Henry Molly J, Herrmann Björn, Obleser Jonas
Max Planck Research Group "Auditory Cognition", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Cereb Cortex. 2015 Feb;25(2):450-9. doi: 10.1093/cercor/bht240. Epub 2013 Aug 26.
Meaningful auditory stimuli such as speech and music often vary simultaneously along multiple time scales. Thus, listeners must selectively attend to, and selectively ignore, separate but intertwined temporal features. The current study aimed to identify and characterize the neural network specifically involved in this feature-selective attention to time. We used a novel paradigm where listeners judged either the duration or modulation rate of auditory stimuli, and in which the stimulation, working memory demands, response requirements, and task difficulty were held constant. A first analysis identified all brain regions where individual brain activation patterns were correlated with individual behavioral performance patterns, which thus supported temporal judgments generically. A second analysis then isolated those brain regions that specifically regulated selective attention to temporal features: Neural responses in a bilateral fronto-parietal network including insular cortex and basal ganglia decreased with degree of change of the attended temporal feature. Critically, response patterns in these regions were inverted when the task required selectively ignoring this feature. The results demonstrate how the neural analysis of complex acoustic stimuli with multiple temporal features depends on a fronto-parietal network that simultaneously regulates the selective gain for attended and ignored temporal features.
诸如语音和音乐等有意义的听觉刺激通常会在多个时间尺度上同时变化。因此,听众必须有选择地关注并选择性地忽略不同但相互交织的时间特征。当前的研究旨在识别并描述专门参与这种对时间的特征选择性注意的神经网络。我们采用了一种新颖的范式,让听众判断听觉刺激的时长或调制率,并且在该范式中,刺激、工作记忆需求、反应要求和任务难度均保持不变。首次分析确定了所有大脑区域,其中个体的脑激活模式与个体的行为表现模式相关,因此这些区域总体上支持时间判断。然后进行的第二次分析分离出了那些专门调节对时间特征的选择性注意的大脑区域:包括岛叶皮质和基底神经节在内的双侧额顶叶网络中的神经反应会随着所关注的时间特征的变化程度而降低。至关重要的是,当任务要求选择性忽略此特征时,这些区域的反应模式会反转。结果表明,对具有多个时间特征的复杂声学刺激的神经分析如何依赖于一个额顶叶网络,该网络同时调节对所关注和忽略的时间特征的选择性增益。