Evidence of a predictive coding hierarchy in the human brain listening to speech.

ABSTRACT: Considerable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, translate and classify texts. Yet, these language models still fail to match the language abilities of humans. Predictive coding theory offers a tentative explanation to this discrepancy: while language models are optimized to predict nearby words, the human brain would continuously predict a hierarchy of representations that spans multiple timescales. To test this hypothesis, we analysed the functional magnetic resonance imaging brain signals of 304 participants listening to short stories. First, we confirmed that the activations of modern language models linearly map onto the brain responses to speech. Second, we showed that enhancing these a

SUBMITTER: Caucheteux C 

PROVIDER: S-EPMC10038805 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature