发现用户对AI的深度倾诉，不只因觉得它像人，更因觉得它不像人且逻辑匹配。
Beyond Anthropomorphism: Exploring the Roles of Perceived Non-humanity and Structural Similarity in Deep Self-Disclosure Toward Generative AI
- 从非人性化与结构相似性切入，突破传统拟人化研究视角。
- 感知非人性化+结构相似性高的用户，自述深度提升11.35倍。
- 适合关注AI心理互动、人机信任机制的研究者阅读。
本研究通过考察感知非人性化和结构相似性这两个超越拟人化的心理因素，探索用户对生成式AI的深度自我披露行为。感知非人性化可能降低评价焦虑，而结构相似性指用户感知到自身思维与AI回应之间的逻辑一致性。基于2025年收集的2400名参与者横断面调查数据，分析了自我披露的发生率与深度。逻辑回归显示，同时高感知非人性化与结构相似性的群体（D组）相比基线组（A组）具有显著更高的披露可能性（OR = 11.35）。方差分析进一步揭示各组间披露深度存在显著差异。结果表明，深度自我披露中的信任行为可能涉及拟人化之外的因素。由于研究为探索性且依赖自我报告数据，结果应视为相关而非因果，未来需纵向或实验研究验证。
原文摘要 · Abstract (English)
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.
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