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1. [SOBC Measures Repository.][4] 2. [SOBC Resource and Coordinating Center Website.][1] 3. [NIH Common Fund SOBC Website.][2] 4. [Manual of Operations][5] Health risk behavior, including poor diet, physical inactivity, tobacco and other substance use, causes as much as 40% of the illness, suffering, and early death related to chronic diseases. Although an array of interventions have been shown to be effective in promoting initiation and maintenance of health behavior change -- including adherence to medical regimens and increasing health behavior -- much of this work has been siloed (focused on one disease or disorder at a time). One promising domain of putative behavior change targets across many populations and types of health behavior is that of self-regulation. Despite the promise of this line of research examining the role of self-regulation as a mechanism of health behavior change, many significant gaps and opportunities exist in this line of scientific inquiry. First, a broad set of constructs related to self-regulation (and tasks used to measure them) have been studied across multiple distinct literatures, but often have little crosstalk. At the psychological level, the cognitive literature has focused on such performance-related processes as goal maintenance in working memory, impulsivity, and cognitive homeostasis, whereas the affective science and social psychology literatures have focused on emotion regulation processes and resource models. In parallel, the health psychology and behavioral medicine literatures have focused on processes such as self-efficacy and outcome expectancies. At the behavioral level, an even broader set of tasks have been developed to assess self-regulation, such as behavioral disinhibition and temporal discounting tasks. At the neural level, self-regulation can be conceptualized in terms of top-down control (implemented by fronto-parietal networks) over impulsive drives or habits (implemented by subcortical and ventromedial prefrontal regions), though this two-system approach has also occasioned some controversy. An emerging framework from neuroeconomics has characterized decision processes in terms of goal-directed versus habitual or Pavlovian control over action. However, the field has yet to achieve a clear mapping of self-regulation processes to specific neural systems. Second, the number of measures for identically-named constructs is increasing and different measures are often used in different studies of the same putative constructs. Consistency in measures will be important in establishing the generality of health behavior intervention effects across settings and populations. Third, research examining mechanisms has tended to examine a small set of potential mechanisms at a specific level of analysis (e.g., brain circuitry measures alone or behavioral tasks alone) and may lead to over-simplified accounts of behavior change. Examining a broad array of mechanisms at multiple levels of analysis will enable a more comprehensive picture of mechanisms of change, and an increased understanding of the conditions under which replications do and do not occur. In this work, we plan to use a systematic, empirical process to integrate concepts across the divergent self-regulation literatures to identify putative mechanisms of behavior change to develop an overarching “ontology” of self-regulatory processes (a formal description of constructs across the self-regulation domain and the tasks use to engage these targets). The first aim of this project is a large Mechanical Turk individual differences study, where 500 subjects (split into a 200 person discovery sample and a 300 person validation sample) complete many tasks and questionnaires. See the [Manual of Operations][3] wiki page for a list and description of each measure, as well as links to the tasks themselves and their github repos. [1]: [2]: [3]: [4]: [5]: