research

The overarching aim of my research programme is to understand the computational principles of neural information processing, focussing on how brains and artificial neural networks understand language and make sense of the world. Currently, I am pre-occupied with the following two research directions:

Studying language understanding in and with Large Language Models (LLMs)

Large language models (LLMs) now broadly approach human-level performance on many language tasks, like summarisation or question answering. Moreover, LLMs are also the best available encoding models for predicting brain responses to linguistic stimuli. A major aim of my current work is to understand the reasons behind their impressive performance, on both domains. Do LLMs “understand” language – and if so, is their understanding in any way like that of humans? Can we dissect their representations into human-interpretable components, like syntax and meaning, or world and agent models? Can we use such dissections to better understand what is driving the alignment between LLM representations and brain responses to language? And can we use what we know from humans to build LLMs that operate in a more human-like manner?

Generative AI for strong and precise tests of the predictive brain hypothesis

A prominent idea in cognitive science is that the brain is a ‘prediction machine’, constantly comparing incoming signals to internal predictions. Advances in AI are allowing for new and better ways to test this hypothesis: using generative AI models to approximate the predictions the brain might be making, and comparing these to brain responses. Previously, I used this approach – which combines deep generative modelling with neural data science – to study prediction in language and music processing. Currently, I am expanding the framework to ask further questions. For instance, do the predictions we find in language generalise to other domains, like vision? At what level of abstraction does the brain predict? And what information is driving these predictions?

Working with me. If you are a student interested in working on anything related to the above, do get in touch. Note that it useful but not necessary to have a formal background in computational modelling or AI. It is, however, important to have ample experience with, and an affinity for, programming in Python.