In sustained one-on-one conversations, large language models, despite their broad capabilities, still appear flat: competent, responsive, yet lacking a sense of mind. We hypothesize that the central missing ingredient is not more capability but dimensional completeness. We propose that the believability of an artificial interlocutor—the degree to which a user attributes an inner life to it, termed perceived mind—is governed by whether the agent expresses a small set of first-person stances that humans use as evidence of mind, separate from task intelligence. We identify four such dimensions—time, truth, entropy, and love—each defined as a behavioral stance rather than a benchmark competency, with a human analog and a concrete emulation path; the time dimension already has an author-reported prototype. We identify an observable behavior layer—initiative (unprompted action) and cadence (the shape and timing of turns)—through which the stances surface in conversation, partially realized as deployed features in a production companion application. We state six falsifiable predictions that a later pre-registered study will test, separating those that are pre-registrable now from those that remain conjectures pending operationalization. This is a conceptual framework: it reports no human-subjects data, and its central comparative claims are predictions, not findings. We maintain a firm boundary—object is inferrable interiority, not interiority; this is perception engineering, not a theory of machine consciousness—and treat the resulting attachment and manipulation risks as load-bearing rather than incidental.
Blogger's Review: This article explores the believability of AI in conversation, emphasizing the importance of dimensional completeness. It introduces specific behavioral stances, providing a new perspective on human-AI interaction. Future research will test these hypotheses, which could lead to new insights for the development of AGI.