Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China.
Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China.
Cereb Cortex. 2018 Sep 1;28(9):3241-3254. doi: 10.1093/cercor/bhx195.
A significant neural challenge in speech perception includes extracting discrete phonetic categories from continuous and multidimensional signals despite varying task demands and surface-acoustic variability. While neural representations of speech categories have been previously identified in frontal and posterior temporal-parietal regions, the task dependency and dimensional specificity of these neural representations are still unclear. Here, we asked native Mandarin participants to listen to speech syllables carrying 4 distinct lexical tone categories across passive listening, repetition, and categorization tasks while they underwent functional magnetic resonance imaging (fMRI). We used searchlight classification and representational similarity analysis (RSA) to identify the dimensional structure underlying neural representation across tasks and surface-acoustic properties. Searchlight classification analyses revealed significant "cross-task" lexical tone decoding within the bilateral superior temporal gyrus (STG) and left inferior parietal lobule (LIPL). RSA revealed that the LIPL and LSTG, in contrast to the RSTG, relate to 2 critical dimensions (pitch height, pitch direction) underlying tone perception. Outside this core representational network, we found greater activation in the inferior frontal and parietal regions for stimuli that are more perceptually similar during tone categorization. Our findings reveal the specific characteristics of fronto-tempo-parietal regions that support speech representation and categorization processing.
在语音感知中,一个重要的神经学挑战是,尽管任务需求和表面声学变化不断,但仍要从连续的多维信号中提取离散的语音类别。虽然以前已经在额颞和后颞顶叶区域确定了语音类别的神经表示,但这些神经表示的任务依赖性和维度特异性仍然不清楚。在这里,我们要求母语为普通话的参与者在进行功能磁共振成像 (fMRI) 的同时,被动聆听、重复和分类任务中听带有 4 个不同的词汇声调类别的语音音节。我们使用搜索灯分类和表示相似性分析 (RSA) 来确定跨任务和表面声学特性的神经表示的维度结构。搜索灯分类分析显示,双侧颞上回 (STG) 和左顶下小叶 (LIPL) 中存在显著的“跨任务”词汇声调解码。RSA 显示,与 RSTG 相比,LIPL 和 LSTG 与声调感知的 2 个关键维度(音高高度、音高方向)有关。在这个核心表示网络之外,我们发现,在进行声调分类时,感知上更相似的刺激在额下和顶下区域的激活程度更高。我们的研究结果揭示了支持语音表示和分类处理的额颞顶叶区域的特定特征。