ArticleNeuroImage2022
Decoding the temporal dynamics of spoken word and nonword processing from EEG.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Utilizing functional neuroimaging to study early language development.Developmental cognitive neuroscience · 2025Review
- Hierarchical dynamic coding coordinates speech comprehension in the human brain.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
- Association of domain-general speed of information processing with spoken language outcomes in prelingually-deaf children with cochlear implants.Hearing research · 2024Article
- Underlying dimensions of real-time word recognition in cochlear implant users.Nature communications · 2024Article
- Delineating memory reactivation in sleep with verbal and non-verbal retrieval cues.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- I'm not sure that curve means what you think it means: Toward a [more] realistic understanding of the role of eye-movement generation in the Visual World Paradigm.Psychonomic bulletin & review · 2023Review
- Cognitive neural responses in the semantic comprehension of sound symbolic words and pseudowords.Frontiers in human neuroscience · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The efficiency of spoken word recognition is essential for real-time communication. There is consensus that this efficiency relies on an implicit process of activating multiple word candidates that compete for recognition as the acoustic signal unfolds in real-time. However, few methods capture the neural basis of this dynamic competition on a msec-by-msec basis. This is crucial for understanding the neuroscience of language, and for understanding hearing, language and cognitive disorders in people for whom current behavioral methods are not suitable. We applied machine-learning techniques to standard EEG signals to decode which word was heard on each trial and analyzed the patterns of confusion over time. Results mirrored psycholinguistic findings: Early on, the decoder was equally likely to report the target (e.g., baggage) or a similar sounding competitor (badger), but by around 500 msec, competitors were suppressed. Follow up analyses show that this is robust across EEG systems (gel and saline), with fewer channels, and with fewer trials. Results are robust within individuals and show high reliability. This suggests a powerful and simple paradigm that can assess the neural dynamics of speech decoding, with potential applications for understanding lexical development in a variety of clinical disorders.
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Registered trials
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.