Rapid Measurement of Routinely Estimated Psychophysical Functions

快速测量常规估计的心理物理功能

• 批准号: 8702799
• 负责人: VIRGINIA M RICHARDS
• 金额: $ 11.58万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-03-01 至 2016-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8702799
• 关键词: Agreement; Algorithms; Attention; Audiometry; Auditory; Auditory system; Behavioral; Characteristics; Clinical; Clinical Research; Communities; Data; Data Collection; Development; Devices; Dimensions; Ensure; Evaluation; Face Processing; Frequencies; Future; Goals; Health; Hearing; Hour; Individual; Individual Differences; Infant; Laboratory Animals; Masks; Measurement; Measures; Methods; Modeling; Noise; Outcome; Outcomes Research; Output; Participant; Patients; Performance; Peripheral; Procedures; Process; Psychophysics; Relative (related person); Research; Resolution; Scientist; Shapes; Signal Transduction; Solutions; Stimulus; Testing; Time; Work; base; clinical Diagnosis; hearing impairment; interest; notch protein; novel; programs; public health relevance; research study; response; sound; user friendly software

DESCRIPTION (provided by applicant): Collecting behavioral data efficiently is a significant challenge faced by many auditory scientists, especially those who conduct clinical or developmental research. The prolonged process of data collection is the bottleneck restricting how much information can be gained from a single test subject and how many participants can be included in a clinical study. The long-term goal of the proposed research is to increase the efficiency of behavioral data collection, making individualized estimation of auditory psychophysical models possible. As the first step toward this goal, the estimation of two important psychophysical models will be studied in detail. The two models are the auditory filter model, a model of spectral resolution, and the cochlear input-output function, a model of peripheral nonlinearity. The parameters of these models, such as the auditory-filter bandwidth and the compression ratio of the cochlear input-output function, have been shown to be reliable indicators of cochlear health and can predict supra-threshold listening deficits. Classical procedures to fit these models use threshold-based approaches: multiple thresholds are measured, and the psychophysical model of interest is fitted using those thresholds. For the proposed procedure, a Bayesian algorithm will used to ensure that the stimulus presented on each trial is the stimulus that maximally accelerates the rate of parameter convergence. This parameter-based approach allows the estimation of the auditory filter or the cochlear input-output function using a single experimental track and fewer than 200 trials. This is approximately ten times faster than procedures currently in use. In the proposed experiments, for both of these models, parameters estimated for normal hearing listeners using the proposed and threshold-based procedures will be compared to determine the relative reliability of the new procedure. The optimal configurations for the new procedure, e.g. how to initiate and terminate an experimental track, will be identified. Additionally, the procedure developed to estimate the auditory filter will be further developed to ensure its suitability for hearing-impaired listeners. Upon the completion of the proposed research program, user-friendly software packages will be made available to hearing research community for the estimation of the auditory filter and the cochlear input-output function. The outcome of this research is expected to have a strong and sustained impact on behavioral studies of hearing and hearing impairment. With the procedures to be developed, the fitting of fundamental auditory models for individual test subjects can become routine. This will open the door to a better understanding of the individual differences in hearing capability because scientists will be able to test more participants and/or make more measurements in their experiments. Moreover, given the efficiency of the procedures, it will be much easier for the future experimenters to track a listener's hearing characteristics longitudinally.

描述（由申请人提供）：有效收集行为数据是许多听觉科学家，特别是那些进行临床或发育研究的科学家面临的重大挑战。漫长的数据收集过程是限制从单个受试者获得多少信息以及临床研究可以纳入多少参与者的瓶颈。该研究的长期目标是提高行为数据收集的效率，使听觉心理物理模型的个性化估计成为可能。作为实现这一目标的第一步，将详细研究两个重要心理物理模型的估计。这两个模型是听觉滤波器模型（频谱分辨率模型）和耳蜗输入输出函数（外围非线性模型）。这些模型的参数，例如听觉滤波器带宽和耳蜗输入输出函数的压缩比，已被证明是耳蜗健康的可靠指标，并且可以预测超阈值听力缺陷。拟合这些模型的经典过程使用基于阈值的方法：测量多个阈值，并使用这些阈值拟合感兴趣的心理物理模型。对于所提出的过程，将使用贝叶斯算法来确保每次试验中呈现的刺激是最大限度地加速参数收敛速度的刺激。这种基于参数的方法允许使用单个实验轨道和少于 200 次试验来估计听觉滤波器或耳蜗输入输出函数。这比当前使用的程序快大约十倍。在所提出的实验中，对于这两个模型，将比较使用所提出的程序和基于阈值的程序为正常听力听众估计的参数，以确定新程序的相对可靠性。新程序的最佳配置，例如将确定如何启动和终止实验轨道。此外，用于估计听觉滤波器的程序将得到进一步开发，以确保其适合听力受损的听众。 拟议的研究计划完成后，将向听力研究界提供用户友好的软件包，用于估计听觉滤波器和耳蜗输入输出函数。这项研究的结果预计将对听力和听力障碍的行为研究产生强烈而持续的影响。随着程序的开发，为个体测试对象拟合基本听觉模型可以成为常规。这将为更好地理解听力能力的个体差异打开大门，因为科学家将能够在实验中测试更多的参与者和/或进行更多的测量。此外，考虑到程序的效率，未来的实验者将更容易纵向跟踪听众的听力特征。