Jylkkä Jussi, Stickley Zachary, Fellman Daniel, Waris Otto, Ritakallio Liisa, Little Todd D, Salmi Juha, Laine Matti
Department of Psychology, Åbo Akademi University, Abo, Finland.
College of Education, Texas Tech University, Lubbock, TX, USA.
Q J Exp Psychol (Hove). 2025 Aug;78(8):1547-1563. doi: 10.1177/17470218241278272. Epub 2024 Sep 18.
Measurement of cognitive functions is typically based on the implicit assumption that the mental architecture underlying cognitive task performance is constant throughout the task. In contrast, skill learning theory implies that cognitively demanding task performance is an adaptive process that progresses from initial heavy engagement of effortful and task-general metacognitive and executive control processes towards more automatic and task-specific performance. However, this hypothesis is rarely applied to the short time spans of traditional cognitive tasks such as working memory (WM) tasks. We utilised longitudinal structural equation models on two well-powered data sets to test the hypothesis that the initial stages of WM task performances load heavily on a task-general g-factor and then start to diverge towards factors specific to task structure. In line with the hypothesis, data from the first experiment ( = 296) were successfully fitted in a model with task-initial unity of the WM paradigm-specific latent factors, after which their intercorrelations started to diverge. The second experiment ( = 201) replicated this pattern except for one paradigm-specific latent factor. These preliminary results suggest that the processes underlying WM task performance tend to progress rapidly from more task-general towards task-specific, in line with the cognitive skill learning framework. Such task-internal dynamics has important implications for the measurement of complex cognitive functions.
认知功能的测量通常基于一个隐含的假设，即认知任务表现背后的心理结构在整个任务过程中是恒定的。相比之下，技能学习理论意味着，需要认知能力的任务表现是一个适应性过程，它从最初对费力的、任务通用的元认知和执行控制过程的大量投入，发展到更自动的、特定于任务的表现。然而，这一假设很少应用于传统认知任务（如工作记忆（WM）任务）的短时间跨度。我们利用两个强大数据集上的纵向结构方程模型来检验这一假设，即WM任务表现的初始阶段在很大程度上依赖于任务通用的g因素，然后开始向特定于任务结构的因素分化。与该假设一致，第一个实验（n = 296）的数据成功拟合到一个模型中，该模型中WM范式特定的潜在因素在任务开始时是统一的，之后它们的相互关系开始分化。第二个实验（n = 201）重复了这种模式，但有一个范式特定的潜在因素除外。这些初步结果表明，WM任务表现背后的过程倾向于迅速从更通用的任务发展到特定于任务的表现，这与认知技能学习框架一致。这种任务内部动态对复杂认知功能的测量具有重要意义。
