Bayesian models allow us to investigate children’s belief revision alongside physiological states, such as “surprise”. Recent work finds that pupil dilation (or the “pupillary surprise response”) following expectancy violations is predictive of belief revision. How can probabilistic models inform the interpretations of “surprise”? Shannon Information considers the likelihood of an observed event, given prior beliefs, and suggests stronger surprise occurs following unlikely events. In contrast, Kullback–Leibler divergence considers the dissimilarity between prior beliefs and updated beliefs following observations—with greater surprise indicating more change between belief states to accommodate information. To assess these accounts under different learning contexts, we use Bayesian models that compare these computational measures of “surprise” to contexts where children are asked to either predict or evaluate the same evidence during a water displacement task. We find correlations between the computed Kullback–Leibler divergence and the children’s pupillometric responses only when the children actively make predictions, and no correlation between Shannon Information and pupillometry. This suggests that when children attend to their beliefs and make predictions, pupillary responses may signal the degree of divergence between a child’s current beliefs and the updated, more accommodating beliefs.

Despite sometimes noisy evidence (e.g., perceptual processing errors), young children are capable of predicting and evaluating events based on complex causal representations. Children rapidly revise their beliefs and learn scientific concepts -sometimes without prior knowledge of an underlying causal system. What might we need in our computational models of belief revision to similarly simulate children’s behaviors when learning such causal systems? Building from experimental data of elementary school children’s intuitive beliefs and predictions of water displacement, we propose three aspects of human inference and belief revision that warrant attention within the subfield of computational cognition. Each aspect is described by identifying the gaps between empirical findings and current computational implementations. Then, specific implementations of these aspects are built using models of Theory-based Bayesian inference. First, we construct children’s prior beliefs at the individual level based on their prior behavior. Second, we approximate children’s learning using an “optimal” Bayesian model, revealing the dynamics of belief revision trial-by-trial. Third, we investigate the role prediction may have in facilitating learning. By performing these key computational steps, we find support for contemporary claims that children may be approximately “Bayesian” learners and increase awareness of the importance of generating predictions in active learning.

There exists a rich literature describing how social context influences decision making. Here, we propose a novel framing of social influences, the Intentional Selection Assumption. This framework proposes that, when a person is presented with a set of options by another social agent, people may treat the set of options as intentionally selected, reflecting the chooser's inferences about the presenter and the presenter's goals. To describe our proposal, we draw analogies to the cognition literature on sampling inferences within concept learning. This is done to highlight how the Intentional Selection Assumption accounts for both normative (e.g., comparing perceived utilities) and subjective (e.g., consideration of context relevance) principles in decision making, while also highlighting how analogous findings in the concept learning literature can aid in bridging these principles by drawing attention to the importance of potential sampling assumptions within decision making paradigms. We present the two behavioral experiments that provide support to this proposal and find that social-contextual cues influence choice behavior with respect to the induction of sampling assumptions. We then discuss a theoretical framework of the Intentional Selection Assumption alongside the possibility of its potential relationships to contemporary models of choice. Overall, our results emphasize the flexibility of decision makers with respect to social-contextual factors without sacrificing systematicity regarding the preference for specific options with a higher value or utility.

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. Summary: We 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.

Bayesian models allow us to investigate children’s belief revision alongside physiological states like “surprise”. Recent work finds that pupil dilation (or the “pupillary surprise response”) following expectancy-violations may be predictive of belief revision. How can probabilistic models inform interpretations of “surprise”? Shannon Information considers the likelihood of an observed event, given prior beliefs – suggesting stronger surprise occurs following unlikely events. In contrast, Kullback-Leibler divergence considers the “dissimilarity” between prior beliefs and updated beliefs following observations – with greater surprise indicating more change between belief states to accommodate information. To assess these accounts under different learning contexts, we use Bayesian models that compare these computational measures of “surprise” to contexts where children are asked to either predict or to evaluate the same evidence during a water displacement task. We find correlations between the computed Kullback-Leibler divergence and children’s pupillometry responses only when children actively make predictions, and no correlation between Shannon Information and pupillometry. This suggests that when children attend to their beliefs and make predictions, pupillary responses may signal the degree of divergence between a child’s current beliefs and updated, more accommodating beliefs.