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瞳孔大小波动和自发眨眼反映故事传递。

Fluctuation in Pupil Size and Spontaneous Blinks Reflect Story Transportation.

作者信息

Kaakinen Johanna K, Simola Jaana

机构信息

University of Turku, Finland.

University of Helsinki, Finland.

出版信息

J Eye Mov Res. 2020 Jun 1;13(3). doi: 10.16910/jemr.13.3.6.

DOI:10.16910/jemr.13.3.6
PMID:33828801
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7993253/
Abstract

Thirty-nine participants listened to 28 neutral and horror excerpts of Stephen King short stories while constantly tracking their emotional arousal. Pupil size was measured with an Eyelink 1000+, and participants rated valence and transportation after each story. In addition to computing mean pupil size across 1-sec intervals, we extracted blink count and used detrended fluctuation analysis (DFA) to obtain the scaling exponents of long-range temporal correlations (LRTCs) in pupil size time-series. Pupil size was expected to be sensitive also to emotional arousal, whereas blink count and LRTC's were expected to reflect cognitive engagement. The results showed that self-reported arousal increased, pupil size was overall greater, and the decreasing slope of pupil size was flatter for horror than for neutral stories. Horror stories induced higher transportation than neutral stories. High transportation was associated with a steeper increase in self-reported arousal across time, stronger LRTCs in pupil size fluctuations, and lower blink count. These results indicate that pupil size reflects emotional arousal induced by the text content, while LRTCs and blink count are sensitive to cognitive engagement associated with transportation, irrespective of the text type. The study demonstrates the utility of pupillometric measures and blink count to study literature reception.

摘要

39名参与者在持续追踪自己的情绪唤起状态时,聆听了斯蒂芬·金短篇小说的28段中性和恐怖情节选段。使用Eyelink 1000+测量瞳孔大小,参与者在听完每个故事后对效价和沉浸感进行评分。除了计算1秒间隔内的平均瞳孔大小外,我们还提取了眨眼次数,并使用去趋势波动分析(DFA)来获得瞳孔大小时间序列中长程时间相关性(LRTC)的标度指数。预计瞳孔大小对情绪唤起也很敏感,而眨眼次数和LRTC则预计反映认知参与度。结果表明,自我报告的唤起感增加,恐怖故事的瞳孔总体上更大,且恐怖故事的瞳孔大小下降斜率比中性故事更平缓。恐怖故事比中性故事引发更高的沉浸感。高沉浸感与自我报告的唤起感随时间更陡峭的增加、瞳孔大小波动中更强的LRTC以及更低的眨眼次数相关。这些结果表明,瞳孔大小反映了文本内容引起的情绪唤起,而LRTC和眨眼次数对与沉浸感相关的认知参与敏感,与文本类型无关。该研究证明了瞳孔测量和眨眼次数在研究文学接受方面的效用。

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本文引用的文献

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J Eye Mov Res. 2020 Feb 27;13(3). doi: 10.16910/jemr.13.3.2.
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Eye movements and mental imagery during reading of literary texts with different narrative styles.阅读不同叙事风格文学文本时的眼动与心理意象
J Eye Mov Res. 2020 Mar 30;13(3). doi: 10.16910/jemr.13.3.3.
3
Reading Shakespeare Sonnets: Combining Quantitative Narrative Analysis and Predictive Modeling -an Eye Tracking Study.阅读莎士比亚十四行诗:结合定量叙事分析与预测建模——一项眼动追踪研究
J Eye Mov Res. 2019 Mar 27;12(5). doi: 10.16910/jemr.12.5.2.
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A linear oscillator model predicts dynamic temporal attention and pupillary entrainment to rhythmic patterns.线性振荡器模型预测了对节奏模式的动态时间注意力和瞳孔夹带。
J Eye Mov Res. 2018 Nov 20;11(2). doi: 10.16910/jemr.11.2.12.
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Modulation of the pupillary response by the content of visual working memory.视觉工作记忆内容对瞳孔反应的调制。
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Arousal Effects on Pupil Size, Heart Rate, and Skin Conductance in an Emotional Face Task.情绪面孔任务中唤醒对瞳孔大小、心率和皮肤电导率的影响。
Front Neurol. 2018 Dec 3;9:1029. doi: 10.3389/fneur.2018.01029. eCollection 2018.
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Neurosci Lett. 2019 Feb 6;693:3-8. doi: 10.1016/j.neulet.2017.07.012. Epub 2017 Jul 10.
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Critical dynamics of endogenous fluctuations predict cognitive flexibility in the Go/NoGo task.内源性波动的临界动力学预测了 Go/NoGo 任务中的认知灵活性。
Sci Rep. 2017 Jun 6;7(1):2909. doi: 10.1038/s41598-017-02750-9.