Recent shifts in the understanding of how the mind and brain retain information in working memory (WM) call for revision to traditional theories. Evidence of dynamic, “activity-silent,” short-term retention processes diverges from conventional models positing that information is always retained in WM by sustained neural activity in buffers. Such evidence comes from machine-learning methods that can decode patterns of brain activity and the simultaneous administration of transcranial magnetic stimulation (TMS) to causally manipulate brain activity in specific areas and time points. TMS can “ping” brain areas to both reactivate latent representations retained in WM and affect memory performance. On the basis of these findings, I argue for a supplement to sustained retention mechanisms. Brain-decoding methods also reveal that dynamic levels of representational codes are retained in WM, and these vary according to task context, from perceptual (sensory) codes in posterior areas to abstract, recoded representations distributed across frontoparietal regions. A dynamic-processing model of WM is advanced to account for the overall pattern of results.

中文翻译：

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工作记忆的动态处理模型

关于思维和大脑如何在工作记忆（WM）中保留信息的理解的最新变化要求对传统理论进行修订。动态的，“沉默活动”的短期保留过程的证据与传统模型不同，后者认为信息始终通过缓冲液中持续的神经活动而保留在WM中。这些证据来自机器学习方法，该方法可以解码大脑活动的模式，并同时给予经颅磁刺激（TMS）以因果地操纵特定区域和特定时间点的大脑活动。TMS可以“ ping”大脑区域以重新激活WM中保留的潜在表示并影响记忆性能。基于这些发现，我主张对持续保留机制进行补充。大脑解码方法还揭示出，WM中保留了代表代码的动态级别，并且这些动态级别根据任务上下文而有所不同，从后部区域的感知（感官）代码到跨额顶区域分布的抽象，重新编码的表示形式。改进了WM的动态处理模型，以考虑结果的整体模式。