用认知网络分析青少年与大学生对理工科的态度，发现数学焦虑导致抽象化认知。
Cognitive networks reconstruct mindsets about STEM subjects and educational contexts in almost 1000 high-schoolers, University students and LLM-based digital twins
- 构建行为心智网络，通过关键词关联和情感标注刻画群体思维模式。
- 数学与统计常被负面情绪包围，高焦虑者表现更抽象、去情境化。
- 大模型数字孪生可模拟文化态度，但缺真实教育焦虑的体验细节。
态度形成源于概念知识、教育经历与情感的交互作用。本文运用认知网络科学，重建群体心智为行为形式心智网络（BFMNs）：节点为提示词与自由联想，边为实证关联，每个概念附带情感极性标注。分析涵盖994名观察对象，包括高中生、大学生及早期职业理工专家，以及基于GPT-oss生成的“数字孪生”模拟相应群体。聚焦关键目标概念（如理工科目或教育角色/场所）周围的语义邻域（“框架”），量化其情感光环、情绪特征、网络重叠度（杰卡德相似性）及相对于随机基线的具象性。结果显示，科学与研究整体被正向建构，但核心数理学科（数学、统计）则呈现更强负面情绪与焦虑感，尤其在高数学焦虑子群体中更为显著，揭示了理工认知与情感间的不协调。高焦虑框架的具象性低于随机水平，表明对威胁性数理领域的表征更具抽象性和去情境化特征。人类网络中数学与焦虑的重叠高于GPT-oss。结果表明，BFMNs能捕捉目标领域的心智—情感特征，且大模型数字孪生虽可近似文化态度，却缺失与具体情境相关的经验成分，难以复现真实教育焦虑。
原文摘要 · Abstract (English)
Attitudes toward STEM develop from the interaction of conceptual knowledge, educational experiences, and affect. Here we use cognitive network science to reconstruct group mindsets as behavioural forma mentis networks (BFMNs). In this case, nodes are cue words and free associations, edges are empirical associative links, and each concept is annotated with perceived valence. We analyse BFMNs from N = 994 observations spanning high school students, university students, and early-career STEM experts, alongside LLM (GPT-oss) "digital twins" prompted to emulate comparable profiles. Focusing also on semantic neighbourhoods ("frames") around key target concepts (e.g., STEM subjects or educational actors/places), we quantify frames in terms of valence auras, emotional profiles, network overlap (Jaccard similarity), and concreteness relative to null baselines. Across student groups, science and research are consistently framed positively, while their core quantitative subjects (mathematics and statistics) exhibit more negative and anxiety related auras, amplified in higher math-anxiety subgroups, evidencing a STEM-science cognitive and emotional dissonance. High-anxiety frames are also less concrete than chance, suggesting more abstract and decontextualised representations of threatening quantitative domains. Human networks show greater overlapping between mathematics and anxiety than GPT-oss. The results highlight how BFMNs capture cognitive-affective signatures of mindsets towards the target domains and indicate that LLM-based digital twins approximate cultural attitudes but miss key context-sensitive, experience-based components relevant to replicate human educational anxiety.
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