Department of Neurophysiology, Donders Institute, Radboud University Nijmegen, The Netherlands.
Psychophysiology Laboratory, Institute of Psychology, Jagiellonian University, Krakow, Poland.
eNeuro. 2018 Apr 13;5(2). doi: 10.1523/ENEURO.0090-18.2018. eCollection 2018 Mar-Apr.
Many natural sounds can be well described on a statistical level, for example, wind, rain, or applause. Even though the spectro-temporal profile of these acoustic textures is highly dynamic, changes in their statistics are indicative of relevant changes in the environment. Here, we investigated the neural representation of change detection in natural textures in humans, and specifically addressed whether active task engagement is required for the neural representation of this change in statistics. Subjects listened to natural textures whose spectro-temporal statistics were modified at variable times by a variable amount. Subjects were instructed to either report the detection of changes (active) or to passively listen to the stimuli. A subset of passive subjects had performed the active task before (passive-aware vs passive-naive). Psychophysically, longer exposure to pre-change statistics was correlated with faster reaction times and better discrimination performance. EEG recordings revealed that the build-up rate and size of parieto-occipital (PO) potentials reflected change size and change time. Reduced effects were observed in the passive conditions. While P2 responses were comparable across conditions, slope and height of PO potentials scaled with task involvement. Neural source localization identified a parietal source as the main contributor of change-specific potentials, in addition to more limited contributions from auditory and frontal sources. In summary, the detection of statistical changes in natural acoustic textures is predominantly reflected in parietal locations both on the skull and source level. The scaling in magnitude across different levels of task involvement suggests a context-dependent degree of evidence integration.
许多自然声音可以在统计水平上得到很好的描述，例如风声、雨声或掌声。尽管这些声纹纹理的时频谱图是高度动态的，但它们的统计变化表明环境发生了相关变化。在这里，我们研究了人类对自然纹理中变化检测的神经表示，特别是解决了主动任务参与是否是统计变化的神经表示所必需的。被试者听其自然纹理，其声纹统计数据在不同时间以不同的量被修改。被试者被指示报告检测到的变化（主动）或被动地听刺激。一部分被动被试在之前已经执行了主动任务（被动意识与被动无知）。心理物理学表明，较长时间暴露于预变统计数据与更快的反应时间和更好的辨别性能相关。脑电图记录显示，顶枕部（PO）电位的建立率和大小反映了变化的大小和时间。在被动条件下观察到的效果减少。虽然 P2 反应在不同条件下相似，但 PO 电位的斜率和高度与任务参与程度成正比。神经源定位确定了一个顶叶源是变化特异性电位的主要贡献者，除了来自听觉和额叶源的更有限的贡献。总之，对自然声纹纹理中统计变化的检测主要反映在头皮和源水平的顶叶位置。在不同任务参与程度下的幅度缩放表明了证据整合的程度取决于上下文。
