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People’s behavior can be roughly categorized into two modes: either reflective and thoughtful, or automatic and rote. Past work on Theory of Mind has focused on the first category. But do children notice when people are acting in an automatic way? This paper examined five- to ten-year-old children’s reasoning about others’ rote behavior, focusing on the consequences of this inference in teaching contexts (N = 660 across four studies, 327 girls). Children’s sensitivity to rote behavior increased with development, with consistent competence emerging around age 7. Rote behavior was also associated with worse teaching. These results indicate when and how reasoning about automatic behavior matters to children’s perception of others, and suggest novel extensions to models of Theory of Mind. 

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. 

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. 

When people make decisions, they act in a way that is either automatic (“rote”), or more thoughtful (“reflective”). But do people notice when others are behaving in a rote way, and do they care? We examine the detection of rote behavior and its consequences in U.S. adults, focusing specifically on pedagogy and learning. We establish repetitiveness as a cue for rote behavior (Experiment 1), and find that rote people are seen as worse teachers (Experiment 2). We also find that the more a person's feedback seems similar across groups (indicating greater rote‐ness), the more negatively their teaching is evaluated (Experiment 3). A word‐embedding analysis of an open‐response task shows people naturally cluster rote and reflective teachers into different semantic categories (Experiment 4). We also show that repetitiveness can be decoupled from perceptions of rote‐ness given contextual explanation (Experiment 5). Finally, we establish two additional cues to rote behavior that can be tied to quality of teaching (Experiment 6). These results empirically show that people detect and care about scripted behaviors in pedagogy, and suggest an important extension to formal frameworks of social reasoning. 

Children do not just learn in the classroom. They engage in “informal learning” every day just by spending time with their family and peers. However, while researchers know this occurs, less is known about the science of this learning—how this learning works. This is so because investigators lack access to those moments of informal learning. In this mini-review we present a technical solution: a mobile-based research platform called “Talk of the Town” that will provide a window into children’s informal learning. The tool will be open to all researchers and educators and is flexibly adaptable to these needs. It allows access to data that have never been studied before, providing a means for developing and testing vast educational interventions, and providing access to much more diverse samples than are typically studied in laboratories, homes, and science museums. The review details the promise and challenges associated with these new methods of data collection and family engagement in STEM learning sciences. 

In studies involving human subjects, voluntary participation may lead to sampling bias, thus limiting the generalizability of findings. This effect may be especially pronounced in developmental studies, where parents serve as both the primary environmental input and decision maker of whether their child participates in a study. We present a novel empirical and modeling approach to estimate how parental consent may bias measurements of children’s behavior. Specifically, we coupled naturalistic observations of parent–child interactions in public spaces with a behavioral test with children, and used modeling methods to impute the behavior of children who did not participate. Results showed that parents’ tendency to use questions to teach was associated with both children’s behavior in the test and parents’ tendency to participate. Exploiting these associations with a model-based multiple imputation and a propensity score–matching procedure, we estimated that the means of the participating and not-participating groups could differ as much as 0.23 standard deviations for the test measurements, and standard deviations themselves are likely underestimated. These results suggest that ignoring factors associated with consent may lead to systematic biases when generalizing beyond lab samples, and the proposed general approach provides a way to estimate these biases in future research. 

Abstract What maximizes instructional impact in early childhood? We propose a simple intervention employing “Pedagogical Questions”. We explore whether swapping some instructional language with questions in psychosomatic storybooks improves preschoolers’ memory, learning, and generalization. Seventy-two preschoolers were randomly assigned to one of three conditions and were read storybooks employing eitherDirect Instruction,Pedagogical Questions, orControlcontent. Posttest measures of psychosomatic understanding, judgments about the possibility of psychosomatic events, and memory for storybook details showed that children in thePedagogical Questionscondition demonstrated greater memory for relevant storybook details and improved psychosomatic understanding. Our results suggest that pedagogical questions are a relatively simple educational manipulation to improve memory, learning, and transfer of theory-rich content. 

Abstract Lieder and Griffiths present the computational framework “resource-rational analysis” to address the reverse-engineering problem in cognition. Here we discuss how developmental psychology affords a unique and critical opportunity to employ this framework, but which is overlooked in this piece. We describe how developmental change provides an avenue for ongoing work as well as inspiration for expansion of the resource-rational approach.