Research published in Nature Neuroscience asked whether brain predictions work like the next-word predictions used in phones and large language models (LLMs). Coauthor David Poeppel explains that while LLMs are trained to predict the next word, the human brain makes predictions by grammatically grouping words into phrases.
The study tested Mandarin Chinese speakers and recorded brain activity with magnetoencephalography (MEG). It also used behavioral Cloze tests, where people fill missing words, and the team examined additional brain data from patients exposed to English to check cross-language consistency.
To compare brain responses and model behavior, the researchers used LLMs to compute entropy (many possible continuations) and surprisal (how unexpected a word is). If the brain worked like an LLM, correlations between brain signals and model predictions would be uniformly high. Instead, brain responses varied with a word’s position inside grammatical structure, showing sensitivity to constituents. The authors conclude human prediction is balanced and modulated by grammatically organized chunks, not just next-word probability.
Difficult words
- entropy — measure of unpredictability among many options
- surprisal — how unexpected an event or item is
- magnetoencephalography — a brain activity recording method using magnetic fieldsmagnetoencephalography (MEG)
- cloze test — task where people fill missing wordsCloze tests
- constituent — a group of words that form a unitconstituents
- modulate — to change strength or level of somethingmodulated
Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.
Discussion questions
- Do you think your brain predicts words more like an LLM or by grouping words into grammatical chunks? Why?
- Why is it useful for researchers to test speakers of different languages, such as Mandarin and English, in this study?
- How might the finding that prediction is modulated by grammatical chunks affect text prediction tools on phones?
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