This chapter provides an overview on the use and validity of student samples in the behavioral and social sciences. In some instances, data collected from students can be of limited value or even inappropriate; however, in other cases, this approach provides useful data. I offer three general ways to evaluate the use of student samples. First, consider the research design. Descriptive studies that rely on students to draw inferences about the overall population are likely problematic. Second, statistical controls such as multivariate analyses that adjust for other factors may reduce some of the biases that may be introduced through sampling. Third, consider the theorized mechanism – a clear theoretical mechanism that does not vary based on the demographics of the sample allows us to put more faith in constrained samples. Despite these approaches, and regardless of our methods, statistics, and theoretical mechanism, we should be cautious with generalizability claims.

The foundations of the General Theory are described at a conceptual level of understanding. The basic terminology of memory research is presented. The main focus is on the proposed memory structures and the control processes that guide the flow of information though them. The assumed memory structures are sensory registers, the short-term store, and the long-term store. Control processes are models of the flow information through these structures to support the performance of tasks that lead to the achievement of a subjective goal. Empirical support for the fundamental assumptions of the General Theory are provided.

How can we get data into a network format? This chapter briefly describes the basics and introduces the main kinds of networks we encounter in network science. It also shows how to take data that may not obviously present itself as a network and transform it into a network format.

This chapter profiles a description of the paths that shaped research on parental monitoring and adolescents’ information management. As these areas developed, accounts of the interplay between parents’ attempts to regulate their adolescents’ behavior and adolescents’ responses grew in breadth and in detail. In this chapter, we introduce readers to the constructs and frameworks that have come to represent monitoring and information management research, including related topics that have been probed in diverse attempts to better understand parenting and adolescents’ behaviors. We track developments in the field from the initial challenges to research on parental monitoring, to the rapid shift emphasizing adolescents’ information management and challenging assumptions about monitoring specifically and parental control more generally. Finally, we not how these broad examinations of monitoring and parental control have led to theory development and offer suggestions for continuing these efforts.

The three initial sources of this book are a Marxist dialectical theory of concepts, an emphasis on the collective and historically evolving nature of concepts, and the turn toward everyday practical activities as legitimate arenas of thinking and conceptualizing. The resources for integrating these sources into a coherent framework come from cultural-historical activity theory and the theory of expansive learning, applied and developed in interventionist studies of various types of work and organizations, from cleaning services to hospitals and courts of law to factories and banks. In recent years, this empirical basis has been broadened to include social movements and societal change efforts championed by broad-based coalitions. The primary focus of this book is on collective creation of culturally new concepts in the wild. The starting point is the realization that culturally novel concepts are not only created by scientists but also by people struggling with persistent problems and challenges in all walks of life. We are all involved in the creation of new concepts. This has been largely omitted by scholars of concept formation. Taking this seriously means that we need to reexamine and perhaps revamp aspects of our basic understanding of concepts.

This chapter presents an overview of the book and positions it in the context of the development of expertise and the pursuit of excellence. It presents the historical context of the development of expertise and the theoretical context of the study of expertise.

This chapter lays out an argument for why a new approach to understanding children’s development in school is necessary. It first reviews the limitations of research with young children in Head Start programs and elementary schools serving large percentages of children in poverty within the US. These approaches are critiqued in light of findings that challenge the validity and reliability of teacher report and other commonly used measures. Issues of bias and cultural relevance in ways of defining the development of children placed at risk are discussed. Promising insights from research using alternative frames and units of analysis are then contrasted with those of large-scale studies. Finally, the rationale for focusing on collaborative competence as a key driver of development throughout childhood is elaborated. The potential of a developmental sequence of collaboration beginning with preschooler free play and leading into more complex collaborations during elementary school is proposed. Summaries of each chapter and how it contributes to this argument is provided.

We explain how a systems conceptualization scaffolds our understanding of the development of coping. First, we describe five developmental systems ideas that open pathways for examining age-graded changes and transformations in coping from infancy through adolescence. A systems conceptualization: (1) defines coping as action regulation under stress; (2) ties coping to basic adaptive tasks; (3) locates the study of coping between regulation and resilience; (4) views coping as hierarchically structured families of action types; and (5) holds that coping comprises an integrated multi-level system that emerges on the levels of action but incorporates both underlying neurophysiological and psychological subsystems and overarching interpersonal and societal contexts. Second, we describe six ways the coping system undergoes successive reorganizations as the coping equipment available to individuals changes with age. We show how children are active participants in the construction of coping tools, the emergence and consolidation of which depend on social partners and encounters with stressors. At every age, qualitative developmental shifts allow coping appraisals and actions to become more effectively calibrated to internal capacities and external affordances, better coordinated with other people, and guided by increasingly autonomous values and goals. We end with implications of this view for translation to practice.

This chapter provides an introduction to the topic of this handbook: prosociality and its development across the first two decades of life, as well as causes, correlates, and consequences across the lifespan. We begin by providing conceptualizations of prosociality and derive an understanding of what prosociality is, and how it is different from related constructs. We then describe core theoretical accounts on prosocial development. Selected historical attempts to understand prosociality in humans are reviewed, along with historical turning points in early theorizing on prosocial development. Last, a brief summary of select mechanisms underlying the development of prosociality is presented.

Vision science combines ideas from physics, biology, and psychology. The language and ideas of mathematics help scientists communicate and provide an initial framing for understanding the visual system. Mathematics combined with computational modeling adds important realism to the formulations. Together, mathematics and computational tools provide a realistic estimate of the initial signals the brain analyzes to render visual judgments (e.g., motion, depth, and color). This chapter first traces calculations from the representation of the light signal, to how that signal is transformed by the lens to the retinal image, and then how the image is converted into cone photoreceptor excitations. The central steps in the initial encoding rely heavily on linear systems theory and the mathematics of signal-dependent noise. We then describe computational methods that add more realism to the description of how light is encoded by cone excitations. Finally, we describe the mathematical formulation of the ideal observer using all the encoded information to perform a visual discrimination task, and Bayesian methods that combine prior information and sensory data to estimate the light input. These tools help us reason about the information present in the neural representation, what information is lost, and types of neural circuits for extracting information.