A hierarchy of linguistic predictions during natural language comprehension

By Micha Heilbron, Kristijan Armeni, Jan-Mathijs Schoffelen, Peter Hagoort, F. D. de Lange. bioRxiv, 2020

320 citations recorded by the source corpus.

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analysing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous, probabilistic predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable signatures of syntactic, phonemic and semantic predictions. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.

Subfields: Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/4b2d04556f7d3377998f4f236b57086a1be74f23

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Citations in the corpus

Brains and algorithms partially converge in natural language processing (2022, background): “Speci ﬁ - cally, while there is growing evidence that the human brain does predict words from context 30,73,74 , this learning rule may not fully account for the complex (and potentially various) tasks performed by the brain (e.g., long-range 75,76 and hierarchical predictions 77 ).”
Shared computational principles for language processing in humans and deep language models (2022, background): “It was already shown that the activation level after onset is correlated with the surprise level 14,21–23 , 41 .”
Cortical encoding of probabilistic temporal predictions during speech perception (2026, unknown): “In this sense, temporal prediction appears to be a distinct mechanism from content prediction, which is time-locked to the unit’s onset and traditionally modeled via surprisal (4, 8, 9).”