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独立的逆向回忆和逆向工作记忆因素的证据:一项大规模潜在变量分析。

Evidence for separate backward recall and -back working memory factors: a large-scale latent variable analysis.

作者信息

Byrne Elizabeth M, Gilbert Rebecca A, Kievit Rogier A, Holmes Joni

机构信息

School of Psychology, University of East Anglia, Norwich, UK.

MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.

出版信息

Memory. 2024 Oct;32(9):1182-1198. doi: 10.1080/09658211.2024.2393388. Epub 2024 Aug 26.

Abstract

Multiple studies have explored the factor structure of working memory (WM) tasks, yet few have done so controlling for both the domain and category of the memory items in a single study. In the current pre-registered study, we conducted a large-scale latent variable analysis using variant forms of n-back and backward recall tasks to test whether they measured a single underlying construct, or were distinguished by stimuli-, domain-, or paradigm-specific factors. Exploratory analyses investigated how the resulting WM factor(s) were linked to fluid intelligence. Participants ( = 703) completed a fluid reasoning test and multiple n-back and backward recall tasks containing memoranda that varied across (spatial or verbal material) and within (verbal digits or letters) domain, allowing the variance specific to task content and paradigm to be assessed. Two distinct but related backward recall and n-back constructs best captured the data, in comparison to other plausible model constructions (single WM factor, two-factor domain, and three-factor materials models). Common variance associated with WM was a stronger predictor of fluid reasoning than a residual n-back factor, but the backward recall factor predicted fluid reasoning as strongly as the common WM factor. These data emphasise the distinctiveness between backward recall and n-back tasks.

摘要

多项研究探讨了工作记忆(WM)任务的因素结构,但很少有研究在单一研究中同时控制记忆项目的领域和类别。在当前的预注册研究中,我们使用n-back和倒序回忆任务的变体形式进行了大规模潜变量分析,以测试它们是测量单一潜在结构,还是由刺激、领域或范式特定因素区分。探索性分析研究了由此产生的WM因素如何与流体智力相关联。参与者(n = 703)完成了一项流体推理测试以及多个n-back和倒序回忆任务,这些任务包含在领域间(空间或语言材料)和领域内(语言数字或字母)有所不同的记忆内容,从而能够评估特定于任务内容和范式的方差。与其他合理的模型构建(单一WM因素、双因素领域和三因素材料模型)相比,两个不同但相关的倒序回忆和n-back结构最能拟合数据。与WM相关的共同方差比剩余的n-back因素更能预测流体推理,但倒序回忆因素对流体推理的预测强度与共同的WM因素相当(预测力相当)。这些数据强调了倒序回忆和n-back任务之间的区别。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e8e/11441403/6cd25a07ca14/PMEM_A_2393388_F0002_OB.jpg

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