Richmond Lauren L, Burnett Lois K, Morrison Alexandra B, Ball B Hunter
Department of Psychology, Stony Brook University, Stony Brook, NY, 11794-2500, USA.
Department of Psychology, California State University, Sacramento, Sacramento, CA, USA.
Behav Res Methods. 2022 Apr;54(2):780-794. doi: 10.3758/s13428-021-01645-y. Epub 2021 Aug 5.
Individual differences in working memory capacity (WMC) have long been known to relate to performance in domains outside of WM, including attentional control, long-term memory, problem-solving, and fluid intelligence to name a few. Complex span WM tasks, composed of a processing component and a storage component, are often used to index WMC in these types of investigations. Capacity estimates are derived from performance on the storage component only, while processing performance is often largely ignored. Here, we explore the relationship between processing performance and WMC in a large dataset for each of three complex span tasks to better characterize how the components of these tasks might be related. We provide evidence that enforcing an 85% or better accuracy criterion for the processing portion of the task results in the removal of a disproportionate number of individuals exhibiting lower WMC estimates. We also find broad support for differences in processing task performance, characterized according to both accuracy and reaction time metrics, as a function of WMC. We suggest that researchers may want to include processing task performance measures, in addition to capacity estimates, in studies using complex span tasks to index WMC. This approach may better characterize the relationships between complex span task performance and performance in disparate domains of cognition.
长期以来，人们都知道工作记忆容量（WMC）的个体差异与工作记忆之外的领域中的表现有关，包括注意力控制、长期记忆、问题解决以及流体智力等等。由一个处理成分和一个存储成分组成的复杂广度工作记忆任务，在这类研究中常被用于衡量WMC。容量估计仅从存储成分的表现得出，而处理表现往往在很大程度上被忽视。在此，我们在一个大型数据集中针对三项复杂广度任务中的每一项，探究处理表现与WMC之间的关系，以更好地描述这些任务的成分可能是如何关联的。我们提供的证据表明，对任务的处理部分实施85%或更高的准确性标准，会导致排除掉不成比例的大量WMC估计值较低的个体。我们还发现，根据准确性和反应时间指标来衡量，处理任务表现存在差异，且这种差异是WMC的函数，这一观点得到了广泛支持。我们建议，在使用复杂广度任务来衡量WMC的研究中，除了容量估计之外，研究人员可能还想纳入处理任务表现的测量。这种方法可能会更好地描述复杂广度任务表现与不同认知领域表现之间的关系。
