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Low-frequency Cortical Activity Reflects Context-dependent Parsing of Word Sequences
Poster Session A, Friday, September 12, 11:00 am - 12:30 pm, Field House
Honghua Chen1, Minhui Zhang1, Tianyi Ye3, Max Wolpert1, Nai Ding1,2,4; 1Zhejiang University, China, 2Nanhu Brain-computer Interface Institute, Hangzhou, China, 3Georgetown University, USA, 4The MOE Frontier Science Center for Brain Science & Brain-machine Integration, China
During speech listening, it has been hypothesized that the brain builds representations of large linguistic structures such as sentences, which are tracked by neural activity entrained to the rhythm of these structures. Alternatively, it has been proposed that the brain may only encode words, and neural activity supposedly tracking structures may be confounded by the predictability or syntactic properties of individual words. Here, to disentangle the neural responses to sentences and words, we design word sequences that are parsed into different sentences in different contexts. By analyzing neural activity recorded by magnetoencephalography, we find that low-frequency neural activity strongly depends on context – the difference between MEG responses to the same word sequence in two contexts yields a low-frequency signal, most strongly generated in the superior temporal gyrus, which precisely tracks sentences. The predictability and syntactic properties of words can partly explain the neural response in each context but cannot explain the difference between contexts. In summary, low-frequency neural activity encodes sentences and can reliably reflect how same-word sequences are parsed in different contexts.
Topic Areas: Syntax and Combinatorial Semantics, Speech Perception