It is now firmly established that long-term memory knowledge, such as semantic knowledge, supports the temporary maintenance of verbal information in Working Memory (WM). This support from semantic knowledge is well-explained by models assuming that verbal items are directly activated in long-term memory, and that this activation provides the representational basis for WM maintenance. However, the exact mechanisms underlying semantic influence on WM performance remain poorly understood. We manipulated the presence of between-item semantic relatedness in an immediate serial recall task, by mixing triplets composed of semantically related and unrelated items (e.g. leaf – tree – branch – wall – beer – dog; hand – father – truck – cloud – sky – rain). Compared to unrelated items, related items were better recalled, as had been classically observed. Critically, semantic relatedness also impacted WM maintenance in a complex manner, as observed by the presence of proactive benefit effects on subsequent unrelated items, and the absence of retroactive effects. The complexity of these interactions is well-captured by TBRS*-S, a decay-based computational architecture in which the activation occurring in long-term memory is described. The present study suggests that semantic knowledge can be used to free up WM resources that can be reallocated for maintenance purposes, and supports models postulating that long-term memory knowledge constrains WM maintenance processes.

现在已经确定，长期记忆知识（例如语义知识）支持工作记忆（WM）中语言信息的临时维护。假设口头项目直接在长期记忆中被激活，并且模型的这种激活为WM维护提供了表示基础，这些模型可以很好地说明语义知识的支持。但是，语义上影响WM性能的确切机制仍然知之甚少。通过混合由语义相关和不相关的项目（例如，叶子–树–树枝–墙–啤酒–狗；手–父亲–卡车–云）组成的三胞胎，我们在立即的序列召回任务中操纵了项目之间语义相关性的存在–天空–雨）。与不相关的项目相比，相关的项目被召回得更好，正如经典所观察到的那样。至关重要的是，语义上的关联性还以一种复杂的方式影响了WM的维护，这可以通过对后续无关项目的前瞻性利益效应的存在和追溯力的缺失来观察。TBRS * -S很好地捕获了这些交互的复杂性，TBRS * -S是一种基于衰减的计算体系结构，其中描述了在长期存储器中发生的激活。本研究表明，语义知识可用于释放可用于维护目的而重新分配的WM资源，并支持假设长期记忆知识会约束WM维护过程的模型。