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.

翻译：对STEM（科学、技术、工程、数学）的态度形成于概念知识、教育经历与情感体验的交互作用。本研究运用认知网络科学方法，将群体思维模式重构为行为心智形态网络。在该网络中，节点为线索词与自由联想词，边为经验性联想连接，每个概念均标注其感知情感效价。我们分析了来自N=994个观测样本的BFMN数据，涵盖高中生、大学生及STEM领域早期职业专家，同时纳入了经提示以模拟相应人群特征的LLM（GPT-oss）“数字孪生”数据。通过聚焦关键目标概念（如STEM学科或教育参与者/场所）的语义邻域（“框架”），我们从情感氛围、情绪轮廓、网络重叠度（杰卡德相似性）及相对于零基线的具体性程度等维度对框架进行量化分析。在所有学生群体中，科学与研究始终呈现积极框架，而其核心量化学科（数学与统计学）则表现出更消极且与焦虑相关的情感氛围，这种效应在数学焦虑较高的子群体中更为显著，揭示了STEM领域内部科学与数学间的认知情感失调。高焦虑框架的具体性程度显著低于随机水平，表明对具有威胁性的量化领域存在更抽象、去情境化的心理表征。人类联想网络中数学与焦虑的关联重叠度高于GPT-oss模型。研究结果凸显了BFMN在捕捉针对目标领域的认知-情感特征方面的有效性，并指出基于LLM的数字孪生虽能近似文化态度，但未能复现人类教育焦虑中关键的情境敏感性与经验依赖成分。