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Humanizing virtual agents through mutual self-disclosure: enhancing interpersonal engagement and cooperative behavior
Jia-Xin Wang1, Yu-Xuan Xue1, Ya-Ting Zhang1
1School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
Acta Psychologica
|April 3, 2026
Summary
Mutual self-disclosure training helps humans cooperate more with virtual agents. Reciprocal sharing of personal information enhances engagement, making AI seem more human-like and fostering collaboration.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence
- Social Psychology
Background:
- A persistent cooperation gap exists between humans and artificial agents.
- Increasing anthropomorphism of virtual agents has not fully closed this gap.
- Understanding factors that enhance human-agent cooperation is crucial.
Purpose of the Study:
- To investigate if mutual self-disclosure training can reduce the cooperation gap.
- To assess the impact of reciprocal personal information sharing on cooperation with virtual agents.
- To explore how self-disclosure affects perceptions of virtual agents and interpersonal engagement.
Main Methods:
- Utilized highly anthropomorphic virtual agents in an immersive virtual reality environment.
- Employed the Chicken game paradigm to measure cooperation rates before and after training.
- Assessed perceptions of human-likeness, social presence, and interpersonal closeness.
Main Results:
- Mutual self-disclosure significantly increased cooperation rates with virtual partners.
- Participants reported heightened perceptions of human-likeness, social presence, and closeness.
- Fairness-oriented motivation increased, driving cooperative behavior, especially in prosocial individuals.
Conclusions:
- Reciprocal, emotionally grounded interactions can partially humanize virtual agents.
- Self-disclosure fosters perceptions of virtual agents as socially meaningful partners.
- Findings have implications for designing AI in collaborative contexts like autonomous driving and robotics.
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