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ABSTRACT Although exploratory play is considered a hallmark of cognitive development and learning, relatively few studies have been able to quantitatively characterize the shifts that may occur in children's approach to exploration. One reason for this gap is due to challenges coding and analyzing children's exploratory play behavior. In our paper, we employ a novel computational modeling approach to understand whether and how children's exploratory play patterns shift in early childhood (3‐ to 11‐years‐old). We analyze data from children (N = 432) across five different experiments that varied in the type of exploration task (including novel toys, novel topics, and novel envelopes). Children's behaviors were coded action‐by‐action according to whether children repeated an action on the same type of target, switched to a novel target, or terminated play. Our computational Markov model searches over the space of possible “stay,” “switch,” and “end” parameters to quantify child‐specific transition probabilities. We find that overall, older children are less likely to perseverate, more likely to switch, and more likely to end the task earlier. Our approach provides a demonstration of how Markov models can be used to map the process of play, providing insight into theories of developmental changes in exploration. SummaryWe use Markov models to quantify developmental shifts in children's exploratory play across five naturalistic tasks.Older children showed increased exploratory variability and decreased perseveration during play.Developmental effects were most consistent in novel toy tasks, but varied across contexts.Our findings help reconcile conflicting prior research by highlighting the role of task structure and developmental changes in exploratory strategy. 

Pedagogy is a powerful way to learn about the world, and young children are adept at both learning from teaching and teaching others themselves. Theoretical accounts of pedagogical reasoning suggest that an important aspect of being an effective teacher is considering what learners need to know, as misconceptions about learners' beliefs, needs, or goals can result in less helpful teaching. One underexplored way in which teachers may fail to represent what learners know is by simply “going through the motions” of teaching, without actively engaging with the learner's beliefs, needs, and goals at all. In the current paper, we replicate ongoing work that suggests children are sensitive to when others are relying on automatic scripts in the context of teaching. We then look at the potential link to two related measures. First, we hypothesize that sensitivity to a teacher's perceived automaticity will be linked to classic measures of pedagogical sensitivity and learning—specifically, how children explore and learn about novel toys following pedagogical vs. non-pedagogical demonstrations. Second, we hypothesize that the development of Theory of Mind (ToM) (and age differences more broadly) relate to these pedagogical sensitivities. Our online adaptation of the novel toy exploration task did not invoke pedagogical reasoning as expected, and so we do not find robust links between these tasks. We do find that ToM predicts children's ability to detect automaticity in teaching when controlling for age. This work thus highlights the connections between sensitivity to teaching and reasoning about others' knowledge, with implications for the factors that support children's ability to teach others.