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

人脑最令人惊叹的技能之一是它能够从许多同时发出的声音的复杂混合中辨别单独的信息流，例如单一的声音。这种能力被称为选择性倾听，它是选择性注意更广泛现象的一个例子。众所周知，选择性听力并不是完美的。有时我们会被其他声音分散注意力，甚至会被输入到其他感官中。令人惊讶的是，认知神经科学领域并没有花太多的精力来理解分心的潜在机制。这可能是因为我们直觉上认为分心就是“不用心”，但这是一个过于简单化的错误。当相互冲突的输入干扰了它们有效处理声音的能力时，听觉回路会发生什么？在大脑皮层加工的不同阶段是否存在多种分心作用机制？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。第一个目标是测试这一理论，即当大脑中振荡的神经电信号在时间上与听觉场景中的声学事件分离时，就会产生听觉分心。由此产生的现象，我称之为分心消相干，将扰乱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脑电设备来检测注意力分散的神经特征的方法。如果我们能够检测到分心的时刻，那么我们就可以开发神经技术，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.