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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.

Publication Date

2026-03-03

Publication Title

Frontiers in Psychology

Volume

17

ISSN

1664-1078

Acceptance Date

2026-02-17

Deposit Date

2026-06-10

Funding

The author(s) declared that financial support was received for this work and/or its publication. EMP was supported by European Office of Aerospace Research and Development (EOARD) grant FA8655-23-1-7220.

Keywords

AI participants (AI subjects), complementarity, conjunction fallacy, disjunction fallacy, GPt-5, human vs. AI cognition, large language models (LLMs), probabilistic reasoning

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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