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在智能手机进行心理声学评估的背景下,注意缺陷如何影响自适应程序的稳健性和效率?

How Does Inattention Influence the Robustness and Efficiency of Adaptive Procedures in the Context of Psychoacoustic Assessments via Smartphone?

机构信息

Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany.

出版信息

Trends Hear. 2024 Jan-Dec;28:23312165241288051. doi: 10.1177/23312165241288051.

Abstract

Inattention plays a critical role in the accuracy of threshold measurements, e.g., when using mobile devices. To describe the influence of distraction, long- and short-term inattention models based on either a stationary or a non-stationary psychometric function were developed and used to generate three simulated listeners: fully-, moderately-, and non-concentrated listeners. Six established adaptive procedures were assessed via Monte-Carlo simulations in combination with the inattention models and compared with a newly proposed method: the graded response bracketing procedure (GRaBr). Robustness was examined by bias and root mean square error between the "true" and estimated thresholds while efficiency was evaluated using rates of convergence and a normalized efficiency index. The findings show that inattention has a detrimental impact on adaptive procedure performance-especially for the short-term inattentive listener-and that several model-based procedures relying on a consistent response behavior of the listener are prone to errors owing to inattention. The model-free procedure GRaBr, on the other hand, is considerably robust and efficient in spite of the (assumed) inattention. As a result, adaptive techniques with desired properties (i.e., high robustness and efficiency) as revealed in our simulations-such as GRaBr-appear to be advantageous for mobile devices or in laboratory tests with untrained subjects.

摘要

不注意在阈值测量的准确性中起着关键作用,例如,在使用移动设备时。为了描述分心的影响,开发了基于固定或非固定心理物理函数的长期和短期注意力不集中模型,并用于生成三个模拟听众:全注意、适度注意和非集中注意听众。通过蒙特卡罗模拟评估了六种已建立的自适应程序,结合不注意模型,并与新提出的方法进行了比较:分级反应分块程序(GRaBr)。通过“真实”和估计阈值之间的偏差和均方根误差来检查稳健性,同时通过收敛率和归一化效率指数来评估效率。研究结果表明,不注意会对自适应程序的性能产生不利影响-特别是对于短期注意力不集中的听众-并且依赖于听众一致反应行为的几种基于模型的程序由于不注意而容易出错。另一方面,无模型程序 GRaBr 尽管(假设)存在不注意,但具有相当的稳健性和效率。因此,在模拟中显示出所需特性(即高稳健性和效率)的自适应技术,例如 GRaBr,对于移动设备或未经训练的受试者的实验室测试似乎是有利的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3048/11574912/6fd8f765b13d/10.1177_23312165241288051-fig1.jpg

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