Dynamic neural reconstructions of attended object location and features using EEG
Dynamic neural reconstructions of attended object location and features using EEG
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DOI：
10.1152/jn.00180.2022
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发表时间：
2023-07-01
影响因子：
2.5
通讯作者：
Golomb，Julie D. D.
中科院分区：
文献类型：
--
作者：
Chen，Jiageng;Golomb，Julie D. D.
注意力使我们能够从复杂的环境中选择相关的信息，忽略不相关的信息。当注意力从一个项目转移到另一个项目时会发生什么？要回答这个问题，关键是要有工具，以高时间分辨率准确地恢复特征和位置信息的神经表示。在本研究中，我们使用人类脑电图（EEG）和机器学习来探索对象特征和位置的神经表征如何在注意力的动态转移中更新。我们证明，EEG可以用来创建同时的时间过程中的神经表征的出席功能（时间点的时间点倒置编码模型重建）和出席的位置（时间点的时间点解码）在稳定的时期和跨动态转移的注意。每次试验都有两个定向光栅，它们以相同的频率闪烁，但方向不同;参与者被提示参加其中一个，一半的试验在试验中期收到了转移提示。我们在保持注意力试验的稳定期训练模型，然后在转移注意力试验的每个时间点重建/解码关注的方向/位置。我们的研究结果表明，无论是特征重建和位置解码动态跟踪的注意力转移，并可能有时间点在转移的注意力时，1）功能和位置表示成为解耦和2）以前关注和当前关注的方向表示大致相同的强度。结果提供了深入了解我们的注意力转移的理解，在本研究中开发的非侵入性技术，以及借给自己的各种各样的未来应用。新&值得注意的是，我们使用人类脑电图和机器学习来重建神经反应曲线在动态转移的注意力。具体来说，我们证明了我们可以同时读出的位置和功能信息，从出席项目在多刺激显示。此外，我们还研究了在注意力转移的动态过程中，这种读出是如何随着时间的推移而演变的。这些结果提供了我们对注意力的理解，这种技术具有广泛的扩展和应用潜力。
Attention allows us to select relevant and ignore irrelevant information from our complex environments. What happens when attention shifts from one item to another? To answer this question, it is critical to have tools that accurately recover neural representations of both feature and location information with high temporal resolution. In the present study, we used human electroencephalography (EEG) and machine learning to explore how neural representations of object features and locations update across dynamic shifts of attention. We demonstrate that EEG can be used to create simultaneous time courses of neural representations of attended features (time point-by-time point inverted encoding model reconstructions) and attended location (time point-by-time point decoding) during both stable periods and across dynamic shifts of attention. Each trial presented two oriented gratings that flickered at the same frequency but had different orientations; participants were cued to attend one of them and on half of trials received a shift cue midtrial. We trained models on a stable period from Hold attention trials and then reconstructed/decoded the attended orientation/location at each time point on Shift attention trials. Our results showed that both feature reconstruction and location decoding dynamically track the shift of attention and that there may be time points during the shifting of attention when1) feature and location representations become uncoupled and2) both the previously attended and currently attended orientations are represented with roughly equal strength. The results offer insight into our understanding of attentional shifts, and the noninvasive techniques developed in the present study lend themselves well to a wide variety of future applications.NEW & NOTEWORTHYWe used human EEG and machine learning to reconstruct neural response profiles during dynamic shifts of attention. Specifically, we demonstrated that we could simultaneously read out both location and feature information from an attended item in a multistimulus display. Moreover, we examined how that readout evolves over time during the dynamic process of attentional shifts. These results provide insight into our understanding of attention, and this technique carries substantial potential for versatile extensions and applications.