A Mentalistic Interface for Probing Folk-Psychological Attribution to Non-Humanoid Robots
🤖 AI Summary
This study investigates how linguistic framing influences human attribution of mental states to non-humanoid robots. By rigorously holding robot behavior constant and systematically manipulating explanatory language through three narrative frameworks—mentalistic, teleological, and mechanistic—the research isolates, for the first time, the independent effect of language on the adoption of the intentional stance. The experimental platform integrates a simulated robot, a realistic task environment, and a large language model–based module that generates multi-perspective explanations. This controlled paradigm reveals the pivotal role of linguistic framing in shaping human attributions and provides a theoretical foundation for designing explainable human-robot interactions.
Application Category
📝 Abstract
This paper presents an experimental platform for studying intentional-state attribution toward a non-humanoid robot. The system combines a simulated robot, realistic task environments, and large language model-based explanatory layers that can express the same behavior in mentalistic, teleological, or mechanistic terms. By holding behavior constant while varying the explanatory frame, the platform provides a controlled way to investigate how language and framing shape the adoption of the intentional stance in robotics.
Problem
Research questions and friction points this paper is trying to address.
intentional stance
folk psychology
non-humanoid robots
mentalistic attribution
explanatory framing
Innovation
Methods, ideas, or system contributions that make the work stand out.
mentalistic interface
intentional stance
large language models
non-humanoid robots
explanatory framing
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