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.
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BibTeX
@misc{bombach2026response,
title = {{Response Variability and Stability in Human Reasoning}},
author = {Clemens Bombach and Rajmadan Lakshmanan and Marco Ragni},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03008},
eprint = {2610.03008},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03008},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Response Variability and Stability in Human Reasoning
AU - Clemens Bombach
AU - Rajmadan Lakshmanan
AU - Marco Ragni
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.03008
UR - https://arxiv.org/abs/2610.03008
AB - 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.
ER -