Modeling emotions with the free-energy reduction in category recognition: A hierarchical Bayesian approach for perception process

Abstract

Emotion modeling has been a significant task in engineering design to optimize the users’ feedback. Psychology studies have established explanatory relation of agent’s outcome emotion with fluency from perceived stimuli. Friston proposed the free-energy principle modeling the human perception process in a Bayesian form to apply the emotion models in engineering. Joffily further suggested that free-energy reduction is encouraged by positive emotion generated. This study proposes a hierarchical Bayesian model that mathematically explains emotion feedback in the perception process. To support the model, our study further linked the free-energy paradigm with fluency and informatics. We suggest a successful category recognition would cause free energy reduction and thus positive emotion valence. The informatics approach based on efficient encoding provides a practical simulation of the human perception process that helps to generate proper visual stimuli for experimental evidence. Two types of stimuli are introduced as evaluation material to compare with their non-category transformation that correspondingly shares the same feature. The result shows that category recognition can reduce the sense of novelty as part of free-energy. The emotion valence is positively correlated with the novelty reduced through the recognition, where a moderate level of novelty reduction can be more effective. In the perspective of engineering, the design features promoting category recognition can contribute to the optimization of the users’ emotion feedback.