Recognition: unknown
Beyond Anthropomorphism: Exploring the Roles of Perceived Non-humanity and Structural Similarity in Deep Self-Disclosure Toward Generative AI
Pith reviewed 2026-05-14 18:03 UTC · model grok-4.3
The pith
Perceived non-humanity and structural similarity together raise the odds of deep self-disclosure to generative AI more than eleven times over baseline.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The study claims that users who simultaneously perceive generative AI as high in non-humanity and high in structural similarity exhibit substantially elevated likelihood and depth of self-disclosure compared with users low on both perceptions, quantified by an odds ratio of 11.35 in logistic regression and by significant between-group differences in ANOVA on disclosure depth scores.
What carries the argument
Four user segments formed by crossing high/low perceived non-humanity with high/low structural similarity, then compared via logistic regression on disclosure occurrence and ANOVA on disclosure depth.
If this is right
- Deep self-disclosure to generative AI can occur without anthropomorphic perceptions of the system.
- Trust-related disclosure behaviors involve psychological factors beyond the desire for human-like interaction.
- The combination of reduced evaluation apprehension and perceived logical match predicts both whether and how deeply users disclose.
- Exploratory cross-sectional patterns indicate the need for longitudinal or experimental follow-up to establish directionality.
Where Pith is reading between the lines
- Interface designs that emphasize logical consistency while downplaying human-like features may increase user willingness to share sensitive information.
- The same pattern could apply to other AI contexts such as mental-health support tools where users seek a non-judgmental but aligned partner.
- Future models of disclosure should test whether controlling for personality traits or prior AI exposure alters the observed group differences.
Load-bearing premise
Self-reported perceptions of non-humanity and structural similarity validly measure the psychological mechanisms at work and cross-sectional group comparisons isolate their effects without major confounding from prior AI experience or personality traits.
What would settle it
A controlled experiment that independently varies an AI system's displayed non-human traits and logical alignment with a user's thinking style, then measures the depth of actual personal information the user volunteers, would directly test whether those perceptions cause higher disclosure.
read the original 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.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This paper reports results from a cross-sectional survey of 2,400 participants examining associations between perceived non-humanity, structural similarity, and deep self-disclosure to generative AI. Logistic regression shows that the group high on both perceptions (Segment D) has substantially elevated odds of disclosure relative to the baseline group (Segment A; OR = 11.35), with ANOVA indicating between-group differences in disclosure depth. The authors frame the work as exploratory and associative, explicitly cautioning against causal interpretation.
Significance. If the reported associations prove robust to confounding, the study contributes to HCI and AI-interaction literature by identifying non-anthropomorphic perceptual factors that may facilitate self-disclosure. The large sample size and dual outcome measures (occurrence and depth) provide reasonable statistical power for detecting group differences, though the cross-sectional design inherently limits causal inference as the authors note.
major comments (2)
- [Results] Results section: The logistic regression yielding OR = 11.35 for Segment D versus Segment A does not report the full covariate set (e.g., prior AI experience, personality traits, or general trust propensity). In a single-wave survey where perceptions and disclosure are measured concurrently, omission of these variables leaves open substantial residual confounding that could account for much of the large observed association.
- [Methods] Methods section: The construction of the four segments (A–D) from the two continuous perception scales is not described in sufficient detail (e.g., whether median splits, quartiles, or other thresholds were used). This ambiguity affects both replicability and the claim that the group contrast isolates distinct psychological mechanisms.
minor comments (1)
- [Abstract] The abstract and discussion could more explicitly list the covariates that were (or were not) included in the logistic models to help readers assess confounding risk.
Circularity Check
No circularity: purely empirical statistical analysis with no derivation chain
full rationale
The paper reports logistic regression (OR=11.35 for Segment D vs A) and ANOVA on cross-sectional survey data (N=2400) measuring perceived non-humanity, structural similarity, and self-disclosure. No equations, fitted parameters renamed as predictions, self-citations, or ansatzes appear in the load-bearing steps. Segments are defined from measured variables and tested against a separate outcome; results are presented as associative only. This matches the default non-circular case for empirical work.
Axiom & Free-Parameter Ledger
axioms (2)
- domain assumption Logistic regression model assumptions (linearity in logit, independence of observations) hold for the survey responses
- domain assumption ANOVA is suitable for detecting between-group differences in disclosure depth scores
Reference graph
Works this paper leans on
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[1]
Introduction Since 2025, the rapid proliferation of generative artificial intelligence (AI), exemplified by models such as ChatGPT and Gemini, has brought about a qualitative transformation in human-computer interaction (HCI). A particularly noteworthy phenomenon is users engaging in "deep self-disclosure" toward AI—the sharing of personally or socially s...
work page 2025
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[2]
Depth of Self-disclosure (Dependent Variable 2): In this study, to perform an exploratory verification not only of the "presence" but also of the "depth" of disclosure using secondary data, the depth of self-disclosure was defined as an operational index consisting of four items. Specifically, four items related to emotional venting, sense of trust, sense...
work page 1973
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[3]
Discussion 5.1. Complementary Perspective to Anthropomorphism-based Models Consistent with the hypotheses, the results suggest that deep self-disclosure to generative AI may be explained not solely by the anthropomorphism of the AI itself, but also in association with a combination of the perception of non-humanity and the perception of structural similar...
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[4]
Future Life Survey with AI 2025
Conclusion and Limitations The present findings suggest, based on a secondary analysis of large-scale survey data collected in 2025, that deep self-disclosure toward generative AI may be associated with the perception of AI’s non-humanity and structural similarity. In particular, the notably higher tendency observed in both the occurrence and depth of sel...
discussion (0)
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