Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their capabilities remain split. Here, we evaluate LLMs using a distinction between formal competence — knowledge of linguistic rules and patterns — and functional competence — understanding and using language in the world. We ground this distinction in human neuroscience, showing that these skills recruit different cognitive mechanisms. Although LLMs are close to mastering formal competence, they still fail at functional competence tasks, which often require drawing on non-linguistic capacities. In short, LLMs are good models of language but incomplete models of human thought.

Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Of crucial importance in all these studies is the mapping model, which defines the space of possible relationships between features and neural data.

The ability to combine individual concepts of objects, properties, and actions into complex representations of the world is often associated with language. Yet combinatorial event-level representations can also be constructed from nonverbal input, such as visual scenes. Here, we test whether the language network in the human brain is involved in and necessary for semantic processing of events presented nonverbally.

Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill?