Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis -Systematic Representative Design (SRD)concurrently enhancing both causal inference and "built-in" generalizability by leveraging today's intelligent agent, virtual environments, and other technologies. In SRD, a "default control group" (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both "bigger theory" and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.

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Background Research suggests that deficits in both executive functioning and trait impulsivity may play a role in risky sexual behavior. At the neural level, differences in regulation of the prefrontal cortex have been linked to impulsivity, measured neurocognitively and through self-report. The relationship between neurocognitive measures of executive control and trait impulsivity in predicting risky sexual behavior has not been investigated. Purpose To investigate the relationship between neural functioning during the Stroop task and risky sexual behavior, as well as the effect of individual differences in urgent (positive and negative) impulsivity on this relationship. Methods 105 sexually active men who have sex with men (MSM) completed the Stroop task during functional magnetic resonance imaging scanning. They also completed impulsivity inventories and self-reported their risky sexual behavior (events of condomless anal sex in the last 90 days). Results Risky participants had greater activation than safe participants during the color congruent condition of the Stroop task in anterior cingulate cortex/dorsomedial prefrontal cortex, dorsolateral prefrontal cortex, left frontal pole, and right insula. Across these regions, this neural activation mediated the link between (positive and/or negative) urgent impulsivity and risky sexual behavior. Conclusions Findings suggest that the brains of men who engage in risky sexual behavior may employ a different distribution of cognitive resources during tasks of executive functioning than men who practice safe sex, and that this may relate to differences in the prefrontal cortical/fronto-insular system responsible for impulse control.

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Narrative games, in which users interact with virtual agents, are increasingly being used in health interventions to change targeted behaviors. In virtual social interactions, based on similar real-life contextual cues, past behavior can predict virtual choices. Here, based on theories in learning and interactivity, we examined the whether following a virtual intervention, choices in social interactions may be particularly diagnostic of future behavior changes. To test this, we needed to: (1) leverage a contextualized risk (e.g., involving alcohol consumption) scenario (e.g., having one more drink with my partner) given a target audience (e.g., sexually risky young men who have sex with men (YMSM)), (2) include within this context an evidence-based virtual intervention (e.g., promoting alcohol reduction), (3) instantiate and record a virtual choice (water or alcohol) in a virtual dating game scenario intervention with IA for that target audience, and (4) assess pre and 6-months post-intervention YMSM’s alcohol use. Using a Socially Optimized Learning Environment (SOLVE) intervention game with IA and alcohol use measures, we found that virtual water choice (versus virtual alcohol choice) significantly predicted real-life alcohol consumption change. Furthermore, personality factors (e.g., Behavioral Approach System) predicted virtual choices and alcohol consumption change. Implications of these findings are discussed.