ArticleCommunications psychology2024
Studying and improving reasoning in humans and machines.
Article in Communications psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- The cognitive biases that may exacerbate inflationary and deflationary positions about large language models.Current opinion in behavioral sciences · 2026Review
- Legal Decision Biases in GPT: A Comparison with Human Judgment.Behavioral sciences (Basel, Switzerland) · 2026Article
- A large-scale comparison of divergent creativity in humans and large language models.Nature human behaviour · 2026Article
- Influence of solution efficiency and valence of instruction on additive and subtractive solution strategies in humans, GPT-4, and GPT-4o.Communications psychology · 2026Article
- Divergent patterns of probabilistic reasoning in humans and GPT-5.Frontiers in psychology · 2026Article
- A moral Turing test: How belief and source shape detection of and agreement with LLM judgments.PloS one · 2026Article
- The simulation of judgment in LLMs.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Humans and LLMs rate deliberation as superior to intuition on complex reasoning tasks.Communications psychology · 2025Article
- Identification of Long-Term Care Facility Residence From Admission Notes Using Large Language Models.JAMA network open · 2025Observational
- How should the advancement of large language models affect the practice of science?Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- How human-AI feedback loops alter human perceptual, emotional and social judgements.Nature human behaviour · 2025Article
- Using large language models to facilitate academic work in the psychological sciences.Current psychology (New Brunswick, N.J.) · 2025Article
- Human reinforcement learning processes and biases: computational characterization and possible applications to behavioral public policy.Mind & society · 2025Article
- Relative Value Encoding in Large Language Models: A Multi-Task, Multi-Model Investigation.Open mind : discoveries in cognitive science · 2025Article
- A tutorial on open-source large language models for behavioral science.Behavior research methods · 2024Article
- Generative artificial intelligence performs rudimentary structural biology modeling.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In the present study, we investigate and compare reasoning in large language models (LLMs) and humans, using a selection of cognitive psychology tools traditionally dedicated to the study of (bounded) rationality. We presented to human participants and an array of pretrained LLMs new variants of classical cognitive experiments, and cross-compared their performances. Our results showed that most of the included models presented reasoning errors akin to those frequently ascribed to error-prone, heuristic-based human reasoning. Notwithstanding this superficial similarity, an in-depth comparison between humans and LLMs indicated important differences with human-like reasoning, with models' limitations disappearing almost entirely in more recent LLMs' releases. Moreover, we show that while it is possible to devise strategies to induce better performance, humans and machines are not equally responsive to the same prompting schemes. We conclude by discussing the epistemological implications and challenges of comparing human and machine behavior for both artificial intelligence and cognitive psychology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.