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

Prediction is often regarded as an integral aspect of incremental language comprehension,
but little is known about the cognitive architectures and mechanisms that support it. We …

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 …

Several deep neural networks have recently been shown to generate activations similar to
those of the brain in response to the same input. These algorithms, however, remain largely …

Abstract Representations from transformer-based unidirectional language models are
known to be effective at predicting brain responses to natural language. However, most …

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

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 …

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

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

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 …