Human reasoning shows stable patterns linked to learning gains
Response Variability and Stability in Human Reasoning
Logic in Computer Science
Summary
People’s ways of thinking and answering questions tend to be consistent over time, even if their responses vary a bit from one task to another. The authors created a new method using geometry to measure how similar or different these reasoning patterns are within individuals. They found that this variation helps predict how much people improve their reasoning over time. This method can be used to study different kinds of reasoning tasks.
What this means in practice
- •For cognitive assessment developers: Design assessments that track individuals’ reasoning stability and variability to better predict learning progress.
- •For educational software designers: Incorporate measures of response variation into learning platforms to tailor reasoning exercises that predict and enhance user improvement.
Authors
Clemens Bombach, Rajmadan Lakshmanan, Marco Ragni
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.