It's time to opt out: Metacognitive analysis of time regulation under uncertainty

When performing cognitive tasks like solving problems in an exam or tackling challenging thinking tasks at work, knowing when to stop struggling with a difficult problem is often crucial for effective global performance. That is, opting out quickly when success is unlikely allows conserving time for other tasks with a higher chance of success. While research has examined how we find answers, giving-up efficiency has been largely ignored. This study delineates the metacognitive process by which individuals manage their mental effort when given a legitimate choice to quit. The present research extends prior metacognitive models, with one and two stopping rules, that did not take the temporal dynamics of opting out into account. The 3-Stopping-Rule Model addresses this gap in understanding time regulation under uncertainty by adding an opting-out confidence criterion to the known confidence criterion and a time limit for providing answers. Three experiments (N = 596) used problem-solving and general knowledge tasks that differ in opt-out rates and patterns of the confidence stopping criterion for submitting answers. The set of opt-out measures revealed that while opting out frequency was sensitive to manipulations, tasks, and individual characteristics, opt-out confidence remained stable and independent of time across incentives, tasks, opt-out wording (do not know, skip, help), and individual-level characteristics. The 3-Stopping-Rule Model advances prior metacognitive frameworks by revealing that opting out is not merely a by-product of failed problem solving but a systematically governed control decision with its own stable confidence threshold by which people counteract the speed-accuracy trade-off. (PsycInfo Database Record (c) 2026 APA, all rights reserved).