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通过智能手机应用众包进行的听觉分离行为大规模分析。

Large-Scale Analysis of Auditory Segregation Behavior Crowdsourced via a Smartphone App.

作者信息

Teki Sundeep, Kumar Sukhbinder, Griffiths Timothy D

机构信息

Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.

Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom.

出版信息

PLoS One. 2016 Apr 20;11(4):e0153916. doi: 10.1371/journal.pone.0153916. eCollection 2016.

Abstract

The human auditory system is adept at detecting sound sources of interest from a complex mixture of several other simultaneous sounds. The ability to selectively attend to the speech of one speaker whilst ignoring other speakers and background noise is of vital biological significance-the capacity to make sense of complex 'auditory scenes' is significantly impaired in aging populations as well as those with hearing loss. We investigated this problem by designing a synthetic signal, termed the 'stochastic figure-ground' stimulus that captures essential aspects of complex sounds in the natural environment. Previously, we showed that under controlled laboratory conditions, young listeners sampled from the university subject pool (n = 10) performed very well in detecting targets embedded in the stochastic figure-ground signal. Here, we presented a modified version of this cocktail party paradigm as a 'game' featured in a smartphone app (The Great Brain Experiment) and obtained data from a large population with diverse demographical patterns (n = 5148). Despite differences in paradigms and experimental settings, the observed target-detection performance by users of the app was robust and consistent with our previous results from the psychophysical study. Our results highlight the potential use of smartphone apps in capturing robust large-scale auditory behavioral data from normal healthy volunteers, which can also be extended to study auditory deficits in clinical populations with hearing impairments and central auditory disorders.

摘要

人类听觉系统擅长从其他几种同时存在的声音的复杂混合中检测出感兴趣的声源。在忽略其他说话者和背景噪音的同时,选择性地关注一个说话者的语音的能力具有至关重要的生物学意义——理解复杂“听觉场景”的能力在老年人群以及听力损失人群中会显著受损。我们通过设计一种合成信号来研究这个问题,这种信号被称为“随机图形-背景”刺激,它捕捉了自然环境中复杂声音的基本特征。此前,我们表明,在受控的实验室条件下,从大学受试者库中抽取的年轻听众(n = 10)在检测嵌入随机图形-背景信号中的目标方面表现非常出色。在这里,我们将这种鸡尾酒会范式的修改版本作为一款智能手机应用程序(“伟大大脑实验”)中的一个“游戏”呈现出来,并从具有不同人口统计学模式的大量人群(n = 5148)中获取了数据。尽管范式和实验设置存在差异,但该应用程序用户观察到的目标检测性能是稳健的,并且与我们之前心理物理学研究的结果一致。我们的结果突出了智能手机应用程序在从正常健康志愿者那里获取稳健的大规模听觉行为数据方面的潜在用途,这些数据也可以扩展到研究有听力障碍和中枢听觉障碍的临床人群中的听觉缺陷。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8e9/4838209/eb6b2843fe9e/pone.0153916.g001.jpg

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