Dynamics of Distraction: Physiological Correlates of Selective Listening Revealed in Brain Electrical Dynamics

人类大脑最令人惊叹的技能之一是它能够从许多同时存在的声源的复杂混合物中辨别出单个信息流，例如单个声音。 这种能力被称为选择性倾听，它是更广泛的选择性注意现象的一个例子。 众所周知，选择性倾听并不完美。 有时我们会被其他声音分散注意力，甚至是其他感官的输入。 令人惊讶的是，认知神经科学领域并没有投入太多精力来理解分心的潜在机制。 这可能是因为我们直觉地认为分心只是“不参加”，但这是一个过度简单化的错误。 当相互冲突的输入干扰了听觉回路有效处理声音的能力时，听觉回路会发生什么？ 在大脑皮层处理的不同阶段是否存在多种分心机制？ 有没有办法通过增强听觉世界中的重要信号来防止分心？ 这里提出的研究有两个目标。 第一个目标是测试的理论，即听觉分心时，在大脑中的振荡神经电信号成为暂时解耦的听觉场景中的声学事件。 由此产生的现象，我称之为分心退相干，会破坏大脑完成选择性倾听的效率。 第二个目标是开发使用最先进的无线EEG设备检测分心的神经特征的方法。 如果我们能检测到分心的时刻，那么我们就能开发出神经技术，将注意力重新引导到感官世界中的重要事件上。

One of the most amazing skills of the human brain is its ability to discern individual streams of information, such as a single voice, from a complex mixture of many simultaneous sources of sound. This ability is called selective listening and it is one example of the broader phenomenon of selective attention. As we all know, selective listening is not perfect. Sometimes we are distracted by other sounds or even input to our other senses. Surprisingly, the field of Cognitive Neuroscience has not devoted much effort to understanding the underlying mechanisms of distraction. This is probably because we intuitively think of distraction as simply "not attending", but this is an error of oversimplification. What happens to auditory circuits when conflicting input disrupts their ability to effectively process sounds? Are there multiple mechanisms of distraction acting at different stages of cortical processing? Are there ways to prevent distraction by augmenting important signals in the auditory world? The research proposed here has two goals. The first goal is to test the theory that auditory distraction arises when oscillating neuroelectric signals in the brain become temporally uncoupled from the acoustic events in the auditory scene. The resulting phenomenon, which I call Distraction Decoherence, would disrupt the efficiency with which the brain accomplishes selective listening. The second goal is to develop ways to detect the neural signatures of distraction using state-of-the-art wireless EEG devices. If we can detect moments of distraction then we can develop neurotechnologies that redirect attention toward important events in the sensory world.