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Related Experiment Video

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The Emotional Stroop Task: Assessing Cognitive Performance under Exposure to Emotional Content

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Published on: June 29, 2016

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

Hideyoshi Yanagisawa¹, Oto Kawamata¹, Kazutaka Ueda²

¹Design Engineering Laboratory, Department of Mechanical Engineering, The University of Tokyo, Tokyo, Japan.

Frontiers in Computational Neuroscience

|February 9, 2019

Summary

Novelty acceptance is linked to emotional state. A new Bayesian model predicts emotions using information gain, successfully validated by an experiment on novelty and arousal crossover effects.

Keywords:

P300 arousal emotion information novelty surprise uncertainty valence

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Area of Science:

Cognitive Science
Neuroscience
Psychology

Background:

Novelty acceptance is influenced by emotional state.
Predicting emotions elicited by novel events is complex.
Existing models often lack a quantitative approach to novelty's emotional impact.

Purpose of the Study:

To propose a novel mathematical model for predicting emotions elicited by novelty.
To model arousal and valence dimensions of emotion.
To investigate the role of uncertainty and prediction error in emotional responses to novelty.

Main Methods:

Developed a Bayesian model using Kullback-Leibler divergence (information gain) to represent arousal.
Formalized valence based on Berlyne's hedonic function, incorporating reward and aversion systems.
Derived information gain as a function of prediction errors, uncertainty, and noise.
Conducted an experiment using videos of percussion instruments to test the model's predictions.

Main Results:

The model predicted an 'arousal crossover effect' where uncertainty's impact on information gain varies with prediction error magnitude.
Experimental results, measuring arousal via P300 amplitudes and surprise reports, supported the arousal crossover effect.
Bayesian information gain was validated as a measure of emotional arousal, decomposable into uncertainty and prediction errors.

Conclusions:

The proposed Bayesian information gain model accurately predicts emotional arousal in response to novelty.
The arousal crossover effect demonstrates a nuanced interaction between uncertainty and prediction error in emotional responses.
This model offers a framework for understanding and potentially predicting the acceptance of novelty.