Research in this axis addresses conceptual questions about representation, reasoning, intentionality, and the relationship between artificial and human cognition. It also explores ethical issues such as how AI systems should be evaluated, how responsibility should be assigned, and whether artificial agents can or should be granted particular forms of moral or legal status.

Research in this axis investigates how humans reason about AI systems, how they interpret machine behavior, and how misunderstandings or biases can arise during interaction. Experimental paradigms adapted from cognitive science are used to compare human and artificial decision processes, including reasoning, learning, and moral judgment. This work helps identify points of convergence and divergence between human cognition and artificial systems.

Research in this axis examines how AI influences information dissemination, social behavior, learning environments, and health outcomes. This includes studying the effects of generative AI on public communication, identifying biases and limitations in real-world AI tools, and developing methods to translate laboratory advances into reliable applications.

Research in this axis uses AI to analyze naturalistic data such as speech, text, behavior, and neural activity. Computational models are treated as explicit hypotheses about cognitive or neural mechanisms, allowing researchers to test how learning, representation, and dynamics give rise to observable behavior. In many cases, this work leads to the development of new analytical and conceptual tools that refine theoretical constructs, such as internal representations, cognitive states, or dimensions of subjective experience.

Historically, insights about human learning, decision making, and representation have played a major role in the development of AI, from early expert systems to reinforcement learning and modern deep neural architectures. This tradition continues today.

Si la recherche en intelligence artificielle s’est particulièrement accélérée et démocratisée ces dernières années avec l’apparition de systèmes de plus en plus performants,

Cette rencontre est organisée en l’honneur de la médaille d’or du CNRS décernée en 2025 à Stéphane Mallat, mathématicien, professeur au département d’informatique de l’ENS-PSL et au Collège de France. Elle sera consacrée aux thématiques de recherche qu’il a profondément marquées, à la croisée des mathématiques appliquées, du traitement du signal et de l’intelligence artificielle, et plus largement aux axes scientifiques portés par le Centre de Sciences des Données (CSD) de l’ENS, dont il est l'un des membres.

Research in this axis addresses conceptual questions about representation, reasoning, intentionality, and the relationship between artificial and human cognition. It also explores ethical issues such as how AI systems should be evaluated, how responsibility should be assigned, and whether artificial agents can or should be granted particular forms of moral or legal status.