PhD Position F/M Neural Representations of Multilingual Language Processing

Understanding how the brain encodes language remains one of the core challenges in cognitive neuroscience. In recent years, advances in nat- ural language processing (NLP)—particularly with brain-inspired neural network architectures—have opened new perspectives to explore how linguistic information is represented. By bringing together tools from both neuroscience and NLP, researchers can now directly compare the represen- tations learned by computational models with patterns of brain activity. This cross-disciplinary approach enables the development of more grounded and data-driven hypotheses about how language is processed in the human brain.

Although some multilingual datasets include stimuli and recordings in multiple languages (Li et al., 2022), to our knowledge, there is no fMRI neuroimaging dataset in which bilingual individuals perform the same task in both of their languages. To address this, our plan is to collect fMRI data from bilingual participants who speak both their first language (L1) and second language (L2) at a native or near-native level. We will examine how their brains respond to each language and how those responses relate to representations generated by computational models.

Overall, this PhD project will use tools from NLP to better understand how the brain understands and organizes multiple languages. By comparing brain activity recorded during natural listening with deep learning model embeddings, we hope to reveal where artificial and biological language systems might converge. Focusing on bilingual individuals will allow us to study how the brain responds to the same meaning expressed in different languages. With this project, our aim is to understand whether the brain uses a shared or language-specific system to represent meaning and how closely these systems resemble the ones used by modern language models, or vice versa.