TY - JOUR
T1 - Free energy model of emotional valence in dual-process perceptions
AU - Yanagisawa, Hideyoshi
AU - Wu, Xiaoxiang
AU - Ueda, Kazutaka
AU - Kato, Takeo
N1 - Funding Information:
This research was supported by the Japan Society for the Promotion of Science (KAKENHI Grant Number 21H03528 , Mathematical model development of emotion dimensions based on variation of uncertainty and its application to inverse problems).
Publisher Copyright:
© 2022
PY - 2023/1
Y1 - 2023/1
N2 - 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.
AB - 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.
KW - Bayesian model
KW - Dual process
KW - Emotional valence
KW - Free energy
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U2 - 10.1016/j.neunet.2022.10.027
DO - 10.1016/j.neunet.2022.10.027
M3 - Article
C2 - 36413849
AN - SCOPUS:85142201630
SN - 0893-6080
VL - 157
SP - 422
EP - 436
JO - Neural Networks
JF - Neural Networks
ER -