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 …

Natural language is often seen as the single factor that explains the cognitive singularity of
the human species. Instead, we propose that humans possess multiple internal languages …

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 …

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 …

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

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

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

We conducted a systematic review and meta-analysis of 30 functional magnetic resonance
imaging studies investigating processing of musical rhythms in neurotypical adults. First, we …

To understand language, we must infer structured meanings from real-time auditory or visual
signals. Researchers have long focused on word-by-word structure building in working …

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 …