Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions, however it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of functional magnetic resonance imaging data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

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. Selfregulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including selfreport 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 testretest 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.self-regulation | retest reliability | individual differences These data were previously presented as a poster at

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.

Cognitive control enables flexible interaction with a dynamic environment. In two experiments, the authors investigated control adjustments in the stop-signal paradigm, a procedure that requires balancing speed (going) and caution (stopping) in a dual-task environment. Focusing on the slowing of go reaction times after stop signals, the authors tested five competing hypotheses for post-stop-signal adjustments: goal priority, error detection, conflict monitoring, surprise, and memory. Reaction times increased after both successful and failed inhibition, consistent with the goal priority hypothesis and inconsistent with the error detection and conflict hypotheses. Poststop-signal slowing was greater if the go task stimulus repeated on consecutive trials, suggesting a contribution of memory. We also found evidence for slowing based upon more than the immediately preceding stop signal. Post-stop-signal slowing was greater when stop signals occurred more frequently (Experiment 1), inconsistent with the surprise hypothesis, and when inhibition failed more frequently (Experiment 2). This suggests that more global manipulations encompassing many trials affect post-stop-signal adjustments. Key Words/PhrasesPost-Stop-Signal Slowing; Proactive Control; Cognitive Control; Inhibition; Stop-Signal Paradigm Cognitive control, the ability to adapt mental processes to the demands of the task environment, is of central importance to goal-directed behavior (Logan, 1985;Miyake et al., 2000). Cognitive control often involves resolving competing demands. Many instances of control involve achieving a balance between going and stopping, speed and caution. For example, driving involves balancing when to accelerate and when to brake. In two experiments, we investigated the adjustments subjects made to balance speed and caution in the stop-signal paradigm (Logan & Cowan, 1984), which directly pits going against stopping. Previous research has shown that reaction time (RT) increases after both successful and failed inhibition (Rieger & Gauggel, 1999;Verbruggen, Logan, Liefooghe, & Vandierendonck, 2008). The first motivation for this research was to evaluate five competing hypotheses for these post-stop-signal adjustments: goal priority, error detection, Correspondence should be addressed to Patrick G. Bissett, (Patrick.g.bissett@vanderbilt.edu) or Gordon D. Logan (Gordon.logan@vanderbilt.edu). Both can be contacted at Department of Psychology, Vanderbilt University, Nashville, TN, 37240. Publisher's Disclaimer: The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this manuscript version, any version derived from this manuscript by NIH, or other third parties. The published version is available at www.apa.org/pubs/journa...