Beyond Anthropomorphism: Exploring the Roles of Perceived Non-humanity and Structural Similarity in Deep Self-Disclosure Toward Generative AI

TL;DR

This study explores how perceived non-humanity and structural similarity influence deep self-disclosure toward generative AI, revealing that trust-related behaviors are affected by factors beyond anthropomorphic perceptions.

Contribution

It introduces the roles of perceived non-humanity and structural similarity as psychological factors in self-disclosure toward AI, beyond traditional anthropomorphism.

Findings

High perceived non-humanity and structural similarity increase likelihood of self-disclosure.

Significant differences in disclosure depth were observed between groups with different perceptions.

Trust-related self-disclosure may involve factors other than anthropomorphic perception.

Abstract

This study investigates deep self-disclosure toward generative AI by examining perceived non-humanity and structural similarity as psychological factors beyond anthropomorphism. Perceived non-humanity may reduce evaluation apprehension, whereas structural similarity refers to the perceived logical alignment between a user's thinking and AI responses. Using cross-sectional survey data from 2,400 participants collected in 2025, this study analyzed associations with both the occurrence and depth of self-disclosure. Logistic regression indicated that the group high in both perceptions (Segment D) showed a significantly higher likelihood of disclosure than the baseline group (Segment A; OR = 11.35). ANOVA further showed significant between-group differences in disclosure depth. The findings suggest that trust-related behavior in deep self-disclosure may involve factors other than anthropomorphic perception. Because the study is exploratory and based on self-reported survey data, the results should be interpreted as associative rather than causal, and future longitudinal or experimental research is needed.