An Agent-Based Model of Opinion Polarization Driven by Emotions

We provide an agent-based model to explain the emergence of collective opinions not based on feedback between different opinions, but based on emotional interactions between agents. The driving variable is the emotional state of agents, characterized by their valence, quantifying the emotion from unpleasant to pleasant, and their arousal, quantifying the degree of activity associated with the emotion. Both determine their emotional expression, from which collective emotional information is generated. This information feeds back on the dynamics of emotional states and individual opinions in a nonlinear manner. We derive the critical conditions for emotional interactions to obtain either consensus or polarization of opinions. Stochastic agent-based simulations and formal analyses of the model explain our results. Possible ways to validate the model are discussed.

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An agent-based model of opinion polarization driven by emotions

10.31235/osf.io/8m2wq ◽

2020 ◽

Author(s):

Frank Schweitzer ◽

Tamas Krivachy ◽

David Garcia

Keyword(s):

Emotional Expression ◽

Emotional State ◽

Critical Conditions ◽

Emotional States ◽

Agent Based Model ◽

Emotional Information ◽

Agent Based ◽

Non Linear ◽

Agent Based Simulations ◽

Linear Manner

We provide an agent-based model to explain the emergence of collective opinions not based on feedback between different opinions, but based on emotional interactions between agents. The driving variable is the emotional state of agents, characterized by their valence, quantifying the emotion from unpleasant to pleasant, and their arousal, quantifying the degree of activity associated with the emotion. Both determine their emotional expression, from which collective emotional information is generated. This information feeds back on the dynamics of emotional states and of individual opinions in a non-linear manner. We derive the critical conditions for emotional interactions to obtain either consensus or polarization of opinions. Stochastic agent-based simulations and formal analyses of the model explain our results. Possible ways to validate the model are discussed.

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Dogs can infer implicit information from human emotional expressions

Animal Cognition ◽

10.1007/s10071-021-01544-x ◽

2021 ◽

Author(s):

Natalia Albuquerque ◽

Daniel S. Mills ◽

Kun Guo ◽

Anna Wilkinson ◽

Briseida Resende

Keyword(s):

Emotional State ◽

Food Resource ◽

Emotional Expressions ◽

Emotional States ◽

Emotional Information ◽

Implicit Information ◽

Emotional Display ◽

Affective Information ◽

Social Decision Making ◽

Current Behaviour

AbstractThe ability to infer emotional states and their wider consequences requires the establishment of relationships between the emotional display and subsequent actions. These abilities, together with the use of emotional information from others in social decision making, are cognitively demanding and require inferential skills that extend beyond the immediate perception of the current behaviour of another individual. They may include predictions of the significance of the emotional states being expressed. These abilities were previously believed to be exclusive to primates. In this study, we presented adult domestic dogs with a social interaction between two unfamiliar people, which could be positive, negative or neutral. After passively witnessing the actors engaging silently with each other and with the environment, dogs were given the opportunity to approach a food resource that varied in accessibility. We found that the available emotional information was more relevant than the motivation of the actors (i.e. giving something or receiving something) in predicting the dogs’ responses. Thus, dogs were able to access implicit information from the actors’ emotional states and appropriately use the affective information to make context-dependent decisions. The findings demonstrate that a non-human animal can actively acquire information from emotional expressions, infer some form of emotional state and use this functionally to make decisions.

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Simplifying the Design of Human-Like Behaviour

International Journal of Synthetic Emotions ◽

10.4018/jse.2010101603 ◽

2010 ◽

Vol 1 (1) ◽

pp. 30-50 ◽

Cited By ~ 13

Author(s):

Joanna J. Bryson ◽

Emmanuel Tanguy

Keyword(s):

Emotional Expression ◽

Autonomous Agents ◽

Emotional State ◽

Emotional Responses ◽

Full Time ◽

Emotional States ◽

Virtual Character ◽

Agent Behavior ◽

Emotion Representation ◽

Representational Framework

Human intelligence requires decades of full-time training before it can be reliably utilized in modern economies. In contrast, AI agents must be made reliable but interesting in relatively short order. Realistic emotion representations are one way to ensure that even relatively simple specifications of agent behavior will be expressed with engaging variation, and those social and temporal contexts can be tracked and responded to appropriately. We describe a representation system for maintaining an interacting set of durative states to replicate emotional control. Our model, the Dynamic Emotion Representation (DER), integrates emotional responses and keeps track of emotion intensities changing over time. The developer can specify an interacting network of emotional states with appropriate onsets, sustains, and decays. The levels of these states can be used as input for action selection, including emotional expression. We present both a general representational framework and a specific instance of a DER network constructed for a virtual character. The character’s DER uses three types of emotional state as classified by duration timescales, keeping with current emotional theory. We demonstrate the system with a virtual actor. We also demonstrate how even a simplified version of this representation can improve goal arbitration in autonomous agents.

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Emotion-Related Traits Moderate the Impact of Emotional State on Creative Performances

Journal of Individual Differences ◽

10.1027/1614-0001.29.3.157 ◽

2008 ◽

Vol 29 (3) ◽

pp. 157-167 ◽

Cited By ~ 25

Author(s):

Franck Zenasni ◽

Todd I. Lubart

Keyword(s):

Divergent Thinking ◽

Emotional State ◽

Emotional Valence ◽

Emotional States ◽

Emotional Information ◽

Creative Cognition ◽

Undergraduate University Students ◽

Valence And Arousal ◽

Affective Intensity ◽

The Impact

The present study shows that the impact of emotional states on creativity depends on individual emotional characteristics as well as the type of task used. The results found during the last 30 years diverge concerning relationships between emotion and creative cognition. For this reason, we conducted a study to explore whether the impact of emotional states on creative potential is moderated by individuals emotional traits. Using a multivariate approach, we measured (1) emotional valence and arousal level of participants after an emotional induction, (2) emotional traits (e.g., alexithymia, emotional expressivity, affective intensity, emotional idiosyncrasy), and (3) quantity, originality, and valence of generated ideas in two distinct divergent thinking tasks. Participants were 107 undergraduate university students. Regression analyses confirm our hypothesis showing that the impact of emotional states on creative performance is not uniform, but depends on participants emotional state and emotion-related traits. For example, we observed that the more individuals had difficulty with emotional information and the higher their level of arousal, the less they generated pleasant ideas. Several processes such as arousal regulation may explain the observed results.

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Effect of Emotions in a Lexical Decision Task

Swiss Journal of Psychology ◽

10.1024/1421-0185/a000193 ◽

2017 ◽

Vol 76 (2) ◽

pp. 71-79 ◽

Cited By ~ 1

Author(s):

Hélène Maire ◽

Renaud Brochard ◽

Jean-Luc Kop ◽

Vivien Dioux ◽

Daniel Zagar

Keyword(s):

Lexical Decision ◽

Lexical Decision Task ◽

Emotional State ◽

Control Group ◽

Decision Task ◽

Attentional Orienting ◽

Emotional States ◽

Sequence Effect ◽

Emotion Induction ◽

Before And After

Abstract. This study measured the effect of emotional states on lexical decision task performance and investigated which underlying components (physiological, attentional orienting, executive, lexical, and/or strategic) are affected. We did this by assessing participants’ performance on a lexical decision task, which they completed before and after an emotional state induction task. The sequence effect, usually produced when participants repeat a task, was significantly smaller in participants who had received one of the three emotion inductions (happiness, sadness, embarrassment) than in control group participants (neutral induction). Using the diffusion model ( Ratcliff, 1978 ) to resolve the data into meaningful parameters that correspond to specific psychological components, we found that emotion induction only modulated the parameter reflecting the physiological and/or attentional orienting components, whereas the executive, lexical, and strategic components were not altered. These results suggest that emotional states have an impact on the low-level mechanisms underlying mental chronometric tasks.

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