When Bayesians take over: A computational model of parental intervention

When children encounter new challenges, parents often ask themselves: should I let my child figure it out, or should I step in and do it for them? How parents resolve this dilemma is linked to various child developmental outcomes, yet we know little about the cognitive computations that underlie parents’ decision to intervene. Here, we model parenting decisions as a Bayesian solution to a Partially Observable Markov Decision Process (POMDP) and qualitatively compare predictions with behavioral data from real-time parent-child interactions. Empirically, we find that parents are more likely to take over when they believe children are less skilled and when tasks are challenging, and more likely to step back when they believe their child can learn from doing the task on their own. The model captures the fine-grained ways in which these factors shape parent decision-making and, alongside the empirical data, uncovers the cognitive computations that drive parental intervention.