چگونگی و زمان تعامل قابلیت پیش بینی با تشدید توجه زمانی انتخابی در طول درک گفتار
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|70200||2014||14 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Neuropsychologia, Volume 64, November 2014, Pages 71–84
The present study used EEG to investigate how and when top-down prediction interacts with bottom-up acoustic signals in temporally selective attention during speech comprehension. Mandarin Chinese spoken sentences were used as stimuli. We systematically manipulated the predictability and de/accentuation of the critical words in the sentence context. Meanwhile, a linguistic attention probe ‘ba’ was presented concurrently with the critical words or not. The results showed that, first, words with a linguistic attention probe elicited a larger N1 than those without a probe. The latency of this N1 effect was shortened for accented or lowly predictable words, indicating more attentional resources allocated to these words. Importantly, prediction and accentuation showed a complementary interplay on the latency of this N1 effect, demonstrating that when the words had already attracted attention due to low predictability or due to the presence of pitch accent, the other factor did not modulate attention allocation anymore. Second, relative to the lowly predictable words, the highly predictable words elicited a reduced N400 and enhanced gamma-band power increases, especially under the accented conditions; moreover, under the accented conditions, shorter N1 peak-latency was found to correlate with larger gamma-band power enhancement, which indicates that a close relationship might exist between early selective attention and later semantic integration. Finally, the interaction between top-down selective attention (driven by prediction) and bottom-up selective attention (driven by accentuation) occurred before lexical-semantic processing, namely before the N400 effect evoked by predictability, which was discussed with regard to the language comprehension models.