Scientific ReportsNeurosciencePeer-reviewed journal article
Overt and covert prosody are reflected in neurophysiological responses previously attributed to grammatical processing
Abstract
AO_SCPLOWBSTRACTC_SCPLOWRecent neurophysiological research suggests that slow cortical activity tracks hierarchical syntactic structure during online sentence processing (e.g., Ding, Melloni, Zhang, Tian, & Poeppel, 2016). Here we tested an alternative hypothesis: electrophysiological activity peaks at sentence constituent frequencies reflect cortical tracking of overt or covert (implicit) prosodic grouping. In three experiments, participants listened to series of sentences while electroencephalography (EEG) was recorded. First, prosodic cues in the sentence materials were neutralized. We found an EEG spectral power peak elicited at a frequency that only tagged covert prosodic change, but not any major syntactic constituents. In the second experiment, participants listened to a series of sentences with overt prosodic grouping cues that either aligned or misaligned with the syntactic phrasing in the sentences (initial overt prosody trials). Immediately after each overt prosody trial, participants were presented with a second series of sentences (covert prosody trial) with all overt prosodic cues neutralized and asked to imagine the prosodic contour present in the previous, overt prosody trial. The EEG responses reflected an interactive relationship between syntactic processing and prosodic tracking at the frequencies of syntactic constituents (sentences and phrases): alignment of syntax and prosody boosted EEG responses, whereas their misalignment had an opposite effect. This was true for both overt and covert (imagined) prosody. We conclude that processing of both overt and covert prosody is reflected in the frequency tagged neural responses at sentence constituent frequencies, whereas identifying neural markers that are narrowly reflective of syntactic processing remains difficult and controversial.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 47
- FWCI
- 4.77
- Field percentile
- top 4%
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