Reported Self-control is not Meaningfully Associated with Inhibition-related Executive Function: A Bayesian Analysis

Incremental Validity of Online Over Offline Reports of Volitional Control in Predicting Learning Success

Abstract. Volitional control (i. e., efforts to maintain goal striving in the face of obstacles) is an integral part of self-regulated learning and an important factor for explaining individual differences in academic performance. However, differences between the various methods for assessing volitional control have rarely been investigated. Two common methods are (a) offline questionnaires, in which respondents aggregate experiences over a longer period of time, and (b) online questionnaires such as learning diaries, which assess respondents’ experiences close to the learning event. We compared these assessment approaches in 96 medical students who prepared for a high-stakes exam. Achievement of self-set learning goals was measured objectively via logfiles of students’ activities on a learning platform. Daily reports of volitional control explained substantial variance in the achievement of learning goals over and above the offline questionnaire, indicating incremental validity of online assessments of self-regulation. Moreover, the daily reports of volitional control could explain intra-individual day-to-day variance in goal achievement. The current study, thus, suggests that learning diaries, albeit cumbersome, have clear advantages over offline questionnaires.

The Past, Present, and Future of Ego Depletion

Abstract. At the center of social psychology just a few years ago, ego depletion is now widely seen as a controversial topic, one of the chief victims of the replication crisis. Despite over 600 studies of apparent support, many are now asking if ego depletion is even real. Here, we comment on the articles included in this Special Issue: Ego Depletion and Self-Control: Conceptual and Empirical Advances. Specifically, we delineate the contributions and limitations of these articles by embedding them in a brief history of ego depletion, describing the current state of uncertainty about ego depletion’s scientific status, and outlining necessary steps for the study of ego depletion to have a healthy future. To us, the most troubling aspect of this controversy is not what it suggests about ego depletion, but what it suggests about social psychology more broadly. If the mere existence of ego depletion is seriously doubted by many, what can be confidently regarded as real in social psychology? By increasing the precision of our theories, continuously validating our manipulations and measures, and practicing the full suite of open science practices, we have the potential to identify legitimate and robust effects and build a cumulative and trustworthy psychological science.

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

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.

Scientific progress relies on integrating and building on existing knowledge. Here, the authors propose improving cumulative science by developing data-driven ontologies, and they apply this approach to understanding the construct of self-regulation.

Large-scale analysis of test-retest reliabilities of self-regulation measures

The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including self-report surveys and behavioral tasks. Accurate characterization of individual differences requires measurement reliability, a property frequently characterized in self-report surveys, but rarely assessed in behavioral tasks. We remedy this gap by (i) providing a comprehensive literature review on an extensive set of self-regulation measures and (ii) empirically evaluating test-retest reliability of this battery in a new sample. We find that dependent variables (DVs) from self-report surveys of self-regulation have high test-retest reliability, while DVs derived from behavioral tasks do not. This holds both in the literature and in our sample, although the test-retest reliability estimates in the literature are highly variable. We confirm that this is due to differences in between-subject variability. We also compare different types of task DVs (e.g., model parameters vs. raw response times) in their suitability as individual difference DVs, finding that certain model parameters are as stable as raw DVs. Our results provide greater psychometric footing for the study of self-regulation and provide guidance for future studies of individual differences in this domain.