Emotion Regulation in Human-Robot Interaction: Insights from Video and Game Simulations
This research article investigates how different robot emotion regulation strategies (empathetic, non-empathetic, and no regulation) influence users' emotional responses, decision-making, and trust across positive and negative contexts. Through video-based and game-based simulations, the authors find that empathetic regulation is more effective in positive scenarios, whereas non-empathetic regulation supports rational decision-making in negative scenarios. The study highlights the importance of adapting robot emotion regulation strategies to context and provides methodological guidance for human-robot interaction research.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers and developers in human-robot interaction who want to understand how emotional intelligence and simulation methods affect user trust and decision-making.
What the document contains
The study addresses how different emotion regulation styles (empathetic, non-empathetic, and no regulation) impact user trust, decision-making, and interaction quality in positive and negative emotional contexts using video and game simulations.
The document does not provide legislative rules, regulatory enforcement mechanisms, software code, or hardware design standards for commercial robotics.
- Empathetic emotion regulation by robots is more effective in positive scenarios, enhancing suggestion acceptance and trust.
- Non-empathetic regulation proves more effective in negative scenarios by enabling more rational decision-making and conveying greater authority.
- Video-based simulations elicit stronger emotional arousal and valence shifts compared to game-based simulations.
- Game-based simulations offer greater ecological validity and realistic feedback despite diluted emotional intensity from interactive elements.
- Robots must adapt their emotion regulation strategies based on the user's emotional state and interaction context.
· Introduction · Approach · Outcome · Takeaways
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
screening score · 2026-10-24
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-24
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-24
Transparency
0/5Openness about how AI systems work and are used
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-24
Impact Assessment
1/5Required assessment of an AI system's risks/impacts before or during use
This study examines how different robot-provided emotion regulation strategies—empathetic, nonempathetic, and no regulation—influence users’ emotional responses, decision-making, and perceptions of trust and interaction quality across both positive and negative emotional contexts.
Abstract
screening score · 2026-10-24