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Many researchers have long assumed imaginative play critical to the healthy cognitive, social, and emotional development of children, which has important implications for early-education policy and practice. But, the authors find, a careful review of the existing literature highlights a need for a better theory to clarify the nature of the relationship between pretend play and childhood development. In particular, they ask why children spend so much time engaging in unreal scenarios at a time when they know relatively little about the real world? The authors review the idea that children pretend because it exercises their developing ability to reason counter-factually-an ability essential for causal reasoning and learning. The authors present a look at their study in progress aimed at assessing their theory. According to the model of play they outline, imaginative play serves as an engine of learning. Such play arises out of the human capacity for causal cognition and feeds back to help develop causal-reasoning skills. [ABSTRACT FROM AUTHOR]

A deep understanding of any phenomenon requires knowing how its causal elements are related to one another. Here, we examine whether children treat causal structure as a metric for assessing similarity across superficially distinct events. In two experiments, we presented 156 4-7-year-olds (approximately 55% of participants identified as White, 29% as multiracial, and 12% as Asian) with three-variable narratives in which story events unfold according to a causal chain or a common effect structure. We then asked children to make judgments about which stories are the most similar. In Experiment 1, we presented all events in the context of simple, illustrated stories. In Experiment 2, we removed all low-level linguistic cues that may have supported children's similarity judgments in Experiment 1 and used animated videos to support understanding of the causal elements in each story. Results indicated a gradual shift between 4 and 7 years in children's use of causal structure as a metric of similarity between narratives: While we found that children as young as five were capable of correctly representing the causal structure of each story individually, only 6- and 7-year-olds relied on shared causal structure across stories when making similarity judgments. We discuss these findings in light of children's developing causal and abstract reasoning and propose directions for future work. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Self-directed learners are described as "intuitive scientists," yet they often struggle in assessments of their scientific reasoning skills. We investigate a novel explanation for this apparent gap between formal and informal scientific inquiry behavior. Specifically, we consider whether learners' documented failure to correctly apply the control of variables strategy might stem from a mismatch between task presentation and their intuitions as causal learners. In Experiment 1, children (7- and 9-year-olds) and adults were tested on a version of a traditional multivariate reasoning task (Tschirgi, 1980) that was modified to clarify ambiguous elements of the causal logic in the original design. In all age groups, a significant majority of participants selected informative experiments on this modified task, avoiding confounded actions with positive tangible outcomes. In Experiment 2, we replicate these results with real-world stimuli, and in Experiments 3 and 4, we provide direct evidence that self-directed learners apply specific causal intuitions to experimentation tasks. Together, these findings support a novel alternative interpretation of the apparently paradoxical gap between learners' success in informal exploration and their error-prone experimentation-both behaviors are consistent with an intuitively causal approach to scientific inquiry. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A large body of research has shown that engaging in self-explanation improves learning across a range of tasks. It has been proposed that the act of explaining draws attention and cognitive resources towards evidence that supports good explanations-information that is broad, abstract, and consistent with prior knowledge-which in turn aids discovery and promotes generalization. However, it remains unclear whether explanation impacts the learning process via improved hypothesis generation, increasing the probability that the most generalizable hypotheses are considered in the first place, or hypothesis evaluation, the appraisal of such hypotheses in light of observed evidence. In two experiments with adults, we address this question by separating hypothesis generation and evaluation in a novel category learning task and quantifying the effect of explaining on each process independently. We find that explanation supports learners' generation of broad and abstract hypotheses but does not impact their evaluation of them. These results provide a more precise account of the process by which explanation impacts learning and offer additional support for the claim that hypothesis generation and evaluation play distinct roles in problem solving., (Copyright © 2022 Elsevier B.V. All rights reserved.)