脑电图
工作记忆
心理学
认知心理学
单变量
神经生理学
任务(项目管理)
计算机科学
多元统计
神经科学
机器学习
认知
经济
管理
作者
Dongwei Li,Yiqing Hu,Mengdi Qi,Chenguang Zhao,Ole Jensen,Jing Huang,Yan Song
出处
期刊:NeuroImage
[Elsevier]
日期:2023-01-25
卷期号:269: 119902-119902
被引量:10
标识
DOI:10.1016/j.neuroimage.2023.119902
摘要
Previous work has proposed two potential benefits of retrospective attention on working memory (WM): target strengthening and non-target inhibition. It remains unknown which hypothesis contributes to the improved WM performance, yet the neural mechanisms responsible for this attentional benefit are unclear. Here, we recorded electroencephalography (EEG) signals while 33 participants performed a retrospective-cue WM task. Multivariate pattern classification analysis revealed that only representations of target features were enhanced by valid retrospective attention during retention, supporting the target strengthening hypothesis. Further univariate analysis found that mid-frontal theta inter-trial phase coherence (ITPC) and ERP components were modulated by valid retrospective attention and correlated with individual differences and moment-to-moment fluctuations on behavioral outcomes, suggesting that both trait- and state-level variability in attentional preparatory processes influence goal-directed behavior. Furthermore, task-irrelevant target spatial location could be decoded from EEG signals, indicating that enhanced spatial binding of target representation is vital to high WM precision. Importantly, frontoparietal theta-alpha phase-amplitude coupling was increased by valid retrospective attention and predicted the reduced random guessing rates. This long-range connection supported top-down information flow in the engagement of frontoparietal networks, which might organize attentional states to integrate target features. Altogether, these results provide neurophysiological bases that retrospective attention improves WM precision by enhancing flexible target representation and emphasize the critical role of the frontoparietal attentional network in the control of WM representations.
科研通智能强力驱动
Strongly Powered by AbleSci AI