An appropriate level of arousal induces positive emotions, and a high arousal potential may provoke negative emotions. To explain the effect of arousal on emotional valence, we propose a novel mathematical framework of arousal potential variations in the dual process of human cognition: automatic and controlled. A suitable mathematical formulation to explain the emotions in the dual process is still absent. Our model associates free energy with arousal potential and its variations to explain emotional valence. Decreasing and increasing free energy consequently induce positive and negative emotions, respectively. We formalize a transition from the automatic to the controlled process in the dual process as a change of Bayesian prior. Further, we model emotional valence using free energy increase (FI) when one tries changing one's Bayesian prior and its reduction (FR) when one succeeds in recognizing the same stimuli with a changed prior and define three emotions: "interest," "confusion," and "boredom" using the variations. The results of our mathematical analysis comparing various Gaussian model parameters reveals the following: 1) prediction error (PR) increases FR (representing "interest") when the first prior variance is greater than the second prior variance, 2) PR decreases FR when the first prior variance is less than the second prior variance, and 3) the distance between priors' means always increases FR. We also discuss the association of the outcomes with emotions in the controlled process. The proposed mathematical model provides a general framework for predicting and controlling emotional valence in the dual process that varies with viewpoint and stimuli, as well as for understanding the contradictions in the effects of arousal on the valence.
翻译:适当的觉醒水平会诱发积极的情绪,而高度的觉醒潜力可能会引发消极情绪。为了解释振奋情绪对情绪价值的影响,我们提议了一个新的数学框架,在人类的双重认知过程:自动和受控制的双重认知过程中,产生潜在的强烈变化。一个解释双重过程情感的适当数学配方仍然不存在。我们的模型将自由能量与刺激潜力及其解释情感价值的变异联系起来。降低和增加自由能量分别产生积极和消极的情绪。我们把双重过程从自动向控制过程的转变正式化为Bayesian以前的改变。此外,当一个人尝试改变巴耶斯人的两种认知过程:自动和控制;当一个人成功地认识到同一刺激和改变前两种过程的变异性时,我们模拟将自由能量值与控制过程的变异性分别化。我们比较各种高斯模型参数的结果显示:(1) 预测错误(PR) 将前者的变异性(FR) 增加第一种变异性(FI), 当先前的变性变性(C) 与先前的变性变异性之间, 前者的变性是前变性的变性(我们之前的变性) 的变性是前变的变的变的变的变性(我们) 之前的变的变的变性) 和前的变的变的变的变的变性(我们的变性) 的变的变的变的变性(我们的变的变性) ) 的变性是前的变的变的变的变的变的变的变) 。