Amplifying the Good, Muting the Bad: A Social Amplifier Model of Embodied Role Adaptation in Human–Robot Interaction
Year: 2026
Type: article
Abstract: As robots increasingly collaborate with humans in dynamic environments, the tension between communication clarity and social appropriateness presents an important design challenge. While communicative clarity is traditionally assumed to enhance human–robot interaction, recent findings suggest that clear communication can backfire when robots violate social norms. This study introduces and validates a “social amplifier” model of embodied communication, proposing that the value of role clarity depends on the appropriateness of the robot’s social behavior. We developed a novel confidence-aware role adaptation paradigm where an NAO robot dynamically switches between socially appropriate and inappropriate roles based on users’ confidence levels in an ambiguous video discrimination task. Using a within-subjects design with 64 participants, we manipulated role complementarity, choice alignment, and embodied communication across 512 total interactions. Results revealed that embodied communication significantly enhanced role clarity but had conditional effects on collaborative outcomes. Contrary to the “bad is stronger than good” principle, embodied communication asymmetrically amplified positive effects of appropriate robot behavior while having minimal impact on inappropriate behavior. This pattern emerged consistently across subjective measures (collaboration quality, role dynamics, trust) and objective behavioral outcomes (confidence change, choice change). The findings also demonstrate that robots with socially appropriate roles became over 7 times more persuasive when enhanced with rich embodied cues, while inappropriate robots showed no benefit. These results provide evidence-based design principles prioritizing social intelligence over mere expressiveness in collaborative decision-making robots.
DOI: 10.1145/3811031
Authors: Yankuan Liu, Pei-Luen Patrick Rau
Institution: Tsinghua University
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7155029751