Computational Process of Sharing Emotion: An Authentic Information Perspective.

Area of Science:

• Psychology
• Computational Modeling
• Affective Science

Background:

• Emotion sharing facilitates dyadic interaction.
• Limited understanding exists regarding the transmission of authentic emotional information from expressions.
• Previous research has not quantitatively assessed the perception of authentic information from facial expressions.

Purpose of the Study:

• To formally quantify the process of emotion sharing.
• To investigate the perception of authentic information from facial expressions.
• To explore the role of computational modeling in understanding emotion transmission.

Main Methods:

• Utilized computational modeling, specifically a multinomial processing tree, for quantitative analysis.
• Analyzed the perception of authentic information from facial expressions (happy and angry).
• Examined the activation of emotional elicitation and emotion sharing in perceivers.

Main Results:

• Perceiving authentic information from happy expressions is more probable than from angry expressions.
• Happy expressions activate both emotional elicitation and emotion sharing; angry expressions primarily activate emotional elicitation.
• Parameters for detecting anger and happiness experiences were positively correlated.
• No significant correlation was found between experimental parameters and self-reported emotional contagion, empathy, or social anxiety.

Conclusions:

• A novel computational approach can describe emotion sharing processes.
• Facial expressions differentially convey authentic emotional information.
• Understanding authentic information transmission is crucial for affective science and psychology.