Working memory (WM) has been defined as the active maintenance and flexible updating of goal-relevant information in a form that has limited capacity and resists interference. Complex measures of WM recruit multiple subprocesses, making it difficult to isolate specific contributions of putatively independent subsystems. The present study was designed to determine whether neurophysiological indicators of proposed subprocesses of WM predict WM performance. We recruited 200 individuals defined by care-seeking status and measured neural responses using electroencephalography (EEG), while participants performed four WM tasks. We extracted spectral and time-domain EEG features from each task to quantify each of the hypothesized WM subprocesses: maintenance (storage of content), goal maintenance, and updating. We then used EEG measures of each subprocess as predictors of task performance to evaluate their contribution to WM. Significant predictors of WM capacity included contralateral delay activity and frontal theta, features typically associated with maintenance (storage of content) processes. In contrast, significant predictors of reaction time and its variability included contingent negative variation and the P3b, features typically associated with goal maintenance and updating. Broadly, these results suggest two principal dimensions that contribute to WM performance, tonic processes during maintenance contributing to capacity, and phasic processes during stimulus processing that contribute to response speed and variability. The analyses additionally highlight that reliability of features across tasks was greater (and comparable to that of WM performance) for features associated with stimulus processing (P3b and alpha), than with maintenance (gamma, theta and cross-frequency coupling).

工作记忆（Working memory，WM）被定义为以容量有限、抗干扰的形式主动维护和灵活更新目标相关信息。WM 的复杂测量需要多个子过程，因此很难分离假定独立子系统的特定贡献。本研究旨在确定所提出的 WM 子过程的神经生理指标是否可以预测 WM 性能。我们招募了 200 名根据求医状态定义的个体，并使用脑电图 (EEG) 测量了神经反应，而参与者则执行了四项 WM 任务。我们从每个任务中提取频谱和时域脑电图特征来量化每个假设的 WM 子过程：维护（内容存储）、目标维护和更新. 然后，我们使用每个子过程的 EEG 测量值作为任务绩效的预测指标，以评估它们对 WM 的贡献。WM 容量的重要预测因素包括对侧延迟活动和额叶θ，这些特征通常与维护（内容存储）过程相关。相比之下，反应时间及其可变性的重要预测因子包括或有负变化和 P3b，这些特征通常与目标维护和更新相关. 从广义上讲，这些结果表明了有助于 WM 性能的两个主要方面，即维持期间有助于容量的强直过程，以及有助于响应速度和可变性的刺激处理期间的阶段性过程。分析还强调，与刺激处理（P3b 和 alpha）相关的特征（P3b 和 alpha）比维护（gamma、theta 和跨频率耦合）相关的特征在任务间的可靠性更高（并且与 WM 性能相当）。