Modeling Emotions Associated With Novelty at Variable Uncertainty Levels: A Bayesian Approach.

ABSTRACT: Acceptance of novelty depends on the receiver's emotional state. This paper proposes a novel mathematical model for predicting emotions elicited by the novelty of an event under different conditions. It models two emotion dimensions, arousal and valence, and considers different uncertainty levels. A state transition from before experiencing an event to afterwards is assumed, and a Bayesian model estimates a posterior distribution as being proportional to the product of a prior distribution and a likelihood function. Our model uses Kullback-Leibler divergence of the posterior from the prior, which we termed information gain, to represent arousal levels because it corresponds to surprise, a high-arousal emotion, upon experiencing a novel event. Based on Berlyne's hedonic function, we formali

SUBMITTER: Yanagisawa H 

PROVIDER: S-EPMC6353852 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature