Laureate Institute for Brain Research.
Children's Mercy Hospital.
Child Dev. 2021 Sep;92(5):2035-2052. doi: 10.1111/cdev.13578. Epub 2021 Apr 26.
This study used a machine learning framework in conjunction with a large battery of measures from 9,718 school-age children (ages 9-11) from the Adolescent Brain Cognitive Development (ABCD) Study to identify factors associated with fluid cognitive functioning (FCF), or the capacity to learn, solve problems, and adapt to novel situations. The identified algorithm explained 14.74% of the variance in FCF, replicating previously reported socioeconomic and mental health contributors to FCF, and adding novel and potentially modifiable contributors, including extracurricular involvement, screen media activity, and sleep duration. Pragmatic interventions targeting these contributors may enhance cognitive performance and protect against their negative impact on FCF in children.
本研究使用机器学习框架,结合来自“青少年大脑认知发展研究”(ABCD 研究)的 9718 名学龄儿童(9-11 岁)的大量测量指标,确定与流体智力功能(FCF)相关的因素,即学习、解决问题和适应新情况的能力。所确定的算法解释了 FCF 方差的 14.74%,复制了先前报告的与 FCF 相关的社会经济和心理健康因素,并增加了新的和潜在可改变的因素,包括课外活动参与、屏幕媒体活动和睡眠时间。针对这些因素的实用干预措施可能会提高儿童的认知表现,并防止它们对 FCF 产生负面影响。