Amsterdam UMC, Vrije Universiteit Amsterdam, Otolaryngology-Head and Neck Surgery, Ear and Hearing, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
Eriksholm Research Centre, Snekkersten, Denmark.
Trends Hear. 2021 Jan-Dec;25:23312165211009351. doi: 10.1177/23312165211009351.
In hearing research, pupillometry is an established method of studying listening effort. The focus of this study was to evaluate several pupil measures extracted from the Task-Evoked Pupil Responses (TEPRs) in speech-in-noise test. A range of analysis approaches was applied to extract these pupil measures, namely (a) pupil peak dilation (PPD); (b) mean pupil dilation (MPD); (c) index of pupillary activity; (d) growth curve analysis (GCA); and (e) principal component analysis (PCA). The effect of signal-to-noise ratio (SNR; Data Set A: -20 dB, -10 dB, +5 dB SNR) and luminance (Data Set B: 0.1 cd/m, 360 cd/m) on the TEPRs were investigated. Data Sets A and B were recorded during a speech-in-noise test and included TEPRs from 33 and 27 normal-hearing native Dutch speakers, respectively. The main results were as follows: (a) A significant effect of SNR was revealed for all pupil measures extracted in the time domain (PPD, MPD, GCA, PCA); (b) Two time series analysis approaches (GCA, PCA) provided modeled temporal profiles of TEPRs (GCA); and time windows spanning subtasks performed in a speech-in-noise test (PCA); and (c) All pupil measures revealed a significant effect of luminance. In conclusion, multiple pupil measures showed similar effects of SNR, suggesting that effort may be reflected in multiple aspects of TEPR. Moreover, a direct analysis of the pupil time course seems to provide a more holistic view of TEPRs, yet further research is needed to understand and interpret its measures. Further research is also required to find pupil measures less sensitive to changes in luminance.
在听力研究中，瞳孔测量法是一种研究听力努力的既定方法。本研究的重点是评估从语音噪声测试中的任务诱发瞳孔反应（TEPR）中提取的几种瞳孔测量值。应用了一系列分析方法来提取这些瞳孔测量值，即(a)瞳孔峰值扩张（PPD）；(b)平均瞳孔扩张（MPD）；(c)瞳孔活动指数；(d)生长曲线分析（GCA）；和(e)主成分分析（PCA）。研究了信噪比（SNR；数据集 A：-20dB、-10dB、+5dB SNR）和亮度（数据集 B：0.1cd/m、360cd/m）对 TEPR 的影响。数据集 A 和 B 是在语音噪声测试中记录的，分别包括 33 名和 27 名正常听力的荷兰母语者的 TEPR。主要结果如下：(a)在时域中提取的所有瞳孔测量值（PPD、MPD、GCA、PCA）均显示出 SNR 的显著影响；(b)两种时间序列分析方法（GCA、PCA）提供了 TEPR 的模型化时间曲线（GCA）；以及跨越语音噪声测试中执行的子任务的时间窗口（PCA）；(c)所有瞳孔测量值均显示出亮度的显著影响。总之，多种瞳孔测量值显示出与 SNR 相似的影响，表明努力可能反映在 TEPR 的多个方面。此外，对瞳孔时间过程的直接分析似乎提供了对 TEPR 的更全面的了解，但需要进一步的研究来理解和解释其测量值。还需要进一步的研究来找到对亮度变化不那么敏感的瞳孔测量值。
