Mental Content and Brain Data

A central question in both neuroscience and AI is how to understand the relationship between mental content and patterns of activation. In neuroscience, we ask: can mental states be decoded from brain activity? In AI, we ask: what do activation patterns in large language models tell us about what these systems “know” or “represent”?

These questions are deeply connected. Both involve machine learning methods applied to high-dimensional activation data, and both raise philosophical questions about the nature of representation, the objectivity of information, and the relationship between mechanism and meaning.

Associated publications