Electrophysiological Correlates of Semantic Dissimilarity Reflect the Comprehension of Natural, Narrative Speech

People routinely hear and understand speech at rates of 120–200 words per minute [1, 2]. Thus, speech comprehension must involve rapid, online neural mechanisms that process words’ meanings in an approximately time-locked fashion. However, electrophysiological evidence for such time-locked processing has been lacking for continuous speech. Although valuable insights into semantic processing have been provided by the “N400 component” of the event-related potential [3–6], this literature has been dominated by paradigms using incongruous words within specially constructed sentences, with less emphasis on natural, narrative speech comprehension. Building on the discovery that cortical activity “tracks” the dynamics of running speech [7–9] and psycholinguistic work demonstrating [10–12] and modeling [13–15] how context impacts on word processing, we describe a new approach for deriving an electrophysiological correlate of natural speech comprehension. We used a computational model [16] to quantify the meaning carried by words based on how semantically dissimilar they were to their preceding context and then regressed this measure against electroencephalographic (EEG) data recorded from subjects as they listened to narrative speech. This produced a prominent negativity at a time lag of 200–600 ms on centro-parietal EEG channels, characteristics common to the N400. Applying this approach to EEG datasets involving time-reversed speech, cocktail party attention, and audiovisual speech-in-noise demonstrated that this response was very sensitive to whether or not subjects understood the speech they heard. These findings demonstrate that, when successfully comprehending natural speech, the human brain responds to the contextual semantic content of each word in a relatively time-locked fashion. •EEG reflects semantic processing of continuous natural speech•Mapping function of semantic features to neural response shares traits with the N400•Computational language models capture neural-related measures of semantic dissimilarity•Index of semantic processing is sensitive to attention and speech intelligibility Electrophysiological studies eliciting semantic-related neural activity typically involve paradigms using incongruous words within specially constructed sentences. Broderick et al. use computational language modeling and linear regression to successfully index the semantic processing of continuous, natural speech using EEG.