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研究生(外文):Ling Tang
論文名稱(外文):Hemispheric Differences in Statistical Learning of Non-Adjacent Dependencies: Evidence from Event-Related Brain Potentials
指導教授(外文):Chia-Lin LeeHsin-Jen Hsu
口試委員(外文):Jenn-Yeu Chen
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本篇研究使用事件相關電位(Event-Related Potential, ERP)技術,配合左右耳交替接收聽覺刺激材料的方式作業(alternate monaural listening)。實驗共收集47 位慣用右手且為右利家族史(FS-)的年輕人之腦波。以過去研究發展完善的人造語言學習實驗為基礎,本實驗的刺激材料為非相鄰依存之規律。為了能系統性的操弄轉移概率(transitional probabilities, TP),實驗中將安排不同數量的介入詞語(intervening items)在非相鄰依存的結構中,設計出分別為低、中、高變化的情境。

本實驗的行為結果顯示若全部受試者的資料皆納入計算,我們得到和先前文獻中部份不一致的結果;但若只納入最終有學習成功之受試者,高變化的情境確實對於非相鄰依存規律之學習最有幫助。推斷其不一致的結果可能為實驗流程設計所導致:在不斷交替的訓練及測驗階段中,各小段實驗的轉移概率並非固定,對於學習結果可能造成一定程度的影響。事件相關電位結果則顯示,在低及中等變化的情境中,左右半腦皆有相似的效果反應:在低變化的情境為句法衝突效果(P600 effect)而中等變化的情境則為語意衝突效果(N400 effect)。僅有在高變化的情境中我們發現左右半腦不一致的效果反應:左腦側化的句法衝突效果和右腦側化的語意衝突效果。上述反應證實了左右半腦皆有處理統計訊息的機制,然而,僅有在高變化的情境中才能發現左右半腦運用不同神經處理機制的現象。最後,綜觀行為和事件相關電位實驗的結果,本篇研究亦發現了句法衝突效果側化的有無和非相鄰依存規律之學習精熟程度有正向關係。
Increasing number of studies have suggested that statistical learning may play a more fundamental role in supporting language acquisition than previously thought. However, the issue of how the left hemisphere (LH) and the right hemisphere (RH) learn to master the statistical regularities in language is still poorly understood. In view of this, the present study aims to take a critical first step toward understanding how the two hemispheres track statistical regularities. In this study, we targeted Mandarin Chinese native speakers to investigate: (1) are both hemispheres capable of picking up non-adjacent statistical regularities from language-like input? (2) If yes, do the learning trajectories differ between the two hemispheres? And critically, (3) do the two hemispheres rely on mechanisms of different nature to track statistical regularities in language, akin to how syntactic information is processed in the two hemispheres?

An ERP experiment with alternate monaural listening mechanism in presenting the materials was conducted in this thesis. In total, forty-seven right-handed young adults without familial sinistrality background (FS-) participated. With the classic artificial language learning paradigm, non-adjacent dependencies were chosen as the learning materials in this experiment. In order to systematically manipulate the transitional probabilities (TP) between test tokens, different numbers of the intervening items were assigned to create three different variability conditions (low, mid, and high).

The behavioral results failed to replicate past findings when all participants were considered; nevertheless, if focused on successful learners only, high variability condition indeed best facilitated the learning of non-adjacent dependencies. Alternating training and test phases might be determinant factors contributing to these conflicting findings since the TPs do not exactly meet the goal of the experimental design. The ERP results displayed symmetric brain responses for low and mid variability conditions, with P600 for low and N400 for mid. Only in high variability condition did LH and RH show asymmetric results—left-lateralized P600 effects and right-lateralized N400 effects. These brain responses have proven that both left and right hemispheres have the ability to undergo statistical learning processes; however, only in high variability condition can we see each hemisphere using different processing approach (LH: syntactic processing approach/ RH: lexical association based approach). Finally, together with behavioral results, we think the lateralization of P600 grammaticality effects may be closely linked to the syntactic proficiency of non-adjacent dependencies. These data patterns are based on a relatively small ERP data set, and more data need to be obtained before firmer conclusions can be drawn.
口試委員會審定書 i
誌謝 ii
中文摘要 iii
Abstract v
Chapter 1. Overview 1
Chapter 2. Literature review 4
2.1 Statistical learning in language acquisition 4
2.2 Non-adjacent dependencies in language 9
2.3 Impaired statistical learning for non-adjacent dependencies 11
2.4 Hemispheric differences in syntactic processing 13
2.5 The present study 18
Chapter 3. Methods 21
3.1 Participants 22
3.1.1 Non-word repetition test 23
3.1.2 Wechsler Adult Intelligence Scale, WAIS-III 23
3.2 Materials 24
3.3 Procedure 27
3.4 EEG recording parameters and data analysis 30
Chapter 4. Results 33
4.1 Behavioral results 33
4.1.1 Learning trajectories for all participants 33
4.1.2 Successful vs. unsuccessful participants 37
4.1.3 Interim summary 41
4.2 ERP results 43
4.2.1 Statistical analysis 47
4.2.2 Interim summary 49
Chapter 5. Discussion 51
5.1 Behavioral results on non-adjacent dependency learning with alternate monaural listening 51
5.1.1 Comparisons with past findings 51
5.1.2 Continuous training versus interleaved training and testing 55
5.1.3 Influences of blink control during SL 58
5.1.4 Hemispheric differences in the behavioral data 60
5.2 ERP results on non-adjacent dependency learning with alternate monaural listening 61
5.2.1 RH’s role in learning non-adjacent dependencies among different variability conditions 62
5.3 Concluding remarks 64
References 67
Appendix A. 79
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