Τρίτη 18 Δεκεμβρίου 2018

Interactive effects of linguistic abstraction and stimulus statistics in the online modulation of neural speech encoding

Abstract

Speech processing is highly modulated by context. Prior studies examining frequency-following responses (FFRs), an electrophysiological 'neurophonic' potential that faithfully reflects phase-locked activity from neural ensembles within the auditory network, have demonstrated that stimulus context modulates the integrity of speech encoding. The extent to which context-dependent encoding reflects general auditory properties or interactivities between statistical and higher-level linguistic processes remains unexplored. Our study examined whether speech encoding, as reflected by FFRs, is modulated by abstract phonological relationships between a stimulus and surrounding contexts. FFRs were elicited to a Mandarin rising-tone syllable (/ji-TR/, 'second') randomly presented with other syllables in three contexts from 17 native listeners. In a contrastive context, /ji-TR/ occurred with meaning-contrastive high-level-tone syllables (/ji-H/, 'one'). In an allotone context, TR occurred with dipping-tone syllables /ji-D/, a non-meaning-contrastive variant of /ji-TR/. In a repetitive context, the same /ji-TR/ occurred with other speech tokens of /ji-TR/. Consistent with prior work, neural tracking of /ji-TR/ pitch contour was more faithful in the repetitive condition wherein /ji-TR/ occurred more predictably (p = 1) than in the contrastive condition (p = 0.34). Crucially, in the allotone context, neural tracking of /ji-TR/ was more accurate relative to the contrastive context, despite both having an identical transitional probability (p = 0.34). Mechanistically, the non-meaning-contrastive relationship may have augmented the probability to /ji-TR/ occurrence in the allotone context. Results indicate online interactions between bottom-up and top-down mechanisms, which facilitate speech perception. Such interactivities may predictively fine-tune incoming speech encoding using linguistic and statistical information from prior context.



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