Response Variability and Stability in Human Reasoning

Abstract: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.

Accepted as full paper with talk at the 48th Annual Conference of the Cognitive Science Society (CogSci 2026). This version contains minor revisions

Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)

arXiv:2610.03008 [cs.LO]

(or arXiv:2610.03008v1 [cs.LO] for this version)

https://doi.org/10.48550/arXiv.2610.03008 Focus to learn more arXiv-issued DOI via DataCite

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