Free energy model of emotional valence in dual-process perceptions

Hideyoshi Yanagisawa, Xiaoxiang Wu, Kazutaka Ueda, Takeo Kato

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)422-436
Number of pages15
JournalNeural Networks
Publication statusPublished - 2023 Jan


  • Bayesian model
  • Dual process
  • Emotional valence
  • Free energy

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


Dive into the research topics of 'Free energy model of emotional valence in dual-process perceptions'. Together they form a unique fingerprint.

Cite this