When Bayesians take over: A computational model of parental intervention

When children encounter challenges, parents often wonder: Should I let my child figure it out or take over? How parents resolve this dilemma shapes key developmental outcomes, yet we know little about the cognitive mechanisms that drive these decisions. Here, we model parental "take over" decisions as a Bayesian solution to a Partially Observable Markov Decision Process (POMDP) and qualitatively compare model predictions with behavioral data from parent-child interactions. We find that two core beliefs guide intervention: the child’s probability of success and the utility of the task. Parents are more likely to take over when they believe their child is less skilled and the task is harder, and more likely to step back when they expect the rewards of independent effort to outweigh the costs. The model captures how these beliefs interact to shape decision-making and, together with the empirical data, reveals the cognitive computations that underlie parental intervention.