This chapter reviews the development of looming motion perception in infancy, tracing the progression from early behavioral defensive behaviors, such as blinking, to contemporary EEG-based brain-computer interface approaches. Grounded in ecological optics, it examines how infants learn to detect approaching objects that signal impending collision and how this skill becomes increasingly refined with age and locomotor experience. Behavioral studies show that younger infants tend to rely on simpler cues, such as the visual angle of the approaching object, whereas older infants and crawlers increasingly use time-to-collision information for more accurate responses. High-density EEG studies extend these findings by revealing developmental changes in parieto-occipital brain activity, theta and alpha synchronization, and more efficient neural timing strategies. The chapter also highlights differences between full-term and preterm children, with preterm children showing poorer prospective control and delayed visual motion processing, resulting in sustained vulnerabilities in dorsal-stream function. Recent work further demonstrates that EEG-based BCI methods can detect infant perceptual responses to looming stimuli with promising classification accuracy, pointing toward future applications in early cognitive monitoring and intervention. Overall, the chapter presents looming perception as a powerful window into the developing perception-action system and a potential tool for identifying atypical developmental trajectories early.
Children's cognitive control increases dramatically across development. These changes reflect a confluence of what children can do (their control capacities), what they know how to do (task understanding, familiarity, and strategies), and what they are willing to do (their propensity to allocate, sustain, or withdraw control depending on the task and context). This chapter focuses on children's willingness to engage control, which has received comparatively less attention even though it has significant implications for understanding performance on cognitive control assessments and links with life outcomes, and for designing interventions to support children's cognitive control. We frame willingness within Expected Value of Control theory, which describes cognitive control allocation as the result of a cost-benefit decision process that integrates subjective effort costs, beliefs about efficacy, and the expected value of outcomes. Children calibrate their cognitive control engagement according to costs, efficacy, and expected value. These factors change based on external contexts, internal states, and developmental processes, while nonetheless showing stability within individuals. Cultural experiences provide one external context that can influence when control feels meaningful and worthwhile, such that cross-cultural variations in cognitive control can also be understood in terms of willingness. Together, this framing has key implications for interpreting why cognitive control predicts life outcomes: Children who perform well on cognitive control tasks may also be more consistently willing to engage control across real-world contexts, and some children may reliably experience contexts that encourage greater willingness, suggesting that the predictive validity of cognitive control task partly reflects stable differences in willingness in addition to control capacities.
A longstanding assumption is that children acquire language mainly from adult models, but newer research suggests this cannot be correct. Children experience a great deal of child speech as well, particularly across a broader sample of cultures. They enjoy listening to child speech. They can comprehend and learn from child speech. This chapter discusses acoustic properties unique to child speech; argues for its prevalence and importance as an input source to other children; outlines an account that children recognize speech contingent on the speaker's age and identity; and discusses some outstanding questions about children's comprehension of and learning from child speech. The goal is to convince readers that there is an enormous gap in our knowledge of a major component of children's language input, which shapes both child comprehension and production.
This chapter provides a developmental perspective on "educational opportunities" from early childhood through adolescence. Educational opportunities have gained increasing attention from scholars in multiple disciplines. Yet a developmental perspective has been lacking. We offer an operational definition of educational opportunities that is grounded in developmental theory, and review milestones in children's growth that are key to the study of educational opportunities. In turn, we highlight when, why, and how key educational opportunities unfold inside and outside of school, including at home, in early education, through afterschool activities, and in neighborhoods. We also highlight theories on the accumulation of opportunities across developmental stages and settings, concluding with research recommendations and policy considerations.
Typical visual working memory tasks involve many trials of rapid presentations of simple stimuli to be remembered across short delays. This is rarely how memory is used in the real world: we are almost always able to resample information by making an action just-in-time (eye or head movements) or create one if our memory is not sufficient (cognitive offloading). Deciding how often to sample vs. how much to remember depends on the costs of each option. In this chapter, we bring together work on active online visual memory, research on the sampling-remembering trade-off, cognitive offloading, and for a broader modeling context, resource rational models, which each provide important building blocks for developing a new, ethological approach to the study of working memory. This new approach not only centers new problems in the field, but also provides new insights into the mechanisms of developmental change and individual differences.
Judgments of whether events are possible or impossible matter when people choose goals and decide what to believe, yet preschoolers and even school-aged children make surprising errors when judging whether events are possible. In contrast with the popular view of children entertaining the possibility of the marvelous and fantastic, children often deny that unusual events and impermissible actions can happen in real life. They even sometimes deny these events can happen in stories and dreams. In this chapter, we review experimental work on these surprising possibility denials, including studies that have attempted to bring children to recognize these events as possible. We also review work examining other aspects of how young children judge whether events are possible, including their judgments that some impossible events are more impossible than others. We review theories that may account for young children's possibility denials and outline remaining questions for future research.
Humans are fundamentally cooperative beings whose survival and success depend on prosocial behavior, including with non-kin and strangers. Even young children engage in prosocial behaviors such as helping, sharing, and comforting that are often intrinsically motivated. While evolutionary accounts explain why prosociality emerged, they leave open the proximate question of what motivates individuals, including young children, to engage in costly actions that benefit others. This chapter argues that evolution has equipped humans with affective mechanisms that motivate us to initiate, sustain, and repair prosocial interactions from early in development. Sympathy motivates us to initiate prosocial responses by creating concern for those in need. Warm glow (positive affect from acting prosocially) and gratitude sustain cycles of prosociality within and between individuals by making cooperation emotionally rewarding and motivating reciprocity. Guilt and forgiveness help repair damaged relationships, with guilt prompting transgressors to make amends and forgiveness enabling victims to restore trust and cooperative bonds. Drawing on behavioral, physiological, and cross-cultural research, we show that these mechanisms emerge within the first years of life and become increasingly flexible and context-sensitive with development. We conclude by outlining a developmental proposal for the emergence of prosocial emotions, discussing methodological and cultural challenges, and highlighting directions for future research. Together, the evidence suggests that early-emerging affective processes work in concert to enable even young children to navigate the cooperative landscape that defines human social life.
In the dynamic and complex process of language development, some populations, like preterm-born children, are at risk of developing language delays. This review addresses possible language-specific (i.e., prosody, word recognition, gesture) and domain-general (i.e., motor, visual perception and attention, joint attention, EFs) child-related precursors to language development of preterm children in the first three years of life. We examine how each factor can contribute to the development of language in this population. We end our review by providing suggestions for future theoretical frameworks and empirical research.
The efficient coding hypothesis states that biological perceptual systems adapt to the statistics of the sensory signals arising in their natural environments. Because infants actively shape these sensory statistics through their own behavior, perception and action form a tightly coupled developmental loop. We present an integrative review of recent extensions of efficient coding into the domain of active perception, with a particular focus on the Active Efficient Coding (AEC) framework. AEC explains the development of perceptions and actions through a unifying computational principle: encoding sensory observations as efficiently as possible. We introduce a novel formalism that frames AEC within rate-distortion theory, interpreting active perception as a problem of lossy compression. We then re-examine AEC models of the autonomous learning and calibration of active binocular vision in a simulated infant embodiment. This work shows how active perception can emerge without external supervision, providing a foundation for the development of complex behaviors and higher cognition.
Human cognitive development unfolds through complex, non-linear interactions among the brain, body, and environment, producing diverse developmental trajectories across individuals and contexts. Capturing such variability requires a generative account that explains how cognition emerges and reorganizes over time, rather than models that describe isolated functions or static developmental stages. In this chapter, we propose Embodied Predictive Processing as a unifying generative framework for cognitive development. Building on predictive processing as a principle of brain function, we extend it to encompass bodily dynamics and social interaction, conceptualizing development as distributed prediction error minimization across brain-body-environment systems. Through integrative evidence from developmental studies and constructive robotic models, we demonstrate how brain-body co-development, body-grounded multimodal integration, and socially coordinated interaction jointly give rise to both developmental change and individual diversity. These findings position embodied predictive processing not only as a theoretical framework for understanding cognitive development, but also as a mechanistic and empirically testable generative account of developmental dynamics.
Human fetuses and young infants behave spontaneously before the emergence of explicit goals and rewards. These behaviors, though appearing random at first glance, give rise to regulated sensorimotor interactions through the developing body and its environment, thereby contributing to neural circuit maturation and later development. In this chapter, we propose a framework that treats early development as the progressive structuring of sensorimotor information. We call this emerging regularity the sensorimotor information structure (SMIS), defined as the dynamic pattern of information flow among motor output and sensory input such as muscle activity, proprioception, touch, and vision. We review evidence that spontaneous activity and activity-dependent plasticity play central roles in early development and that embodiment provides strong constraints that organize these signals. We then present an empirical approach to early spontaneous movements that combines quantitative analysis with a biologically grounded musculoskeletal model. This approach enables the estimation of whole-body muscle activity and proprioceptive signals, revealing modular sensorimotor organization and recurrent state transitions during spontaneous movements. These SMIS patterns exhibit developmental changes over the first three months after birth, reflecting spontaneous exploration, which we term sensorimotor wandering. To move from description to mechanism, we introduce a constructive approach based on an embodied simulation that integrates a musculoskeletal model, multimodal sensory models, and spontaneous spinal activity, enabling key aspects of SMIS to be reproduced and their roles to be examined. Finally, we propose that early SMIS can serve as a developmental prior for later learning and provide a computational bridge from spontaneous movement to the emergence of agency and goal-directed behavior.
This paper reviews research on problem solving from infancy through childhood, with particular attention to tool use, means-end behavior, planning, and action selection. Across classic and recent studies, we show that developmental progress is not captured fully by whether children succeed on a task, but also by how they solve it in real time. Findings from behavioral, kinematic, eye-tracking, neural, and computational work suggest that successful problem solving depends on the timing and coordination of multiple processes, including information gathering, motor preparation, and online adjustment. The chapter also examines how exploration, variability, and low-cost error support learning and generalization across contexts. We conclude by arguing for a more integrated developmental science of problem solving that combines structured experiments, naturalistic observation, and embodied computational models to explain how flexible problem-solving skills emerge over development and in everyday settings.
Decades of research into infants' early word productions has shown that the early vocabulary includes systematic templates, in which children use a small number of word-based patterns to produce many different words, both accurately and inaccurately. Recent studies have attempted to test for this early systematicity across large samples of data using network modelling to identify similarity across the early vocabulary. In this chapter, we draw together the traditional 'by hand' approach with a network approach to determine whether computational analysis of early production data might help us understand the nature of early production. We draw on data from 29 infants acquiring one of six languages to compare these methodological approaches. We discuss how they complement one another and address limitations.
One of the central goals of scientific inquiry is to understand the mechanism underlying the observed phenomena. Scientific methods (such as experimental design, measurement theory, and statistics) help solving these problems. Like other methods, computational modeling, the subject of this chapter, offers a collection of tools that advance scientific understanding. We consider five case studies in which computational modeling used to examine issues in infant memory for specific and general information, attention and category learning, memory development, and decision making. We demonstrate how computational modeling may advance our understanding of developmental changes across these broad domains in developmental science. We discuss how modeling complements experimentation and how it allows researchers to test hypotheses about theoretical variables giving rise to various data patterns.
Economic inequality has reached extreme levels worldwide, and children will bear its burden. How do children perceive and explain economic inequality? And how do these perceptions and explanations, in turn, shape their development? We address these questions by proposing an integrative social-cognitive developmental framework. According to the framework, when children enter a new context, they consider a series of questions: (1) Is there inequality? (2) What causes inequality? (3) What does inequality say about me? (4) What does inequality say about us? (5) And do I address inequality? We suggest that children’s perceptions and explanations of economic inequality—above and beyond exposure to the inequality in itself—have profound consequences for mental health, social relationships, motivation, and egalitarianism (e.g., whether children rectify or perpetuate inequality). Children’s perceptions and explanations, then, might serve as levers for intervention to help children navigate the current age of inequality.
This chapter examines the relationship between curiosity and metacognition as critical drivers of autonomous and self-regulated learning. We synthesize recent research to propose a unified framework integrating behavioral, computational, and psychoeducational dimensions, arguing that curiosity-the intrinsic drive to acquire new knowledge-relies fundamentally on metacognitive monitoring and control. From an educational perspective, we evaluate interventions designed to enhance curiosity in classroom settings. While promising, our review indicates that these interventions yield mixed results, often proving differentially effective for struggling learners, thereby underscoring the necessity for approaches tailored to individual profiles. Finally, we address the paradigm shift introduced by Generative AI. While Large Language Models (LLMs) offer unprecedented scalability for personalized inquiry, we argue that their default interaction modes pose significant risks to the dynamics of curiosity-driven learning. To mitigate these challenges, we review strategies to transform AI from a potential cognitive shortcut into a powerful partner for sustained epistemic development.
Learning to understand speech appears almost effortless for typically developing infants, yet from an information-processing perspective, acquiring a language from acoustic speech is an enormous challenge. This chapter reviews recent developments in using computational models to understand early language acquisition from speech and audiovisual input. The focus is on self-supervised and visually grounded models of perceptual learning. We show how these models are becoming increasingly powerful in learning various aspects of speech without strong linguistic priors, and how many features of early language development can be explained through a shared set of learning principles-principles broadly compatible with multiple theories of language acquisition and human cognition. We also discuss how modern learning simulations are gradually becoming more realistic, both in terms of input data and in linking model behavior to empirical findings on infant language development.
This manuscript examines how home visiting by educators aligns with and informs culturally responsive and sustaining pedagogy (CR-SP) through the lens of developmental theory. Drawing on a synthesis of multiple qualitative studies, we analyze evidence of home visiting's influence on four dimensions of CR-SP: teachers' beliefs, dispositions, instructional practices, and integration of family and community knowledge. We find the strongest alignment between home visiting and teacher beliefs, including enhanced self-reflection, cultural competence, and asset-based thinking. While evidence of impact on responsive instructional practices is emerging, findings show limited influence on teachers' sociopolitical consciousness, curriculum adaptation, or integration of family knowledge into classroom environments. To guide our analysis, we apply asset-based educational frameworks alongside developmental theories, including Bronfenbrenner's ecological systems theory, Vygotsky's sociocultural theory, and developmental systems theory. This interdisciplinary approach highlights home visiting as a potentially powerful relational and developmental bridge between families and schools, while also underscoring the need for structural supports-such as targeted professional learning, reflection tools, and policy guidance-to realize its full promise. We conclude with implications for theory, research, and practice, calling for more robust and longitudinal inquiry into how home visiting can support equity-driven, developmentally aligned teaching across varied educational contexts.
