Uncovering the structure of self-regulation through data-driven ontology discovery

Abstract

Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issues that are particularly damaging for the study of multifaceted constructs like self-regulation. Here, we derive a psychological ontology from a study of individual differences across a broad range of behavioral tasks, self-report surveys, and self-reported real-world outcomes associated with self-regulation. Though both tasks and surveys putatively measure self-regulation, they show little empirical relationship. Within tasks and surveys, however, the ontology identifies reliable individual traits and reveals opportunities for theoretic synthesis. We then evaluate predictive power of the psychological measurements and find that while surveys modestly and heterogeneously predict real-world outcomes, tasks largely do not. We conclude that self-regulation lacks coherence as a construct, and that data-driven ontologies lay the groundwork for a cumulative psychological science.

Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood

“…Hence, future work that seeks to utilize EEA or other computationally derived risk factors to make real-world predictions about individuals' substance use problems should consider identifying neural correlates of these risk factors that can be feasibly measured in applied settings (e.g., electrophysiological measures of errormonitoring). Additionally, as recent work suggests that self-report measures have greater predictive power than task-based measures 18 , identification of self-report measures that index similar mechanistic dimensions to EEA may aid prediction. Future work establishing the place of EEA in a larger nomological network of task and survey measures is therefore needed.…”

“…Substance use increases throughout adolescence and peaks during a period characterized as "emerging adulthood" (ages [18][19][20][21][22][23][24][25] 6 . According to an ongoing national survey on US substance use, rates of past-month marijuana use were highest at ages 21-22 (27.5%), and pastmonth alcohol use was highest at ages 23-24 (75.1%) 7 .…”

“…Although these studies suggest that individual differences in neurocognition predict problematic substance use at critical developmental periods, they have exclusively focused on neurocognitive constructs derived from earlier factor analytic work (e.g., working memory, inhibition 16 ). Such a focus is problematic because these cognitive ontologies have recently been found to display questionable replicability 17,18 , and because constructs defined mainly based on behavioral test score covariation, rather than on an understanding of the mechanistic processes involved in cognition, are likely to display ambiguous links to neural circuitry 19 . Computational psychiatry, an emerging field in which biologically plausible formal models are used to describe performance on clinically relevant tasks 19 , offers a promising alternative framework.…”

“…We recently found 29 that EEA on a go/no-go task (measured across both "go" and "nogo" trials) was strongly related to the inhibitory performance (false alarm rate) of young adults (18)(19)(20)(21) and was robustly positively correlated with error-related activation in the anterior cingulate cortex (ACC) and anterior insula. Both regions are considered key hubs of the salience network 30 , a brain network involved in triggering cognitive control in response to errors and other salient events.…”

Under the Hood: Using Computational Psychiatry to Make Psychological Therapies More Mechanism-Focused

“…Ideally, computational model parameters should capture real-life phenomena in ecological settings, and changes seen in controlled settings must generalize between the two. There does not appear to be good support for this at present (39). Others have argued the need for ecologically valid tasks and models (40) and this argument becomes stronger when wanting to demonstrate functional improvement following psychological therapy.…”