Uncanny Valley in Conversation? Effects of Internal and External Human-Likeness on Intimacy and Trust

Abstract

Conversational agents built on large language models increasingly converse like humans, expressing emotions and disclosing their own experiences. Human-likeness, however, has been studied largely as a matter of appearance—how human an agent looks. Language now also lets an agent display internal human-likeness by conveying an inner life, raising the question of how deeply an AI should express its own experiences—and whether that depends on its appearance. In a 2 (appearance: machine-like vs. human-like) × 3 (self-disclosure: none vs. low vs. high) between-subjects experiment, 193 university students completed a single, brief, fully scripted counseling session with an AI counselor prototype and evaluated it on six measures, controlling for prior mind perception. The effects of disclosure depth depended on appearance: with the machine-like counselor, intimacy and social presence peaked at low and declined at high disclosure, with trust and perceived authenticity showing the same direction—an uncanny valley-like pattern—whereas with the human-like counselor evaluations generally rose with depth. Deeper disclosure was not judged more inappropriate, and moderated mediation analyses showed that deepening disclosure from low to high reduced anthropomorphism—and, through it, intimacy and trust—only for the machine-like counselor. Internal and external human-likeness were read together: how deeply an AI should speak about itself cannot be decided apart from how it looks.