Abstract Language behaviour is complex, but neuroscientific evidence disentangles it into
distinct components supported by dedicated brain areas or networks. In this Review, we …

It has long been argued that only humans could produce and understand language. But
now, for the first time, artificial language models (LMs) achieve this feat. Here we survey the …

Large language models (LLMs) have demonstrated remarkable prediction performance for a
growing array of tasks. However, their rapid proliferation and increasing opaqueness have …

During language acquisition, children follow a typical sequence of learning stages, whereby
they first learn to categorize phonemes before they develop their lexicon and eventually …

Speech in psychosis has long been ascribed as involving 'loosening of associations'. We
pursued the aim to elucidate its underlying cognitive mechanisms by analysing picture …

We introduce Metric-Learning Encoding Models (MLEMs) as a new approach to understand
how neural systems represent the theoretical features of the objects they process. As a proof …

Humans understand sentences word-by-word, in the order that they hear them. This
incrementality entails resolving temporary ambiguities about syntactic relationships. We …

Over the past decade, studies of naturalistic language processing where participants are
scanned while listening to continuous text have flourished. Using word embeddings at first …

Two fundamental questions in neurolinguistics concerns the brain regions that integrate
information beyond the lexical level, and the size of their window of integration. To address …

A generative incremental dependency parser enhanced with sequence encodings from a
large language model was used to calculate a syntactic surprisal measure in order to …