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提高分类响度标度效率的最大期望信息方法

Maximum Expected Information Approach for Improving Efficiency of Categorical Loudness Scaling.

作者信息

Fultz Sara E, Neely Stephen T, Kopun Judy G, Rasetshwane Daniel M

机构信息

Center for Hearing Research, Boys Town National Research Hospital, Omaha, NE, United States.

出版信息

Front Psychol. 2020 Nov 17;11:578352. doi: 10.3389/fpsyg.2020.578352. eCollection 2020.

Abstract

Categorical loudness scaling (CLS) measures provide useful information about an individual's loudness perception across the dynamic range of hearing. A probability model of CLS categories has previously been described as a multi-category psychometric function (MCPF). In the study, a representative "catalog" of potential listener MCPFs was used in conjunction with maximum-likelihood estimation to derive CLS functions for participants with normal hearing and with hearing loss. The approach of estimating MCPFs for each listener has the potential to improve the accuracy of the CLS measurements, particularly when a relatively low number of data points are available. The present study extends the MCPF approach by using Bayesian inference to select stimulus parameters that are predicted to yield maximum expected information (MEI) during data collection. The accuracy and reliability of the MCPF-MEI approach were compared to the standardized CLS measurement procedure (ISO 16832:2006, 2006). A non-adaptive, fixed-level, paradigm served as a "gold-standard" for this comparison. The test time required to obtain measurements in the standard procedure is a major barrier to its clinical uptake. Test time was reduced from approximately 15 min to approximately 3 min with the MEI-adaptive procedure. Results indicated that the test-retest reliability and accuracy of the MCPF-MEI adaptive procedures were similar to the standardized CLS procedure. Computer simulations suggest that the reliability and accuracy of the MEI procedure were limited by intrinsic uncertainty of the listeners represented in the MCPF catalog. In other words, the MCPF provided insufficient predictive power to significantly improve adaptive-tracking efficiency under practical conditions. Concurrent optimization of both the MCPF catalog and the MEI-adaptive procedure have the potential to produce better results. Regardless of the adaptive-tracking method used in the CLS procedure, the MCPF catalog remains clinically useful for enabling maximum-likelihood determination of loudness categories.

摘要

分类响度标度(CLS)测量提供了关于个体在听力动态范围内响度感知的有用信息。CLS类别的概率模型先前已被描述为多类别心理测量函数(MCPF)。在该研究中,一个具有代表性的潜在听众MCPF“目录”与最大似然估计结合使用,以推导听力正常和听力损失参与者的CLS函数。为每个听众估计MCPF的方法有可能提高CLS测量的准确性,特别是当可用数据点数量相对较少时。本研究通过使用贝叶斯推理来选择预计在数据收集期间产生最大预期信息(MEI)的刺激参数,扩展了MCPF方法。将MCPF-MEI方法的准确性和可靠性与标准化CLS测量程序(ISO 16832:2006,2006)进行了比较。一种非自适应、固定水平的范式用作该比较的“金标准”。在标准程序中获得测量所需的测试时间是其临床应用的主要障碍。使用MEI自适应程序,测试时间从大约15分钟减少到大约3分钟。结果表明,MCPF-MEI自适应程序的重测可靠性和准确性与标准化CLS程序相似。计算机模拟表明,MEI程序的可靠性和准确性受到MCPF目录中所代表听众的内在不确定性的限制。换句话说,在实际条件下,MCPF提供的预测能力不足,无法显著提高自适应跟踪效率。同时优化MCPF目录和MEI自适应程序有可能产生更好的结果。无论CLS程序中使用何种自适应跟踪方法,MCPF目录在临床上仍然有助于实现响度类别的最大似然确定。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47f2/7705216/fac05858e09b/fpsyg-11-578352-g001.jpg

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