Delta-band neural activity primarily tracks sentences instead of semantic properties of words

• Published in: NeuroImage (Orlando, Fla.) Vol. 251; p. 118979
• Main Authors: Lu, Yuhan, Jin, Peiqing, Pan, Xunyi, Ding, Nai
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-05-2022; Elsevier Limited; Elsevier
• Subjects: Auditory Perception - physiology; Brain research; Chinese languages; Chunk; EEG; Electroencephalography; Experiments; Grouping; Humans; Language; Neural coding; Semantics; Speech; Speech - physiology; Speech; Grouping; Language; Chunk; Semantics; EEG
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.118979

Human language is generally combinatorial: Words are combined into sentences to flexibly convey meaning. How the brain represents sentences, however, remains debated. Recently, it has been shown that delta-band cortical activity correlates with the sentential structure of speech. It remains debated, however, whether delta-band cortical tracking of sentences truly reflects mental representations of sentences or is caused by neural encoding of semantic properties of individual words. The current study investigates whether delta-band neural tracking of speech can be explained by semantic properties of individual words. Cortical activity is recorded using electroencephalography (EEG) when participants listen to sentences repeating at 1 Hz and word lists. The semantic properties of individual words, simulated using a word2vec model, predict a stronger 1 Hz response to word lists than to sentences. When listeners perform a word-monitoring task that does not require sentential processing, the 1 Hz response to word lists, however, is much weaker than the 1 Hz response to sentences, contradicting the prediction of the lexical semantics model. When listeners are explicitly asked to parse word lists into multi-word chunks, however, cortical activity can reliably track the multi-word chunks. Taken together, these results suggest that delta-band neural responses to speech cannot be fully explained by the semantic properties of single words and are potentially related to the neural representation of multi-word chunks.