Children adapt their questions to achieve efficient search

When is it appropriate to ask a question? The role of age, social context, and personality

How do children decide when it is appropriate to ask a question? In Study 1 (preregistered), 50 4- and 5-year-olds, 50 7- and 8-year-olds, and 100 adults watched vignettes featuring a child who had a question, and participants indicated whether they thought the child should ask the question "right now." Both adults and children endorsed more question-asking to a well-known informant than to an acquaintance and to someone doing nothing than to someone busy working or busy socializing. However, younger children endorsed asking questions to someone who was busy more often than older children and adults. In addition, Big Five personality traits predicted endorsement of question-asking. In Study 2 (preregistered, N = 500), mothers' self-reports showed that children's actual question-asking varied with age, informant activity, and informant familiarity in ways that paralleled the results of Study 1. In Study 3 (N = 100), we examined mothers' responses to their children's question-asking and found that mothers' responses to their children's question-asking varied based on the mother's activity. In addition, mothers high in authoritarianism were less likely to answer their children's questions when they were busy than mothers low in authoritarianism. In sum, across three studies, we found evidence that the age-related decline in children's question-asking to their parents reflects a change in children's reasoning about when it is appropriate to ask a question.

Toddlers strategically adapt their information search

Adaptive information seeking is essential for humans to effectively navigate complex and dynamic environments. Here, we developed a gaze-contingent eye-tracking paradigm to examine the early emergence of adaptive information-seeking. Toddlers (N = 60, 18-36 months) and adults (N = 42) either learnt that an animal was equally likely to be found in any of four available locations, or that it was most likely to be found in one particular location. Afterwards, they were given control of a torchlight, which they could move with their eyes to explore the otherwise pitch-black task environment. Eye-movement data and Markov models show that, from 24 months of age, toddlers become more exploratory than adults, and start adapting their exploratory strategies to the information structure of the task. These results show that toddlers' search strategies are more sophisticated than previously thought, and identify the unique features that distinguish their information search from adults'.

Seeking versus receiving help: How children integrate suggestions in memory decisions

The current research examined how seeking versus receiving help affected children's memory and confidence decisions. Baseline performance, when no help was available, was compared to performance when help could be sought (Experiment 1: N = 83, 41 females) or was provided (Experiment 2: N = 84, 44 females) in a sample of predominately White 5-, 7-, and 9-year-olds from Northern California. Data collection occurred from 2018 to 2019. In Experiment 1, 5-year-olds agreed most often with sought-help, whereas 9-year-olds were the only age group reporting lower confidence for sought-help relative to baseline trials. In Experiment 2, agreement and confidence after provided help were similar across age groups. Different developmental patterns when help was sought versus provided underscore the importance of active help-seeking for memory decision-making.

The Efficiency of Question-Asking Strategies in a Real-World Visual Search Task

In recent years, a multitude of datasets of human-human conversations has been released for the main purpose of training conversational agents based on data-hungry artificial neural networks. In this paper, we argue that datasets of this sort represent a useful and underexplored source to validate, complement, and enhance cognitive studies on human behavior and language use. We present a method that leverages the recent development of powerful computational models to obtain the fine-grained annotation required to apply metrics and techniques from Cognitive Science to large datasets. Previous work in Cognitive Science has investigated the question-asking strategies of human participants by employing different variants of the so-called 20-question-game setting and proposing several evaluation methods. In our work, we focus on GuessWhat, a task proposed within the Computer Vision and Natural Language Processing communities that is similar in structure to the 20-question-game setting. Crucially, the GuessWhat dataset contains tens of thousands of dialogues based on real-world images, making it a suitable setting to investigate the question-asking strategies of human players on a large scale and in a natural setting. Our results demonstrate the effectiveness of computational tools to automatically code how the hypothesis space changes throughout the dialogue in complex visual scenes. On the one hand, we confirm findings from previous work on smaller and more controlled settings. On the other hand, our analyses allow us to highlight the presence of "uninformative" questions (in terms of Expected Information Gain) at specific rounds of the dialogue. We hypothesize that these questions fulfill pragmatic constraints that are exploited by human players to solve visual tasks in complex scenes successfully. Our work illustrates a method that brings together efforts and findings from different disciplines to gain a better understanding of human question-asking strategies on large-scale datasets, while at the same time posing new questions about the development of conversational systems.

Multifaceted information-seeking motives in children

From an early age, children need to gather information to learn about their environment. Deciding which knowledge to pursue can be difficult because information can serve several, sometimes competing, purposes. Here, we examine the developmental trajectories of such diverse information-seeking motives. Over five experiments involving 521 children (aged 4-12), we find that school-age children integrate three key factors into their information-seeking choices: whether information reduces uncertainty, is useful in directing action, and is likely to be positive. Choices that likely reveal positive information and are useful for action emerge as early as age 4, followed by choices that reduce uncertainty (at ~age 5). Our results suggest that motives related to usefulness and uncertainty reduction become stronger with age, while the tendency to seek positive news does not show a statistically significant change throughout development. This study reveals how the relative importance of diverging, sometimes conflicting, information-seeking motives emerges throughout development.

Active inductive inference in children and adults: A constructivist perspective

A defining aspect of being human is an ability to reason about the world by generating and adapting ideas and hypotheses. Here we explore how this ability develops by comparing children's and adults' active search and explicit hypothesis generation patterns in a task that mimics the open-ended process of scientific induction. In our experiment, 54 children (aged 8.97±1.11) and 50 adults performed inductive inferences about a series of causal rules through active testing. Children were more elaborate in their testing behavior and generated substantially more complex guesses about the hidden rules. We take a 'computational constructivist' perspective to explaining these patterns, arguing that these inferences are driven by a combination of thinking (generating and modifying symbolic concepts) and exploring (discovering and investigating patterns in the physical world). We show how this framework and rich new dataset speak to questions about developmental differences in hypothesis generation, active learning and inductive generalization. In particular, we find children's learning is driven by less fine-tuned construction mechanisms than adults', resulting in a greater diversity of ideas but less reliable discovery of simple explanations.

Self-motivated and directed learning across the lifespan

Self-motivated and directed learning is integral to knowledge base expansion for learners of all ages. Both motivational and cognitive processes drive self-motivated and directed lifelong learning, yet how these different processes operate together from childhood through adulthood is largely unknown. In this review, we discuss the role of personal motivators, such as beliefs in self-efficacy and personality traits in self-motivated and directed learning across the lifespan. We then consider the role of cognitive processes that contribute to knowledge base expansion in learners of all ages, specifically executive functions. We focus on working memory, inhibitory control, and task switching as potential determinants of lifelong learning. Finally, we integrate the two literatures, to discuss ways in which personal motivators may influence deployment of executive functions under self-motivated and directed conditions as a learner advances along a developmental trajectory. We also suggest ways to move the study of self-motivated and directed learning beyond observation and self-report measures thus affording experimental control. We aim to provide a more comprehensive understanding and novel insight to the mechanisms and processes of self-motivated and directed learning across the lifespan.

Curiosity as a metacognitive feeling

Curious information-seeking is known to be a key driver for learning, but characterizing this important psychological phenomenon remains a challenge. In this article, we argue that solving this challenge requires qualifying the relationships between metacognition and curiosity. The idea that curiosity is a metacognitive competence has been resisted: researchers have assumed both that young children and non-human animals can be genuinely curious, and that metacognition requires conceptual and culturally situated resources that are unavailable to young children and non-human animals. Here, we argue that this resistance is unwarranted given accumulating evidence that metacognition can be deployed procedurally, and we defend the view that curiosity is a metacognitive feeling. Our metacognitive view singles out two monitoring steps as a triggering condition for curiosity: evaluating one's own informational needs, and predicting the likelihood that explorations of the proximate environment afford significant information gains. We review empirical evidence and computational models of curiosity, and show that they fit well with this metacognitive account, while on the contrary, they remain difficult to explain by a competing account according to which curiosity is a basic attitude of questioning. Finally, we propose a new way to construe the relationships between curiosity and the human-specific communicative practice of questioning, discuss the issue of how children may learn to express their curiosity through interactions with others, and conclude by briefly exploring the implications of our proposal for educational practices.

Rational information search in welfare-tradeoff cognition

One of the most important dimensions along which we evaluate others is their propensity to value our welfare: we like people who are disposed to incur costs for our benefit and who refrain from imposing costs on us to benefit themselves. The evolutionary importance of social valuation in our species suggests that humans have cognitive mechanisms that are able to efficiently extract information about how much another person values them. Here I test the hypothesis that people are spontaneously interested in the kinds of events that have the most potential to reveal such information. In two studies, I presented participants (Ns = 216; 300) with pairs of dilemmas that another individual faced in an economic game; for each pair, I asked them to choose the dilemma for which they would most like to see the decision that the individual had made. On average, people spontaneously selected the choices that had the potential to reveal the most information about the individual's valuation of the participant, as quantified by a Bayesian ideal search model. This finding suggests that human cooperation is supported by sophisticated cognitive mechanisms for information-gathering.

Visuospatial information foraging describes search behavior in learning latent environmental features

In the real world, making sequences of decisions to achieve goals often depends upon the ability to learn aspects of the environment that are not directly perceptible. Learning these so-called latent features requires seeking information about them. Prior efforts to study latent feature learning often used single decisions, used few features, and failed to distinguish between reward-seeking and information-seeking. To overcome this, we designed a task in which humans and monkeys made a series of choices to search for shapes hidden on a grid. On our task, the effects of reward and information outcomes from uncovering parts of shapes could be disentangled. Members of both species adeptly learned the shapes and preferred to select tiles expected to be informative earlier in trials than previously rewarding ones, searching a part of the grid until their outcomes dropped below the average information outcome-a pattern consistent with foraging behavior. In addition, how quickly humans learned the shapes was predicted by how well their choice sequences matched the foraging pattern, revealing an unexpected connection between foraging and learning. This adaptive search for information may underlie the ability in humans and monkeys to learn latent features to support goal-directed behavior in the long run.

Preschoolers' information search strategies: Inefficient but adaptive

Although children's sensitivity to others' informativeness emerges early in life, their active information search becomes robustly efficient only around age 10. Young children's difficulty in asking efficient questions has often been hypothesized to be linked to their developing verbal competence and growing vocabulary. In this paper, we offer for the first time a quantitative analysis of 4- to 6-year-old children's information search competence by using a non-verbal version of the 20-questions game, to gain a more comprehensive and fair picture of their active learning abilities. Our results show that, even in this version, preschoolers performed worse than simulated random agents, requiring more queries to reach the solution. However, crucially, preschoolers performed better than the simulated random agents when isolating the extra, unnecessary queries, which are made after only one hypothesis is left. When additionally isolating all the unnecessary queries, children's performance looked on par with that of the simulated optimal agents. Our study replicates and enriches previous research, showing an increase in efficiency across the preschool-aged years, but also a general lack of optimality that seems to be fundamentally driven by children's strong tendency to make unnecessary queries, rather than by their verbal immaturity. We discuss how children's non-optimal, conservative information-search strategies may be adaptive, after all.

Balancing novelty and appropriateness leads to creative associations in children

Creative problem solving is a fundamental skill of human cognition and is conceived as a search process whereby a novel and appropriate solution is generated. However, it is unclear whether children are able to balance novelty and appropriateness to generate creative solutions and what are the underlying computational mechanisms. Here, we asked children, ranging from 10 to 11 years old, to perform a word association task according to three instructions, which triggered a more appropriate (ordinary), novel (random), or balanced (creative) response. Results revealed that children exhibited greater cognitive flexibility in the creative condition compared to the control conditions, as revealed by the structure and resiliency of the semantic networks. Moreover, responses' word embeddings extracted from pretrained deep neural networks showed that semantic distance and category switching index increased in the creative condition with respect to the ordinary condition and decreased compared to the random condition. Critically, we showed how children efficiently solved the exploration/exploitation trade-off to generate creative associations by fitting a computational reinforcement learning (RL) model that simulates semantic search strategies. Our findings provide compelling evidence that children balance novelty and appropriateness to generate creative associations by optimally regulating the level of exploration in the semantic search. This corroborates previous findings on the adult population and highlights the crucial contribution of both components to the overall creative process. In conclusion, these results shed light on the connections between theoretical concepts such as bottom-up/top-down modes of thinking in creativity research and the exploration/exploitation trade-off in human RL research.

Children's failure to control variables may reflect adaptive decision-making

Changing one variable at a time while controlling others is a key aspect of scientific experimentation and a central component of STEM curricula. However, children reportedly struggle to learn and implement this strategy. Why do children's intuitions about how best to intervene on a causal system conflict with scientific practices? Mathematical analyses have shown that controlling variables is not always the most efficient learning strategy, and that its effectiveness depends on the "causal sparsity" of the problem, i.e., how many variables are likely to impact the outcome. We tested the degree to which 7- to 13-year-old children (n = 104) adapt their learning strategies based on expectations about causal sparsity. We report new evidence demonstrating that some previous work may have undersold children's causal learning skills: Children can perform and interpret controlled experiments, are sensitive to causal sparsity, and use this information to tailor their testing strategies, demonstrating adaptive decision-making.

An Introduction to Ecological Active Learning

This article introduces ecological active learning, a developmental framework that focuses on children’s ability to adapt and tailor their active-learning strategies to the particular structure and characteristics of a learning environment. Results of seminal studies indicate that efficient, adaptive search strategies emerge around 3 years of age, much earlier than previously assumed. This work highlights the importance of developing age-appropriate paradigms that capture children’s early competence to gain a more comprehensive and fair picture of their active-learning abilities. Also, it offers a process-oriented theoretical framework that can accommodate and reconcile a sparse but growing body of work documenting children’s active and adaptive learning. Three of the most promising avenues for future research on children’s ecological active learning are discussed.

“What makes this a wug?” Relations among children’s question asking, memory, and categorization of objects

Children ask many questions, but do not always receive answers to the questions they ask. We were interested in whether the act of generating questions, in the absence of an answer, is related to children’s later thinking. Two experiments examined whether children retain the questions they ask in working memory, and whether the type of questions asked relate to their categorization. Four to ten-year-old children (N = 42 in Experiment 1, N = 41 in Experiment 2) were shown 12 novel objects, asked three questions about each, and did not receive answers to their questions. Children recalled their questions in the first experiment and categorized variants of the novel objects in the second experiment. We found that children have robust working memory for their questions, indicating that these questions may relate to their subsequent thinking. Additionally, children generalize category boundaries more narrowly or broadly depending on the type of question they ask, indicating that children’s questions may reflect an underlying bias in how they think about the world. These findings suggest that future research should examine questions in the absence of answers to understand how inquiry affects children’s cognitive development.

Adaptive search space pruning in complex strategic problems

People have limited computational resources, yet they make complex strategic decisions over enormous spaces of possibilities. How do people efficiently search spaces with combinatorially branching paths? Here, we study players’ search strategies for a winning move in a “k-in-a-row” game. We find that players use scoring strategies to prune the search space and augment this pruning by a “shutter” heuristic that focuses the search on the paths emanating from their previous move. This strong pruning has its costs—both computational simulations and behavioral data indicate that the shutter size is correlated with players’ blindness to their opponent’s winning moves. However, simulations of the search while varying the shutter size, complexity levels, noise levels, branching factor, and computational limitations indicate that despite its costs, a narrow shutter strategy is the dominant strategy for most of the parameter space. Finally, we show that in the presence of computational limitations, the shutter heuristic enhances the performance of deep learning networks in these end-game scenarios. Together, our findings suggest a novel adaptive heuristic that benefits search in a vast space of possibilities of a strategic game.

Search problems usually have a common trade-off between accuracy and computational resources; Finding the best solution usually requires an exhaustive search, while limiting computations usually decreases the quality of solutions. Yet, humans provide high-quality solutions for complex problems despite having limited computational resources. How do they do that? Here, we analyze people’s behavior in a strategic game of “k-in-a-row” that has an enormous space of possibilities. We find that people strongly prune the search space by using scoring strategies to evaluate each possibility and augment this pruning with a shutter heuristic that limits their search to the possible winning paths from their last move. Similar to other adaptive heuristics, the shutter heuristic provides a strong reduction in the computations the searcher needs to carry out while maintaining on par accuracy rates. Finally, this adaptive heuristic generalizes to the performance of deep learning networks when playing with limited computational resources.

Causal Information-Seeking Strategies Change Across Childhood and Adolescence

Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine their underlying structures. Each puzzle comprised a three- or four-node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip's hidden wires and had to select a single node to activate. After observing the outcome of their intervention, participants selected a wire configuration and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures versus those that had high causal centrality in one of the two causal hypotheses but did not necessarily discriminate between them. Our model estimates revealed that the use of a discriminatory strategy increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that interventions revealed, as indexed by participants' confidence in their selections. Our results suggest that improvements in causal information-seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas.

Children's exploratory play tracks the discriminability of hypotheses

Effective curiosity-driven learning requires recognizing that the value of evidence for testing hypotheses depends on what other hypotheses are under consideration. Do we intuitively represent the discriminability of hypotheses? Here we show children alternative hypotheses for the contents of a box and then shake the box (or allow children to shake it themselves) so they can hear the sound of the contents. We find that children are able to compare the evidence they hear with imagined evidence they do not hear but might have heard under alternative hypotheses. Children (N = 160; mean: 5 years and 4 months) prefer easier discriminations (Experiments 1-3) and explore longer given harder ones (Experiments 4-7). Across 16 contrasts, children's exploration time quantitatively tracks the discriminability of heard evidence from an unheard alternative. The results are consistent with the idea that children have an "intuitive psychophysics": children represent their own perceptual abilities and explore longer when hypotheses are harder to distinguish.

In Pursuit of Knowledge: Preschoolers Expect Agents to Weigh Information Gain and Information Cost When Deciding Whether to Explore

When deciding whether to explore, agents must consider both their need for information and its cost. Do children recognize that exploration reflects a trade-off between action costs and expected information gain, inferring epistemic states accordingly? In two experiments, 4- and 5-year-olds (N = 144; of diverse race and ethnicity) judge that an agent who refuses to obtain low-cost information must have already known it, and an agent who incurs a greater cost to gain information must have a greater epistemic desire. Two control studies suggest that these findings cannot be explained by low-level associations between competence and knowledge. Our results suggest that preschoolers' theory of mind includes expectations about how costs interact with epistemic desires and states to produce exploratory action.

How to Help Young Children Ask Better Questions?

In this paper, we investigate the informativeness of 4- to 6-year-old (N = 125) children’s questions using a combined qualitative and quantitative approach. Children were presented with a hierarchical version of the 20-questions game, in which they were given an array of objects that could be organized into three category levels based on shared features. We then tested whether it is possible to scaffold children’s question-asking abilities without extensive training. In particular, we supported children’s categorization performance by providing the object-related features needed to ask effective constraint-seeking questions. We found that with both age and scaffolding children asked more effective questions, targeting higher category levels and therefore reaching the solution with fewer questions. We discuss the practical and theoretical implications of these results.

Developing an Understanding of Science

Young children are adept at several types of scientific reasoning, yet older children and adults have difficulty mastering formal scientific ideas and practices. Why do “little scientists” often become scientifically illiterate adults? We address this question by examining the role of intuition in learning science, both as a body of knowledge and as a method of inquiry. Intuition supports children's understanding of everyday phenomena but conflicts with their ability to learn physical and biological concepts that defy firsthand observation, such as molecules, forces, genes, and germs. Likewise, intuition supports children's causal learning but provides little guidance on how to navigate higher-order constraints on scientific induction, such as the control of variables or the coordination of theory and data. We characterize the foundations of children's intuitive understanding of the natural world, as well as the conceptual scaffolds needed to bridge these intuitions with formal science.

Individual differences in strategy use and performance during fault diagnosis

Background

Research on causal reasoning often uses group-level data analyses that downplay individual differences and simple reasoning problems that are unrepresentative of everyday reasoning. In three empirical studies, we used an individual differences approach to investigate the cognitive processes people used in fault diagnosis, which is a complex diagnostic reasoning task. After first showing how high-level fault diagnosis strategies can be composed of simpler causal inferences, we discussed how two of these strategies—elimination and inference to the best explanation (IBE)—allow normative performance, which minimizes the number of diagnostic tests, whereas backtracking strategies are less efficient. We then investigated whether the use of normative strategies was infrequent and associated with greater fluid intelligence and positive thinking dispositions and whether normative strategies used slow, analytic processing while non-normative strategies used fast, heuristic processing.

Results

Across three studies and 279 participants, uses of elimination and IBE were infrequent, and most participants used inefficient backtracking strategies. Fluid intelligence positively predicted elimination and IBE use but not backtracking use. Positive thinking dispositions predicted avoidance of backtracking. After classifying participants into groups that consistently used elimination, IBE, and backtracking, we found that participants who used elimination and IBE made fewer, but slower, diagnostic tests compared to backtracking users.

Conclusions

Participants’ fault diagnosis performance showed wide individual differences. Use of normative strategies was predicted by greater fluid intelligence and more open-minded and engaged thinking dispositions. Elimination and IBE users made the slow, efficient responses typical of analytic processing. Backtracking users made the fast, inefficient responses suggestive of heuristic processing.

Strategies for selecting and evaluating information

Within the domain of psychology, Optimal Experimental Design (OED) principles have been used to model how people seek and evaluate information. Despite proving valuable as computational-level methods to account for people's behaviour, their descriptive and explanatory powers remain largely unexplored. In a series of experiments, we used a naturalistic crime investigation scenario to examine how people evaluate queries, as well as outcomes, in probabilistic contexts. We aimed to uncover the psychological strategies that people use, not just to assess whether they deviated from OED principles. In addition, we explored the adaptiveness of the identified strategies across both one-shot and stepwise information search tasks. We found that people do not always evaluate queries strictly in OED terms and use distinct strategies, such as by identifying a leading contender at the outset. Moreover, we identified aspects of zero-sum thinking and risk aversion that interact with people's information search strategies. Our findings have implications for building a descriptive account of information seeking and evaluation, accounting for factors that currently lie outside the realm of information-theoretic OED measures, such as context and the learner's own preferences.

Supporting Early Scientific Thinking Through Curiosity

Curiosity and curiosity-driven questioning are important for developing scientific thinking and more general interest and motivation to pursue scientific questions. Curiosity has been operationalized as preference for uncertainty (Jirout and Klahr, 2012), and engaging in inquiry-an essential part of scientific reasoning-generates high levels of uncertainty (Metz, 2004; van Schijndel et al., 2018). This perspective piece begins by discussing mechanisms through which curiosity can support learning and motivation in science, including motivating information-seeking behaviors, gathering information in response to curiosity, and promoting deeper understanding through connection-making related to addressing information gaps. In the second part of the article, a recent theory of how to promote curiosity in schools is discussed in relation to early childhood science reasoning. Finally, potential directions for research on the development of curiosity and curiosity-driven inquiry in young children are discussed. Although quite a bit is known about the development of children’s question asking specifically, and there are convincing arguments for developing scientific curiosity to promote science reasoning skills, there are many important areas for future research to address how to effectively use curiosity to support science learning.

Exploration, Explanation, and Parent-Child Interaction in Museums

Young children develop causal knowledge through everyday family conversations and activities. Children's museums are an informative setting for studying the social context of causal learning because family members engage together in everyday scientific thinking as they play in museums. In this multisite collaborative project, we investigate children's developing causal thinking in the context of family interaction at museum exhibits. We focus on explaining and exploring as two fundamental collaborative processes in parent-child interaction, investigating how families explain and explore in open-ended collaboration at gear exhibits in three children's museums in Providence, RI, San Jose, CA, and Austin, TX. Our main research questions examined (a) how open-ended family exploration and explanation relate to one another to form a dynamic for children's learning; (b) how that dynamic differs for families using different interaction styles, and relates to contextual factors such as families' science background, and (c) how that dynamic predicts children's independent causal thinking when given more structured tasks. We summarize findings on exploring, explaining, and parent-child interaction (PCI) styles. We then present findings on how these measures related to one another, and finally how that dynamic predicts children's causal thinking. In studying children's exploring we described two types of behaviors of importance for causal thinking: (a) Systematic Exploration: Connecting gears to form a gear machine followed by spinning the gear machine. (b) Resolute Behavior: Problem-solving behaviors, in which children attempted to connect or spin a particular set of gears, hit an obstacle, and then persisted to succeed (as opposed to moving on to another behavior). Older children engaged in both behaviors more than younger children, and the proportion of these behaviors were correlated with one another. Parents and children talked to each other while interacting with the exhibits. We coded causal language, as well as other types of utterances. Parents' causal language predicted children's causal language, independent of age. The proportion of parents' causal language also predicted the proportion of children's systematic exploration. Resolute behavior on the part of children did not correlate with parents' causal language, but did correlate with children's own talk about actions and the exhibit. We next considered who set goals for the play in a more holistic measure of parent-child interaction style, identifying dyads as parent-directed, child-directed, or jointly-directed in their interaction with one another. Children in different parent-child interaction styles engaged in different amounts of systematic exploration and had parents who engaged in different amounts of causal language. Resolute behavior and the language related to children engaging in such troubleshooting, seemed more consistent across the three parent-child interaction styles. Using general linear mixed modeling, we considered relations within sequences of action and talk. We found that the timing of parents' causal language was crucial to whether children engaged in systematic exploration. Parents' causal talk was a predictor of children's systematic exploration only if it occurred prior to the act of spinning the gears (while children were building gear machines). We did not observe an effect of causal language when it occurred concurrently with or after children's spinning. Similarly, children's talk about their actions and the exhibit predicted their resolute behavior, but only when the talk occurred while the child was encountering the problem. No effects were found for models where the talk happened concurrently or after resolving the problem. Finally, we considered how explaining and exploring related to children's causal thinking. We analyzed measures of children's causal thinking about gears and a free play measure with a novel set of gears. Principal component analysis revealed a latent factor of causal thinking in these measures. Structural equation modeling examined how parents' background in science related to children's systematic exploration, parents' causal language, and parent-child interaction style, and then how those factors predicted children's causal thinking. In a full model, with children's age and gender included, children's systematic exploration related to children's causal thinking. Overall, these data demonstrate that children's systematic exploration and parents' causal explanation are best studied in relation to one another, because both contributed to children's learning while playing at a museum exhibit. Children engaged in systematic exploration, which supported their causal thinking. Parents' causal talk supported children's exploration when it was presented at certain times during the interaction. In contrast, children's persistence in problem solving was less sensitive to parents' talk or interaction style, and more related to children's own language, which may act as a form of self-explanation. We discuss the findings in light of ongoing approaches to promote the benefit of parent-child interaction during play for children's learning and problem solving. We also examine the implications of these findings for formal and informal learning settings, and for theoretical integration of constructivist and sociocultural approaches in the study of children's causal thinking.

The Development of Cumulative Cultural Learning

Human culture is unique among animals in its complexity, variability, and cumulative quality. This article describes the development and diversity of cumulative cultural learning. Children inhabit cultural ecologies that consist of group-specific knowledge, practices, and technologies that are inherited and modified over generations. The learning processes that enable cultural acquisition and transmission are universal but are sufficiently flexible to accommodate the highly diverse cultural repertoires of human populations. Children learn culture in several complementary ways, including through exploration, observation, participation, imitation, and instruction. These methods of learning vary in frequency and kind within and between populations due to variation in socialization values and practices associated with specific educational institutions, skill sets, and knowledge systems. The processes by which children acquire and transmit the cumulative culture of their communities provide unique insight into the evolution and ontogeny of human cognition and culture.

Opacity, obscurity, and the geometry of question-asking

Asking questions is a pervasive human activity, but little is understood about what makes them difficult to answer. An analysis of a pair of large databases, New York Times crosswords and questions from the quiz-show Jeopardy, establishes two orthogonal dimensions of question difficulty: obscurity (the rarity of the answer) and opacity (the indirectness of question cues, operationalized with word2vec). The importance of opacity, and the role of synergistic information in resolving it, suggests that accounts of difficulty in terms of prior expectations captures only a part of the question-asking process. A further regression analysis shows the presence of additional dimensions to question-asking: question complexity, the answer's local network density, cue intersection, and the presence of signal words. Our work shows how question-askers can help their interlocutors by using contextual cues, or, conversely, how a particular kind of unfamiliarity with the domain in question can make it harder for individuals to learn from others. Taken together, these results suggest how Bayesian models of question difficulty can be supplemented by process models and accounts of the heuristics individuals use to navigate conceptual spaces.

Searching for Rewards Like a Child Means Less Generalization and More Directed Exploration

How do children and adults differ in their search for rewards? We considered three different hypotheses that attribute developmental differences to (a) children's increased random sampling, (b) more directed exploration toward uncertain options, or (c) narrower generalization. Using a search task in which noisy rewards were spatially correlated on a grid, we compared the ability of 55 younger children (ages 7 and 8 years), 55 older children (ages 9-11 years), and 50 adults (ages 19-55 years) to successfully generalize about unobserved outcomes and balance the exploration-exploitation dilemma. Our results show that children explore more eagerly than adults but obtain lower rewards. We built a predictive model of search to disentangle the unique contributions of the three hypotheses of developmental differences and found robust and recoverable parameter estimates indicating that children generalize less and rely on directed exploration more than adults. We did not, however, find reliable differences in terms of random sampling.

Stepwise versus globally optimal search in children and adults

How do children and adults search for information when stepwise-optimal strategies fail to identify the most efficient query? The value of questions is often measured in terms of stepwise information gain (expected reduction of entropy on the next time step) or other stepwise-optimal methods. However, such myopic models are not guaranteed to identify the most efficient sequence of questions, that is, the shortest path to the solution. In two experiments we contrast stepwise methods with globally optimal strategies and study how younger children (around age 8, N = 52), older children (around age 10, N = 99), and adults (N = 101) search in a 20-questions game where planning ahead is required to identify the most efficient first question. Children searched as efficiently as adults, but also as myopically. Both children and adults tended to rely on heuristic stepwise-optimal strategies, focusing primarily on questions' implications for the next time step, rather than planning ahead.

Shake it baby, but only when needed: Preschoolers adapt their exploratory strategies to the information structure of the task

Previous research has suggested that active engagement with the world drives children's remarkable learning capabilities. We investigated whether preschoolers are "ecological learners," that is, whether they are able to select those active learning strategies that are most informative in a given task. Children had to choose which of two exploratory actions (open vs. shake) to perform to find an egg shaker hidden in one of four small boxes, contained in two larger boxes. Prior to this game, children either learnt that the egg was equally likely to be found in any of the four small boxes (Uniform condition), or that it was most likely to be found in one particular small box (Skewed condition). Results of Study 1 show that 3- and 4-year-olds successfully tailored their exploratory actions to the different likelihood-distributions: They were more likely to shake first in the Uniform compared to the Skewed condition. Five-year-olds were equally likely to shake first, irrespective of condition, even when incentivized to shake only when needed (Study 2a). However, when the relevance of the frequency training for the hiding game was highlighted (Study 2b and Study 2c), the 5-year-olds showed the same behavioural pattern as the younger preschoolers in Study 1. We suggest that ecological learning may be a key mechanism underlying children's effectiveness in active learning.

Using data to solve problems: Children reason flexibly in response to different kinds of evidence

This study examined children's (5- to 9-year-olds, N = 363) abilities to use information seeking and explanation to solve problems using conclusive or inconclusive (i.e., consistent, inconsistent, or ambiguous) evidence. Results demonstrated that inconsistent and ambiguous evidence, not consistent evidence, motivate more requests for information than conclusive evidence. In addition, children's explanations were flexible in response to evidence; explanations based on transitive inference were more likely to be associated with an accurate conclusion than other explanation types. Children's requests for additional information in response to inconclusive evidence increased with age, as did their problem-solving accuracy. The data demonstrate that children's capacity to use information seeking and explanation develop in tandem as tools for problem solving.

Effects of explanation on children's question asking

The capacity to search for information effectively by asking informative questions is crucial for self-directed learning and develops throughout the preschool years and beyond. We tested the hypothesis that explaining observations in a given domain prepares children to ask more informative questions in that domain, and that it does so by promoting the identification of features that apply to multiple objects, thus supporting more effective questions. Across two experiments, 4- to 7-year-old children (N  = 168) were prompted to explain observed evidence or to complete a control task prior to a 20-questions game. We found that prior prompts to explain led to a decrease in the number of questions needed to complete the game, but only for older children (ages 6-7). Moreover, we found that effects of explanation manifested as a shift away from questions that targeted single objects. These findings shed light on the development of question asking in childhood and on the role of explanation in learning.

Memory enhancements from active control of learning emerge across development

This paper investigates whether active control of study leads to enhanced learning in 5- to 11-year-old children. In Experiments 1 and 2, participants played a simple memory game with the instruction to try to remember and later recognize a set of 64 objects. In Experiment 3, the goal was to learn the French names for the same objects. For half of the materials presented, participants could decide the order and pacing of study (Active condition). For the other half, they passively observed the study decisions of a previous participant (Yoked condition). Recognition memory was more accurate for objects studied in the active as compared to the yoked condition. However, the active learning advantage was relatively small among 5-year-olds and increased with age, becoming comparable to adults' by age 8. Our results show that the ability to actively control study develops during early childhood and results in memory benefits that last over a week-long delay. We discuss possible interpretations for the observed developmental change, as well as the implications of these results for educational implementations.

Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search

Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma-Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information-theoretic formalism.

Opening the cuebox: the information children and young adults generate and rely on when making inferences from memory

We used a cue-generation and a cue-selection paradigm to investigate the cues children (9- to 12-year-olds) and young adults (17-year-olds) generate and select for a range of inferences from memory. We found that children generated more cues than young adults, who, when asked why they did not generate some particular cues, responded that they did not consider them relevant for the task at hand. On average, the cues generated by children were more perceptual but as informative as the cues generated by young adults. When asked to select the most informative of two cues, both children and young adults tended to choose a hidden (i.e., not perceptual) cue. Our results suggest a developmental change in the cuebox (i.e., the set of cues used to make inferences from memory): New cues are added to the cuebox as more cues are learned, and some old, perceptual cues, although informative, are replaced with hidden cues, which, by both children and young adults, are generally assumed to be more informative than perceptual cues.