Dynamics of Mind Perception in Human-Robot Interaction: Investigating Determinants Related to the Perceiver and the Perceived Using Real-Time Implicit and Explicit Measurements

Date

2024-9-3

Author

Pekçetin, Tuğçe Nur

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Humans have long been curious about other minds; a fascination rooted in ancient philosophy that shapes key debates in modern cognitive science. As artificial intelligences become more widespread, the human tendency to attribute mental states—known as mind perception—to non-human entities has found new relevance in human-robot interaction. This thesis explores the dynamics of mind perception in this context, focusing on determinants related to both the perceiver and the perceived entity. In multiple-step experiments involving 160 participants from four generations, we examined how agent type (human vs. robot), action type (communicative vs. noncommunicative), individual traits, and generational differences influence mental capacity attributions. We measured mind perception along Agency (ability to do) and Experience (ability to feel) dimensions. Our methodology combined implicit and explicit tasks in a real-time, naturalistic lab setting with live actors, enhancing ecological validity while maintaining experimental control. We collected both behavioral measurements and self-report answers, addressing the current discussions in the field. Findings revealed that humans were consistently attributed higher mental capacities than robots. Action type effects were varied and context-dependent. Younger participants were more likely to attribute mental states to robots, while individual traits showed weak influences. Explicit measures aligned with implicit ones in showing higher mind attribution to humans; however, implicit measures revealed subtler effects, particularly for action type and agency. Response times and mouse trajectories captured nuances and interactions that were not apparent in explicit ratings. This thesis highlights the significance of considering both perceiver- and perceived-related factors and using implicit and explicit assessment methods to reveal layered interactions among determinants by uncovering distinctions between these two measurement types.

Subject Keywords

mind perception, human-robot interaction, implicit measures, naturalistic lab setup, generational differences

URI

https://hdl.handle.net/11511/111545

Collections

Graduate School of Informatics, Thesis