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在听力科学中使用复杂网络

Using Complex Networks in the Hearing Sciences.

作者信息

Vitevitch Michael S, Pisoni David B, Soehlke Lauren, Foster Tabitha A

机构信息

University of Kansas, Lawrence, Kansas, USA.

Indiana University, Indianapolis, Indiana, USA.

出版信息

Ear Hear. 2024;45(1):1-9. doi: 10.1097/AUD.0000000000001395. Epub 2023 Jun 15.

Abstract

In this Point of View, we review a number of recent discoveries from the emerging, interdisciplinary field of Network Science , which uses graph theoretic techniques to understand complex systems. In the network science approach, nodes represent entities in a system, and connections are placed between nodes that are related to each other to form a web-like network . We discuss several studies that demonstrate how the micro-, meso-, and macro-level structure of a network of phonological word-forms influence spoken word recognition in listeners with normal hearing and in listeners with hearing loss. Given the discoveries made possible by this new approach and the influence of several complex network measures on spoken word recognition performance we argue that speech recognition measures-originally developed in the late 1940s and routinely used in clinical audiometry-should be revised to reflect our current understanding of spoken word recognition. We also discuss other ways in which the tools of network science can be used in Speech and Hearing Sciences and Audiology more broadly.

摘要

在这篇观点文章中,我们回顾了网络科学这一新兴跨学科领域的一些最新发现,该领域运用图论技术来理解复杂系统。在网络科学方法中,节点代表系统中的实体,相互关联的节点之间建立连接以形成类似网络的结构。我们讨论了几项研究,这些研究展示了语音词形网络的微观、中观和宏观层面结构如何影响听力正常的听众和听力损失听众的口语单词识别。鉴于这种新方法带来的发现以及几种复杂网络度量对口语单词识别性能的影响,我们认为最初在20世纪40年代末开发并常规用于临床听力测定的语音识别度量应该进行修订,以反映我们目前对口语单词识别的理解。我们还更广泛地讨论了网络科学工具可用于言语和听力科学以及听力学的其他方式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a58c/10721731/f0e24dc249da/nihms-1902256-f0001.jpg

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