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 …

Efforts to understand the brain bases of language face the Map** Problem: At what level
do linguistic computations and representations connect to human neurobiology? We review …

Recent advancements in artificial intelligence have sparked interest in the parallels between
large language models (LLMs) and human neural processing, particularly in language …

Current large language models (LLMs) rely on word prediction as their backbone pretraining
task. Although word prediction is an important mechanism underlying language processing …

Deep language algorithms, like GPT-2, have demonstrated remarkable abilities to process
text, and now constitute the backbone of automatic translation, summarization and dialogue …

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 …

Human language is expressive because it is compositional: The meaning of a sentence
(semantics) can be inferred from its structure (syntax). It is commonly believed that language …

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

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 …

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