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

This study empirically evaluates the perceived realism of six micromovement 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.