Self-reported mentalizing and AI differentiation: An empirical study with implications for construct validity

Engineering notes

Key topics: autonomous driving, large language model. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Generative artificial intelligence (gAI) systems are increasingly capable of producing human-like writing, yet little is known about the cognitive factors that influence an individual’s ability to distinguish AI-generated from human-authored text. This study investigated whether self-reported mentalizing, measured using the Multidimensional Mentalizing Questionnaire (MMQ), was associated with performance on an AI differentiation task involving scientific abstracts and news headlines generated using a contemporary large language model. To evaluate this relationship, multiple complementary analytical approaches were employed, including Pearson correlation, multiple linear regression, principal component analysis, bootstrap resampling, cross-validation, and model comparison. Participants performed near chance overall, and no MMQ composite, individual factor, or latent component demonstrated a robust association with AI differentiation performance after correction for multiple comparisons and evaluation of out-of-sample generalizability. Although one MMQ factor (Reflexivity) showed a nominal association with AI news headline differentiation, this relationship was not robust across validation procedures.These findings indicate that self-reported mentalizing, as operationalized by the MMQ, did not reliably predict performance on the present AI differentiation task. However, the results should be interpreted cautiously because AI differentiation itself remains an emerging construct. Unlike many psychological tasks that involve relatively stable human behaviors, AI differentiation depends on identifying outputs from rapidly evolving algorithmic systems whose stylistic characteristics change across model generations. Consequently, it remains uncertain whether AI differentiation represents a stable psychological construct with enduring cognitive determinants or performance against a temporally specific technological artifact. The present findings therefore apply to the specific experimental paradigm and LLM generation examined and highlight the need for future research to establish the construct validity and temporal stability of AI differentiation before drawing broader conclusions regarding its cognitive predictors.

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