Casey Michael A
Bregman Music and Audio Lab, Computer Science and Music Departments, Dartmouth CollegeHanover, NH, United States.
Front Psychol. 2017 Jul 14;8:1179. doi: 10.3389/fpsyg.2017.01179. eCollection 2017.
Underlying the experience of listening to music are parallel streams of auditory, categorical, and schematic qualia, whose representations and cortical organization remain largely unresolved. We collected high-field (7T) fMRI data in a music listening task, and analyzed the data using multivariate decoding and stimulus-encoding models. Twenty subjects participated in the experiment, which measured BOLD responses evoked by naturalistic listening to twenty-five music clips from five genres. Our first analysis applied machine classification to the multivoxel patterns that were evoked in temporal cortex. Results yielded above-chance levels for both stimulus identification and genre classification-cross-validated by holding out data from multiple of the stimuli during model training and then testing decoding performance on the held-out data. Genre model misclassifications were significantly correlated with those in a corresponding behavioral music categorization task, supporting the hypothesis that geometric properties of multivoxel pattern spaces underlie observed musical behavior. A second analysis employed a spherical searchlight regression analysis which predicted multivoxel pattern responses to music features representing melody and harmony across a large area of cortex. The resulting prediction-accuracy maps yielded significant clusters in the temporal, frontal, parietal, and occipital lobes, as well as in the parahippocampal gyrus and the cerebellum. These maps provide evidence in support of our hypothesis that geometric properties of music cognition are neurally encoded as multivoxel representational spaces. The maps also reveal a cortical topography that differentially encodes categorical and absolute-pitch information in distributed and overlapping networks, with smaller specialized regions that encode tonal music information in relative-pitch representations.
聆听音乐的体验背后存在着并行的听觉、类别和图式感受质流,其表征和皮层组织在很大程度上仍未得到解决。我们在音乐聆听任务中收集了高场(7T)功能磁共振成像(fMRI)数据,并使用多变量解码和刺激编码模型对数据进行了分析。20名受试者参与了该实验,该实验测量了自然聆听来自五种流派的25个音乐片段所诱发的血氧水平依赖(BOLD)反应。我们的首次分析将机器分类应用于颞叶皮层诱发的多体素模式。通过在模型训练期间留出多个刺激的数据,然后在留出的数据上测试解码性能进行交叉验证,结果在刺激识别和流派分类方面均产生了高于机会水平的结果。流派模型的错误分类与相应行为音乐分类任务中的错误分类显著相关,支持了多体素模式空间的几何特性是观察到的音乐行为基础这一假设。第二项分析采用了球形搜索光回归分析,该分析预测了跨大面积皮层对代表旋律与和声的音乐特征的多体素模式反应。所得的预测准确性图谱在颞叶、额叶、顶叶和枕叶以及海马旁回和小脑中产生了显著的聚类。这些图谱为支持我们的假设提供了证据,即音乐认知的几何特性在神经上被编码为多体素表征空间。这些图谱还揭示了一种皮层拓扑结构,该结构在分布式和重叠网络中对类别和绝对音高信息进行差异编码,同时具有较小的专门区域,以相对音高表征对调性音乐信息进行编码。