Lexical Indicators of Mind Perception in Human-AI Companionship
Authors: Jaime Banks, Jianghui Li
Organizations: School of Information Studies, Syracuse University, Syracuse, NY, USA · School of Information, University of Texas at Austin, Austin, TX, USA
Mind perception (MP) is a psychological phenomenon in which humans automatically infer that another entity has a mind and/or mental capacities, usually understood in two dimensions (perceived agency and experience capacities). Despite MP's centrality to many social processes, understanding how MP may function in humans' machine companionship relations is limited. This is in part due to reliance on self reports and the gap between automatic MP processes and more purposeful and norm governed expressions of MP. We here leverage MP signaling language to explore the relationship between MP and AI companionship in humans' natural language. We systematically collected discussions about companionship from AI dedicated Reddit forums and examined the cooccurrence of words (a) known to signal agentic and experiential MP and those induced from the data and (b) discussion topics related to AI companionship. Using inductive and deductive approaches, we identify a small set of linguistic indicators as reasonable markers of MP in human/AI chat, and some are linked to critical discussions of companion authenticity and philosophical and ethical imaginaries.
AI companions are conversational or multimodal agents designed for ongoing emotional, social, or practical support, raising questions about safety, memory, and dependence.