Department of Psychology and Behavioral Science, Zhejiang University, 148 Tianmushan Road, Xihu District, Hangzhou, 310007, China.
Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, 100000, China.
Cereb Cortex. 2023 Mar 10;33(6):2774-2787. doi: 10.1093/cercor/bhac241.
Working memory (WM) is essential for cognition, but the underlying neural mechanisms remain elusive. From a hierarchical processing perspective, this paper proposed and tested a hypothesis that a domain-general network at the top of the WM hierarchy can interact with distinct domain-preferential intermediate circuits to support WM. Employing a novel N-back task, we first identified the posterior superior temporal gyrus (pSTG), middle temporal area (MT), and postcentral gyrus (PoCG) as intermediate regions for biological motion and shape motion processing, respectively. Using further psychophysiological interaction analyses, we delineated a frontal-parietal network (FPN) as the domain-general network. These results were further verified and extended by a delayed match to sample (DMS) task. Although the WM load-dependent and stimulus-free activations during the DMS delay phase confirm the role of FPN as a domain-general network to maintain information, the stimulus-dependent activations within this network during the DMS encoding phase suggest its involvement in the final stage of the hierarchical processing chains. In contrast, the load-dependent activations of intermediate regions in the N-back task highlight their further roles beyond perception in WM tasks. These results provide empirical evidence for a hierarchical processing model of WM and may have significant implications for WM training.
工作记忆(WM)对于认知至关重要,但其潜在的神经机制仍难以捉摸。从分层处理的角度来看,本文提出并检验了一个假设,即在 WM 层次结构的顶部有一个通用的域网络,可以与不同的域偏好中间电路相互作用,以支持 WM。采用一种新颖的 N 回任务,我们首先确定了后上颞回(pSTG)、中颞区(MT)和后中央回(PoCG)分别是生物运动和形状运动处理的中间区域。使用进一步的心理生理交互分析,我们描绘了一个额顶网络(FPN)作为通用的域网络。这些结果通过延迟匹配样本(DMS)任务进一步得到验证和扩展。虽然 DMS 延迟阶段中 WM 负荷相关且无刺激的激活证实了 FPN 作为维持信息的通用域网络的作用,但在 DMS 编码阶段中这个网络内的刺激相关激活表明它参与了分层处理链的最后阶段。相比之下,N 回任务中中间区域的负荷相关激活强调了它们在 WM 任务中除了感知之外的进一步作用。这些结果为 WM 的分层处理模型提供了经验证据,可能对 WM 训练具有重要意义。