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This paper introduces a novel geometry-based framework to quantify the stability and variability of individual human reasoning patterns, moving beyond simple accuracy metrics. By analyzing response distances and internal variability, the authors demonstrate that this variation measure, when combined with correctness, predicts learning gains in reasoning tasks. The findings also confirm that individual reasoning patterns exhibit significant stability over time.
Human reasoning patterns are surprisingly stable, and their internal variability predicts learning gains, suggesting a new avenue for personalized cognitive training.
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.