发表机构
TU Chemnitz（开姆尼茨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于几何的正式方法，量化人类推理模式的变异性与稳定性，并通过广义线性混合效应模型和聚类验证，发现变异度量与正确性交互可预测学习效果，且推理模式随时间稳定。
AI 中文摘要
理解人类如何推理——以及推理响应如何因任务和个体而异——仍然是认知科学中建模与解释的核心挑战。我们研究了推理者内部响应模式的稳定性，以及这些模式的变化是否可用于预测学习效果。我们提出了一种基于几何的正式方法，以启发式理论为基础，量化个体推理模式之间的距离及其内部变异性。所提出的框架通过广义线性混合效应模型和聚类方法对实验数据进行测试，我们发现所提出的变异度量与正确性交互作用，可预测表现提升。此外，我们发现同一推理者的推理模式随时间保持稳定。该方法具有足够的通用性，可应用于其他推理领域。
英文摘要
Understanding how humans reason -- and how reasoning responses vary across tasks and individuals -- remains a core challenge for modeling and explanation in cognitive science. We investigate the stability of response patterns within reasoners and whether variation in these patterns can be used to predict learning effects. We introduce a formal, geometry-based method to quantify distances between individual reasoning patterns and their internal variability, grounded in heuristic theories. The proposed framework is tested against experimental data via generalized linear mixed-effects models and clustering, where we find that our proposed variation measure interacts with correctness to predict performance gains. Moreover, we find that reasoning patterns are stable over time within the same reasoner. The method is general enough to be applied to other reasoning domains.
CommentsAccepted as full paper with talk at the 48th Annual Conference of the Cognitive Science Society (CogSci 2026). This version contains minor revisions
Journal refProceedings of the 48th Annual Meeting of the Cognitive Science Society (2026)
