Dynamical Systems Modeling of Early Childhood Self-Regulation
Dynamical Systems Modeling of Early Childhood Self-Regulation
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DOI：
10.1037/emo0000268
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发表时间：
2017-06-01
期刊：
影响因子：
4.2
通讯作者：
Chow, Sy-Miin
中科院分区：
文献类型：
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作者：
Cole, Pamela M.;Bendezu, Jason J.;Chow, Sy-Miin
自我调节可以概念化为极有可能的反应(优势反应)和可以调节这些反应的策略(执行过程)之间的动态紧张关系。本研究探讨了动力系统方法在儿童早期自我调节研究中的价值。具体地说，常微分方程式(ODE)被用来模拟115个36个月大婴儿的执行过程(策略使用)和对等待打开礼物的强势反应(对礼物的渴望和等待打开礼物的挫败感)的交互影响。使用非线性混合效应框架中的一对耦合的二阶常数项，该研究测试了对卓越反应-执行过程耦合的特定儿童内部和儿童之间模式的预测。动态建模结果清楚地表明了36个月大婴儿的战略努力的局限性。当他们的优势反应水平较高时，他们参与了执行过程，这延迟了优势反应的重新出现，但最终并没有抑制等待过程中的优势反应。然而，有初步证据表明，36个月大的婴儿自我调节的有效性取决于儿童的特征。外部化行为问题与更多的监管干预有关。气质上的负性情感与更多的监管无效略有关联。与研究自我调节的传统方法相比，动态建模产生了互补和独特的结果，表明了其潜力。
Self-regulation can be conceptualized in terms of dynamic tension between highly probable reactions (prepotent responses) and use of strategies that can modulate those reactions (executive processes). This study investigated the value of a dynamical systems approach to the study of early childhood self-regulation. Specifically, ordinary differential equations (ODEs) were used to model the interactive influences of 115 36-month-olds' executive processes (strategy use) and prepotent responses to waiting to open a gift (desire for the gift and frustration about waiting to open it). Using a pair of coupled second-order ODEs in a nonlinear mixed effects framework, the study tested predictions for specific within-and between-child patterns of prepotent response-executive process coupling. Dynamic modeling results articulated the limits of 36-month olds' strategic efforts. They engaged executive processes when their prepotent responding levels were high, which delayed the resurgence of prepotent responses, but ultimately did not damp prepotent responding over the course of the wait. There was, however, preliminary evidence that the effectiveness of 36-month-olds' self-regulation depended upon child characteristics. Externalizing behavior problems were associated with more regulatory interference. Temperamental negative affectivity was marginally associated with more regulatory inefficiency. Compared with conventional methods of studying self-regulation, dynamic modeling yielded complementary and unique findings, suggesting its potential.