Par Michael Pichat, William Pogrund, Armanush Gasparian, Paloma Pichat, Samuel Demarchi, et Michael Veillet-Guillem.
Article publié au sein de la revue en ligne Arxiv (Cornell University) le 26 décembre 2024.
Abstract
How do the synthetic neurons in language models create « thought categories » to segment and analyze their informational environment? What are the cognitive characteristics, at the very level of formal neurons, of this artificial categorical thought? Based on the mathematical nature of algebraic operations inherent to neuronal aggregation functions, we attempt to identify mathematico-cognitive factors that genetically shape the categorical reconstruction of the informational world faced by artificial cognition. This study explores these concepts through the notions of priming, attention, and categorical phasing.
DOI : https://doi.org/10.48550/arXiv.2501.06196
Michael Pichat est MCF en psychologie, fondateur du Cabinet Chrysippe-R&D et de Neocognition, co-directeur Diplômes « Management & Coaching » Université Paris Dauphine & Cabinet Chrysippe-R&D et membre titulaire de l’ER IPC.
