Search

The emergence of deliberate ignorance, i.e. what influences children's deliberate decisions not to seek information, is a phenomenon so far notably overlooked. This project addresses this gap by investigating various factors that systematically modulate such decisions in children and adults. Across five studies, we presented participants with short stories illustrating social situations where a misdeed occurs, and measured participants' preference for knowing the identity of the wrongdoer.Studies 1-3 (N = 550) shows that both children and adults systematically manifest a preference for ignorance when the information value is low, and when the potential wrongdoer is suspected to be a friend. Studies 4-5 (N = 333) further investigate the role of information probability in this phenomenon.This first contribution shows that children's preference for deliberate ignorance is modulated by information value and the relationship frame proposed, suggesting a rational approach to ignorance.

This study explores the influence of social contexts on the efficiency of information search in children (6-14 years), adolescents (15-17 years), and adults. Participants are placed on a team for a competition. When the championship trophy goes missing, the participant's team has either won or lost. Participants are then tasked with playing a 20-Questions game to try to find the trophy. Beyond the developmental trajectory in their ability to select the most informative questions, we found, as hypothesized, that all participants actively biased their search strategies: the efficiency of their questions was contingent upon whether it was in their best interest to find the culprit. In particular, they were more likely to select the most efficient question when they were winning and were more motivated to identify the target. Overall, our findings suggest that social contexts play a strong role in modulating the efficiency of information search across age groups.

Young children, like adults, conform to group consensus. However, it is unclear when children develop more sophisticated intuitions about how the composition of groups, the way in which they acquire and aggregate information, impacts advice quality. This experiment assessed children's developing sensitivity to source independence - that is, do they understand that statistically independent sources of information are more valuable than correlated ones? Children (5-to-11-year-olds, N=106) and teenagers and adults (N=99) played a space exploration game in which they made multiple 2AFC decisions based on the advice of 8 friendly aliens. Across trials and participants, we manipulated consensus (the relative number of advisers endorsing each option) and source diversity (the relative number of independent advisers endorsing each option). Results indicated that children were able to detect the correlations between sources, but their ability to exploit this knowledge was late emerging, likely in the early adolescence years.

Past research examining developmental differences in exploration behavior has shown that children are more likely than adults to seek out uncertainty. However, children's exploration behavior may be shaped by their distinct prior experiences and assumptions, differing from those of adults. We investigate these differences and their potential impact on exploration, using the game “Little Alchemy”, in which players can create new elements (e.g. clay) by combining previously discovered elements (e.g. stone and mud). Previous work found that adults use an empowerment strategy: They combine elements with the goal of creating new elements with the potential for many successful combinations. We observed that children were less likely to use an empowerment strategy, but relied more on their uncertainty compared to adults. This discrepancy decreased over age. In a follow-up experiment, we showed that this difference was indeed due to children using different strategies rather than the influence of different semantic priors.

What drives children to explore and learn when external rewards are uncertain or absent? Across three studies, we tested whether information gain itself acts as an internal reward and suffices to motivate children's actions. We measured 24-56-month-olds' persistence in a game where they had to search for an object (animal or toy), which they never find, hidden behind a series of doors, manipulating the degree of uncertainty about "which specific object" was hidden. We found that children were more persistent in their search when there was higher uncertainty, and therefore, more information to be gained with each action, highlighting the importance of research on artificial intelligence to invest in curiosity-driven algorithms.

Previous research shows that children evaluate the competence of others based on how effectively someone accomplished a goal, that is, based on the observed outcome of an action (e.g., number of attempts needed). Here, we investigate whether 5- to 10-year-old children and adults infer competence from how efficiently someone solves a task by implementing question-asking strategies of varying expected information gains (EIG). Whereas the efficiency of a strategy defined as EIG is a reliable indicator of competence, the observed effectiveness of actions may depend on unrelated external factors, such as luck. Across two experiments conducted in Germany, we varied how efficiently and how effectively different agents solved a 20-questions game (Experiments 1 and 2) and a maze-exploration game (Experiment 2). In Experiment 1 (N = 121), only adults identified a more efficient agent as more competent, and all participants attributed higher competence to agents needing fewer questions even when they employed the same inefficient strategy. In Experiment 2 (N = 220), adults and children from about 8 years onward successfully identified the agents using the more efficient strategy as more competent. Overall, our results suggest that observed effectiveness is a powerful cue for competence even when such an inference may not be warranted and that the ability to make explicit competence judgments based on the efficiency of a strategy alone emerges around 8 years of age, although, as shown in previous work, a more implicit understanding of competence may already be present during the preschool years.

Previous research suggests that children’s information search remains largely inefficient until age 4. Here, we investigate the early emergence of children’s information-search competence using a simplified version of Lindow’s (2021) finding-presents game. Children (n = 86, 24- to 59-months old) had to find a present hidden in one of three closed boxes. All boxes were identical but for one feature (e.g., all boxes were blue and had a flower icon on top, but one box was round, one heart-shaped, and one squared). To identify the target box, children received three information cards revealing one feature of the target box (i.e., its color, shape, or icon). As the boxes differed in only one feature (e.g., their shape), only one information card contained the relevant information to the decision (i.e., the information card indicating the correct shape). Children could flip one information card to learn about one particular feature before deciding which box to open. This was our dependent measure. Our findings indicate that children as young as 2 years can efficiently search for information to guide their decisions and underline the importance of using age-appropriate paradigms.

Previous research documented adults’ preference for stablecausal relationships that do not vary in strength across backgrounds(Vasilyeva, Blanchard, & Lombrozo, 2018). In thisstudy, we investigate the role of causal stability in guidingchildren’s exploration behavior. We developed a computerizedversion of an active information-search paradigm to studyhow children dynamically explore different agents and backgroundsto learn more about their causal stability. Five- toseven-year-old children (n = 60) were presented with stableand unstable causes (i.e., causes with fixed or variable causalefficacy across backgrounds). We assessed children’s causalattributions of outcomes and their exploratory behavior as theytried out previously observed and novel causes across previouslyobserved and novel backgrounds. We find that childrenin this age range acknowledge causal instability in their causalattributions, and they become increasingly adept at trackingcausal efficacy across multiple factors simultaneously (causesand backgrounds), but this does not translate into a blanketpreference for exploring stable or unstable causes. We suggesta possibility that causal (in)stability guides exploration inmore subtle and indirect ways and discuss the implications ofour findings for the development of active exploration.

Recent studies suggest that children are \emph{ecological active learners} who recognize and exploit the ecology of their learning environment (Ruggeri, 2022). However, when assessed by verbal tasks such as the 20-questions game, systematic search only matures around age 7. The current study examined if even young children can adapt their information-search strategies in a developmentally-appropriate task requiring minimal verbal or conceptual abstraction skills. Three to 7-year-olds (N = 76, M = 5.7 years) played a search game with a structure analogous to the 20-questions game. We manipulated whether children received predecisional cues about the past location of the solution or not, across two search phases, and further varied whether the cues follow a Uniform or Skewed distribution. Children adapted their information-search strategies as predicted: They followed a constraint-seeking strategy in the absence of cues, and only switched to hypothesis-scanning when exposed to the Skewed cues.

Do children consider how others learned when seeking help? Across three experiments, German children (N = 536 3-to-8 year olds, 49% female, majority White, tested 2017-2019) preferred to learn from successful active learners selectively by context: They sought help solving a problem from a learner who had independently discovered the solution to a previous problem over those who had learned through instruction or observation, but only when the current problem was novel, yet related, to the learners' problem (Experiment 1). Older, but not younger, children preferred the active learner even when she was offered help (Experiment 2), though only when her discovery was deliberate (Experiment 3). Although a preference to learn from successful active learners emerges early, a genuine appreciation for "process beyond outcome" increases across childhood.

We explore how children and adults actively experiment within the physical world to achieve different epistemic goals. In our experiment, one hundred one 4- to 10-year-old children and 24 adults either passively observed or used a touchscreen interface to actively interact with objects in a dynamic physical microworld with the goal of inferring one of two latent physical properties: relative object masses or local forces of attraction and repulsion. We find an age improvement in judgments as well as an advantage for active over passive learning. With the help of Bayesian statistics and a computational modeling framework for the quantitative analysis of participants' actions, we show that children's and adults' actions are equally successful in targeting their goal-relevant uncertainty, but that adults and older children are better able to use this information to respond correctly. We further unpack children's and adults' experimental strategies qualitatively, finding adults more likely to use a "deconfounding" strategy to isolate properties of interest, potentially creating evidence less susceptible to cognitive and perceptual errors.

We investigate whether a spatial representation of a search task supports 4- to 7-year-old children's information-search strategies, relative to their performance in a question-asking game. Children played two computationally and structurally analogous search games: a spatial search task, the maze-exploration game, in which they had to discover the path through a maze by removing masks covering its passages; and a "verbal search task," the 20-questions game, where they had to identify a target monster from a set of eight monsters by asking yes-no questions. Across four experiments, we found that children searched more efficiently when they could make queries nonverbally (Experiments 1 and 2a). We also found that merely providing children with a visual conceptual aid that supports their representation of the hypothesis space (Experiment 2b), or familiarizing them with the hypothesis-space structure (Experiment 3) was not sufficient to improve their search strategies. Together, our results suggest that young children's difficulties in the 20-questions game are mainly driven by the verbal requirements of the task. However, they also demonstrate that efficient search strategies emerge much earlier than previously assumed in tasks that do not rely on verbal question generation. These findings highlight the importance of developing age-appropriate paradigms that capture children's early competence, in order to gain a more comprehensive picture of their emerging information-search abilities.

Many of our decisions are based on probabilistic information. While probability theory is a useful tool for quantifying probabilities mathematically, subjective probability is a complex psychological phenomenon. We investigated developmental changes in subjective probability and the modulating role of emotions in probabilistic cognition. For this, we asked N = 45 children (M = 10.59, SD = 2.28, range 7-15) and N = 160 adults (M = 25.20, SD = 14.35, range 18-88) to estimate the probability of a series of three-item compound events generated from a known probability distribution. While children’s estimates largely resembled those of adults, conservatism (avoidance of the extremes) and representativeness judgments (basing estimates on similarity) were modulated by age and emotions. Our findings suggest that the way in which people use the representativeness heuristic develops with age and that emotions modulate subjective probability in children and adults.

Effective practice is key to learning. Yet, it is unclear whetheryoung children have the ability to make effective and adaptivetraining choices. In this project, we investigated 4- to 7-year-oldchildren’s (n=146) ability to tailor their training strategiesto optimize performance outcomes. Children were presentedwith one easy and one difficult guessing game and were askedto choose which game they wanted to practice. Crucially, beforethey chose, they were told that they would eventually betested either on the game of their choice (Choice condition) oron the game the computer would randomly pick (Random condition).Contrary to our hypotheses, we found that conditionper se did not predict children’s training choices. However, wefound that older children were more likely to make effectiveand adaptive training choices than younger children. Overall,our results indicate that children’s training choices improvefrom ages 4 to 7 and inform the development of interventionsto support strategic learning.

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.

This paper investigates the emergence and development ofchildren’s ability to adapt their information search to differentgoals. In Study 1, 3- to 7-year-olds had to decide whether tostudy the arms or legs of two monsters to predict which wouldsucceed at a throwing vs. jumping challenge. Children’s abil-ity to adaptively select the relevant piece of information andtailor their search to the given goal increased with age, surpass-ing chance level around 4;6. Study 2 investigated additionaladaptation to distributions of, e.g., long arms in the search do-main. Preliminary results confirm the observed developmentaltrend in search adaptiveness and effectiveness, suggesting anability to tailor information search to the relevant distributionsin the environment. These studies provide first insights intothe development of adaptive information search given complexgoals, deepening our understanding of this key aspect of learn-ing, judgment and decision-making.

How do people learn complex rules? We introduce a novel paradigm called ”Track-A-Mole”, in which participants have tolearn about and predict the moves of a cartoon mole, whose movements are generated by graphical programs. Our resultsshow that participants can learn to predict richly structured programs, and often require only few observations to do so,showing rapid learning and early insights about the underlying patterns. Moreover, we found that how learnable a programis can be predicted by features related to its complexity and compressibility. Finally, participants also show interestingpatterns of generalizations, assuming more parsimonious rules first and then gradually adjusting their predictions to morecomplex regularities, as well as matching their predictions to the general direction of movements and producing sensi-ble errors. These results extend our understanding of complex rule learning and open up future opportunities to modelsequential pattern predictions as graphical program induction.

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.

To successfully navigate an uncertain world, one has to learn the relationship between cues (e.g., wind speed, atmospheric pressure) and outcomes (e.g., rain). When learning, it is possible to actively manipulate the cue values to test hypotheses about this relationship directly. Across two studies, we investigated how 5- to 7-year-olds actively learned cue-outcome relationships, and what their behavior revealed about how they represented the hypothesis space. Children learned how two cues (color and shape) predicted some monsters' relative speed, by selecting which monster pairs to see racing. We compared two computational models in their ability to capture children's behavior: a "cue-abstraction" model, which organizes the hypothesis space based on abstracted cue-outcome relationships, and a permutation-based model, which represents the hypothesis space based on the relative speed of individual monsters. The results of Study 1 (26 five-year-olds, 14 female and 25 six-year-olds, 15 female; predominantly White, fluent in English) provided the first evidence that 5- and 6-year-olds can use cue-abstraction hypothesis space representations when provided with scaffolding. However, Study 2 (65 five-year-olds, 33 female; 67 six-year-olds, 33 female; 68 seven-year-olds, 33 female; predominantly White, fluent in German) showed that young children were best described by the "permutation-based" model, and that only 7-year-olds, when provided with memory aids, were best captured by the cue-abstraction model. Overall, our results highlight the guiding role of the hypothesis space for active search and learning, suggesting that these two phases might trigger different representations, and indicating a developmental shift in how children represent the hypothesis space.

Are young children just random explorers who learn serendipitously? Or are even young children guided by uncertainty-directed sampling, seeking to explore in a systematic fashion? We study how children between the ages of 4 and 9 search in an explore-exploit task with spatially correlated rewards, where exhaustive exploration is infeasible and not all options can be experienced. By combining behavioral data with a computational model that decomposes search into similarity-based generalization, uncertainty-directed exploration, and random exploration, we map out developmental trajectories of generalization and exploration. The behavioral data show strong developmental differences in children's capability to exploit environmental structure, with performance and adaptiveness of sampling decisions increasing with age. Through model-based analyses, we disentangle different forms of exploration, finding signature of both uncertainty-directed and random exploration. The amount of random exploration strongly decreases as children get older, supporting the notion of a developmental "cooling off" process that modulates the randomness in sampling. However, even at the youngest age range, children do not solely rely on random exploration. Even as random exploration begins to taper off, children are actively seeking out options with high uncertainty in a goal-directed fashion, and using inductive inferences to generalize their experience to novel options. Our findings provide critical insights into the behavioral and computational principles underlying the developmental trajectory of learning and exploration.

Prior research showed that young children prefer to seek helpfrom actors who have demonstrated active learning compe-tence. What inferences do people make based on the abil-ity to search effectively, for example by asking informativequestions? This project explores across two experiments towhat extent adults and children (3- to 9-year-olds) general-ize the ability to ask informative questions to other abili-ties/characteristics. We presented participants with one mon-ster who always asked informative questions and one whoalways asked uninformative questions. Participants had tochoose which monster they thought was more likely to pos-sess/was better at 12 different characteristics/abilities. Our re-sults show a clear developmental trend. Three- and 4-year-olds draw unsystematic inferences from the monsters question-asking expertise. Five- and 6-year-olds identified the betterquestion asker as better at everything. Seven- to 9-year-oldsshowed adult-like response patterns, selectively associating theability to ask good questions to related characteristics/abilities.

Previous research with typically developing (TD) childrenand adults show an advantage of active control for episodicmemory as compared to conditions lacking this control. Thepresent study attempts to replicate this effect in autisticchildren. Six- to 12-year-old autistic children (n = 30) wereinstructed to remember as many of 64 presented objects aspossible. For half of the materials presented, participantscould decide the order and pacing of study (Active condition).For the other half, they passively observed the study decisionsof a previous participant (Yoked condition). We found thatrecognition memory was more accurate for objects studied inthe active as compared to the yoked condition, even after aweek-long delay. The magnitude of the effect was comparableto that obtained in previous studies with TD children andadults, suggesting a strong robustness for the benefits ofactive learning. We discuss how pedagogical approaches maybe encouraged to utilize self-directed learning strategies topromote inclusive learning.

Previous research with typically developing children and adults shows that active control of the learning experience leads to enhanced episodic memory, as compared with conditions lacking this control. The present study investigates whether similar advantages can be found in children with autism spectrum disorder. In this study, 6-12-year-old autistic children (N=29) participated in a simple memory game on a touchscreen tablet, in which they were asked to remember 64 objects presented in four blocks of 16. In two of the blocks, children could decide the order and pacing of study (active condition), whereas in the other two blocks, they passively observed the active study decisions of a previous participant (yoked condition). We found that recognition memory was more accurate for objects studied in the active compared with the yoked condition, even after a week-long delay. The magnitude of the effect was comparable with that obtained in previous studies with typically developing children and adults, suggesting a robustness for the benefits of active learning that goes beyond what previously hypothesized, extending to special populations. We discuss how these findings may help develop pedagogical interventions that leverage the active learning approach to promote inclusive learning.

Previous research has shown that active engagement driveschildren’s remarkable learning capabilities. We investigatedwhether preschoolers are ecological learners, able to selectthose active learning strategies that are most informative in agiven task. Children (n = 114; 3 to 5 years old) chose betweentwo exploratory actions (opening vs. shaking) to find an eggshaker hidden in one of four small boxes, contained in twolarger boxes. Prior to this game, children learnt that the eggwas equally likely to be in any of the four small boxes(Uniform condition), or that it was most likely to be in oneparticular small box (Skewed condition). Results show that 3-and 4-year-olds, but not 5-year-olds, successfully tailored theirexploratory actions to these different likelihood distributions.We suggest that ecological learning may be a key mechanismexplaining how children can efficiently learn about the worldaround them.

Prior work suggests that young children consider others’knowledge and expertise to decide from whom to learn. Dochildren also consider how others came to know what theyknow? Here we investigate young children’s sensitivity to theprocess by which people have learned. In Exp.1, 3- to 6-year-olds preferentially sought help from an active learner, who hadfigured out how to solve a problem by herself, over learnerswho had learned through passive observation or direct instruc-tion. Yet, this preference emerged only when the problem chil-dren needed to solve was related to the one the learners hadpreviously solved (i.e., when they thought the active learner’scompetence would be relevant). These findings suggest chil-dren inferred competence from the process of active learning,but considered this competence to be constrained to a partic-ular task rather than more broadly generalizeable. The resultsof Exp.2 (3- to 7-year-olds) suggest that younger children’slearner preference might be driven by more superficial cues re-lated to active learning such as being alone and that a moreabstract understanding of the process of active learning mightdevelop with age.

Explanations often highlight inductively rich relationships thatsupport further generalizations: learning that the knife is sharpbecause it is for cutting, we correspondingly infer that other thingsfor cutting might also be sharp. When do children appreciate thatexplanations are good guides to generalization? We report a study inwhich 108 4- to 7-year-old children evaluated mechanistic,functional, and categorical explanations for the properties of objects,and subsequently generalized those properties to novel objects onthe basis of shared mechanisms, functions, or category membership.Older children, but not younger children, were significantly morelikely to generalize when the explanation they had received matchedthe subsequent basis for generalization (e.g., generalizing on thebasis of a shared mechanism after hearing a mechanisticexplanation). These findings shed light on how explanation andgeneralization are coordinated over development, as well as the roleof explanations in young children’s learning

People use hypothesis-driven search to learn about novel concepts, favoring information sources that reduce uncertaintyacross a set of hypotheses about a target concept. We used childrens information search to investigate their reliance on twotypes of hypothesis spaces: exemplar-based representations or a hierarchical hypothesis space based on cue abstraction.Five- to seven-year-olds learned to rank monsters according to a hierarchical decision rule involving two cues (shape andcolor). Children generated evidence by selecting pairs of monsters and observing which one ranked higher; they werethen tested on whether they learned the decision rule and correct ranking. A comparison of exemplar-based and cue-based Bayesian models revealed that all children made search decisions predicted by the exemplar-based model, but olderchildren could use collected evidence to infer the underlying hierarchical structure. These results suggest a dissociationbetween the representations used to drive search and to make inferences from evidence during development.

In everyday life, before deciding what to do, one has to thinkabout what could be done. We investigate option generationfrom a developmental perspective, testing the predictions of theTake-The-First-heuristic (TTF). Moreover, we examine theinfluence of time limitation on decision-making processes.Using soccer as a testbed, 6- to 13-year-old children (N = 97)were tested in a video-based option-generation paradigm.Children’s performance was aligned with predictions of TTF:Children generated a mean of 2.21 options, did so in ameaningful way and selected the first as final option in 74%.With shorter time, children generated fewer and higher qualityoptions, selected better options and more often the first optionas final decision. Further, with age, an increase of the numberof options generated and an increase in quality of the finaldecisions emerged. This age effect was more pronounced withshorter time. Implications for real-life decision-making arediscussed.

What is the best way of figuring out the structure of a causalsystem composed of multiple variables? One prominent ideais that learners should manipulate each candidate variable inisolation to avoid confounds (known as the “Control of Vari-ables” strategy). Here, we demonstrate that this strategy is notalways the most efficient method for learning. Using an opti-mal learner model which aims to minimize the number of tests,we show that when a causal system is sparse, that is, whenthe outcome of interest has few or even just one actual causeamong the candidate variables, it is more efficient to test mul-tiple variables at once. In a series of behavioral experiments,we then show that people are sensitive to causal sparsity whenplanning causal experiments.

What is the best way of discovering the underlying structure of a causal system composed of multiple variables? One prominent idea is that learners should manipulate each candidate variable in isolation to avoid confounds (sometimes known as the control of variables [CV] strategy). We demonstrate that CV is not always the most efficient method for learning. Using an optimal actor model, which aims to minimize the average number of tests, we show that when a causal system is sparse (i.e., when the outcome of interest has few or even just one actual cause among the candidate variables), it is more efficient to test multiple variables at once. Across a series of behavioral experiments, we then show that people are sensitive to causal sparsity and adapt their strategies accordingly. When interacting with a dense causal system (high proportion of actual causes among candidate variables), they use a CV strategy, changing one variable at a time. When interacting with a sparse causal system, they are more likely to test multiple variables at once. However, we also find that people sometimes use a CV strategy even when a system is sparse.

Little is known about how children generate options for taking action in familiar situations or how they select which action option to actually perform. In this article, we explore the interplay between option generation and selection from a developmental perspective using sports as a testbed. In a longitudinal design with four measurement waves, we asked 6- to 13-year-old children (N = 73) to generate and select action options in a soccer-related task. Children generated and selected options in accordance with the predictions of the take-the-first heuristic, which served as a theoretical starting point: They generated only a few options in decreasing order of validity (i.e., better options were generated earlier) and selected the first options they had generated. Older children selected the first option generated more often than younger children and generated options faster. Longitudinal effects revealed that both age groups generated fewer options and faster across waves. Time limitation fostered fewer and higher quality options being generated and selected. Overall, our results highlight the importance of considering the predecisional process of option generation to deepen our understanding of developmental changes in decision-strategy use. Future research directions and implications for children's real-life decision making are discussed.

Research on the development of active learning and information search behaviors has been growing rapidly, drawing interest from multiple disciplines, from developmental psychology to cognitive science and artificial intelligence. These different perspectives can open pathways to understanding how preschool‐age children grow into adaptive and efficient active learners. However, the lack of a shared vocabulary, operationalizations, and research paradigms has led to limited cross‐talk and some conflicting findings. In this article, we advocate for using a shared operationalization of a "good" information‐search strategy, as a function of its efficiency and effectiveness within a given ecology, based on the information‐theoretic measure of expected information gain and observed behavioral outcomes, respectively. We also discuss factors that should be considered when designing experiments that examine children's information‐search competence, specifically, using formal models as performance benchmarks and accounting for children's prior knowledge, assumptions, and self‐generated goals. [ABSTRACT FROM AUTHOR]

This paper reports an experiment testing whether volitionalcontrol over the presentation of stimuli leads to enhancedrecognition memory in 6- to 8-year-old children. Childrenwere presented with a simple memory game on an iPad.During the study phase, for half of the materials childrencould decide the order and pacing of stimuli presentation(active condition). For the other half of the materials, childrenobserved the study choices of another child (yokedcondition). We found that recognition performance was betterfor the objects studied in the active condition as compared tothe yoked condition. Furthermore, we found that the memoryadvantages of active learning persisted over a one-week delaybetween study and test. Our results support pedagogicalapproaches that emphasize self-guided learning and show thateven young children benefit from being able to control howthey learn.

Like scientists, children and adults learn by asking questionsand making interventions. How does this ability develop? Weinvestigate how children (7- and 10-year-olds) and adultssearch for information to learn which kinds of objects share anovel causal property. In particular, we consider whetherchildren ask questions and select interventions that are asinformative as those of adults, and whether they recognizewhen to stop searching for information to provide a solution.We find an anticipated developmental improvement ininformation search efficiency. We also present a formalanalysis that allows us to identify the basis for children’sinefficiency. In our 20-questions-style task, children initiallyask questions and make interventions no less efficiently thanadults do, but continue to search for information past the pointat which they have narrowed their hypothesis space to asingle option. In other words, the performance change fromage seven to adulthood is due largely to a change inimplementing a “stopping rule”; when considering only theminimum number of queries participants would have neededto identify the correct hypothesis, age differences disappear.