Human language processing is poorly matched by artificial intelligence algorithms. We analysed fMRI brain recordings of 304 participants while they listened to short stories and compared brain activations to artificial intelligence algorithms. Unlike such algorithms, we found that the human brain operates with a hierarchy of predictions that anticipate incoming words and phrases.
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References
Friston, K. & Kiebel, S. Predictive coding under the free-energy principle. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 1211–1221 (2009). A review that presents predictive coding as a general framework for neuroscience.
Goldstein, A. et al. Shared computational principles for language processing in humans in deep language models. Nat. Neurosci. 3, 369–380 (2022). This study reports neural correlates of next-word prediction in the brain.
Huth, A. G. et al. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016). This study shows how word embeddings linearly map and explain brain responses to spoken stories.
Fedorenko, E. et al. Neural correlate of the construction of sentence meaning. Proc. Natl Acad. Sci. USA 113, E6256–E6262 (2016). This study compares meaningful and meaningless sentences in the brain to understand their underlying computations.
Brown, T. B. et al. Language models are few-shot learners. In NIPS’20: Proc. 34th Internatl Conf. Neural Information Processing Systems (eds Larochelle, H. et al.) 1877–1901 (2020). This paper introduces GPT-3, a large language model with powerful generalization abilities.
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This is a summary of: Caucheteux, C., Gramfort, A. & King, J.-R. Evidence of a predictive coding hierarchy in the human brain listening to speech. Nat. Hum. Behav., https://doi.org/10.1038/s41562-022-01516-2 (2023).
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Hierarchical organization of language predictions in the brain. Nat Hum Behav 7, 308–309 (2023). https://doi.org/10.1038/s41562-023-01534-8
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DOI: https://doi.org/10.1038/s41562-023-01534-8
