Dynamic modeling of EEG responses to natural speech reveals earlier processing of predictable words

• DOI: 10.1371/journal.pcbi.1013006
• Publication Date: 2025-04-28T17:51:02Z
• Authors: Jin Dou; Andrew J. Anderson; Aaron S. White; Samuel V. Norman-...; Edmund C. Lalor
• Keywords: Predictability

In recent years, it has become clear that EEG indexes the comprehension of natural, narrative speech. One particularly compelling demonstration this fact can be seen by regressing responses to speech against measures how individual words in linguistically relate their preceding context. This approach produces a so-called temporal response function displays centro-parietal negativity reminiscent classic N400 component event-related potential. shortcoming previous implementations is they have typically assumed linear, time-invariant relationship between linguistic features and responses. other words, analysis assumes same shape timing for every word – only varies (linearly) terms its amplitude. present work, we relax assumption under hypothesis may processed more rapidly when are predictable. Specifically, introduce framework wherein standard linear modulated amplitude, latency, scale based on predictability current prior words. We use proposed model recorded from set participants who listened an audiobook narrated single talker, separate attended one two concurrently presented audiobooks. show expected faster evoking lower amplitude N400-like with earlier peaks effect driven both word’s own immediately word. Additional suggests finding not simply explained quickly disambiguated phonetic neighbors. As such, our study demonstrates brain natural depend predictability. By accounting these effects, also improves accuracy which neural modeled.