Dynamic modeling of EEG responses to natural speech reveals earlier processing of predictable words Article Swipe
Jin Dou
,
Andrew J. Anderson
,
Aaron Steven White
,
Sam V. Norman-Haignere
,
Edmund C. Lalor
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1013006
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1013006
In recent years, it has become clear that EEG indexes the comprehension of natural, narrative speech. One particularly compelling demonstration of this fact can be seen by regressing EEG responses to speech against measures of how individual words in that speech linguistically relate to their preceding context. This approach produces a so-called temporal response function that displays a centro-parietal negativity reminiscent of the classic N400 component of the event-related potential. One shortcoming of previous implementations of this approach is that they have typically assumed a linear, time-invariant relationship between the linguistic speech features and the EEG responses. In other words, the analysis typically assumes that the response has the same shape and timing for every word – and only varies (linearly) in terms of its amplitude. In the present work, we relax this assumption under the hypothesis that responses to individual words may be processed more rapidly when they are predictable. Specifically, we introduce a framework wherein the standard linear temporal response function can be modulated in terms of its amplitude, latency, and temporal scale based on the predictability of the current and prior words. We use the proposed approach to model EEG recorded from a set of participants who listened to an audiobook narrated by a single talker, and a separate set of participants who attended to one of two concurrently presented audiobooks. We show that expected words are processed faster – evoking lower amplitude N400-like responses with earlier peaks – and that this effect is driven both by the word’s own predictability and the predictability of the immediately preceding word. Additional analysis suggests that this finding is not simply explained based on how quickly words can be disambiguated from their phonetic neighbors. As such, our study demonstrates that the timing and amplitude of brain responses to words in natural speech depend on their predictability. By accounting for these effects, our framework also improves the accuracy with which neural responses to natural speech can be modeled.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pcbi.1013006
- OA Status
- gold
- Cited By
- 2
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409882414
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409882414Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pcbi.1013006Digital Object Identifier
- Title
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Dynamic modeling of EEG responses to natural speech reveals earlier processing of predictable wordsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-28Full publication date if available
- Authors
-
Jin Dou, Andrew J. Anderson, Aaron Steven White, Sam V. Norman-Haignere, Edmund C. LalorList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pcbi.1013006Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pcbi.1013006Direct OA link when available
- Concepts
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N400, Electroencephalography, Computer science, Speech recognition, Set (abstract data type), Context (archaeology), Event-related potential, Predictability, Psychology, Mathematics, Neuroscience, Biology, Statistics, Paleontology, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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