Evaluating Perceived Realism of Micro-Movement Strategies in Artificial Social Agents

Abstract

This study empirically evaluates the perceived realism of six micro-movement strategies implemented in two agent embodiments (Humanoid and Dummy). Thirty participants (ages 18–55) recruited via the Gorilla platform viewed randomised 5-second video clips (six micro-movement strategies × two embodiments). After each clip, participants rated five dimensions—Resemblance, Daily-Life Likelihood, Efficiency, Movement Realism, and Overall Agent Realism—on a continuous 0–100 scale. Descriptive statistics for a representative movement (TurnBackward) revealed that the Dummy variant scored lower in Overall Agent Realism than the Humanoid, despite similar Movement Realism ratings. Two-way ANOVAs showed significant main effects of Movement Type on Resemblance, Day-to-day, Efficiency, and Movement Realism but no significant Movement × Embodiment interactions. Agent type significantly influenced only Overall Agent Realism. Mixed Linear Models corroborated that CurveForward and Forward movements positively impacted Resemblance and Day-to-day, while Backward and Strafe had negative effects. Correlation analysis revealed strong positive associations among Resemblance, Day-to-day, Efficiency, and Movement Realism, but weaker links to Overall Agent Realism. These findings validate the proposed micro-movement taxonomy and indicate that movement kinematics drive perceived realism more than agent appearance, guiding future ASA controller design.