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Oscillatory Dynamics Supporting Semantic Cognition: MEG Evidence for the Contribution of the Anterior Temporal Lobe Hub and Modality-Specific Spokes

机译:支持语义认知的振荡动力学:MEG证据为前颞叶枢纽和特定模式的辐形的贡献。

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摘要

The “hub and spoke model” of semantic representation suggests that the multimodal features of objects are drawn together by an anterior temporal lobe (ATL) “hub”, while modality-specific “spokes” capture perceptual/action features. However, relatively little is known about how these components are recruited through time to support object identification. We used magnetoencephalography to measure neural oscillations within left ATL, lateral fusiform cortex (FC) and central sulcus (CS) during word-picture matching at different levels of specificity (employing superordinate vs. specific labels) for different categories (manmade vs. animal). This allowed us to determine (i) when each site was sensitive to semantic category and (ii) whether this was modulated by task demands. In ATL, there were two phases of response: from around 100 ms post-stimulus there were phasic bursts of low gamma activity resulting in reductions in oscillatory power, relative to a baseline period, that were modulated by both category and specificity; this was followed by more sustained power decreases across frequency bands from 250 ms onwards. In the spokes, initial power increases were not stronger for specific identification, while later power decreases were stronger for specific-level identification in FC for animals and in CS for manmade objects (from around 150 ms and 200 ms, respectively). These data are inconsistent with a temporal sequence in which early sensory-motor activity is followed by later retrieval in ATL. Instead, knowledge emerges from the rapid recruitment of both hub and spokes, with early specificity and category effects in the ATL hub. The balance between these components depends on semantic category and task, with visual cortex playing a greater role in the fine-grained identification of animals and motor cortex contributing to the identification of tools.
机译:语义表示的“枢纽模型”表明,对象的多峰特征由前颞叶(ATL)“枢纽”组合在一起,而特定于形式的“辐条”则捕获了感知/动作特征。但是,关于如何随时间招募这些组件以支持对象识别的了解相对较少。我们使用磁脑描记法在不同类别(人工或动物)的不同特异性级别(使用上级标签或特定标签)匹配文字图片时,测量了左ATL,外侧梭形皮质(FC)和中央沟(CS)内的神经振荡。 。这使我们能够确定(i)每个站点何时对语义类别敏感,以及(ii)是否受任务需求调节。在ATL中,有两个响应阶段:从刺激后约100毫秒开始,出现低伽马活性的阶段性爆发,导致振荡功率相对于基线期降低,并受类别和特异性调节。随后,从250 ms开始,整个频带上的功率将持续降低。在辐条中,动物的FC和人造物体的CS的初始功率增加并不明显,而特定级别的识别则后来的功率下降更强(分别从150 ms和200 ms开始)。这些数据与时间序列不一致,在该时间序列中,早期的感觉运动活动随后是在ATL中的后期检索。取而代之的是,知识的积累是通过快速招聘中心和分支机构而产生的,并且在ATL中心具有早期的特殊性和类别影响。这些组件之间的平衡取决于语义类别和任务,其中视觉皮层在动物的细粒度识别中起着更大的作用,运动皮层在工具识别中起着重要作用。

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