Children adapt their information search to goal and statistical structure of a problem.

To be efficient, problem solvers need to be adaptive, tailoring their information search to the specific problem at hand. Across two studies, we investigated the emergence and early development of children's ability to adapt their information search to a given goal (Studies 1 and 2) and to the statistical structure of the problem space (Study 2) to maximize effectiveness. In Study 1, 3-6-year-olds (n = 105) decided which of two cues to look up, the arms or the legs of two monsters, to predict the winner of a throwing or jumping challenge, knowing that monsters with long arms were good throwers and those with long legs were good jumpers. Children's ability to adaptively select relevant information and tailor their search to the goal increased with age, surpassing chance level between the ages of 4 and 5. Study 2 (7-14-year-olds and adults, n = 175) demonstrated this competence in a more complex task, additionally investigating whether children tailor their search to the statistical structure, that is, the distribution of cue values, in their search environment (e.g., how common long legs are). The results suggest high reliability in ignoring irrelevant cues (confirming the results from Study 1) and developmental patterns in children's preferential treatment of cues of differing statistical frequency. Together, these studies contribute to the literature on information search adaptiveness by tracing for the first time the emergence and developmental trajectory of children's ability to tailor predecisional search to the changing goals and environmental resources of the problem at hand. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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'.

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.

The differential impact of active learning on children's memory.

Successful active learning has often been quantified with respect to either the efficiency of information search or the accuracy of subsequent recall. In this article, we explored the hypothesis that children's memory is influenced by the types of information search strategies they implement, which may emphasize different aspects of the task stimuli. As a consequence, younger children's well-documented search inefficiency may turn out to be advantageous and result in better memory for some aspects of the task. In the current experiment, 5- to 10-year-old children (N = 124) played (active condition) or observed an agent play (passive condition) 20-questions games, and were then tested for their memory of several different aspects of the game both immediately after and a week later. Children showed overall improved recall in the active condition. Search efficiency was positively related to recall of the game's solution, but did not significantly impact performance on the other memory tests. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Young children's adaptive partner choice in cooperation and competition contexts.

Choosing adequate partners is essential for cooperation, but how children calibrate their partner choice to specific social challenges is unknown. In two experiments, 4- to 7-year-olds (N = 189, 49% girls, mostly White, data collection: 03.2021-09.2022) were presented with partners in possession of different positive qualities. Children then recruited partners for hypothetical tasks that differed with respect to the quality necessary for success. Children and the selected partner either worked together toward a common goal or competed against each other. From age 5, children selectively chose individuals in possession of task-relevant qualities as cooperative partners while avoiding them as competitors. Younger children chose partners indiscriminately. Children thus learn to strategically adjust their partner choice depending on context-specific task demands and different social goals.

Preschoolers search longer when there is more information to be gained.

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. RESEARCH HIGHLIGHTS: Across three studies, we tested whether information gain itself acts as an internal reward and suffices to motivate preschoolers' actions. We measured preschoolers' persistence when searching for an object behind a series of doors, manipulating the uncertainty about which specific object was hidden. We found that preschoolers were more persistent when there was higher uncertainty, and therefore, more information to be gained with each action. Our results highlight the importance of research on artificial intelligence to invest in curiosity-driven algorithms.

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.

Developmental changes in exploration resemble stochastic optimization.

Human development is often described as a 'cooling off' process, analogous to stochastic optimization algorithms that implement a gradual reduction in randomness over time. Yet there is ambiguity in how to interpret this analogy, due to a lack of concrete empirical comparisons. Using data from n = 281 participants ages 5 to 55, we show that cooling off does not only apply to the single dimension of randomness. Rather, human development resembles an optimization process of multiple learning parameters, for example, reward generalization, uncertainty-directed exploration and random temperature. Rapid changes in parameters occur during childhood, but these changes plateau and converge to efficient values in adulthood. We show that while the developmental trajectory of human parameters is strikingly similar to several stochastic optimization algorithms, there are important differences in convergence. None of the optimization algorithms tested were able to discover reliably better regions of the strategy space than adult participants on this task.

Children seek help based on how others learn.

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.

Smart or just lucky? Inferring question-asking competence from strategies' efficiency versus effectiveness.

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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Children's active physical learning is as effective and goal-targeted as adults'.

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. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

Finding the (most efficient) way out of a maze is easier than asking (good) questions.

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. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Developmental Trajectories in the Understanding of Everyday Uncertainty Terms.

Dealing with uncertainty and different degrees of frequency and probability is critical in many everyday activities. However, relevant information does not always come in the form of numerical estimates or direct experiences, but is instead obtained through qualitative, rather vague verbal terms (e.g., "the virus often causes coughing" or "the train is likely to be delayed"). Investigating how people interpret and utilize different natural language expressions of frequency and probability is therefore crucial to understand reasoning and behavior in real-world situations. While there is considerable work exploring how adults understand everyday uncertainty phrases, very little is known about how children interpret them and how their understanding develops with age. We take a developmental and computational perspective to address this issue and examine how 4- to 14-year-old children and adults interpret different terms. Each participant provided numerical estimates for 14 expressions, comprising both frequency and probability phrases. In total we obtained 2856 quantitative judgments, including 2240 judgments from children. Our findings demonstrate that adult-like intuitions about the interpretation of everyday uncertainty terms emerge fairly early in development, with the quantitative estimates of children converging to those of adults from around 9 years on. We also demonstrate how the vagueness of verbal terms can be represented through probability distributions, which provides additional leverage for tracking developmental shifts through cognitive modeling techniques. Taken together, our findings provide key insights into the developmental trajectories underlying the understanding of everyday uncertainty terms, and open up novel methodological pathways to formally model the vagueness of probability and frequency phrases, which are abundant in our everyday life and activities.

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.

How is the hypothesis space represented? Evidence from young children's active search and predictions in a multiple-cue inference task.

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. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Movement Matters! Understanding the Developmental Trajectory of Embodied Planning.

Human motor skills are exceptional compared to other species, no less than their cognitive skills. In this perspective paper, we suggest that "movement matters!," implying that motor development is a crucial driving force of cognitive development, much more impactful than previously acknowledged. Thus, we argue that to fully understand and explain developmental changes, it is necessary to consider the interaction of motor and cognitive skills. We exemplify this argument by introducing the concept of "embodied planning," which takes an embodied cognition perspective on planning development throughout childhood. From this integrated, comprehensive framework, we present a novel climbing paradigm as the ideal testbed to explore the development of embodied planning in childhood and across the lifespan. Finally, we outline future research directions and discuss practical applications of the work on developmental embodied planning for robotics, sports, and education.

Development of directed and random exploration in children.

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.

Memory enhancements from active control of learning in children with autism spectrum disorder.

LAY ABSTRACT: Research with adults and typically developing children has shown that being able to actively control their learning experience, that is, to decide what to learn, when, and at what pace, can boost learning in a variety of contexts. In particular, previous research has shown a robust advantage of active control for episodic memory as compared with conditions lacking this control. In this article, we explore the potential of active control to improve learning of 6- to 12-year-old children diagnosed with autism spectrum disorder. We presented them with a simple memory game on a touchscreen tablet, in which children were asked to recall as many of the presented objects as possible. For half of the objects, children 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). We found that recognition memory was more accurate when children could actively control the order, pace, and frequency of the study experience, even after a week-long delay. We discuss how teachers and educators might promote active learning approaches in educational and pedagogical applications to support inclusive learning.