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date: 03 June 2020

Developmental Psychology: A New Synthesis

Abstract and Keywords

This Handbook surveys what is now known about psychological development from birth to biological maturity, and it reflects the emergence of a new synthetic approach to developmental science that is based on several theoretical and methodological commitments. According to this new view: (1) psychological phenomena are usefully studied at multiple levels of analysis; (2) psychological development depends on neural plasticity, which extends across the lifespan; (3) the effect of any particular influence on psychological development will depend on the context in which it occurs; (4) psychological phenomena, and developmental changes in psychological phenomena, typically reflect multiple, simultaneous causal influences; and (5) these causal influences are often reciprocal. Research based on this synthetic approach provides new insights into the way in which processes operating at many levels of analysis (cultural, social, cognitive, neural, and molecular) work together to yield human behavior and changes in human behavior.

Keywords: ontogeny, developmental systems, multiple levels of analysis, epistemology, ontology, reciprocal causality, philosophy of science

Developmental Psychology: A New Synthesis

Psychologists study many things, including social, cognitive, and emotional functions and their neural correlates, various aspects of subjective experience (e.g., of perception and emotion), and individual differences in personality. For many psychologists, however, observable behavior is the dependent variable of choice: it is considered a level of analysis of singular significance.

That said, psychologists have generally come to realize that although behavior may be relatively easy to measure, it is much more difficult to understand. Consider, for example, that our behavior is obviously influenced by what we hope and fear. Hope and fear are psychological states with clear emotional and cognitive aspects (e.g., involving imagination and memory). Psychological states like these are a consequence, in part, of a history of reinforcement predicated on fundamental approach and avoidance tendencies, and relying on highly conserved mechanisms of neuronal adaptation operating via genetic activity; in this way, our behavior is linked to activity in the brain, genetic and epigenetic processes, and to our basic subjective experiences of pleasure and pain. It is also linked to our sociocultural context, including our culture, our socioeconomic status (SES), and our relationships with peers and parents. In short, behavior can be described at many levels of analysis (e.g., phenomenological, functional, neural, and genetic levels), and explaining it requires understanding how processes operating at all these levels of analysis contribute to behavior.

Developmental psychology, which aims to understand the history, origins, and causes of behavior and age-related changes in behavior, clearly recognizes (p. 4) the wide range of relevant levels of analysis, as any single issue of Child Development or Developmental Psychology will reveal. Beyond this, however, developmental psychology is now in the process of constructing a complex multilevel characterization of behavior as it unfolds in time across a range of time scales, from the milliseconds of reaction time (RT) to the days and weeks of microgenetic studies to the years of childhood, the decades of the human lifespan, and even beyond, to multiple generations. Behavior, on this view, is embedded within what is essentially a dynamic system of relations extending deep within individuals, and deep without. This Handbook is intended to survey what is now known about psychological development, from birth to biological maturity, and in so doing, to highlight the extent to which the most cutting-edge developmental science reflects a new kind of intellectual synthesis: one that reveals how processes operating at many levels of analysis (cultural, social, cognitive, neural, and molecular) work together to yield human behavior and changes in human behavior.

Understanding is often accomplished through a sequential process of analysis followed by synthesis: we tear things apart, scrutinize the pieces in relative isolation, and only then begin to explore how the pieces interact to comprise a whole. Research on human development is now emerging from a protracted period of analysis during which various aspects of human function were studied one piece at a time.

(Zelazo, Chandler, & Crone, 2010, p. 3)

This quotation was intended to describe the circumstances surrounding the birth of a new scientific specialization or subfield: developmental social cognitive neuroscience. The name of this new subfield is a lengthy concatenation of adjectives that calls attention to the multidimensionality of its subject matter, and to the fact that it is simultaneously a subfield of several larger fields or disciplines, including developmental psychology; social, cognitive, and personality psychology; social, cognitive, and affective neuroscience; and developmental psychopathology. The very rapid growth of this hyperinterdisciplinary subfield (or sub-subfield) (e.g., see chapters in Zelazo et al., 2010) speaks to the current collective enthusiasm for putting pieces back together. Understanding is often accomplished through a sequential process of analysis followed by synthesis, although of course any given synthesis also provides a basis for reanalysis.1

Collectively, the chapters in this volume provide a comprehensive survey of the field that illustrates how, through interdisciplinary collaborations and what Seth Pollak has called “methodological promiscuity,” developmental psychology has become increasingly well integrated with several cognate disciplines, including neuroscience, molecular genetics/epigenetics, and cognitive science, among other fields of inquiry. Co-occurring with this integration has been the continuing differentiation of developmental psychology into increasingly specialized (yet increasingly interdisciplinary) investigations. As will be explored in the balance of this chapter, this new synthetic approach supports a reanalysis of fundamental assumptions underlying research in developmental psychology.

There are probably many reasons for the emergence of this synthesis at this particular period in our history, and I will leave it to future historians to explore them. One obvious, practical impetus, however, has been the availability of new technologies and techniques (e.g., for studying complex interactions among genes and environment, for measuring neural activity in young children, and for modeling developmental change using sophisticated computational techniques). These methods have made it possible to investigate many more aspects of human behavior and to explore them all at new levels of analysis—and often at multiple levels simultaneously. Doing so has made it clear that no single level of analysis will suffice; the interactions among levels cannot be ignored, nor can the contexts in which these interactions occur. The resulting research supports a new view of development that is much more comprehensive (and far more complex) than was possible even in the late 1990s, when developmental psychologists still generally focused on one domain of behavior (e.g., theory of mind) at a time, considered it at one level of analysis (e.g., cognitive), in one age range (e.g., the preschool years), and studied it mainly in one context (e.g., a university research laboratory in late-twentieth-century North America), often relying on variations of a single procedure or set of measures (e.g., the false belief task; see Astington & Hughes, this volume 2, for a review of this research and for a more contemporary perspective).

Of course, this new synthetic perspective on human behavior isn’t entirely new: it has numerous historical precursors (e.g., in the work of Hegel, Dewey, Baldwin, and Piaget), and aspects of it were widely called for by developmental scholars writing in the latter half of the twentieth century (see Collins & Hartup, this volume 1). Sameroff and Chandler (1975), for example, articulated (p. 5) a transactional model of developmental risk for poor outcomes, according to which development, healthy or unhealthy, can be described as a process that results from the mutual and ongoing influence of the child and the child’s environment (including caretaking and other aspects of the social environment). As they put it (p. 234), “The child alters his environment and in turn is altered by the changed world he has created.” From this perspective, development cannot be reduced to a simple interaction among influences because the nature of the influences themselves changes over time, as does the nature of the interaction among them.

Gottlieb (1992) also emphasized the bidirectional (“coactive”) nature of interactions among a wide range of endogenous and exogenous influences on behavior and provided a compelling illustration of how new forms of behavior emerge probabilistically from these interactions.Figure 1.1 nicely captures the notion of multiple simultaneous levels of analysis, and it implies (or is at least consistent with) a view of the developing human being as a multidimensional phenomenon that is simultaneously behavioral and neural, cognitive and emotional, and individual and social, with events at any of these levels affecting all other levels. One could not, on this view, instantiate any single polarity of any of the three dualistic distinctions just mentioned without necessarily also instantiating all five of the others.

Developmental PsychologyA New Synthesis

Figure 1.1. Probabilistic-epigenetic framework. Depiction of the bidirectional and coactional nature of genetic, neural, behavioral, and environmental influences over the course of individual development. Reprinted with permission from Individual Development and Evolution: The Genesis of Novel Behavior (p. 186) by Gilbert Gottlieb, 1992, New York: Oxford University Press. Copyright 1992 by Oxford University Press, Inc.

There are many more examples of early developmental systems views to draw from (e.g., see Bronfenbrenner, 1979; Lerner, 1991; Oyama et al., 2001; see Bjorklund, this volume 1; Cicchetti, this volume 2; Lickliter, this volume 1; Moore, this volume 1), and many more relevant influences on current thinking about developmental psychology. These include the following: connectionist models of behavior (Rumelhart, McClleland, & the PDP research group, 1986; Elman et al., 1996; see Shultz, this volume 1); the constructs of embodiment and enaction (Varela, Thompson, & Rosch, 1991; see Lewis, this volume 2; Savelsbergh et al., this volume 1; Schmuckler, this volume 1); dynamic systems approaches to motor development and then cognition (Thelen & Smith, 1994; see Adolph & Robinson, this volume 2; Lewis, this volume 2; Schmuckler, this volume 1); efforts to integrate evolutionary theory with developmental biology and developmental psychology (Bjorklund & Pellegrini, 2002; Hall, 1992; Lickliter & Honeycutt, 2003; see Bjorklund, this volume 1; Lickliter, this volume 1; Moore, this volume 1); and research from the perspectives of developmental psychopathology (see Cicchetti, this volume 2) and neuroconstructivism (see Thomas, Purser, & Richardson, this volume 2). It is only in the twenty-first century, however, that the key features of this new synthetic approach have become sufficiently widespread in practice— across the entire field of developmental psychology—to say that they constitute a dominant theoretical paradigm. A full treatment of this approach is beyond the scope of this introduction, but in what follows, I provide a brief summary of several important features.

Features of the New Synthesis

Psychology has sometimes been characterized as “pre-paradigmatic” (e.g., Staats, 1981) in the sense that there is little consensus concerning objectives, methods, and philosophical assumptions, but regardless of whether this has ever been true of the field as a whole, it is clearly not true of developmental psychology today. The contents of this Handbook provide unambiguous evidence of a set of widely shared theoretical and methodological commitments, all of which are currently being introduced in leading graduate programs, along with a strong emphasis on neuroscience. These commitments, to be discussed in turn, include the following: (1) psychological phenomena are usefully studied at multiple levels of analysis; (2) psychological development depends on neural plasticity, which extends across the lifespan; (3) the effect of any particular influence on psychological development will depend on the context in which it occurs; (4) psychological phenomena, and developmental changes in psychological phenomena, typically reflect multiple, simultaneous causal influences; and (5) these causal influences are often reciprocal.

The Importance of Multiple Levels of Analysis

This first feature is obvious from even a cursory look at the Table of Contents, which makes (p. 6) reference to everything from genetic activity to brain development to all manner of psychological processing (perception/language/cognition/concepts/emotion), frequently across the full range of typical individual differences and extending well into the realm of the atypical. Even looking within each chapter reveals the widespread acceptance of the importance of multiple levels of analysis to developmental psychology. I list this feature first because I believe it was instrumental in ushering in acceptance of the others.

An influential, early example of research examining multiple levels of analysis simultaneously is functional research in developmental neuroscience (i.e., measurement of neural activity interpreted in light of corresponding behavior or age-related changes in behavior; see Markant & Thomas, this volume 1). This research, which has exploded during the past decade, has reinforced our appreciation of the importance of multiple levels of analysis by allowing us to see vividly (e.g., in moving pictures) how our behavior co-occurs with dynamic patterns of neural activation and how neural development unfolds over time (e.g., as in the time-lapse movies of age-related changes in gray matter volume created by Gogtay et al., 2004). As discussed in the subsequent section, it has also demonstrated how age-related and/or experience-dependent changes in our behavior correspond to systematic reorganizations in neural function and structure (vis-à-vis the environment, broadly defined), and this, in turn, has served as a bridge between more molar and more molecular levels of analysis.


At the neural level of analysis, developmental processes correspond to a set of adaptations in an inherently plastic or malleable organ, the brain. Although the importance of neural plasticity has long been recognized (e.g., Hebb, 1949) and the role of expectable environmental input (and its absence) on brain development is well known (e.g., from sensory deprivation studies; Wiesel & Hubel, 1963), an early influential demonstration of the way in which behavioral adaptations to more idiosyncratic (i.e., experience-dependent) aspects of the environment co-occur with neural adaptations appeared in the work of Greenough and colleagues (e.g., Greenough, Black, & Wallace, 1987), who examined rats raised in “enriched” or relatively complex environments that included other rats and the opportunity to explore and play (see also Rosenzweig, Krach, Bennet, & Diamond, 1962). Compared to rats raised as usual, these rats showed better learning and memory (e.g., on maze learning) as well as effects on brain development, including heavier and thicker cortices, more dendrites per neuron, and more spines per dendrite.

The implications of these and other findings became more widely appreciated in light of some well-publicized research with adults (see May, 2011, for review). A famous study by Maguire and coworkers (2000), for example, examined brain regions related to spatial memory in a sample of taxi drivers in London, England. Taxi drivers, who have to pass a rigorous test demonstrating knowledge of London streets, were found to have larger posterior hippocampi (and smaller anterior hippocampi) than age-matched controls. Moreover, the number of years that they had been driving a cab was positively related to the volume of their posterior hippocampi and negatively related to the volume of their anterior hippocampi. Although correlational, this finding suggests that engaging regularly in navigation (and relying heavily on spatial memory) leads to the reshaping of relevant regions of the brain.

Similar findings have been obtained for white matter and also for measures of brain function. Elbert, Pantev, Wuenbruch, Rockstroh, and Taub (1995), for example, used magnetoencephalography (MEG) to measure cortical representations of fingers in violin players and found larger representations in sensorimotor cortex of the digits of the left (fingering) hand (but not the thumb), as would be expected if experience produced these changes. In addition, the number of hours spent practicing the piano (especially as a child) has been found to be related to myelination, with different neural regions being implicated at different ages (Bengtsson, Nagy, Skare, Forsman, Forssberg, & Ullén, 2005). Findings like these, which are increasingly supported by experimental research involving human beings (see May, 2011, for review), suggest that we grow our brains by using them, and that we grow our brains in particular ways by using them in particular ways.

Research on neural plasticity has implications for a longstanding debate in the field regarding the semantic relation between two closely related constructs: development and learning. Some have argued that psychological changes can best be understood as learning, such that development is really just the acquisition of expertise in various domains of experience. Examples of this approach have been influential and include social learning (e.g., Bandura & Walters, 1963; see Meltzoff & Williamson, this volume 1; (p. 7) Thompson, this volume 2; Rubin et al., this volume 2), skill acquisition (e.g., Fischer, 1981; Thelen & Smith, 1994), and conceptual change (e.g., Carey, 2009; see Gelman, this volume 1). In contrast, others have suggested that development refers to a particular kind of change—typically, changes in the way in which change (or learning) occurs. These include qualitative or quantitative changes in the way in which information is represented and/or processed: from the acquisition of learning sets (Harlow & Harlow, 1949) to the emergence of new representational formats (Bruner, Olver, & Greenfield, 1966; Piaget, 1976) to the functional specialization of neural circuits (e.g., Johnson, 2011). Naturally, this is an important debate for developmental psychology because it concerns the definition of the field’s defining characteristic.

Research on neuroplasticity has begun to examine the ways in which a range of psychologically relevant changes (e.g., procedural learning, conceptual change, and changes in information processing) are brought about as the brain interacts with the environment and undergoes changes. As one examines more and more basic neural processes (e.g., from the level of gross neuroanatomy to the level of number of synapses to the level of the thickness of dendritic spines to the level of neurochemistry and genetic activity), the neural correlates of different instances of change look increasingly alike, and distinctions relevant at the level of behavior collapse. Galván (2010), for example, has suggested that whereas both development and learning reflect the same basic mechanisms of neural plasticity (e.g., synapse formation, synaptic pruning, long-term potentiation), they exist on a continuum from experience-expectant processes of change to experience-dependent processes (Greenough et al., 1987). That is, development results from interactions with aspects of the environment that were generally pervasive during the course of our evolutionary history, and hence corresponds to normative ontogenetic change. Learning, in contrast, is a consequence of interactions with environments more likely to be unique to an individual, and hence contributes to individual differences. Of course, many aspects of human function (e.g., literacy) studied by developmental psychologists clearly depend on cultural learning (e.g., Tomasello, Kruger, & Ratner, 1993) and cannot easily be understood in terms of experience-expectant processes.

Another way to distinguish between learning and development is to note that there are characteristic ways in which the brain grows in interaction with experienced environments—whether expectable or not. In addition to changes at the level of neurons (e.g., the formation or pruning of synapses), it is increasingly clear that experience in a given domain is associated with increases in the integrity of white matter tracts linking relevant regions. Hu and colleagues (Hu, Geng, Tao, Hu, Du, Fu, et al., 2011), for example, found that school-age children with abacus training showed evidence of greater myelination in parts of corpus callosum as well as other fiber tracts involved in motor and visuospatial function. These use-related differences in myelination were correlated with behavioral performance and presumably correspond to differences in the functional efficiency of relevant neural circuits.

It is also likely that experience contributes to other age-related changes in neural structure, and some of these changes may lead to more clearly qualitative changes in brain function and information processing. Consider, for example, that at the level of gross anatomical regions, the brain is organized hierarchically (Luria, 1966). Neural development generally proceeds in a bottom-up fashion and appears to follow the likely phylogenetic sequence: lower-order somatosensory and visual cortices develop first (e.g., reach adult levels of gray matter density; Gogtay et al., 2004), followed by higher-order association areas that integrate information from the lower-order areas. Within cortices, too, regions that evolved later and modulate earlier-developing regions generally develop more slowly (e.g., within prefrontal cortex; Bunge & Zelazo, 2006; see Carlson, Zelazo, & Faja, this volume 1). The development of higher-order regions appears to correspond to the emergence of more complex functions that involve the integration and control of simpler functions that rely on lower-order, earlier-developing structures. Increases in the structural and functional complexity of the brain are clear examples of neural plasticity that are more naturally described as development than as learning, but there is obviously semantic overlap between these two constructs.

Finally, thinking about development in terms of neural plasticity informs our characterization of the directionality of psychological development. Development does tend to happen in particular ways, but rather than imply that development has an endpoint, an “ideal ultimate model” (Overton, 2010, p. 6), this merely implies that certain patterns of organization (e.g., increases in hierarchical complexity; Bunge & Zelazo, 2006) tend to emerge as developing human beings adapt to the range of environments currently considered typical.

(p. 8) The Importance of Context

The widespread recognition of the importance of context is related (transactionally) to the first features. In fact, this recognition is so widespread that it is sometimes difficult to believe that it was only in the past 15 years or so that scientific journals such as Child Development began to insist on the inclusion of demographic descriptions of the participants involved in published research (e.g., Sifers, Puddy, Warren, & Roberts, 2002). Before that, as in these examples from the late 1990s, samples were commonly described only in the most general terms: “healthy, normal-term infants,” for example, or “3-year-olds recruited from a large metropolitan area.” Perhaps we have been humbled by the horizons now visible via our new tools and are consequently less inclined to treat our observations as universal, but in any case it is increasingly obvious that our observations and our generalizations need to be more carefully contextualized—that the seemingly incidental correlates of these observations almost always matter. As Dewey (1931/1985) puts it (succinctly) in his essay, Context and Thought, “every occurrence is a concurrence” (p. 9, italics in original).

From a developmental perspective, one aspect of context that is sure to be important is that of developmental timing. Developmental timing is important because the system is always changing. In human beings, there are now a number of striking examples of sensitivity to developmental timing. In addition to sensitive periods for language acquisition (e.g., for phonemic discrimination, e.g., Kuhl, Tsao, & Liu, 2003; Werker & Desjardins, 1995; see Werker & Gervain, this volume 1) and vision (e.g., in research examining the effects of early vs. later cataract removal; see Atkinson & Braddick, this volume 1, Maurer & Lewis, this volume 1), research suggests that the timing and duration of early deprivation (e.g., through institutionalization) have consequences for a wide range of functions (e.g., stress reactivity, social/cognitive function, neural function; Gunnar & Herrera, this volume 2). More generally, researchers are increasingly aware that the significance of psychologically relevant events (including behavior) is always in flux. Bjorklund (1997), for example, considers ways in which behaviors that might be considered immature nonetheless fulfill important functions during particular ontogenetic periods. Examples include play (see Pellegrini, this volume 2) and the way in which slow perceptual development may protect infants from information overload (Turkewitz & Kenny, 1982; Lickliter, this volume 1).

Multiple Simultaneous Causes

The appreciation of context has changed our philosophy of science in crucial ways. Not long ago, following an old-fashioned idea of what it means to do good science, developmental scientists would try to pit different main-effect hypotheses against one another in the hope of disconfirming all but one. If a particular causal account generated a hypothesis that was disconfirmed, or failed to predict the influence of a particular manipulation, this was taken as evidence that the causal account was incorrect, not merely incomplete. Unfortunately, however, this particular approach to theory-driven science generally presupposes that hypotheses are formulated at the same level of analysis and that they are mutually exclusive. Critically, it also ignores the literally infinite range of unmanipulated boundary conditions in any single experiment or particular methodology—conditions that include sociohistorical context, cultural background, and all the things that are frequently correlated with cultural background, like genes, family history, and language. When researchers began more routinely to assess phenomena at multiple levels of analysis and started deliberately to vary the boundary conditions of traditional paradigms (e.g., examining cognitive development across cultures or as a function of socioeconomic status), this led both to a more widespread appreciation of the fact that psychological phenomena may be influenced by variations or manipulations at different levels of analysis and to the discovery that interactions among influences are pervasive. This, in turn, has affected our basic assumptions about causal influences on human behavior. In a relatively brief time span, our field has moved away from a default assumption of single causes to the awareness that any psychological event of interest surely depends on multiple interacting causal influences. These influences may be noticed at, or naturally described at, different levels of description, but within levels, too, it is increasingly obvious that there are often complex interactions among different dimensions of variation, each of which can be shown via experimental manipulations to be related causally to a single psychological phenomenon. Consider, for example, influences on the development of self and social understanding, widely studied under the rubric of theory of mind. Does the development of theory of mind depend on the acquisition of new conceptual understandings (e.g., theories), or does it depend on the prior development of executive function? Are individual differences in theory of mind genetic in origin, or (p. 9) are they a consequence of socialization (e.g., parenting style or the presence of older siblings)? As is clear from this volume (Astington & Hughes, this volume 2; Carlson et al., this volume 1; Eisenberg et al., this volume 2), the answer to both questions is surely “both.”

Another example of multiple simultaneous causes is research on the way in which genetic variation interacts with environmental variation (i.e., gene-times-environment [G×E] interactions). A famous longitudinal study by Caspi and colleagues (2002) found that a low-efficiency allele of the gene involved in the production of the neurotransmitter metabolizing enzyme monoamine oxidase A (MAOA) was associated with antisocial behavior only when it co-occurred with severe maltreatment. As Lickliter (this volume 1) notes, “Exactly how early life experience interacts with gene expression and MAOA activity in humans remains to be determined, but it is clear that the process would be poorly understood by focusing solely on genes.” In any case, as this example shows, any single-bullet, simple-and-sovereign, main-effects model of human behavior is bound to be incomplete.

Reciprocal Causality

Developmental PsychologyA New Synthesis

Figure 1.2. Gottlieb’s probabilistic-epigenetic framework, modified to illustrate the notion of reciprocal causality, which referes to bidirectional influences that may occur either simultaneously or in sequence. Modified, with permission, from Individual Development and Evolution: The Genesis of Novel Behavior (p. 186) by Gilbert Gottlieb, 1992, New York: Oxford University Press. Copyright 1992 by Oxford University Press, Inc.

Just as an appreciation of context revealed psychological phenomena routinely to be affected by multiple causal influences, it has also increased our awareness of reciprocal causality, which is embodied in constructs such as probabilistic epigenesis, coaction, enaction, and interdependence (see, e.g., Gottlieb, 1992; Thompson, 2007; see Lewis, this volume 2; Lickliter, this volume 1). In contrast to our usual way of conceptualizing causes and effects (e.g., as distinct entities, strictly sequenced in time; see Gopnik, this volume 1, for a discussion of children’s developing notions of causation), reciprocal causality describes bidirectional effects that may occur either simultaneously or in sequence. Figure 1.2 adds simultaneous reciprocal effects to Gottlieb’s (1992) figure. It also expands the level of behavior to distinguish among cognitive function, subjective experience, and motor responses, although it is clear that many other levels may also be noted, both within behavior (e.g., the level of stress physiology) and within the other broad categories. For example, environment includes everything from parenting and peer relationships (Rubin et al., this volume 2) to linguistic input (see Werker & Gervain, this volume 1) and spans extreme variations in risk (Cicchetti, this volume 1; Deater-Deckard, this volume 2; Gunnar & Herrera, this volume 2; Masten, this volume 2). These modifications to Gottlieb’s figure highlight two things. First, subjective experience, cognitive function, and neural activity appear to co-occur. Second, motor responses transform the experienced environment (e.g., as when a novel candle becomes something-capable-of-pain), as Dewey (1896) noted in his discussion of the limitations of the reflex arc concept in psychology.

There is nothing mysterious about reciprocal causality; in fact, it is mundane: one billiard ball may cause another to move, but when objects collide there are (simultaneous) effects on both. Even a hammer is deformed, however slightly, by a nail. A well-known and compelling demonstration of reciprocal causality comes from research by Meaney and colleagues on epigenetic changes as a consequence of experience (e.g., see Meaney, 2010). These researchers examined the effects of maternal licking and grooming on genetic activity related to responses to stress and the development of the hippocampus (see Gunnar & Herrera, this volume 2). Whereas previously it was often supposed that genes had a unidirectional effect on neural activity and behavior (Crick’s [1970] central dogma of molecular biology), it is now clear that behavior and experience alter gene expression (e.g., through DNA methylation and histone acetylation). In addition, alterations in gene expression can then be transmitted across generations, including via maternal behavior (Francis et al., 1999; Lickliter, this volume 1).

Other examples of reciprocal causality are described by Smetana (this volume 1), who discusses reciprocal effects of theory of mind and moral reasoning, and by Flynn and Blair (this volume 1), who discuss the multiplier effect whereby small initial variations in a skill can lead to larger (p. 10) later variations when individuals select their environments according to their skill (Dickens & Flynn, 2001). Related phenomena are also discussed by Dweck (this volume 2), who explores the effects on children’s behavior of their beliefs about themselves and about the nature of human skills.

Examples of reciprocal causality invite us to reconsider the relation between different levels of analysis. Consider, for example, that depressive behavior can be altered either pharmaceutically or through interpersonal psychotherapy, and that both types of manipulation produce changes in metabolic activity in parts of prefrontal cortex (e.g., Brody et al., 2001). Evidently, causal events naturally described at one level of analysis (e.g., the levels of belief or social interaction) coact with causal events naturally described at a very different level (e.g., neurophysiological levels), as illustrated in Figure 1.2. In addition, however, this kind of coaction raises the possibility that what appears to be a reciprocal causal relation between two different phenomena, such as depression and particular patterns of neural activity, may instead be better understood as changes in a single phenomenon that has simply been measured in two different ways (e.g., at the level of behavior and at the level of neural activity).

The Value of Intervention for Testing Causal Hypotheses About Development

A major methodological and, in turn, interpretive challenge in developmental psychology has always been the inherently correlational nature of much of the research (see Reznick, this volume 1). For example, research examining developmental sequences, longitudinal research examining continuity and change, twin studies and adoption studies, social cognitive developmental neuroscience research, and of course any individual differences research is almost always correlational in design. The problem, as is well known, is that mere correlations, even those that link events in childhood to those occurring much later in life, simply do not support causal inferences. Increasingly, however, there is interest in intervention research. This interest has been fueled by pragmatic funding priorities, to be sure, but it also reflects the appeal of taking an experimental approach to developmental outcomes, preferably in a randomized controlled trial, and ideally using a double-blind placebo-controlled design. As Bryck and Fisher (2012) put it in a review of this literature:

Such interest exists among neuroscientists focused on understanding the basic science of brain development, developmental psychologists focused on the emergence of key competencies necessary for healthy adjustment over time, child psychologists and other clinicians focused on understanding and treating psychological disorders, prevention scientists and educators focused on designing effective programs for reducing risks and promoting resiliency in high-risk populations, and policymakers focused on allocating funding and resources for such programs. (p. 1)

It is only through experimental research, with random assignment and proper controls, that it is possible to provide unambiguous evidence of causal influence.

One area where an experimental approach to developmental changes is popular is in research on executive function (e.g., see Carlson et al., this volume 1). A growing body of research has now demonstrated conclusively that the development of executive function can be cultivated through exercises that require the use of prefrontal cortical circuits (cf. Hebb, 1949). Much of this research has focused on the preschool period (see Diamond & Lee, 2011, for review), which may be a period of relative plasticity in prefrontal cortex. For example, Rueda, Rothbart, McCandliss, Saccomanno, and Posner (2005) improved 4- and 6-year-olds’ performance on a computerized attention task with five training sessions using computerized games. Children in the training condition showed improvement on an attention task and a measure of general intelligence, as well as more adultlike patterns in an event-related potential (ERP) component (the N2) located over frontoparietal and prefrontal areas (see Rueda & Posner, this volume 1).

Prefrontal cortical plasticity is clearly not limited to the preschool period, however, and an example of a successful intervention with older children and adults is CogMed, designed to train working memory. Following five weeks of training, Klingberg and colleagues (2005) found improved working memory and reduced attention-deficit/hyperactivity disorder (ADHD) symptomatology in a group of 7- to 12-year-olds with ADHD. In a study of CogMed with adults, Olesen, Westerberg, and Klingberg (2003) found training-related changes in activity in cortical regions known to be involved in working memory (i.e., increases in activity in frontal and parietal areas, as well as decreases in activity in cingulate cortex). This type of research has potentially profound practical implications for children developing in atypical ways (e.g., see Deater-Deckard, this volume 2; (p. 11) Masten, this volume 2; Seguin & Tremblay, this volume 2; Sonuga-Barke, this volume 2).


Developmental psychology has changed considerably since the turn of the twenty-first century. Various methodological advances and increased interdisciplinary communication—all of which has been further encouraged by funding priorities—contributed to the emergence of a new synthetic approach to research in the field. This approach examines psychological phenomena at multiple, interacting levels of analysis and seeks to explain processes of reorganization as they unfold on various timescales. While this approach may be especially well suited to the study of ontogenetic changes, it should also be of tremendous value to the larger fields of psychology, cognitive science, neuroscience, education, social work, and many others, because viewing the human being as a complex, multidimensional, and dynamic process adds an important etiological perspective and because it has broad epistemological and ontological implications. Epistemologically, it demands new ways of understanding human behavior, including new methods of measuring and modeling dynamic interactions among events occurring at different levels of analysis. Ontologically, it involves a substantial expansion and reconceptualization of our subject matter. A much wider range of levels of analysis is now recognized to be relevant to the psychology of the developing human being. Research now aims to comprehend how aspects of the environment, including social relationships and culture, interact with genes and everything in between to yield a developing person. This Handbook attests to the widespread acceptance of a developmental systems approach, and it offers a state-of-the-art assessment of what this approach has revealed so far.


Bandura, A., & Walters, R. H. (1963). Social learning and personality development. New York: Holt, Rinehart, & Winston.Find this resource:

Bengtsson, S. L., Nagy, Z., Skare, S., Forsman, L., Forssberg, H., & Ullén, F. (2005). Extensive piano practicing has regionally specific effects on white matter development. Nature Neuroscience 8, 1148–1150.Find this resource:

Bjorklund, D. F. (1997). The role of immaturity in human development. Psychological Bulletin, 122, 153–169.Find this resource:

Bjorklund, D. F., & Pellegrini, A. D. (2002). The origins of human nature: Evolutionary developmental psychology. Washington, DC: American Psychological Association.Find this resource:

Brody, A. L., Saxena, S., Stoessel, P., Gillies, L. A., Fairbanks, L. A., Alborzian, S., Phelps, M. E., Huang, S. C., Wu, H. M., Ho, M. L., Ho, M. K., Au, S. C., Maidment, K., & Baxter, L. R., Jr (2001). Regional brain metabolic changes in patients with major depression treated with either paroxetine or interpersonal therapy: preliminary findings. Archives of General Psychiatry, 58, 631–640.Find this resource:

Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard University Press.Find this resource:

Bruner, J. S., Olver, R. R., & Greenfield, P. M. (Eds.) (1966). Studies in cognitive growth. New York: Wiley.Find this resource:

Bryck, R. L., & Fisher, P. A. (2012). Training the brain: Practical applications of neural plasticity from the intersection of cognitive neuroscience, developmental psychology, and prevention science. American Psychologist, 67, 87–100.Find this resource:

Bunge, S., & Zelazo, P. D. (2006). A brain-based account of the development of rule use in childhood. Current Directions in Psychological Science, 15, 118–121.Find this resource:

Carey, S. (2009). The origin of concepts. New York: Oxford University Press.Find this resource:

Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., Craig, I. W., Taylor, A., & Poulton, R. (2002). Role of genotype in the cycle of violence in maltreated children. Science, 297, 851–854.Find this resource:

Crick, F. (1970). Central dogma of molecular biology. Nature, 227, 561–563.Find this resource:

Dewey, J. (1896). The reflex arc concept in psychology. Psychological Review, 3, 357–370.Find this resource:

Dewey, J. (1985). Context and thought. In J. A. Boydston (Ed.) & A. Sharpe (Textual Ed.), John Dewey: The later works, 19251953 (Vol. 6, 1931–1932; pp. 3–21). Carbondale, IL: Southern Illinois University Press. (Original work published in 1931).Find this resource:

Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in children 4 to 12 years old. Science, 333, 959–964.Find this resource:

Dickens, W. T., & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved. Psychological Review, 108, 346–369.Find this resource:

Elbert, T., Pantev, C., Wienbruch, C., Rockstroh, B., & Taub, E. (1995). Increased cortical representation of the fingers of the left hand in string players. Science, 270, 305–307.Find this resource:

Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking innateness: A connectionist perspective on development. Cambridge, MA: MIT Press.Find this resource:

Fischer, K. W. (1981). A theory of cognitive development: The control and construction of hierarchies of skills. Psychological Review, 87, 477–531.Find this resource:

Francis, D., Diorio, J., Liu, D., & Meaney, M. J. (1999). Nongenomic transmission across generations of maternal behavior and stress responses in the rat. Science, 286, 1155–1158. (p. 12) Find this resource:

Galván, A. (2010). Neural plasticity in development and learning. Human Brain Mapping, 31, 879–890.Find this resource:

Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C.,Nugent III, T. F., Herman, D. H., Clasen, L. S., Toga, A. W., Rapoport, J. L., & Thompson, P. M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences USA, 101, 8174–8179.Find this resource:

Gottlieb, G. (1992). Individual development and evolution: The genesis of novel behavior. New York: Oxford University Press.Find this resource:

Greenough, W. T., Black, J. E., & Wallace, C. S. (1987). Experience and brain development. Child Development, 58, 539–559.Find this resource:

Hall, B. K. (1992). Evolutionary developmental biology. London: Chapman & Hall.Find this resource:

Harlow, H. F., & Harlow, M. K. (1949). Learning to think. Scientific American, 181, 36–39.Find this resource:

Hebb, D. O. (1949). The organization of behavior. New York: Wiley.Find this resource:

Hu, Y., Geng, F., Tao, L., Hu, N., Du, F., Fu, K., et al. (2011). Enhanced white matter tracts integrity in children with abacus training. Human Brain Mapping, 32, 10–21.Find this resource:

Johnson, M. H. (2011). Interactive specialization: A domain-general framework for human functional brain development? Developmental Cognitive Neuroscience, 1, 7–21.Find this resource:

Klingberg, T., Fernell, E., Olesen, P., Johnson, M., Gustafsson, P., Dahlström, K., et al. (2005). Computerized training of working memory in children with ADHD—a randomized, controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 177–186.Find this resource:

Kuhl, P. K., Taso, F. M., & Liu, H. M. (2003). Foreign-language experience in infancy: Effects of short-term exposure and social interaction on phonetic learning. Proceedings of the National Academy of Sciences USA, 100, 9096–9101.Find this resource:

Lerner, R. M. (1991). Changing organism-context relations as the basic process of development: A developmental contextual perspective. Developmental Psychology, 27, 27–32.Find this resource:

Lickliter, R., & Honeycutt, H. (2003). Developmental dynamics: Toward a biologically plausible evolutionary psychology. Psychological Bulletin, 129, 819–835.Find this resource:

Luria, A. R. (1966). Higher cortical functions in man (2nd ed.). New York: Basic Books. (Original work published in 1962.)Find this resource:

Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences USA, 97, 4398–4403.Find this resource:

May, A. (2011). Experience-dependent structural plasticity in the adult human brain. Trends in Cognitive Sciences, 15, 475–482.Find this resource:

Meaney, M. J. (2010). Epigenetics and the biological definition of gene× environment interactions. Child Development, 81, 41–79.Find this resource:

Olesen, P. J., Westerberg, H., & Klingberg, T. (2003). Increased prefrontal and parietal activity after training of working memory. Nature Neuroscience, 7, 75–79.Find this resource:

Overton, W. F. (2010). Life-span development: Concepts and issues. In W. F. Overton (Ed.) & R. M. Lerner (Ed. in Chief), The handbook of life-span development, vol. 1: Cognition, biology, and methods (pp. 1–29). Hoboken, NJ: John Wiley & Sons, Inc.Find this resource:

Oyama, S., Griffiths P. E., & Gray R. D. (2001). Cycles of contingency: Developmental systems and evolution. Cambridge, MA: MIT Press.Find this resource:

Piaget, J. (1976). The child and reality. New York: Penguin Books.Find this resource:

Rosenzweig, M. R., Krech, D., Bennett, E. L., & Diamond, M. C. (1962). Effects of environmental complexity and training on brain chemistry and anatomy: A replication and extension. Journal of Comparative Physiological Psychology, 55, 429–437.Find this resource:

Rueda, M. R., Rothbart, M. K., McCandliss, B. D., Saccomanno, L., & Posner, M. I. (2005). Training, maturation and genetic influences on the development of executive attention. Proceedings of the National Academy of Sciences USA, 102, 14931–14936.Find this resource:

Rumelhart, D. E., McClelland, J. L., & the PDP research group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition, Volume I. Cambridge, MA: MIT Press.Find this resource:

Sameroff, A. J., & Chandler, M. J. (1975). Reproductive risk and the continuum of caretaker casualty. Review of Child Development Research, 4, 187–244.Find this resource:

Sifers, S. K., Puddy, R. W., Warren, J. S., & Roberts, M. C. (2002). Reporting of demographics, methodology, and ethical procedures in journals in pediatric and child psychology. Journal of Pediatric Psychology, 27, 19–25.Find this resource:

Staats, A. R. (1981). Paradigmatic behaviorism, unified theory, unified theory construction methods, and the zeitgeist of separatism. American Psychologist, 36, 239–256.Find this resource:

Thelen, E., & Smith, L. B. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press.Find this resource:

Thompson, E. (2007). Mind in life: Biology, phenomenology, and the sciences of mind. Cambridge, MA: Harvard University Press.Find this resource:

Tomasello, M., Kruger, A. C., & Ratner, H. H. (1993). Cultural learning. Behavioral and Brain Sciences, 16, 495–552.Find this resource:

Turkewitz, G., & Kenny, P. (1982). Limitations on input as a basis for neural organization and perceptual development: A preliminary theoretical statement. Developmental Psychobiology, 15, 357–368.Find this resource:

Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. Cambridge, MA: MIT Press.Find this resource:

Werker, J. F., & Desjardins, R. N. (1995). Listening to speech in the 1st year of life: Experiential influences on phoneme perception. Current Directions in Psychological Science, 4, 76–81.Find this resource:

Wiesel, T. N., & Hubel, D. H. (1963). Single-cell responses in striate cortex of kittens deprived of vision in one eye. Journal of Neurophysiology, 26, 1003–1017.Find this resource:

Zelazo, P. D., Chandler, M., & Crone, E. A. (2010). Developmental social cognitive neuroscience. New York: Psychology Press.Find this resource:


(1) . In fact, this dynamic, dialectical process of understanding, with its reciprocal and generative nature, provides a good illustration of a psychological phenomenon that may best be understood from a developmental perspective—as an ongoing process of transformation that cannot be fully understood via examination at any single time point, without consideration of its context, its history, and its functions, and without considering relevant constraints on the way in which the transformation occurs.