London South Bank University, London SE1 0AA, UK.
London South Bank University, London SE1 0AA, UK.
J Exp Child Psychol. 2020 Mar;191:104736. doi: 10.1016/j.jecp.2019.104736. Epub 2019 Nov 23.
This study examined working memory (WM) using complex span tasks (CSTs) to improve theoretical understanding of the relationship between WM and high-level cognition (HLC) in children. A total of 92 children aged 7 and 8 years were tested on three computer-paced CSTs and measures of nonverbal reasoning, reading, and mathematics. Processing times in the CSTs were restricted based on individually titrated processing speeds, and performance was compared with participant-led tasks with no time restrictions. Storage, processing accuracy, and both processing and recall times within the CSTs were used as performance indices to understand the effects of time restrictions at a granular level. Restricting processing times did not impair storage, challenging models that argue for a role of maintenance in WM. A task-switching account best explained the effect of time restrictions on performance indices and their interrelationships. Principal component analysis showed that a single factor with all performance indices from just one CST (counting span) was the best predictor of HLC. Storage in both the participant-led and computer-paced versions of this task explained unique and shared variance in HLC. However, the latter accounted for more variance in HLC when contributions from processing time were included in the model. Processing time in this condition also explained variance above and beyond storage. This suggests that faster processing is important to keep information active in WM; however, this is evident only when time restrictions are placed on the task and important when WM performance is applied in broader contexts that rely on this resource.
本研究使用复杂跨度任务 (CST) 来检验工作记忆 (WM),以增进对儿童 WM 与高级认知 (HLC) 之间关系的理论理解。共有 92 名 7 岁和 8 岁的儿童接受了三种计算机 paced CST 和非言语推理、阅读和数学的测试。CST 中的处理时间根据个体调整的处理速度进行限制,并且与没有时间限制的参与者主导任务进行了比较。存储、CST 中的处理准确性以及处理和回忆时间都被用作性能指标,以在细粒度层面上理解时间限制的影响。限制处理时间不会损害存储,这挑战了维持在 WM 中起作用的观点。任务转换解释了时间限制对性能指标及其相互关系的影响。主成分分析表明,仅从一个 CST(计数跨度)中的所有性能指标就可以得出最佳预测 HLC 的单一因素。在参与者主导和计算机 paced 版本的任务中,存储都可以解释 HLC 中的独特和共享方差。然而,当在模型中包含处理时间的贡献时,后者可以更好地解释 HLC 中的方差。在这种情况下,处理时间甚至可以解释存储之外的方差。这表明,更快的处理对于将信息保持在 WM 中很重要;但是,只有在任务受到时间限制并且在依赖此资源的更广泛背景下应用 WM 性能时,这才是明显的。