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Evidence of a predictive coding hierarchy in the human brain listening to speech | Nature Human Behaviour

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> Language models still fail to match the language abilities of humans. **[Predictive coding theory](tag:predictive_coding.html)** 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 algorithms with predictions that span multiple timescales improves this brain mapping. Finally, we showed that these predictions are organized hierarchically: frontoparietal cortices predict higher-level, longer-range and more contextual representations than temporal cortices. Overall, **these results strengthen the role of hierarchical predictive coding in language processing and illustrate how the synergy between neuroscience and artificial intelligence can unravel the computational bases of human cognition.**

> our
results support the idea that, unlike current language algorithms, the
brain is not limited to predict word-level representations but rather
predicts multiple levels of representations.

> our study thus calls for systematically training algorithms to predict
multiple timescales and levels of representations

2023-03-03 About

Meta AI sur Twitter : "New in Nature Human Behavior, Meta AI researchers show how current language models differ from the human brain & highlight the role of long-range & hierarchical predictions..."

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2023-03-03 About

AlphaFold’s new rival? Meta AI predicts shape of 600 million proteins

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2023-01-11 About

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sl:creationDate : 2023-01-12
sl:creationTime : 2023-01-12T09:19:47Z
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skos:prefLabel : AI@Meta