ArticlePLoS biology2023
A deep hierarchy of predictions enables online meaning extraction in a computational model of human speech comprehension.
Article in PLoS biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Shared spatial and temporal principles govern connectome dynamics across timescales.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- "What" and "When" Predictions Jointly Modulate Speech Processing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- A primate grammar enabling incremental processing.iScience · 2025Article
- Humans and great apes visually track event roles in similar ways.PLoS biology · 2024Article
- Dog-human vocal interactions match dogs' sensory-motor tuning.PLoS biology · 2024Article
- A universal preference for animate agents in hominids.iScience · 2024Article
- Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an Agent Preference Principle.Neurobiology of language (Cambridge, Mass.) · 2024Article
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4 authors.
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Abstract
Understanding speech requires mapping fleeting and often ambiguous soundwaves to meaning. While humans are known to exploit their capacity to contextualize to facilitate this process, how internal knowledge is deployed online remains an open question. Here, we present a model that extracts multiple levels of information from continuous speech online. The model applies linguistic and nonlinguistic knowledge to speech processing, by periodically generating top-down predictions and incorporating bottom-up incoming evidence in a nested temporal hierarchy. We show that a nonlinguistic context level provides semantic predictions informed by sensory inputs, which are crucial for disambiguating among multiple meanings of the same word. The explicit knowledge hierarchy of the model enables a more holistic account of the neurophysiological responses to speech compared to using lexical predictions generated by a neural network language model (GPT-2). We also show that hierarchical predictions reduce peripheral processing via minimizing uncertainty and prediction error. With this proof-of-concept model, we demonstrate that the deployment of hierarchical predictions is a possible strategy for the brain to dynamically utilize structured knowledge and make sense of the speech input.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.