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.

翻译：本研究通过考察感知非人化与结构相似性作为超越拟人化的心理因素，探讨用户对生成式AI的深度自我表露行为。感知非人化可能降低评价顾虑，而结构相似性指用户思维与AI应答之间感知到的逻辑一致性。基于2025年收集的2400名参与者的横截面调查数据，本研究分析了上述因素与自我表露发生概率及深度的关联。逻辑回归分析表明，两种感知均高的群体（D象限）自我表露概率显著高于基线群体（A象限；OR=11.35）。方差分析进一步揭示不同群体在自我表露深度上存在显著差异。研究结果表明，深度自我表露中的信任相关行为可能涉及拟人化感知以外的因素。鉴于本研究为探索性研究且基于自我报告调查数据，结果应被视为关联性而非因果性结论，未来需开展纵向或实验研究进行验证。