Studying and improving reasoning in humans and machines.

Autor: Yax N; Laboratoire de neurosciences cognitives et computationnelles, Institut national de la santé et de la recherche médicale, Paris, France.; Département d'études cognitives, Ecole normale supérieure - PSL Research University, Paris, France.; FLOWERS Lab, Institut national de recherche en informatique et en automatique, Bordeaux, France., Anlló H; Laboratoire de neurosciences cognitives et computationnelles, Institut national de la santé et de la recherche médicale, Paris, France.; Département d'études cognitives, Ecole normale supérieure - PSL Research University, Paris, France., Palminteri S; Laboratoire de neurosciences cognitives et computationnelles, Institut national de la santé et de la recherche médicale, Paris, France. stefano.palminteri@ens.fr.; Département d'études cognitives, Ecole normale supérieure - PSL Research University, Paris, France. stefano.palminteri@ens.fr.
Jazyk: angličtina
Zdroj: Communications psychology [Commun Psychol] 2024 Jun 03; Vol. 2 (1), pp. 51. Date of Electronic Publication: 2024 Jun 03.
DOI: 10.1038/s44271-024-00091-8
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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