Attention is an important resource for prioritizing information in working memory (WM), and it can be deployed both strategically and automatically. Most research investigating the relationship between WM and attention has focused on strategic efforts to deploy attentional resources toward remembering relevant information. However, such voluntary attentional control represents a mere subset of the attentional processes that select information to be encoded and maintained in WM (Theeuwes, Journal of Cognition, 1[1]: 29, 1–15, 2018). Here, we discuss three ways in which information becomes prioritized automatically in WM—physical salience, statistical learning, and reward learning. This review integrates findings from perception and working memory studies to propose a more sophisticated understanding of the relationship between attention and working memory.

注意力是在工作记忆 (WM) 中优先处理信息的重要资源，它可以战略性地和自动地部署。大多数调查 WM 和注意力之间关系的研究都集中在部署注意力资源以记住相关信息的战略努力上。然而，这种自愿注意控制仅代表了选择要在 WM 中编码和维护的信息的注意过程的一个子集（Theeuwes，认知杂志，1[1]：2018 年 1 月 29 日至 15 日）。在这里，我们讨论了信息在 WM 中自动优先排序的三种方式——物理显着性、统计学习和奖励学习。这篇综述整合了感知和工作记忆研究的结果，提出了对注意力和工作记忆之间关系的更复杂的理解。