The study, published in the Proceedings of the National Academy of Sciences, shows that the function of these AI language models resembles the method of language processing in the human brain, suggesting that the human brain may use next-word prediction to drive language processing.

In this new study, a team of researchers at MIT analysed 43 different language models, many of which were optimized for next-word prediction.

These models include the GPT-3 (Generative Pre-trained Transformer 3), which can generate realistic text when given a prompt, or other ones designed to provide a fill-in-the-blanks function. Researchers presented each model with a string of words to measure the activity of its neural nodes. They then compared these patterns to activity in the human brain, measured when test subjects performed language tasks like listening, reading full sentences, and reading one word at a time.

The study showed that the best performing next-word prediction models had activity patterns that bore the most resemblance to those of the human brain. In addition, activity in those same models also correlated with human behavioural measures, such as how fast people could read the text.

The new study results suggest that next-word prediction is one of the key functions in language processing, supporting a previously proposed hypothesis but has yet to be confirmed.

Boffins have not found any brain circuits or mechanisms that conduct that type of processing. Moving forward, the researchers plan to build variants of the next-word prediction models to see how small changes between each model affect their processing ability.

They plan to combine these language models with computer models developed to perform other brain-like tasks, such as perception of the physical world.