The growth of neural systems in the brain clearly relates to children’s psy-chological development, but the state of knowledge about brain–behavior relations in development has been primitive, with most research merely establishing global correlations. Fortunately, the substantial neuroscientific knowledge of recent years provides possibilities for moving beyond global correlations to major breakthroughs in understanding. Especially promis-ing is the analysis of patterns of growth in which development of brain and behavior show complex variations that share many characteristics. Based on new findings about development of brain and behavior, we have created a dynamic model of cyclical growth that hypothesizes common develop-mental mechanisms behind development of skills and brain activity—which we proposed over a decade ago (Fischer & Rose, 1994) and have subse-quently elaborated and revised (Fischer, in press; Fischer & Bidell, 2006;
Fischer & Rose, 1996). One of the central mechanisms is cyclical growth of cortical connections within and between the right and left hemispheres.
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Two characteristics are especially important for analyzing and explain-ing these common developmental mechanisms. First, both skills and brain activity move repeatedly through periods of rapid change or developmental discontinuity that reflect dynamic underlying processes, such as connec-tions among skills and brain regions. Second, these growth patterns take place in recurring cycles that show similar discontinuities across skills and brain regions over time. Recent advances in methods and concepts for studying and modeling development provide ways of using these two char-acteristics to analyze the processes of development of brain and behavior.
Discontinuities Mark Emergence of Developmental Levels
The discovery that many growth functions for brain and behavior show marked discontinuities has provided a powerful tool for establishing major developmental changes and for relating changes in brain and behavior (Thatcher, 1994). Brain characteristics such as myelination, synaptic den-sity, dendritic branching, brain mass, pruning of neurons and synapses, and brain electrical activity all change systematically with age during childhood (Conel, 1939–1963; Dawson & Fischer, 1994; Yakovlev & Lecours, 1967; see also various chapters in this volume). Simultaneously, children’s actions, speech, problem solving, concepts, social interactions, and emo-tions likewise change systematically. The result is that growth in all these characteristics necessarily correlates globally, but the nonlinear characteris-tics of growth make it possible to demarcate when major changes occur and provide tools for relating changes in skills and brain activity (Fischer &
Bidell, 1998).
Brain activity evidences a series of discontinuities in growth, as illus-trated in Figure 4.1 from a study by Matousek and Petersén (1973). These authors recorded several measures of brain activity that showed patterns of discontinuous growth, including relative power (reflecting level of cortical activation at the neuronal level) in the alpha band of the EEG for the occipital–parietal region of the cortex, as shown in Figure 4.1. Notice that besides the general upward growth, there are regular cycles of rapid growth (spurts) followed by slower growth or even decrease (plateaus).
Similar discontinuities in growth are common for psychological devel-opment, as illustrated in Figure 4.2, which shows growth at later stages of a complex kind of reasoning called reflective judgment (Kitchener, Lynch, Fischer, & Wood, 1993). Participants considered complex knowledge dilemmas for which there were no simple answers, such as whether chemi-cal additives (preservatives) to food are helpful (preventing illness, such as
food poisoning) or harmful (producing illness, such as cancer). Prior research with these dilemmas has found that people develop through seven stages of increasing sophistication in their ability to coordinate viewpoint, argument, and evidence. Adolescents and young adults showed spurts in their level of understanding with each new stage of reflective judgment, both in their reasoning at a specific stage (Y2 shows stage 6 in Figure 4.2) and in their reasoning when all stages were combined (Y1). This kind of spurt-and-plateau pattern does not occur for all developing behaviors but is common in people’s optimal performance—the most complex skills that they can control with contextual support in a given domain (Fischer &
Bidell, 2006).
Figures 4.1 and 4.2 illustrate the kinds of parallels that are often found between discontinuities in development of brain and behavior. There are spurts at approximately 15 and 20 years for both EEG power and reflective judgment. Figure 4.2 shows an additional discontinuity in reflective judg-ment at approximately age 25, and we posit a discontinuity in brain activ-ity at the same age. However, we have been unable to find EEG research to test this hypothesis, because brain development research typically does not record variations in age in subjects in their 20s.
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FIGURE 4.1. Development of spurts in relative energy in alpha EEG in the occip-ital–parietal (O-P) area in Swedish children and adolescents. (Relative energy—also called relative power—is the amplitude [in microvolts] of absolute energy in one region divided by the sum of the amplitudes in all regions, calculated as a percent-age.) Data from Matousek and Petersén (1973).
With these kinds of findings, research on both brain development and skill development (including cognition and emotion) has set the stage for better research and theory about relations between the two. For neural sys-tems, key knowledge about specific growth functions goes beyond global descriptions, on the one hand, and narrow analyses of isolated local brain systems, on the other. For example, across large areas of the cerebral cortex in rhesus monkeys, synapses grow and are pruned in approximately paral-lel cycles that show clear discontinuities (Goldman-Rakic, 1987; Rakic, Bourgeois, Eckenhoff, Zecevic, & Goldman-Rakic, 1986), and human cor-tical activity and networks show distinctive patterns of spurts and drops (Fischer, in press; Hudspeth & Pribram, 1992; Thatcher, 1994).
Likewise for behavior, key knowledge about specific growth functions, especially for cognitive development, goes beyond global stage theories, on the one hand, and narrow analysis of one local behavior or knowledge domain, on the other. Evidence is now extensive that skills develop along a systematic scale in which new levels are marked by discontinuities, both in growth functions and in Rasch scaling patterns (Dawson-Tunik, Com-mons, Wilson, & Fischer, 2005; Fischer & Bidell, 2006). We have
pro-FIGURE 4.2. Development of three stages (levels) of reflective judgment in ado-lescents and young adults. Optimal performance spurts with the emergence of stages 5, 6, and 7 of reflective judgment. The top line (solid) shows the general score for reflective judgment across all tasks, and the dotted line shows the percentage of correct performance for the subset of tasks assessing stage 6, which is the beginning of true reflective thinking. Based on Kitchener, Lynch, Fischer, and Wood (1993).
posed that this is a universal scale reflecting general processes of skill acqui-sition; a person uses common processes to construct specific skills, yet the skills are distinctive and separate for each domain.
The dynamic skill model describes a growth cycle that repeats with each new developmental level. The coordination of components of brain and behavior into higher-order control systems, which are called dynamic skills, produces clusters of discontinuities in both brain and behavior with each major reorganization. The skills are composed of many elements in brain, body, and environment, all interacting according to the principles of dynamic systems (Fischer & Bidell, 1998; Fischer & Rose, 1994; van Geert, 1991). Before coordination, these elements are mostly independent, with only weak connections as a result of being components in the brain of a person. As coordination develops, the connections become powerful and strongly influence the shapes of growth functions for the dynamic skills, producing clusters of discontinuities. The parallel, distributed networks grow in regular cycles in the sense that each new system starts the coordi-nation process anew, becoming a potential component for a new coordina-tion into a still more complex system. The cycles typically produce periods of rapid growth in alternation with periods of slower growth, and so dis-continuities (sudden changes) are a primary index of a cycle.
The mechanism producing synchronous spurts is the development of a new capacity to build skills at a given level of complexity, which we hypothesize also involves emergence of a new level of neural network. This capacity is not to be confused with the powerful, general competencies hypothesized by Piaget (1957) and Chomsky (1965) (which we argue do not exist). The change in cognitive capacity does not automatically eventu-ate in skill changes (Fischer & Bidell, 1998), but instead, people must take time and effort to actually build the changed skills that the capacity makes possible, and factors such as state and task contribute to the actual skills produced. An individual’s skills vary routinely between a higher, optimal level in situations that provide contextual support and a lower, functional level in ordinary situations that provide no support. Discontinuities in level are consistently evident only under optimal assessment conditions. Most conditions in which researchers have traditionally assessed development are nonoptimal and produce evidence for slow, gradual, continuous growth, even when people are performing the same tasks that show discontinuities under optimal conditions.
Finding and interpreting such discontinuities is no simple matter, be-cause in dynamic systems, spurts and drops in growth can occur for many dif-ferent reasons (van Geert, 1991). For example, cognitive and emotional activ-ities can vary abruptly as a result of changes in context, emotional state, social
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interaction, and many other component factors. However, research shows that the discontinuities are highly robust, appearing not only in development but also within a person’s performances at a given time in tests or interviews (Dawson & Stein, in press; Dawson et al., 2005). Rasch scaling of test items, for instance, shows gaps between clusters of items that demonstrate the same skill scale that developmental research has documented.
Although the vagaries of dynamically growing systems make the search challenging, the complex patterns of discontinuities in growth func-tions for brain and behavior provide a methodological tool for testing rela-tions across skill domains or brain regions and between brain and behav-ior. Researchers can search for links between specific discontinuities or other characteristics of the shapes of growth functions.
Brain-Growth Hypothesis: Parallel Cycles in Brain and Cognitive Growth
Fortunately, the framework we propose provides clear guidelines about where and how to look for developmental relations between brain and behavior. Emergence of a new level is typically marked by points of discon-tinuity or sharp change in growth of brain and behavior, such as sharp spurts or drops (Fischer & Bidell, 1998; van der Maas & Molenaar, 1992).
Contrary to popular belief, most body organs grow in fits and starts, just like the brain (Lampl, Veldhuis, & Johnson, 1992). Individual growth curves routinely show jumps interspersed with periods of little change, whether in bones, muscles, brain, or skill.
A good model for studying brain–behavior relations in development is changes at approximately 8 months of age, where extensive evidence from multiple indices of behavior and brain development indicates a cluster of discontinuous changes. For behavior, the evidence is vast because so many studies of cognitive and emotional development have focused on the second half of the first year. Included in the behaviors that show discontinuities such as spurts and drops are search for hidden objects, distress at separa-tion from mother, and general infant test performance. There is also sub-stantial knowledge about one of the behavioral mechanisms behind these changes: Campos and his colleagues have shown that self-produced loco-motion (crawling with the head up, not dragging on the belly or other less effective forms of locomotion) facilitates development of an array of spatial skills during this period (Campos et al., 2000). The evidence is also exten-sive for the brain: EEG frequency and power, some aspects of EEG coher-ence, glucose metabolism, and head circumference all spurt at approxi-mately 8 months (Dawson & Fischer, 1994).
The frontal cortex seems to play a major role in the reorganization at 8 months of age. In searching for hidden objects, infants who exhibited a spurt during this age period in EEG power and coherence involving the frontal cortex also produced an advance in search behaviors, whereas infants who did not exhibit the EEG changes did not produce the cognitive advance (Bell & Fox, 1994). Research with rhesus monkeys likewise pin-points cortical changes at the age of emergence of these search skills (Goldman-Rakic, 1987; Rakic et al., 1986): a spurt in synaptic density in the frontal area as well as other parts of the cortex. In addition, research has shown that a specific column of frontal cells holds information about the location of the hidden object while the monkey is searching. Removal of these cells prevents correct search.
This cluster of changes at approximately 8 months suggests a hypothe-sis about brain growth and cognitive development: When there is sudden emergence of a cluster of new cognitive capacities, there will also be sudden changes in indexes of brain growth, including connections between the frontal cortex and other regions. Prefrontal functions facilitate holding information “online” for co-occurrence and coordination of components.
According to the brain-growth hypothesis, each new developmental level for behavioral control systems is supported by growth of a new type of neural network that facilitates construction of skills at that level.
Growth of the network is evident in discontinuities in both brain and cog-nitive development. The new networks are then gradually pruned to form efficient neural systems, during which time some indices of relevant brain growth decrease slowly and some indices of relevant cognitive growth increase gradually. Eventually, as the networks are consolidated, another new type of network begins to grow for the next developmental level, and another cluster of discontinuities begins. Most likely, the cycles are not all or none, with changes occurring everywhere at once, but instead involve a cascade of changes that moves through brain areas systematically, includ-ing systematic shifts between the right and left hemispheres. But before we elaborate a model of growth of neural networks, we need to describe the developmental scale for skill and brain that emerges from evidence for dis-continuities.
Developmental Scale Marked by Clusters of Discontinuities
Evidence for clusters of discontinuities in behavior indicates development of a series of 10 levels between early infancy and 30 years of age, as shown in Figure 4.3 (Fischer, in press; Fischer & Bidell, 1998; Fischer & Rose, 1994, 1996). (There is some evidence for three additional levels in the first
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months after birth.) The ages listed for each level mark its emergence—
when the person (child or adult) can first control a number of skills at that level. During infancy, levels grow in four rapidly occurring spurts separated by a few months—at approximately 4, 8, 12, and 20 months. Intervals become larger after infancy, with spurts at approximately 4, 7, 11, 15, 20, and 25 years of age.
Of course, individuals vary somewhat in the age of emergence of each level, and skills in specific domains and contexts also vary. There is an approximate synchrony of discontinuities within a definable time interval, along with substantial variation, as a developmental scale of control struc-tures for the coordination of increasingly complex skills is formed. Accord-ing to the brain-growth hypothesis, each level corresponds to a neural net-work reorganization reflected in brain-growth discontinuities.
Qualitative changes in development can be described in three different grains of detail. At the finest grain, skills develop through a sequence of small, microdevelopmental steps, which skill theory predicts via a set of transformation rules for explaining skill coordination and differentiation (Fischer, 1980). Most steps do not involve developmental discontinuities FIGURE 4.3. Developmental scale of skill levels and tiers. Development moves through 10 skill levels, starting with the tier of actions (solid lines), then representa-tions (lines with small squares), and finally abstracrepresenta-tions (lines with large squares).
Levels first emerge under optimal conditions, with high social–contextual support, and they move through a recurring cycle of single components, mappings, systems, and systems of systems—which create a new unit for the next tier, as when systems of systems of representations (Rp4) create single abstractions (Ab1). The ages are based on research with middle-class American or European children.
but are simply points along a pathway of skill construction. Certain steps in a sequence mark the emergence of a new developmental level; that is, a capacity to construct a new type of control system or skill. As the person enters a new level, he or she shows a stage-like spurt in optimal perfor-mance. Each of the developmental levels involves a large, indeterminate number of steps that extends beyond the initial period of developmental discontinuity, and assessment of fine-grained steps greatly facilitates detec-tion of levels via discontinuities.
At the broadest grain, skills develop through a series of tiers, each involving four successive levels, as shown in Figure 4.3. Tiers mark the emergence of a radically new type of unit for controlling behavior—
reflexes (not shown), actions, representations, or abstractions, respectively—
showing an especially strong discontinuity (marked by the arrow head). For example, the emergence of the representational tier late in the second year produces the onset of complex language and a host of other changes that radically transform children’s behavior. Each tier involves the same cycle of development through levels from single units to mappings to systems and finally to systems of systems, shown by the cycle of lines leading to an arrow in Figure 4.3. With the level of systems of systems, a new unit emerges and a new tier begins so that the final level of one tier is also the start of the next tier. For more detailed specification of skill levels and tiers, see Fischer (1980) and Fischer and Bidell (1998); see Fischer and Hogan (1989) for infancy; Fischer, Hand, Watson, Van Parys, and Tucker (1984) for early childhood; and Fischer, Yan, and Stewart (2003) for adolescence and early adulthood.
Growth Cycle of Neural Networks with Shifts between the Right and Left Hemispheres
The few comprehensive studies of brain development show clear growth cycles and provide evidence to help us build a model of developmental cycles of brain growth that shows remarkable parallels with the levels of skill development (Fischer & Rose, 1994, 1996; Marshall, Bar-Haim, &
Fox, 2002; Somsen, van ‘t Klooster, van der Molen, van Leeuwen, & Licht, 1997; Thatcher, 1994). For the first 9 of the 10 developmental levels, evi-dence shows brain growth spurts at ages that parallel skill levels, as we illustrated for the changes at 8 months of age. As mentioned above, evi-dence is not available for the 10th level, at approximately 25 years, because there has been little research to assess brain growth in the 20s.
Existing evidence about changes in brain activity suggests a specific model of how cycles of brain change map onto levels of skill development.
The most relevant findings come from Swedish studies of development of
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EEG power (Hagne, Persson, Magnusson, & Petersén, 1973; Hudspeth &
Pribram, 1992; Matousek & Petersén, 1973), Thatcher’s (1994) large-sample study of development of EEG coherence as a measure of neural con-nectivity, Somsen and colleagues’ (1997) study of EEG in middle child-hood, and a Japanese study of infant brain activity (Mizuno et al., 1970).
These studies sample wide age ranges and divide the samples into groups separated by no more than 1 year in age, thus providing data for modeling growth functions of brain activity. With them, we have built a model of how neural networks grow at each developmental level by connecting vari-ous cortical regions.
According to the model, each skill level is marked by a similar cycle of growth of connections, as shown in Figure 4.4. Network peak growth begins with growth of front-to-back connections in both hemispheres and then moves primarily to the right hemisphere, where growth of connections gradually contracts, changing from more distant (global) to more local.
After a brief period of peak growth of frontal connections in both hemi-spheres, growth of connections moves to the left hemisphere, where it grad-ually expands, shifting from more local to more global. Then the cycle completes by returning to front-to-back growth, which begins the cycle again. Figure 4.4 shows the hypothesized growth cycle for the levels that typically emerge at approximately 7 and 11 years of age: representational systems and single abstractions.
The cycle refers to the leading edge of growth—the areas of peak growth in connections; other, less salient changes undoubtedly occur simul-taneously in other cortical areas. Based on the nature of cognitive-developmental changes within each level (Fischer & Bidell, 2006) and evi-dence about differences between the two hemispheres (Immordino-Yang, 2005; Ivry & Robertson, 1997), this cycle would seem to start with a more global, integrative orientation at the beginning of construction of a net-work and move toward a more focal, differentiated orientation later in the construction.
This model of growth of a neural network is a generalization and mod-ification of Thatcher’s (1994) analysis, which was based on his large-sample study. His findings of connection spurts fit the ages, in general, for the levels emerging during childhood and early adolescence, but he has not analyzed his data for the years of infancy or later adolescence. Findings from the other studies listed above support the existence of clusters of dis-continuities for each level during infancy and later adolescence, but they provide no evidence concerning growth of connections between specific cortical regions.
Extensive research will be required to discover where this model works and where it does not. In brain development research, the primary
empha-sis has traditionally been placed on measurement of neural functions, with extremely limited assessments of behavior—even when behavior is an explicit focus of investigation. Richer behavioral assessments will be required for strong research on how brain and behavior develop together.
Most studies use a single behavioral task, sometimes with a few parametric variations. Psychological development involves much more than actions on a single task. It involves a rich diversity of activities, wide variations in the forms of those activities, and powerful effects of emotions on the activities.
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FIGURE 4.4. An illustration of the cortical network cycle for two successive developmental levels: representational systems and single abstractions. Jagged lines mark the leading edge of growth of coherence, which indicates increasing connec-tion between cortical areas. Peak connecconnec-tions move around the cortex, starting with both hemispheres connecting front to back, moving to the right hemisphere, then to the prefrontal cortex in both hemispheres, and finally to the left hemisphere. This growth cycle repeats as a new network grows for each developmental level. The cycle for the level of Rp3, representational systems, lead to a new cycle for level Ab1, single abstractions, which in turn leads to a new cycle for the next level, Ab2, abstract mappings (not shown). (Connections between the middle and back of the left hemisphere are more prevalent than similar connections for the right hemi-sphere; the temporal–central connection for the left hemisphere is shown as an example of that difference.)
Conclusion: Cycles of Skill Development and Brain Growth
Each developmental level requires a new type of control system to coordi-nate component skills, and each produces a cluster of discontinuities in behavioral growth and apparently brain growth as well. We hypothesize that each skill level is grounded in a broadly based brain-growth spurt that produces a new type of neural network and thus a new type of control sys-tem. The convergence is remarkable between the growth spurts evident in EEG and other brain-growth findings and the spurts for cognitive and emo-tional developments (summarized in Figure 4.4). Unfortunately, almost all relevant studies have investigated either brain growth or behavior develop-ment, not both, so extensive research will be required to test and improve this model of specific connections between brain-growth discontinuities and developmental changes in behavior.
Development is often analyzed as a linear process similar to climbing a ladder. The growth patterns that we have described suggest a different kind of model based on growth cycles. Biology is replete with cycles, and we propose that developmental science needs to move away from linear growth models toward cyclical models (Fischer & Bidell, 2006). The brain has many parts that work together in complicated ways. One part of the brain growth pattern seems to be that new learning and development begin with a bias toward the right hemisphere, with its more global approach, and then gradually move to more involvement with the left hemisphere and a focus on differentiation and specificity. This promising idea can help ground the search for organizational principles of learning, cognitive devel-opment, and brain growth. It can help explain how brain components work together to produce an emerging developmental level or an emerging set of expert skills. The growth cycles that we propose for skill and brain development provide a beginning.