Decoding the content of working memory in school-aged children.
Decoding the content of working memory in school-aged children.
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解码学龄儿童工作记忆的内容。
DOI：
10.1101/2023.02.10.527990
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Vergauwe,Evie
中科院分区：
文献类型：
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作者：
Turoman,Nora;Fiave,ProsperAgbesi;Zahnd,Clélia;deBettencourt,MeganT;Vergauwe,Evie
工作记忆（WM）维持的发展性改善可以预测许多现实世界的结果，包括教育程度。因此，关键是要了解哪些WM机制支持这些行为的改善，以及WM维护策略如何通过发展而改变。一个挑战是，具体的WM神经机制不能很容易地测量行为，特别是在儿童人群。然而，新的多变量解码技术已经被设计出来，主要是在成年人群中，可以灵敏地解码WM的内容。本研究的目的是部署多元解码技术已知的解码记忆表征在成人解码的内容，工作记忆在儿童。我们为儿童创造了一个简单的计算机化工作记忆游戏，儿童在游戏中保持不同类别的信息（视觉，空间或语言）。我们收集了20名儿童（7-12岁）玩游戏时的脑电图（EEG）数据。使用多变量模式分析（MVPA）对儿童的EEG信号，我们可靠地解码的类别的维持信息的感觉和维护期间。在探索性的可靠性和有效性分析中，我们检查了在较少的数据上训练时这些结果的稳健性，以及这些模式在整个测试过程中如何在个体中推广。此外，这些结果与基于理论的对不同个体和年龄的WM的预测相匹配。我们的概念验证研究提出了一个直接的和年龄适当的潜在替代完全行为WM维护措施的儿童。我们的研究表明MVPA的效用，以衡量和跟踪儿童的WM的未经指示的代表性内容。未来的研究可以使用我们的技术来探讨儿童的WM维护和策略。
Developmental improvements in working memory (WM) maintenance predict many real-world outcomes, including educational attainment. It is thus critical to understand which WM mechanisms support these behavioral improvements, and how WM maintenance strategies might change through development. One challenge is that specific WM neural mechanisms cannot easily be measured behaviorally, especially in a child population. However, new multivariate decoding techniques have been designed, primarily in adult populations, that can sensitively decode the contents of WM. The goal of this study was to deploy multivariate decoding techniques known to decode memory representations in adults to decode the contents of WM in children. We created a simple computerized WM game for children, in which children maintained different categories of information (visual, spatial or verbal). We collected electroencephalography (EEG) data from 20 children (7–12-year-olds) while they played the game. Using Multivariate Pattern Analysis (MVPA) on children's EEG signals, we reliably decoded the category of the maintained information during the sensory and maintenance period. Across exploratory reliability and validity analyses, we examined the robustness of these results when trained on less data, and how these patterns generalized within individuals throughout the testing session. Furthermore, these results matched theory-based predictions of WM across individuals and across ages. Our proof-of-concept study proposes a direct and age-appropriate potential alternative to exclusively behavioral WM maintenance measures in children. Our study demonstrates the utility of MVPA to measure and track the uninstructed representational content of children's WM. Future research could use our technique to investigate children's WM maintenance and strategies.