Applying novel technologies and methods to inform the ontology of self-regulation. (February 2018)

• Record Type: Journal Article
• Authors: Eisenberg, Ian W.; Bissett, Patrick G.; Canning, Jessica R.; Dallery, Jesse; Enkavi, A. Zeynep; Whitfield-Gabrieli, Susan; Gonzalez, Oscar; Green, Alan I.; Greene, Mary Ann; Kiernan, Michaela; Kim, Sunny Jung; Li, Jamie; Lowe, Michael R.; Mazza, Gina L.; Metcalf, Stephen A.; Onken, Lisa; Parikh, Sadev S.; Peters, Ellen; Prochaska, Judith J.; Scherer, Emily A.; Stoeckel, Luke E.; Valente, Matthew J.; Wu, Jialing; Xie, Haiyi; MacKinnon, David P.; Marsch, Lisa A.; Poldrack, Russell A.
• Is Part Of: Behaviour research and therapy. Volume 101(2018)
• Page Start: 46
• Page End: 57
• Publication Date: 2018-02
• Subjects: Self-regulation -- Ontology -- Neuroimaging -- Intervention -- Obesity -- Smoking; Cognitive therapy -- Periodicals; Psychotherapy -- Periodicals; 616.891
• DOI: 10.1016/j.brat.2017.09.014
• Languages: English
• ISSNs: 0005-7967

Abstract

Abstract: 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. Highlights: We outline a research program to develop an ontology of self-regulation. The ontology will be informed by behavioral, psychological and neuroimaging data. We will validate the ontology in two behavioral groups: smokers and obese individuals. We overview plans for intervention development: both in lab, and using a mobile platform.