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Alternative TitleThe cognitive mechanisms of predictive processing during language comprehension


Other Abstract

Predictive processing plays an important role during language comprehension. Although numerous studies have examined the integration stage of predictive processing (namely, integration between top-down predictions and bottom-up new inputs), there is still no clear picture of the anticipatory stage of predictive processing (namely, before the predicted/unpredicted information appears in the bottom-up signal). This electroencephalograph study examined the cognitive mechanisms underlying anticipatory processing during language comprehension, and the relationship between anticipatory language processing and working memory. Participants read Mandarin Chinese sentences that were either strongly, moderately, or weakly constraining, and we examined the effects of semantic predictability on anticipatory processing prior to the onset of the critical nouns and on integration of the critical nouns. Meanwhile, two groups of participants were selected (namely, participants with high working memory capacity and those with low working memory capacity) and their executive-control abilities (inhibiting, updating, and shifting) were measured.
The result showed that, first, for the high-working-memory-capacity group, at the integration stage, both strong-constraint and moderate-constraint conditions elicited a smaller N400 than weak-constraint condition, and strong-constraint condition further evoked a smaller N400 than moderate-constraint condition. At the anticipation stage, both strong-constraint and moderate-constraint condition elicited a  reduced anterior negativity compared to weak-constraint condition, and strong-constraint condition further elicited a reduced anterior negativity compared to moderate-constraint condition. That is, the magnitude of N400 or anterior negativity decreases gradually from strong-constraint to moderate-constraint and from moderate-constraint to weak-constraint conditions. Second, for the low-working-memory-capacity group, at the integration stage, strong-constraint condition elicited a smaller N400 than weak-constraint condition, whereas there was no significant difference between moderate- and weak-constraint conditions. At the anticipatory stage, a reversed patter of results was observed: strong-constraint condition evoked an enhanced (rather than a reduced) anterior negativity compared to weak-constraint conditions; moderate-constraint condition also elicited an enhanced anterior negativity compared to weak-constraint condition; no significant difference was observed between strong- and moderate-constraint conditions. That is, relative to weakly constraining context, strongly constraining context facilitated semantic processing at the integration, but put a higher cognitive cost at the anticipatory stage of processing; the moderately constraining context might have been used to form predictions, but didn’t lead to processing benefit at a later stage; the ease/difficulty of processing didn’t show gradual changes from strong-constraint to moderate-constraint and then to weak-constraint conditions. Third, there is a significant negative correlation between the ease of anticipatory processing and updating ability: the slower the process of updating, the more difficulty the anticipatory language processing is.
There results indicated that, during language comprehension, anticipatory language processing is closely correlated with working memory capacity and the speed of updating; the high-working-memory-capacity group and the low-working-memory-capacity group recruit different cognitive mechanisms to perform anticipatory language processing, with the later relying more on top-down control processes; only the high-working-memory-capacity group’s anticipatory
language processing works as the Bayesian model hypothesizes.

Keyword语言预期加工 认知机制言 言语工作记忆广度 中央执行功能
Degree Discipline基础心理学
Degree Grantor中国科学院研究生院
Place of Conferral北京
Document Type学位论文
Recommended Citation
GB/T 7714
张玉萍. 语言理解中预期加工的认知机制[D]. 北京. 中国科学院研究生院,2017.
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