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).

翻译：贝叶斯推断过去曾被用于建模视觉感知（Kersten, Mamassian, & Yuille, 2004），该模型将亥姆霍兹知觉原理解释为受自下而上感官证据（似然）约束，同时受自上而下的预期、启动或其他情境影响（先验偏差）的“无意识推断”；此处的“无意识”仅涉及吉布森意义上的知觉“直接性”。本文采用相同的贝叶斯框架，依据沙赫特-辛格的双因素理论对情绪过程进行建模。该理论认为情绪是对弥散性自主神经唤醒模式的认知标签化或归因的结果（Schachter & Singer, 1962）。类比视觉感知，我们将情绪过程概念化为贝叶斯推断的一个实例，将情境信息与个体的生理唤醒模式相结合。研究构建了漂移扩散模型来模拟情绪过程，其中决策边界对应参与者体验到的情绪状态，而边界跨越则构成沙赫特-辛格理论意义上的“标签化”。我们的模型使用沙赫特与辛格（1962）以及罗斯等人（1969）研究中的实验数据进行了检验。研究探讨了两种模型情境：在第一种情境中，唤醒模式作为一个因素，与同谋者的情境互动（沙赫特-辛格案例）作为另一因素相互对照；在第二种情境中，唤醒模式与明确指导的错误归因（罗斯等人案例）相互对照。这两种情境分别映射到贝叶斯先验（漂移的初始位置）和似然函数（证据积累或漂移速率）。研究发现，第一种情境（唤醒作为先验，情境作为似然）与沙赫特和辛格（1962）的数据拟合更佳，而第二种情境（情境作为先验，唤醒作为似然）与罗斯等人（1969）的数据拟合更佳。