's Two-Factor theory to a prior–likelihood decomposition and implementing it with drift-diffusion dynamics where boundary crossing encodes emotional labeling. It compares two mappings: (i) arousal as the prior with context as the likelihood, and (ii) context as the prior with arousal as the likelihood, evaluating them against Schachter–Singer (1962) and Ross et al. (1969) data. The findings indicate Model 1 best fits Schachter–Singer data, while Model 2 aligns better with Ross data, illustrating how attribution processes and experimental design shape the neural and cognitive underpinnings of emotion. By integrating Bayesian inference with sequential-sampling models, the paper provides a quantitative account of arousal-context interactions, misattribution effects, and time-pressured emotion labeling with potential implications for affective neuroscience and cognitive modeling of emotion.

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).
Paper Structure (17 sections, 3 equations, 4 figures, 4 tables)

This paper contains 17 sections, 3 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. The Bayesian Approach to Perception
  3. Bayes Formula
  4. Bayesian Inference and Perception
  5. Theories and Paradigms of Emotion
  6. Earlier theories
  7. Two-Factor theory and its critique
  8. Misattribution of arousal paradigm
  9. Bayesian Inference on Emotion Process
  10. Drift-Diffusion Model
  11. Study 1: The Schachter & Singer Experiment
  12. Methods
  13. Results
  14. Study 2: Time-Course in Misattribution Effect
  15. Methods
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Sample simulation paths of Model 1 (left) and Model 2 (right)
  • Figure 2: Experimental and simulation results for the Euphoria State (left) and Anger State (right)
  • Figure 3: The percentage of subjects in each condition working on the shock-avoidance puzzle during each 10-sec time interval (data from Ross et al. (1969) replotted).
  • Figure 4: Simulated results of Model 1 (top) and Model 2 (bottom)