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 …

Considerable progress has recently been made in natural language processing: deep
learning algorithms are increasingly able to generate, summarize, translate and classify …

Transformer models such as GPT generate human-like language and are predictive of
human brain responses to language. Here, using functional-MRI-measured brain responses …

High-performing neural networks for vision have dramatically advanced our ability to
account for neural data in biological systems. Recently, further improvement in performance …

Many recent studies have shown that representations drawn from neural network language
models are extremely effective at predicting brain responses to natural language. But why …

Effective communication hinges on a mutual understanding of word meaning in different
contexts. We recorded brain activity using electrocorticography during spontaneous, face-to …

Abstract Language neuroscience currently relies on two major experimental paradigms:
controlled experiments using carefully hand-designed stimuli, and natural stimulus …

Abstract Language models have been shown to be very effective in predicting brain
recordings of subjects experiencing complex language stimuli. For a deeper understanding …

A comprehensive neural model of language must accommodate four components:
representations, operations, structures and encoding. Recent intracranial research has …