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Divergent patterns of probabilistic reasoning in humans and GPT-5

Pegah Imannezhad,E. Pothos,Andy J. Wills

Published 2026 in Frontiers in Psychology

ABSTRACT

Large Language Models (LLMs) such as GPT‑5 are increasingly consulted for advice across a wide range of domains, yet little is known about how their probability judgments compare to those of humans. This study examined GPT‑5’s adherence to classical probability rules, focusing on conjunction fallacies, disjunction fallacies, and violations of binary complementarity. Using a large dataset on human probabilistic judgments, in which participants displayed multiple types of fallacies, we tested GPT‑5 on the same task and with matched participant profiles. GPT‑5 produced only single conjunction or disjunction fallacies and showed near‑perfect compliance with binary complementarity constraints. Its overall response pattern aligned with predictions of early quantum‑probabilistic models rather than more recent variants incorporating noise. These findings suggest that GPT‑5 implements a more coherent and internally consistent form of probabilistic reasoning compared to naïve human participants.

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