Stanislas Dehaene

Collège de France

Psychologie cognitive expérimentale

Année 2025-2026

06 - Qu'est-ce que la conscience, et quels sont ses mécanismes cérébraux ? : Relation entre conscience et mémoire de travail, et applications cliniques

Stanislas Dehaene

Collège de France

Psychologie cognitive expérimentale

Année 2025-2026

05 - Qu'est-ce que la conscience, et quels sont ses mécanismes cérébraux ? : Décours temporel du traitement conscient

Stanislas Dehaene

Collège de France

Psychologie cognitive expérimentale

Année 2025-2026

04 - Qu'est-ce que la conscience, et quels sont ses mécanismes cérébraux ? : La « distillation » des corrélats neuronaux de la perception consciente

Stanislas Dehaene

Collège de France

Psychologie cognitive expérimentale

Année 2025-2026

03 - Qu'est-ce que la conscience, et quels sont ses mécanismes cérébraux ? : L'ignition, une signature cérébrale de l'accès à la conscience

Stanislas Dehaene

Collège de France

Psychologie cognitive expérimentale

Année 2025-2026

02 - Qu'est-ce que la conscience, et quels sont ses mécanismes cérébraux ? : Profondeur et limites du traitement non-conscient : données récentes

Stanislas Dehaene

Collège de France

Psychologie cognitive expérimentale

Année 2025-2026

01 - Qu'est-ce que la conscience, et quels sont ses mécanismes cérébraux ? : Bref historique des recherches sur la conscience et modèle de l'espace de travail neuronal global

Stanislas Dehaene

Chaire Psychologie cognitive expérimentale

Année 2025-2026

Collège de France

Colloque : Seeing the Mind, Educating the Brain

Concluding Remarks

Colloque - Stanislas Dehaene : Concluding Remarks

Stanislas Dehaene

Stanislas Dehaene

Chaire Psychologie cognitive expérimentale

Année 2025-2026

Collège de France

Colloque : Seeing the Mind, Educating the Brain

Theme: Human Singularity

Scaling Intelligence the Human Way

Colloque - Josh Tenenbaum : Scaling Intelligence the Human Way

Josh Tenenbaum

Stanislas Dehaene

Chaire Psychologie cognitive expérimentale

Année 2025-2026

Collège de France

Colloque : Seeing the Mind, Educating the Brain

Theme: Human Singularity

Dissecting the Language of Thought Hypothesis across Marr's Levels

Colloque - Mathias Sablé-Meyer : Dissecting the Language of Thought Hypothesis across Marr's Levels

Mathias Sablé-Meyer

Résumé

The Language of Thought (LoT) hypothesis posits that mental representations are best understood as programme-like objects; indeed, "thoughts" share properties such as productivity and systematicity with programming languages. I tackle questions that arise from taking this hypothesis at face value and unfolding its predictions, from computational accounts to mechanistic implementation. First, zooming on humans' cognition of geometric shapes, I show that in all human groups tested (adults, children, congenitally blind), the perception of shapes is heavily influenced by geometric features. Then, I show using MEG and fMRI that the neural signature of these exact geometric properties is separate both in timing and localisation from typical visual processes. To generalise beyond quadrilaterals, I commit to a proposition for a generative language of shapes to account for the complexity of geometric shapes in humans, while implementing an algorithm for perception-as-program-inference. Finally, building on recent results in rodent neuroscience, I sketch a research programme and give preliminary results on a mechanistic understanding of how program-like representations might be implemented by populations of neurons.

Stanislas Dehaene

Chaire Psychologie cognitive expérimentale

Année 2025-2026

Collège de France

Colloque : Seeing the Mind, Educating the Brain

Theme: Human Singularity

The What?, How? And Why? Of Behavior: Using Cognitive Computational Models to Answer Distinct Questions about Human Cognition

Colloque - Valentin Wyart : The What?, How? And Why? Of Behavior: Using Cognitive Computational Models to Answer Distinct Questions about Human Cognition

Valentin Wyart

Résumé

Quantitative modeling approaches are routinely used in cognitive science to make sense of behavior. Statistical models are designed to test *what* specific patterns are present in behavior, whereas cognitive computational models are developed to describe *how* specific behavioral patterns may emerge from latent cognitive processes. These two types of modeling approaches have successfully identified characteristic (and sometimes suboptimal) features of human learning and decision-making under uncertainty. In this talk, I will argue that cognitive computational models can be used to answer the distinct question of *why* these characteristic features are there. I will use recent studies that rely on different classes of models (low-dimensional algorithmic models, high-dimensional neural networks) to explain characteristic features of human cognition in terms of latent objectives and constraints.