Menu
Sign In Search Podcasts Charts People & Topics Add Podcast API Pricing
Podcast Image

Colloques du Collège de France - Collège de France

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

03 Oct 2025

Description

Stanislas DehaeneChaire Psychologie cognitive expérimentaleAnnée 2025-2026Collège de FranceColloque : Seeing the Mind, Educating the BrainTheme: Human SingularityDissecting the Language of Thought Hypothesis across Marr's LevelsColloque - Mathias Sablé-Meyer : Dissecting the Language of Thought Hypothesis across Marr's LevelsMathias Sablé-MeyerRésuméThe 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.

Audio
Featured in this Episode

No persons identified in this episode.

Transcription

This episode hasn't been transcribed yet

Help us prioritize this episode for transcription by upvoting it.

0 upvotes
🗳️ Sign in to Upvote

Popular episodes get transcribed faster

Comments

There are no comments yet.

Please log in to write the first comment.