Angonese Giulia, Buhl Mareike, Gößwein Jonathan A, Kollmeier Birger, Hildebrandt Andrea
Cluster of Excellence 'Hearing4all', Oldenburg, Germany.
Department of Psychology, Psychological Methods and Statistics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
Trends Hear. 2025 Jan-Dec;29:23312165251362018. doi: 10.1177/23312165251362018. Epub 2025 Aug 7.
Individuals have different preferences for setting hearing aid (HA) algorithms that reduce ambient noise but introduce signal distortions. "Noise haters" prefer greater noise reduction, even at the expense of signal quality. "Distortion haters" accept higher noise levels to avoid signal distortion. These preferences have so far been assumed to be stable over time, and individuals were classified on the basis of these stable, trait scores. However, the question remains as to how stable individual listening preferences are and whether day-to-day state-related variability needs to be considered as further criterion for classification. We designed a mobile task to measure noise-distortion preferences over 2 weeks in an ecological momentary assessment study with = 185 (106 f, = 63.1, SD = 6.5) individuals. Latent State-Trait Autoregressive (LST-AR) modeling was used to assess stability and dynamics of individual listening preferences for signals simulating the effects of noise reduction algorithms, presented in a web browser app. The analysis revealed a significant amount of state-related variance. The model has been extended to mixture LST-AR model for data-driven classification, taking into account state and trait components of listening preferences. In addition to successful identification of noise haters, distortion haters and a third intermediate class based on longitudinal, outside-of-the-lab data, we further differentiated individuals with different degrees of variability in listening preferences. Individualization of HA fitting could be improved by assessing individual preferences along the noise-distortion trade-off, and the day-to-day variability of these preferences needs to be taken into account for some individuals more than others.
个体在设置助听器（HA）算法时存在不同偏好，这些算法可降低环境噪声，但会引入信号失真。“讨厌噪声者”更喜欢更大程度的降噪，即使以牺牲信号质量为代价。“讨厌失真者”则接受较高的噪声水平以避免信号失真。到目前为止，这些偏好一直被认为随时间是稳定的，并且个体是根据这些稳定的特质分数进行分类的。然而，个体听力偏好的稳定性如何，以及与日常状态相关的变异性是否需要作为分类的进一步标准，这一问题仍然存在。我们设计了一项移动任务，在一项生态瞬时评估研究中，对185名（106名女性，平均年龄=63.1岁，标准差=6.5岁）个体进行为期2周的噪声-失真偏好测量。潜在状态-特质自回归（LST-AR）建模用于评估在网络浏览器应用程序中呈现的、模拟降噪算法效果的信号的个体听力偏好的稳定性和动态性。分析揭示了大量与状态相关的方差。该模型已扩展为混合LST-AR模型用于数据驱动的分类，同时考虑了听力偏好的状态和特质成分。除了基于纵向的实验室外数据成功识别出讨厌噪声者、讨厌失真者和第三个中间类别外，我们还进一步区分了听力偏好具有不同程度变异性的个体。通过沿着噪声-失真权衡评估个体偏好，可以改善HA拟合的个性化，并且对于一些个体而言，这些偏好的日常变异性比其他个体更需要考虑。
