Do reflection prompts promote children's conflict monitoring and revision of misconceptions?

We tested whether reflection prompts enhance conflict monitoring and facilitate the revision of misconceptions. German children (N = 97, Mage = 7.20, 56% female) were assigned to a prediction or a prediction with reflection condition that included reflection prompts. Children in the prediction with reflection condition (1) showed greater error-related response times and pupil dilation responses, indicating better conflict monitoring, and (2) performed closer to an optimal Bayesian learner, indicating better monitoring-based control. However, by the end of the study, all children had similar levels of misconception revision. Thus, reflection prompts can enhance learning from anomalous evidence by improving conflict monitoring, but they may need to be repeated often to sustain their beneficial effects.

Seeing the Error in My "Bayes": A Quantified Degree of Belief Change Correlates with Children's Pupillary Surprise Responses Following Explicit Predictions.

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.

Children With More Uncertainty in Their Intuitive Theories Seek Domain-Relevant Information.

How do changes in learners' knowledge influence information seeking? We showed preschoolers (N = 100) uncertain outcomes for events and let them choose which event to resolve. We found that children whose intuitive theories were at immature stages were more likely to seek information to resolve uncertainty about an outcome in the related domains, but children with more mature knowledge were not. This result was replicated in a second experiment but with the nuance that children at intermediate stages of belief development-when the causal outcome would be most ambiguous-were the most motivated to resolve the uncertainty. This effect was not driven by general uncertainty at the framework level but, rather, by the impact that framework knowledge has in accessing uncertainty at the model level. These results are the first to show the relationship between a learning preference and the developmental stage of a child's intuitive theory.

Partial mental simulation explains fallacies in physical reasoning.

People can reason intuitively, efficiently, and accurately about everyday physical events. Recent accounts suggest that people use mental simulation to make such intuitive physical judgments. But mental simulation models are computationally expensive; how is physical reasoning relatively accurate, while maintaining computational tractability? We suggest that people make use of partial simulation, mentally moving forward in time only parts of the world deemed relevant. We propose a novel partial simulation model, and test it on the physical conjunction fallacy, a recently observed phenomenon [Ludwin-Peery et al. (2020). Broken physics: A conjunction-fallacy effect in intuitive physical reasoning. Psychological Science, 31(12), 1602-1611. https://doi.org/10.1177/0956797620957610] that poses a challenge for full simulation models. We find an excellent fit between our model's predictions and human performance on a set of scenarios that build on and extend those used by Ludwin-Peery et al. [(2020). Broken physics: A conjunction-fallacy effect in intuitive physical reasoning. Psychological Science, 31(12), 1602-1611. https://doi.org/10.1177/0956797620957610], quantitatively and qualitatively accounting for deviations from optimal performance. Our results suggest more generally how we allocate cognitive resources to efficiently represent and simulate physical scenes.

Evaluating recall error in preschoolers: Category expectations influence episodic memory for color.

Despite limited memory capacity, children are exceptional learners. How might children engage in meaningful learning despite limited memory systems? Past research suggests that adults integrate category knowledge and noisy episodic traces to aid recall when episodic memory is noisy or incomplete (e.g. Hemmer & Steyvers, 2009a,b). We suspect children utilize a similar process but integrate category and episodic traces in recall to a different degree. Here we conduct two experiments to empirically assess children's color category knowledge (Study 1) and recall of target hue values (Study 2). In Study 1, although children's generated hue values appear to be noisier than adults, we found no significant difference between children and adult's generated color category means (prototypes), suggesting that preschool-aged children's color categories are well established. In Study 2, we found that children's (like adult's) free recall of target hue values regressed towards color category means. We implemented three probabilistic memory models: one that combines category knowledge and specific target information (Integrative), a category only (Noisy Prototype) model, and a target only (Noisy Target) model to computationally evaluate recall performance. Consistent with previous studies with older children (Duffy, Huttenlocher, & Crawford, 2006), quantitative fits of the models to aggregate group-level data provided strong support for the Integrative process. However, at the individual subject level, a greater proportion of preschoolers' recall was better fit by a Prototype only model. Our results provide evidence that the integration of category knowledge in episodic memory comes online early and strongly. Implications for how the greater reliance on category knowledge by preschoolers relative to adults might track with developmental shifts in relational episodic memory are discussed.

Design Drives Discovery in Causal Learning.

We assessed whether an artifact's design can facilitate recognition of abstract causal rules. In Experiment 1, 152 three-year-olds were presented with evidence consistent with a relational rule (i.e., pairs of same or different blocks activated a machine) using two differently designed machines. In the standard-design condition, blocks were placed on top of the machine; in the relational-design condition, blocks were placed into openings on either side. In Experiment 2, we assessed whether this design cue could facilitate adults' (N = 102) inference of a distinct conjunctive cause (i.e., that two blocks together activate the machine). Results of both experiments demonstrated that causal inference is sensitive to an artifact's design: Participants in the relational-design conditions were more likely to infer rules that were a priori unlikely. Our findings suggest that reasoning failures may result from difficulty generating the relevant rules as cognitive hypotheses but that artifact design aids causal inference. These findings have clear implications for creating intuitive learning environments.

Opportunities and challenges integrating resource-rational analysis with developmental perspectives.

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.

Pedagogical Questions in Parent-Child Conversations.

Questioning is a core component of formal pedagogy. Parents commonly question children, but do they use questions to teach? This article defines "pedagogical questions" as questions for which the questioner already knows the answer and intended to help the questionee learn. Transcripts of parent-child conversations were collected from the CHILDES database to examine the frequency and distribution of pedagogical questions. Analysis of 2,166 questions from 166 mother-child dyads and 64 father-child dyads (child's age between 2 and 6 years) showed that pedagogical questions are commonplace during day-to-day parent-child conversations and vary based on child's age, family environment, and historical era. The results serve as a first step toward understanding the role of parent-child questions in facilitating children's learning.

Questioning supports effective transmission of knowledge and increased exploratory learning in pre-kindergarten children.

How can education optimize transmission of knowledge while also fostering further learning? Focusing on children at the cusp of formal schooling (N = 180, age = 4.0-6.0 y), we investigate learning after direct instruction by a knowledgeable teacher, after questioning by a knowledgeable teacher, and after questioning by a naïve informant. Consistent with previous findings, instruction by a knowledgeable teacher allows effective information transmission but at the cost of exploration and further learning. Critically, we find a dual benefit for questioning by a knowledgeable teacher: Such pedagogical questioning both effectively transmits knowledge and fosters exploration and further learning, regardless of whether the question was directed to the child or directed to a third party and overheard by the child. These effects are not observed when the same question is asked by a naïve informant. We conclude that a teacher's choice of pedagogical method may differentially influence learning through their choices of how, and how not, to present evidence, with implications for transmission of knowledge and self-directed discovery. A video abstract of this article can be viewed at: https://www.youtube.com/watch?v=FJXH2b65wL8.

Win-Stay, Lose-Sample: a simple sequential algorithm for approximating Bayesian inference.

People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm "Win-Stay, Lose-Sample", inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a "mini-microgenetic method", investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people's judgments.

Early environments and exploration in the preschool years.

A great deal of research has demonstrated how children's exploration is driven by opportunities for learning. However, less work has investigated how individual differences across children and their environmental contexts relate to patterns in playful exploration. We performed a "mega-analysis" in which we pooled preschool-aged children's play data from four past experiments in our lab (N = 278; M(age) = 56 months) and correlated play behaviors with age and socioeconomic status (median income, modal education in children's home zip codes). We found that, with age, children performed more unique actions during play. Additionally, children from lower SES areas explored more variably; the link between this play and tendencies to focus on pedagogically demonstrated features traded off differently than it did for higher SES children. This work lays critical groundwork for understanding exploration across developmental contexts.

Teaching Without Thinking: Negative Evaluations of Rote Pedagogy.

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.

What's in the Box? Preschoolers Consider Ambiguity, Expected Value, and Information for Future Decisions in Explore-Exploit Tasks.

Self-directed exploration in childhood appears driven by a desire to resolve uncertainties in order to learn more about the world. However, in adult decision-making, the choice to explore new information rather than exploit what is already known takes many factors beyond uncertainty (such as expected utilities and costs) into account. The evidence for whether young children are sensitive to complex, contextual factors in making exploration decisions is limited and mixed. Here, we investigate whether modifying uncertain options influences explore-exploit behavior in preschool-aged children (48-68 months). Over the course of three experiments, we manipulate uncertain options' ambiguity, expected value, and potential to improve epistemic state for future exploration in a novel forced-choice design. We find evidence that young children are influenced by each of these factors, suggesting that early, self-directed exploration involves sophisticated, context-sensitive decision-making under uncertainty.

Children Use Teachers' Beliefs About Their Abilities to Calibrate Explore-Exploit Decisions.

Models of the explore-exploit problem have explained how children's decision making is weighed by a bias for information (directed exploration), randomness, and generalization. These behaviors are often tested in domains where a choice to explore (or exploit) is guaranteed to reveal an outcome. An often overlooked but critical component of the assessment of explore-exploit decisions lies in the expected success of taking actions in the first place-and, crucially, how such decisions might be carried out when learning from others. Here, we examine how children consider an informal teacher's beliefs about the child's competence when deciding how difficult a task they want to pursue. We present a simple model of this problem that predicts that while learners should follow the recommendation of an accurate teacher, they should exploit easier games when a teacher overestimates their abilities, and explore harder games when she underestimates them. We tested these predictions in two experiments with adults (Experiment 1) and 6- to 8-year-old children (Experiment 2). In our task, participants' performance on a picture-matching game was either overestimated, underestimated, or accurately represented by a confederate (the "Teacher"), who then presented three new matching games of varying assessed difficulty (too easy, too hard, just right) at varying potential reward (low, medium, high). In line with our model's predictions, we found that both adults and children calibrated their choices to the teacher's representation of their competence. That is, to maximize expected reward, when she underestimated them, participants chose games the teacher evaluated as being too hard for them; when she overestimated them, they chose games she evaluated as being too easy; and when she was accurate, they chose games she assessed as being just right. This work provides insight into the early-emerging ability to calibrate explore-exploit decisions to others' knowledge when learning in informal pedagogical contexts.

Talk of the Town mobile app platform: New method for engaging family in STEM learning and research in homes and communities.

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.

Children balance theories and evidence in exploration, explanation, and learning.

We look at the effect of evidence and prior beliefs on exploration, explanation and learning. In Experiment 1, we tested children both with and without differential prior beliefs about balance relationships (Center Theorists, mean: 82 months; Mass Theorists, mean: 89 months; No Theory children, mean: 62 months). Center and Mass Theory children who observed identical evidence explored the block differently depending on their beliefs. When the block was balanced at its geometric center (belief-violating to a Mass Theorist, but belief-consistent to a Center Theorist), Mass Theory children explored the block more, and Center Theory children showed the standard novelty preference; when the block was balanced at the center of mass, the pattern of results reversed. The No Theory children showed a novelty preference regardless of evidence. In Experiments 2 and 3, we follow-up on these findings, showing that both Mass and Center Theorists selectively and differentially appeal to auxiliary variables (e.g., a magnet) to explain evidence only when their beliefs are violated. We also show that children use the data to revise their predictions in the absence of the explanatory auxiliary variable but not in its presence. Taken together, these results suggest that children's learning is at once conservative and flexible; children integrate evidence, prior beliefs, and competing causal hypotheses in their exploration, explanation, and learning.

The effects of information utility and teachers' knowledge on evaluations of under-informative pedagogy across development.

Teaching is a powerful way to transmit knowledge, but with this power comes a hazard: When teachers fail to select the best set of evidence for the learner, learners can be misled to draw inaccurate inferences. Evaluating others' failures as teachers, however, is a nontrivial problem; people may fail to be informative for different reasons, and not all failures are equally blameworthy. How do learners evaluate the quality of teachers, and what factors influence such evaluations? Here, we present a Bayesian model of teacher evaluation that considers the utility of a teacher's pedagogical sampling given their prior knowledge. In Experiment 1 (N=1168), we test the model predictions against adults' evaluations of a teacher who demonstrated all or a subset of the functions on a novel device. Consistent with the model predictions, participants' ratings integrated information about the number of functions taught, their values, as well as how much the teacher knew. Using a modified paradigm for children, Experiments 2 (N=48) and 3 (N=40) found that preschool-aged children (2a, 3) and adults (2b) make nuanced judgments of teacher quality that are well predicted by the model. However, after an unsuccessful attempt to replicate the results with preschoolers (Experiment 4, N=24), in Experiment 5 (N=24) we further investigate the development of teacher evaluation in a sample of seven- and eight-year-olds. These older children successfully distinguished teachers based on the amount and value of what was demonstrated, and their ability to evaluate omissions relative to the teacher's knowledge state was related to their tendency to spontaneously reference the teacher's knowledge when explaining their evaluations. In sum, our work illustrates how the human ability to learn from others supports not just learning about the world but also learning about the teachers themselves. By reasoning about others' informativeness, learners can evaluate others' teaching and make better learning decisions.

"There's something inside": Children's intuitions about animate agents.

From infancy, humans have the ability to distinguish animate agents from inert objects, and preschoolers map biological and mechanical insides to their appropriate kinds. However, less is known about how identifying something as an animate agent shapes specific inferences about its internal properties. Here, we test whether preschool children (N = 92; North American population) have specifically biological expectations about animate agents, or if they have more general expectations that animate agents should have an internal source of motion. We presented preschoolers with videos of two puppets: a "self-propelled" fur-covered puppet, and a fur-covered puppet that is seen to be moved by a human actor. In addition, we presented preschoolers with images of a familiar artifact (motorcycle) and familiar animal (sheep). For each item, we asked them to choose what they thought was inside each of these entities: nothing, biological insides, or mechanical insides. Preschoolers were less likely to say that a self-propelled fur-covered object was empty, compared to a fur-covered object that was moved by a human actor, which converges with past work with infants. However, preschoolers showed no specifically biological expectations about these objects, despite being able to accurately match biological insides to familiar animals and mechanical insides to familiar artifacts on the follow-up measure. These results suggest that preschoolers do not have specifically biological expectations about animate agents as a category, but rather general expectations that such agents should not be empty inside.

The Intentional Selection Assumption.

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.

Balancing exploration and exploitation with information and randomization.

Explore-exploit decisions require us to trade off the benefits of exploring unknown options to learn more about them, with exploiting known options, for immediate reward. Such decisions are ubiquitous in nature, but from a computational perspective, they are notoriously hard. There is therefore much interest in how humans and animals make these decisions and recently there has been an explosion of research in this area. Here we provide a biased and incomplete snapshot of this field focusing on the major finding that many organisms use two distinct strategies to solve the explore-exploit dilemma: a bias for information ('directed exploration') and the randomization of choice ('random exploration'). We review evidence for the existence of these strategies, their computational properties, their neural implementations, as well as how directed and random exploration vary over the lifespan. We conclude by highlighting open questions in this field that are ripe to both explore and exploit.