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

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.Author summary: Speech is central to human life. As such, an enormous amount of research has been done in recent decades aimed at understanding how the human brain processing speech and language. Much of this work has involved quantitatively analyzing how brain responses reflect the comprehension of words. However, the bulk of this work has assumed that brain responses to words occur at a fixed interval after hearing that word. In this study, we wished to test the hypothesis that words that are more predictable – in the context of a narrative story – would be processed more rapidly. Using a novel computational analysis, we found that the timing and amplitude of the brain’s response to a word is affected by the predictability of that word and the predictability of the immediately preceding word. This finding has implications for theories of language processing. Moreover, it has implications for improved analysis of brain responses to speech.