Social Cognition & Mind Perception in HRI

Overview

During my doctoral research, I explored the cognitive mechanisms of mind perception, specifically how humans attribute intentions, desires, and feelings to robots. To investigate the causes and consequences of this process, I employed a comprehensive mixed-methods approach combining real-time implicit metrics (e.g., reaction times, mouse trajectories) with explicit surveys and semi-structured interviews.

A key contribution of this work was the development of the Real-World Implicit Association Task (RW-IAT), designed to capture the dynamic nature of mind perception in live interactions. Additionally, to ensure methodological rigor, we conducted normative studies to validate experimental stimuli, addressing critical gaps in the literature.

These investigations involved extensive laboratory experiments, including a large-scale cross-generational study with 160 participants. Our findings showed that while people consistently view humans as mentally superior to robots, this judgment changes significantly depending on the action performed and the observer's age. More importantly, implicit measures (like response times and mouse trajectories) revealed differences in mental capacity attributions that standard surveys missed. These results emphasize why we need multi-layered methods to truly capture the complexity of mind perception dynamics in HRI.

Key Research Questions

• What factors influence people's attributions of mental states and capacities to robots?
• How do agent type (human vs. robot) and action type (communicative vs. noncommunicative) influence mind attribution in real-time interactions?
• How do generational differences and individual traits shape the perception of robot agency and experience?
• To what extent are implicit behavioral metrics (e.g., response times, mouse trajectories) consistent with explicit verbal judgments of mind perception?

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