Sussman Elyse S
Departments of Neuroscience and Otorhinolaryngology-Head and Neck Surgery, Albert Einstein College of Medicine, Bronx, NY.
J Speech Lang Hear Res. 2017 Oct 17;60(10):2989-3000. doi: 10.1044/2017_JSLHR-H-17-0041.
This review article provides a new perspective on the role of attention in auditory scene analysis.
A framework for understanding how attention interacts with stimulus-driven processes to facilitate task goals is presented. Previously reported data obtained through behavioral and electrophysiological measures in adults with normal hearing are summarized to demonstrate attention effects on auditory perception-from passive processes that organize unattended input to attention effects that act at different levels of the system. Data will show that attention can sharpen stream organization toward behavioral goals, identify auditory events obscured by noise, and limit passive processing capacity.
A model of attention is provided that illustrates how the auditory system performs multilevel analyses that involve interactions between stimulus-driven input and top-down processes. Overall, these studies show that (a) stream segregation occurs automatically and sets the basis for auditory event formation; (b) attention interacts with automatic processing to facilitate task goals; and (c) information about unattended sounds is not lost when selecting one organization over another. Our results support a neural model that allows multiple sound organizations to be held in memory and accessed simultaneously through a balance of automatic and task-specific processes, allowing flexibility for navigating noisy environments with competing sound sources.
这篇综述文章为注意力在听觉场景分析中的作用提供了一个新视角。
提出了一个理解注意力如何与刺激驱动过程相互作用以促进任务目标达成的框架。总结了先前通过对听力正常的成年人进行行为和电生理测量获得的数据，以证明注意力对听觉感知的影响——从组织未被注意的输入的被动过程到在系统不同层面起作用的注意力效应。数据将表明，注意力可以使信息流组织朝着行为目标锐化，识别被噪声掩盖的听觉事件，并限制被动处理能力。
提供了一个注意力模型，该模型说明了听觉系统如何执行涉及刺激驱动输入与自上而下过程之间相互作用的多级分析。总体而言，这些研究表明：(a) 流分离自动发生，并为听觉事件的形成奠定基础；(b) 注意力与自动处理相互作用以促进任务目标；(c) 在选择一种组织而非另一种组织时，关于未被注意声音的信息不会丢失。我们的结果支持一种神经模型，该模型允许多个声音组织存储在记忆中，并通过自动过程和特定任务过程的平衡同时进行访问，从而在有竞争声源的嘈杂环境中灵活导航。
