Department of Psychology, Stanford University, Stanford, CA 94305, USA.
Department of Psychology, Stanford University, Stanford, CA 94305, USA.
Behav Res Ther. 2018 Feb;101:46-57. doi: 10.1016/j.brat.2017.09.014. Epub 2017 Oct 5.
Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psychological and neural components of self-regulation remain poorly understood, which contributes to theoretical inconsistencies and hinders maximally effective intervention development. We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers. After identifying ontological targets that precipitate maladaptive behavior, we will craft interventions that engage these targets. If successful, this work will provide a structured, holistic account of self-regulation in the form of an explicit ontology, which will better clarify the pattern of deficits related to maladaptive health behavior, and provide direction for more effective behavior change interventions.
自我调节是一个广泛的概念，代表着招募认知、动机和情感资源以实现长期目标的一般能力。这个概念与许多健康风险行为有关，是培养有益行为改变的有前途的目标。尽管它非常重要，但自我调节的行为、心理和神经成分仍未得到很好的理解，这导致了理论上的不一致，并阻碍了最有效的干预措施的发展。我们概述了一个研究计划，旨在定义自我调节的神经心理学本体论，阐明构成自我调节的认知成分、它们之间的关系以及它们相关的测量方法。该本体论将通过两种大规模的评估个体差异的方法来提供信息：首先是纯粹通过亚马逊的 Mechanical Turk 收集的数据进行行为评估，然后再结合来自另一个人群的神经影像学数据。为了验证本体论并展示其效用，我们将使用它来分析两个典型行为群体中的健康风险行为：暴饮暴食的超重/肥胖成年人和吸烟者。在确定引发适应不良行为的本体论目标后，我们将制定干预措施来针对这些目标。如果成功，这项工作将以明确的本体论形式提供自我调节的结构化、整体描述，这将更好地阐明与适应不良健康行为相关的缺陷模式，并为更有效的行为改变干预措施提供指导。
