Mathematical Model of Emotional Habituation to Novelty: Modeling with Bayesian Update and Information Theory
| Autor: | Hideyoshi Yanagisawa, Yuki Sakai, Takahiro Sekoguchi |
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| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Information Theory Information Theory (cs.IT) media_common.quotation_subject Bayesian probability Novelty 02 engineering and technology Stimulus (physiology) Information theory Arousal 03 medical and health sciences Surprise 020901 industrial engineering & automation 0302 clinical medicine Quantitative Biology - Neurons and Cognition FOS: Biological sciences Neurons and Cognition (q-bio.NC) Habituation Valence (psychology) Psychology 030217 neurology & neurosurgery media_common Cognitive psychology |
| Zdroj: | Publons SMC |
| Popis: | Novelty is an important factor of creativity in product design. Acceptance of novelty, however, depends on one's emotions. Yanagisawa, the last author, and his colleagues previously developed a mathematical model of emotional dimensions associated with novelty such as arousal (surprise) and valence. The model formalized arousal as Bayesian information gain and valence as a function of arousal based on Berlyne's arousal potential theory. One becomes accustomed to novelty by repeated exposure. This so-called habituation to novelty is important in the design of long-term product experience. We herein propose a mathematical model of habituation to novelty based on the emotional dimension model. We formalized the habituation as a decrement in information gain from a novel event through Bayesian update. We derived the information gained from the repeated exposure of a novel stimulus as a function of three parameters: initial prediction error, initial uncertainty, and noise of sensory stimulus. With the proposed model, we discovered an interaction effect of the initial prediction error and initial uncertainty on habituation. Furthermore, we demonstrate that a range of positive emotions on prediction errors shift toward becoming more novel by repeated exposure. Comment: PrePrint submitted to IEEE SMC 2019 |
| Databáze: | OpenAIRE |
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