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 …

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

To understand the architecture of human language, it is critical to examine diverse
languages; however, most cognitive neuroscience research has focused on only a handful …

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 …

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 …

Two analytic traditions characterize fMRI language research. One relies on averaging
activations across individuals. This approach has limitations: because of inter-individual …

Reading a sentence entails integrating the meanings of individual words to infer more
complex, higher-order meaning. This highly rapid and complex human behavior is known to …

Word co‐occurrence patterns in language corpora contain a surprising amount of
conceptual knowledge. Large language models (LLMs), trained to predict words in context …

Aside from the language-selective left-lateralized frontotemporal network, language
comprehension sometimes recruits a domain-general bilateral frontoparietal network …

Abstract Representations from artificial neural network (ANN) language models have been
shown to predict human brain activity in the language network. To understand what aspects …