Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

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

Departing from traditional linguistic models, advances in deep learning have resulted in a
new type of predictive (autoregressive) deep language models (DLMs). Using a self …

Deep learning algorithms trained to predict masked words from large amount of text have
recently been shown to generate activations similar to those of the human brain. However …

During real-time language comprehension, our minds rapidly decode complex meanings
from sequences of words. The difficulty of doing so is known to be related to words' …

Contextual embeddings, derived from deep language models (DLMs), provide a continuous
vectorial representation of language. This embedding space differs fundamentally from the …

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 …

Humans can attribute beliefs to others. However, it is unknown to what extent this ability
results from an innate biological endowment or from experience accrued through child …

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