My research explores different facets of language representation and comprehension. Some questions I'm interested in include: How do neural systems learn and represent content and structure? How do different knowledge types flexibly and quickly assembled into new configurations to help us with a wide range of tasks, like solving problems, or understanding a work of fiction?

Neural bases of language comprehension

I explore the neural bases of syntactic and semantic representations as a window into our generative conceptual capacity. As it is an interdisciplinary endeavour, I try my best to do my homework, grounding my work in linguistic theory, attending to constraints from neuropsychology, and borrowing designs and paradigms from cognitive science and psycholinguistics. [Papers: Law & Pylkkänen, 2021; Tulling, Law et al., 2021; Law et al., 2026]

Structure learning in neural systems

More recently, I began seeking explanations in computational terms, using tools from deep learning to test intuitions about our semantic competence and to better characterise its computational bases. I'm currently working on a project exploring how neural systems learn structural representations like number and magnitude.

AI and human and animal cognitive science

My colleagues and I have also recently argued that AI can benefit from learning from human and animal cognition research, and we outlined a way forward in a comparative approach to the evaluation of large language models.