research on children's cognitive development

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National Research Council (US) Panel to Review the Status of Basic Research on School-Age Children; Collins WA, editor. Development During Middle Childhood: The Years From Six to Twelve. Washington (DC): National Academies Press (US); 1984.

Development During Middle Childhood: The Years From Six to Twelve.

• Hardcopy Version at National Academies Press

Chapter 3 Cognitive Development In School-Age Children: Conclusions And New Directions

Kurt W. Fischer and Daniel Bullock

What is the nature of children's knowledge? How does their knowledge change with development? In pursuing these fundamental questions in the study of cognitive development, researchers often expand their focus to include a range of children's behaviors extending far beyond the standard meaning of knowledge.

In the two primary cognitive-developmental traditions, the questions typically take different forms. In the structuralist tradition, influenced strongly by the work of Jean Piaget, Heinz Werner, and others, the questions are: How is behavior organized, and how does the organization change with development? In the functionalist tradition, influenced strongly by behaviorism and information processing, the question is: What are the processes that produce or underlie behavioral change? In this chapter we review major conclusions from both traditions about cognitive development in school-age children.

The study of cognitive development, especially in school-age children, has been one of the central focuses of developmental research over the last 25 years. There is an enormous research literature, with thousands of studies investigating cognitive change from scores of specific perspectives. Despite this diversity, there does seem to be a consensus emerging about (1) the conclusions to be reached from research to date and (2) the directions new research and theory should take. A major part of this consensus grows from an orientation that seems to be pervading the field: It is time to move beyond the opposition of structuralism and functionalism and begin to build a broader, more integrated approach to cognitive development (see Case, 1980; Catania, 1973; Fischer, 1980; Flavell, 1982a). Indeed, we argue that without such an integration attempts to explain the development of behavior are doomed.

The general orientations or investigations of cognitive development are similar for all age groups—infancy, childhood, and adulthood. The vast majority of investigations, however, involve children of school age and for those children a number of specific issues arise, including in particular the relationship between schooling and cognitive development.

This chapter first describes the emerging consensus about the patterns of cognitive development in school-age children. A description of this consensus leads naturally to a set of core issues that must be dealt with if developmental scientists are to build a more adequate explanation of developmental structure and process. How do the child and the environment collaborate in development? How does the pattern of development vary across traditional categories of behavior, such as cognition, emotion, and social behavior? And what methods are available for addressing these issues in research?

Under the framework provided by these broad issues, there are a number of different directions research could take. Four that seem especially promising to us involve the relationship between cognitive development and emotional dynamics, the relationship between brain changes and cognitive development, the role of informal teaching and other modes of social interaction in cognitive development, and the nature and effects of schooling and literacy. These four directions are taken up in a later section.

• Patterns Of Developmental Change

One of the central focuses in the controversies between structuralist and functionalist approaches has been whether children develop through stages. Much of this controversy has been obscured by fuzzy criteria for what counts as a stage, but significant advances have been made in pinning down criteria (e.g., Fischer and Bullock, 1981; Flavell, 1971; McCall, 1983; Wohlwill, 1973). In addition, developmentalists seem to be moving away from pitting structuralism and functionalism against each other toward viewing them as complementary; psychological development can at the same time be stagelike in some ways and not at all stagelike in other ways. As a result of these recent advances in the field, it is now possible to sketch a general portrait of the status of stages in the development of children.

The General Status Of Stages

Children do not develop in stages as traditionally defined. That is, (1) their behavior changes gradually not abruptly, (2) they develop at different rates in different domains rather than showing synchronous change across domains, and (3) different children develop in different ways (Feldman, 1980; Flavell, 1982b).

Cognitive development does show, however, a number of weaker stagelike characteristics. First, within a domain, development occurs in orderly sequences of steps for relatively homogeneous populations of children (Flavell, 1972). That is, for a given population of children, development in a domain can be described in terms of a specific sequence, in which behavior a develops first, then behavior b , and so forth. For example, with Piaget and Inhelder's (1941/1974) conservation tasks involving two balls or lumps of clay, there seems to be a systematic three-step sequence (see Hooper et al., 1971; Uzgiris, 1964): (1) conservation of the amount of clay (Is there more clay in one of the balls, even though they are different shapes, or do they both have the same amount of clay?), (2) conservation of the weight of clay (Does one of the balls weigh more?), and (3) conservation of the volume of clay (Does one of the balls displace more water?). The explanation and prediction of such sequences is not always easy, but there do seem to be many instances of orderly sequences in particular domains.

Second, these steps often mark major qualitative changes in behavior—changes in behavioral organization. That is, in addition to developing more of the abilities they already have, children also seem to develop new types of abilities. This fact is reflected in the appearance of behaviors that were not previously present for some particular context or task. For example, in pretend play the understanding of concrete social roles, such as that of a doctor interacting with a patient, emerges at a certain point in a developmental sequence for social categories and is usually present by the age at which children begin school (Watson, 1981). Likewise, the understanding of conservation of amount of clay develops at a certain point in a developmental sequence for conservation.

More generally, there appear to be times of large-scale reorganization of behaviors across many (but not all) domains. At these times, children show more than the ordinary small qualitative changes that occur every day. They demonstrate major qualitative changes, and these changes seem to be characterized by large, rapid change across a number of domains (Case, 1980; Fischer et al., in press; Kenny, 1983; McCall, 1983). Indeed, the speed of change is emerging as a promising general measure for the degree of reorganization. We refer to these large-scale reorganizations as levels . We use the term steps to designate any qualitative change that can be described in terms of a developmental sequence, regardless of whether it involves a new level.

Third, there seem to be some universal steps in cognitive development, but their universality appears to depend on the way they are defined. When steps are defined abstractly and in broad terms or when large groups of skills are considered, developmental sequences seem to show universality across domains and across children in different social groups. When skills of any specificity are considered, however, the numbers and types of developmental steps seem to change as a function of both the context and the individual child (Bullock, 1981; Feldman and Toulmin, 1975; Fischer and Corrigan, 1981; Roberts, 1981; Silvern, 1984). For large-scale (macrodevelopmental) changes, then, there seem to be some universals, but for small-scale (microdevelopmental) changes, individual differences appear to be the norm. The nature of individual differences seems to be especially important for school-age children and is discussed in greater depth in a later section.

Large-Scale Developmental Reorganizations

In macrodevelopment there seem to be several candidates for universal large-scale reorganizations—times when major new types of skills are emerging and development is occurring relatively fast. Different structuralist frameworks share a surprising consensus about most of these levels, although opinions are not unanimous (Kenny, 1983). The exact characterizations of each level also vary somewhat across frameworks. Our descriptions of each level, including the age of emergence, are intended to capture the consensus.

Between 4 and 18 years of age—the time when many children spend long periods of time in a school setting—there seem to be four levels. The first major reorganization, apparently beginning at approximately age 4 in middle-class children in Western cultures, is characterized by the ability to deal with simple relations of representations (Bickhard, 1978; Biggs and Collis, 1982; Case and Khanna, 1981; Fischer, 1980; Isaac and O'Connor, 1975; Siegler, 1978; Wallon, 1970). Children acquire the ability to perform many tasks that involve coordinating two or more ideas. For example, they can do elementary perspective-taking, in which they relate a representation of someone else's perceptual viewpoint with a representation of their own (Flavell, 1977; Gelman, 1978). Similarly, they can relate two social categories, e.g., understanding how a doctor relates to a patient or how a mother relates to a father (Fischer et al., in press).

The term representation here follows the usage of Piaget (1936/1952; 1946/1951), not the meaning that is common in information-processing models (e.g., Bobrow and Collins, 1975). Piaget hypothesized that late in the second year children develop representation, which is the capacity to think about things that are not present in their immediate experience, such as an object that has disappeared. He suggested that, starting with these initial representations, children show a gradual increase in the complexity of representations throughout the preschool years, culminating in a new stage of equilibrium called ''concrete operations'' beginning at age 6 or 7.

Research has demonstrated that children acquire more sophisticated abilities during the preschool years than Piaget had originally described (Gelman, 1978), and theorists have hypothesized the emergence of an additional developmental level between ages 2 and 6—one involving simple relations of representations. The major controversy among the various structural theories seems to be whether this level is in fact the beginning of Piagetian concrete operations or a separate reorganization distinct from concrete operations. Many of the structural approaches recasting Piaget's concepts in information-processing terms have treated this level as the beginning of concrete operations (Case, 1980; Halford and Wilson, 1980; Pascual-Leone, 1970).

For Piaget (1970), the second level, that of concrete operations, first appears at age 6-7 in middle-class children. In many of the new structural theories, concrete operations constitute an independent level, not merely an elaboration of the level involving simple relations of representations (Biggs and Collis, 1982; Fischer, 1980; Flavell, 1977). The child comes to be able to deal systematically with the complexities of representations and so can understand what Piaget described as the logic of concrete objects and events. For example, conservation of amount of clay first develops at this level. In social cognition the child develops the capacity to deal with complex problems about perspectives (Flavell, 1977) and to coordinate multiple social categories, understanding, for example, role intersections, such as that a man can simultaneously be a doctor and a father to a girl who is both his patient and his daughter (Watson, 1981).

The third level, usually called formal operations (Inhelder and Piaget, 1955/1958), first emerges at age 10-12 in middle-class children in Western cultures. Children develop a new ability to generalize across concrete instances and to handle the complexities of some tasks requiring hypothetical reasoning. Preadolescents, for example, can understand and use a general definition for a concept such as addition or noun (Fischer et al., 1983), and they can construct all possible combinations of four types of colored blocks (Martarano, 1977). Some theories treat this level as the culmination of concrete operations, because it involves generalizations about concrete objects and events (Biggs and Collis, 1982). Others consider it to be the start of something different—the ability to abstract or to think hypothetically (Case, 1980; Fischer, 1980; Gruber and Voneche, 1976; Halford and Wilson, 1980; Jacques et al., 1978; Richards and Commons, 1983; Selman, 1980).

Recent research indicates that cognitive development does not stop with the level that emerges at age 10-12. Indeed, performance on Piaget's formal operations tasks even continues to develop throughout adolescence (Martarano, 1977; Neimark, 1975). A number of theorists have suggested that a fourth level develops after the beginning of formal operations—the ability to relate abstractions or hypotheses, emerging at age 14-16 in middle-class Western children (Biggs and Collis, 1982; Case, 1980; Fischer et al., in press; Gruber and Voneche, 1976; Jacques et al., 1978; Richards and Commons, 1983; Selman, 1980; Tomlinson-Keasey, 1982). At this fourth level, adolescents can generate new hypotheses rather than merely test old ones (Arlin, 1975); they can deal with relational concepts, such as liberal and conservative in politics (Adelson, 1975); and they coordinate and combine abstractions in a wide range of domains.

Additional levels may also develop in late adolescence and early adulthood (Biggs and Collis, 1980; Case, 1980; Fischer et al., 1983; Richards and Commons, 1983). At these levels, individuals may able to deal with complex relations among abstractions and hypotheses and to formulate general principles integrating systems of abstractions.

Unfortunately, criteria for testing the reality of the four school-age levels have not been clearly explicated in most cognitive-developmental investigations. There seems to be little question that some kind of significant qualitative change in behavior occurs during each of the four specified age intervals, but researchers have not generally explicated what sort of qualitative change is substantial enough to be counted as a new level or stage. Learning a new concept, such as addition, can produce a qualitative change in behavior; but by itself such a qualitative change hardly seems to warrant designation as a level. Thus, clearer specification is required of what counts as a developmental level.

Research on cognitive development in infancy can provide some guidelines in this regard. For infant development, investigators have described several patterns of data that index emergence of a new level. Two of the most promising indexes are (1) a spurt in developmental change measured on some continuous scale (e.g., Emde et al., 1976; Kagan, 1982; Seibert et al., in press; Zelazo and Leonard, 1983) and (2) a transient drop in the stability of behaviors across a sample of tasks (e.g., McCall, 1983). Research on cognitive development in school-age children would be substantially strengthened if investigators specified such patterns for hypothesized developmental levels and tested for them. Available evidence suggests that these patterns may index levels in childhood as well as they do in infancy (see Fischer et al., in press; Kenny, 1983; Peters and Zaidel, 1981; Tabor and Kendler, 1981).

In summary, there seem to be four major developmental reorganizations, commonly called levels, between ages 4 and 18. Apparently, the levels do not exist in a strong form such as that hypothesized by Piaget (1949, 1975) and others (Pinard and Laurendeau, 1969). Consequently, the strong stage hypothesis has been abandoned by many cognitive-developmental researchers, including some Piagetians (e.g., Kohlberg and Colby, 1983). Yet the evidence suggests that developmental levels fitting a weaker concept of stages probably do exist.

Relativity And Universality Of Developmental Sequences

One of the best-established facts in cognitive development is that performance does not strictly adhere to stages. On the contrary, developmental stages vary widely with manipulations of virtually every environmental factor studied (Flavell, 1971, 1982b). Developmental unevenness, also called horizontal decalage (Piaget, 1941), seems to be the rule for development in general (Biggs and Collis, 1982; Fischer, 1980). During the school years it may well become even more common than in earlier years. By the time children reach school age they seem to begin to specialize on distinct developmental paths based on their differential abilities and experiences (Gardner, 1983; Horn, 1976; McCall, 1981). Some weak forms of developmental stages—what we have called levels—probably exist, as we have noted, but they occur in the face of wide variations in performance.

Since developmental unevenness has been shown to be pervasive, it seems inevitable that developmental sequences will vary among children and across contexts. Unfortunately, there have been few investigations testing for variations in sequence. Most of the studies documenting the prevalence of decalage are designed in such a way that they can detect only variations in the speed of development on a fixed sequence, not variations in the sequence itself. The dearth of studies testing for individual differences in sequence, apparently arises from the fact that cognitive developmentalists have been searching for commonalities in sequence, not differences.

Nevertheless, a few studies have expressly assessed individual differences, and their results indicate that different children and different situations do in fact produce different sequences (Knight, 1982; McCall et al., 1977; Roberts, 1981). A plausible hypothesis is that developmental sequences are relative, changing with the child, the immediate situation, and the culture.

To examine this hypothesis researchers must face an important hidden issue—the nature and generality of the classifications used to code successive levels or steps of behavioral organization. Indeed, when issues of classification are brought into the analysis, it becomes clear that universality and relativity of sequence are not opposed. With a general mode of analysis, children can all show the same developmental sequence in some domain, while with a more specific mode of analysis they can all demonstrate different sequences in the same domain.

Figure 3-1 helps show why. The arrows and solid boxes depict developmental paths taken by two children, boy X on the left and girl Y on the right. The letters in the boxes indicate the specific content of the behaviors at each step, and the hyphens connecting letters indicate that two contents have been coordinated or related. The word step is used to describe a specific point in a sequence without implying how that step relates to developmental levels such as those described above.

Two developmental sequences demonstrating both commonalities and individual differences.

Depending on how these sequences are analyzed, they can demonstrate either commonalities or individual differences—that is, that both children move through the same sequences or that each child moves through a different sequence. When viewed in terms of the specific steps each child traverses, the figure shows different developmental sequences. At step 1, child X can control skill or behavior F, and at step 2 he can control skills F and M separately but prefers F. Finally he reaches step 3, where he can relate F to M. Child Y at step 1 can control skill M, and at step 2 she can control both M and F but prefers M. Finally she reaches step 3, where she can relate M to F. For example, in social play, F might represent the social category for father, M the social category for mother, F-M an interaction in which the father dominates, controlling what the mother does, and M-F an interaction in which the mother dominates, controlling what the father does. Thus, all three steps clearly differ for the two children.

Such plurality would seem to contradict the idea of a universal developmental sequence, since the two children are demonstrating different sequences for similar content. Yet when the specific steps are characterized more generally, it is possible to see these different paths as variations on a common theme. Analysis in terms of the social categories present, for instance, leads to the conclusion that steps 2 and 3 are the same in the two children: At step 2 both children comprehend the two separate categories of mother and father, and at step 3 they both understand how a mother and a father can interact.

In a still more general classification, the steps can be defined in terms of social category structure rather than the particular categories. Then, steps 2 and 3 remain equivalent for the children, and, in addition, step 1 becomes equivalent, since both children control similar structures, a single category (mother or father). In addition, skills that deal with markedly different contents can also be considered equivalent. An interaction between a doctor and a patient is equivalent structurally to the interaction between mother and father at step 3, since both interactions involve a social role relation between two categories.

When cognitive-developmental theorists posit general developmental levels, they are defining developmental sequences even more abstractly—in terms of highly general, structural classes of behaviors. For the level of concrete operations, for example, the conservation of amount of clay can be considered structurally equivalent to the intersection of social categories (Fischer, 1980). Conservation of clay involves the coordination of two dimensions (length and width) in two balls of clay, and the intersection of categories involves the coordination of two social categories for two people (such as doctor/father with patient/daughter).

These considerations lead to a reconceptualization of the controversy over whether developmental sequences are relative or universal. For highly specific classes of behavior, universality would seem impossible, relativity inevitable. At the extreme, even the social category of mother is not the same for the two children, since the behaviors and characteristics that each child includes in the category undoubtedly differ. As a result of such variations, no two randomly chosen children could be expected to show the same specific developmental sequences. Even identical twins exposed to, say, a common mathematics curriculum would follow developmental paths for mathematics that differed in detail. Thus, a useful analysis must distinguish irrelevant from relevant detail and generalize over the latter.

Of course, what counts as relevant detail depends on the researcher's purpose. And care must be taken to avoid trivialization of the issue of universality in a second way—by using overly general or ill-defined classes. It is important that what counts as an equivalent structure be specified with some precision. For example, all instances of two units of something cannot be counted as equivalent unless there is a clear rationale for classifying the units as equivalent. With social categories, it would seem unwise to treat "mother" as structurally equivalent to "corporation president." One of the primary tasks for cognitive developmentalists is to devise a system for analyzing structural equivalences across domains (Flavell, 1972, 1982a; Wohlwill, 1973).

Assuming an opposition between relativity and universality, then, is too simple, because at times individual differences may usefully be seen as variations on a common theme. Many of the current disagreements among researchers about universality and relativity in sequences could be clarified by consideration of the nature of the structural classifications being used. In practice, investigators can use a straightforward rule of thumb: They can construct their classes at an intermediate degree of abstraction—neither so specific as to miss valid generalization nor so general that they serve only the purpose of imposing order.

How the controversy about relativity and universality will be resolved rests in part on whether the structures and processes of developmental reorganization can be usefully regarded as similar across different domains of cognition and across children who differ in their achievements within domains. Can the growth of linguistic skill be usefully described in the same terms as the growth of mathematical skill? Or are there distinct linguistic and mathematical faculties whose development remains fundamentally dissimilar in any useful system for classifying sequences (Gardner, 1983)? Is the difference between a retarded child and a prodigy a difference of sequence or a difference in the speed of mastering what can usefully be considered the same sequence (Feldman, 1980)? These questions are just beginning to be addressed in a sophisticated manner.

Processes Of Development

Many of the questions about the nature of developmental stages, their universality, and the extent of individual differences would be substantially clarified by a solid analysis of the processes underlying cognitive development. However, the best way to conceptualize the results of the extensive research literature on developmental processes is very much an open question. No emerging consensus is evident here, except perhaps that none of the traditional explanations is adequate. Three main types of models have dominated research to date.

The first type of model grows out of Piaget's approach. The developing organization of behavior is said to be based fundamentally in logic (Piaget, 1957, 1975). Developmental change results from the push toward logical consistency. Stages are defined by the occurrence of an equilibrium based on logical reversibility, and two such equilibria develop during the school years—one at concrete operations and one at formal operations.

Tests of this process model have proved to be remarkably unsuccessful. The primary empirical requirement of the model is that, when a logical equilibrium is reached, individuals must demonstrate high synchrony across domains. The prediction of synchrony arises from the fact that at equilibrium a logical structure of the whole ( structure d'ensemble ) emerges and quickly pervades the mind, catalyzing change in most or all of the child's schemes. Consequently, when a 6-year-old girl develops her first concrete operational scheme, such as conservation of number, the logical structure of concrete operations should pervade her intelligence in a short time, according to Piaget's model. Her other schemes should quickly be transformed into concrete operations.

Such synchrony across diverse domains has never been found. Instead, synchrony is typically low, even for closely related schemes such as different types of conservation (e.g., number, amount of clay, and length). Even if one allows that several concrete operational schemes might have to be constructed before the rapid transformation occurs, the evidence does not support the predicted synchrony (Biggs and Collis, 1982; Fischer and Bullock, 1981; Flavell, 1982b).

Efforts to study other implications of the logic model also have failed to support it (e.g., Braine and Rumain, 1983; Ennis, 1976; Osherson, 1974). Several attempts have been made to build alternative models based on some different kind of logic (e.g., Halford and Wilson, 1980; Jacques et al., 1978). But thus far there have been only a few studies testing these models, and it is therefore not yet possible to evaluate their success.

The second type of process model in cognitive-developmental theories is based on the information-processing approach. The child is analyzed as an information-processing system with a limited short-term memory capacity. In general, the numbers of items that can be maintained in short-term memory are hypothesized to increase with age, thereby enabling construction of more complex skills. The exact form of the capacity limitation is a matter of controversy, but in all existing models it involves an increase in the number of items that can be processed in short-term or working memory. The increase is conceptualized as a monotonic numerical increment from 1 to 2 to 3, and so forth, until some upper limit is reached.

This memory model has been influential and has generated a large amount of interesting research, although it has not yet produced any consensus about the exact form of the hypothesized memory process (Dempster, 1981; Siegler, 1978, 1983). One of the primary problems with the model seems to be the difficulty of using changes in the number of items in short-term memory to explain changes in the organization of complex behavior. Although analysis of behavioral organization is always difficult, the distance between the minimal structure in short-term memory and the complex structure of a behavior such as conservation or perspective-taking seems to be particularly difficult to bridge. How can a linear numerical growth in memory be transformed into a change from, for example, concrete operational to formal operational perspective-taking skills (Elkind, 1974)? Although such a transformation may be possible, its nature has not proved to be transparent or simple (Flavell, 1984).

Moreover, how to conceptualize working memory is itself a controversial issue. Various investigators have challenged the traditional conceptualization that there is an increase in the size of the short-term memory store (Chi, 1978; Dempster, 1981; see also Grossberg, 1982: chs. 11 and 13). Fortunately, ever richer developmental models involving ideas about working memory capacity have continued to appear since Pascual-Leone's (1970) ground-breaking work (see Case, 1980; Halford and Wilson, 1980), and perhaps one of these will be successful in overcoming the problems mentioned.

The third common type of model assumes that development involves continuous change instead of general reorganizations of behavior like those predicted by the logic and limited-memory models. The fundamental nature of intelligence is laid down early in life, and development involves the accumulation of more and more learning experiences. Behaviorist analyses of cognitive development constitute one of the best-known forms of this functionalist model. A small set of processes defines learning capacity, such as conditioning and observational learning, and all skills—ranging from the reflexes of the newborn infant to the creative problem solving of the artist, scientist, or statesman—are said to arise from these same processes (Bandura and Walters, 1963; Skinner, 1969). Any behavioral reorganizations that might occur are local, involving the learning of a new skill that happens to be useful in several contexts.

Some information-processing approaches also assume that the nature of intelligence is laid down early and that development results from a continuous accumulation of many learning experiences: The child builds and revises a large number of cognitive "programs," often called production systems (Gelman and Baillargeon, 1983; Klahr and Wallace, 1976). Children construct many such systems, such as one for conservation of amount of clay and one for conservation of amount of water in a beaker. At times they can combine several systems into a more general one, as when conservation of clay and conservation of water are combined to form a system for conservation of continuous quantities. These reorganizations remain local, however. There are no general levels or stages in cognitive development—no all-encompassing logical reorganizations and no general increments in working memory capacity.

Researchers who believe in the continuous-change model tend to investigate the effects of specific types of processes or content domains on the development of particular skills. One of the processes emphasized within the continuous change framework has been automatization, the movement from laborious execution of a skill or production system to execution that is smooth and without deliberation. Several studies have demonstrated that automatization can produce what seem to be developmental anomalies. When school-age children are experts in some domain, such as chess, they can perform better than adults who are not experts (Chi, 1978). More generally, many types of tasks produce no differences between the performances of children and adults (Brown et al., 1983; Goodman, 1980).

In research on specific content domains, the general question is typically how the nature of a domain affects a range of developing behaviors. For example, the nature of language, mathematics, or morality is said to produce "constraints" on the form of development in relevant behaviors (Keil, 1981; Turiel, 1977). Development in domains that involve self-monitoring, such as knowledge about one's own memory processes (metamemory), is hypothesized to have general effects on many aspects of cognitive development (Brown et al., 1983; Flavell and Wellman, 1977).

Within the continuous-change, functionalist framework, investigators often assume that there is some intrinsic incompatibility between general cognitive-developmental reorganizations and effects of specific domains or processes. Yet it is far from obvious that any such incompatibility exists. The process of automatization can have powerful effects on developing behaviors, and at the same time children can show general reorganizations in those behaviors (Case, 1980). The domain of mathematics can produce constraints on the types of behaviors children can demonstrate, and at the same time those behaviors can be affected by general reorganizations. The reason for the assumption of incompatibility seems to be that developmentalists view the logic and limited-memory models as incompatible with the continuous-change model.

The assumption of incompatibility between reorganization and continuous change seems to stem from the fundamental starting points of the models: The logic and short-term memory models focus primarily on the organism as the locus of developmental change, whereas the continuous models focus on environmental factors. Several recent theoretical efforts have sought to move beyond this limit of the three standard models by providing a more genuinely interactional analysis, with major roles for both organismic and environmental influences (Fischer, 1980; Halford and Wilson, 1980; Silvern, 1984). Approaches that explicitly include both organism and environment in the working constructs for explaining developmental processes may provide the most promise for future research.

• The Central Issues In The Field Today

The differences among the traditional approaches to development are important to understand, but they seem much less significant today than they did 10 years ago. A pervasive change in orientation seems to be taking place among behavioral scientists—a shift away from emphases on competing theories toward integrating whatever tools are available to explain behavior in the whole person, in all of his or her complexity. The present era seems to be a time of integrating rather than splitting. Structuralism and functionalism, for example, are seen not as competing approaches but as complementary ones, emphasizing different aspects of behavior and development. This new orientation is evident throughout this volume.

In the study of cognitive development, this change in the field appears to be associated with attempts to go beyond certain fundamental limitations of previous approaches and to move toward a more comprehensive framework for characterizing and explaining cognitive development. At least three basic questions have arisen as part of this movement toward a new, integrative framework. All three involve efforts to avoid conceptual orientations that have proved problematic in past research. The most fundamental of the three questions is: How do child and environment jointly contribute to cognitive development? The other two questions involve elaborations of this question: How do developing behaviors in different contexts and domains relate to each other? What methods are appropriate for analyzing cognitive development? In a general way the answers to these questions apply to development at any age, but the answers apply in particular ways to school-age children.

The Collaboration Of Child And Environment

The central unresolved issue in the study of cognitive development today seems to be the manner in which child and environment collaborate in development. As a result of the cognitive revolution, it is generally accepted that the child is an active organism striving to control his or her world. But this emphasis on the active child often seems to lead to a neglect of the environment. Contrary to the structural approaches of such theorists as Piaget (1975) and Chomsky (1965), it appears to be impossible to explain developing behavior without giving a central role to the specific contexts of the child's action, including those in the school environment (see Scribner and Cole, 1981; Flavell, 1982b).

Giving context a central role does not mean merely demonstrating once again that environmental factors affect assessments of developmental steps. Researchers have documented these effects in thousands of studies, thus pointing out the inadequacies of the Piagetian approach to explaining the unevenness of development. Surely Piaget, Kohlberg (1969, 1978), and other traditional structural theorists have failed to deal adequately with the environment. It is also true, however, that the functionalists have not produced a satisfactory alternative—an approach that both deals with the environment's roles in development and treats children as active contributors to their own development (Lerner and Busch-Rossnagel, 1981). An analysis of the collaboration of child and environment in development is just as unlikely to arise from a functionalist emphasis on the environment as from a structuralist emphasis on the child.

A Diagnosis

Why has the study of cognitive development repeatedly fallen back on approaches that focus primarily on either the child or the environment? Why have developmentalists failed to build approaches based on the collaboration of child with environment?

Historically, developmental psychology has been plagued by repeated failures to accept what should be one of its central tasks: to explain the emergence of new organization or structure. These failures have most commonly taken either of two complementary forms. In one form, nativism, the structures evident in the adult are seen as already preformed in the infant. These structures need only be expressed when they are somehow stimulated or nourished at the appropriate time in development. In the second form, environmentalism, the structures in the adult are treated as already preformed in the environment. These structures need only be internalized by some acquisition process, such as conditioning or imitation. Typically, structuralist approaches assume some form of nativism, and functionalist approaches assume some type of environmentalism.

Although it is common to focus on the difference between nativism and environmentalism, there is a fundamental similarity, a common preformism.

Both approaches reduce the phenomena of development to the realization of preformed structures. The mechanisms by which the structures are realized are clearly different, but in both cases the structures are present somewhere from the start—either in the child or in the world (Feffer, 1982; Fischer, 1980; Sameroff, 1975; Silvern, 1984; Westerman, 1980).

A mature developmental theory, we believe, must move beyond explanation by reduction to preexisting forms. It must build constructs that explain how child and environment collaborate in development, and one of the primary tasks of such constructs must be to explain how new structures emerge in development (Bullock, 1981; Dennett, 1975; Haroutunian, 1983).

If the future is not to be a reenactment of the past, it is important to ask why it has been so difficult to avoid drifting toward one or another type of preformism. Why has no well-articulated, compelling alternative to preformism been devised? Any compelling alternative to preformism must describe how child and environment collaborate to produce new structures during development. Constructing such a framework is an immensely difficult task. At the very least, the framework must make reference to cognitive structure, environmental structure, the interaction of the two, and mechanisms for change in structure. The scope of these issues makes such a framework difficult to formulate and difficult to communicate once formulated.

Unfortunately, even approaches that have explicitly attempted to move beyond preformist views have typically failed to do so. Piaget provides a case in point. He set out expressly to build an interactionist position, an approach that would deal with both child and environment and thus avoid the pitfalls of nativism and environmentalism (Piaget, 1947/1950). Yet the theory he eventually built placed most of its explanatory weight on the child and neglected the environment.

Consider, for example, his famous digestive metaphor for cognitive development. Just as the digestive system assimilates food to the body and accommodates to the characteristics of the particular type of food, so children assimilate an object or event to one of their schemes and accommodate the scheme to the object or event. Piaget seems to have chosen this metaphor expressly as a device to avoid preformist thinking, yet he still drifted back toward preformism. In practice, the focus for applications of the metaphor was the assimilation of experience to preexisting schemes. The other side of the metaphor—accommodation to experience—was systematically neglected. For example, Piaget (1936/1952, 1975) differentiated many different types of assimilation but generally spoke of accommodation in only global, undifferentiated terms.

Similarly, the structures behind Piaget's developmental stages—concrete operations and formal operations in school-age children—were treated as static characteristics of the child. The environment was granted an ill-defined role in supporting the emergence of the structures, but the structures themselves were treated as if they came to be fixed characteristics of the child's mind (Piaget and Inhelder, 1966/1969). In a genuinely interactionist position, these structures would have been attributed to the collaboration of the mind with particular contexts. Piaget's neglect of the environment became particularly evident when he was faced with a host of environmentally induced cases of developmental unevenness (termed horizontal decal-age). His response was that it was simply impossible to explain them (Piaget, 1971:11). Because of Piaget's neglect of the environment, even supporters of his position have argued that it is essentially nativist (Beilin, 1971; Broughton, 1981; Flavell, 1971).

Toward A Remedy

If the foregoing diagnosis is accurate, any remedy must explicitly counteract the tendency to drift toward attributing cognitive structures to either the child or the environment. What is needed seems to be a framework providing constructs and methods that force researchers to explicitly deal with both child and environment when they characterize how new structures emerge in development.

What might such a framework look like? Many would recommend general systems theory, because it views the child as an active component in a larger-scale dynamic system that includes the environment. To date, however, systems theory does not seem to have been successful in promoting research explicating the interaction between child and environment in development. Many investigators appear simply to have learned the vocabulary of the approach without changing the way they study development. Apparently, the concepts of systems theory lack the definiteness needed to guide empirical research in cognitive development toward a new interactional paradigm. A few provocative approaches based on general systems concepts have begun to appear in the developmental literature (e.g., Sameroff, 1983; Silvern, 1984), but they seem to bring to bear additional tools that specifically promote interactional analyses.

It is in such practical tools that the proposed remedy lies. To promote interactional analyses, a framework needs to affect the actual practice of cognitive-developmental research. We would like to suggest that the concept of collaboration may provide the basis for such a framework.

The Collaborative Cycle

Human beings are social creatures, who commonly work together for shared goals. That is, people collaborate. Often when two people collaborate to solve a problem, neither one possesses all the elements that will eventually appear in the solution. During their collaboration, a social system (Kaye, 1982) emerges in which each person's behavior supports the other's behavior and thought in directions that would not have been taken by the individuals alone. Eventually a solution—a new cognitive structure—emerges. It bears some mark of each individual, yet it did not exist in either person prior to the collaboration, nor would it have developed in either one without the collaboration. Indeed, even after the structure has developed, the individuals may be able to access it only by reconstituting the collaboration. Of course, besides having the same two people collaborate again, it is also possible for one of them to collaborate with a different partner (Bereiter and Scardamalia, 1982; Brown et al., 1983; Maccoby and Hartup, in this volume).

Figure 3-2 shows this developmental process as a collaborative cycle. The two left circles represent, respectively, structures that are external and internal to an individual. Consider a girl engaged in solving a puzzle with her father. The father provides external structures to support or scaffold her puzzle solving by stating the goal of the task, lining up a puzzle piece to highlight how it fits in its particular place, providing verbal hints, and so forth (Brown, 1980; Kaye, 1982; Wertsch, 1979; Wood, 1980). The child's knowledge and skills for solving the puzzle constitute the core of the developing internal structures.

Development schematized as a collaborative cycle.

The collaboration of external and internal structures produces the behavioral episodes represented in the right circle. The girl and her father work at solving the puzzle, and, as a result of the collaboration, she can achieve a scaffolded mental state, which she could not achieve by herself as quickly or in the same form.

The feedback arrows running from the right circle to the left ones in Figure 3-2 show the dependence of developmental change on collaboration. By performing the task in a scaffolded interaction, the girl learns the goal of the puzzle and how to go about solving it without her father's help. She develops more sophisticated internal structures so that she is less dependent on the complex external structures provided by her father. Of course, the development of this ability takes many steps: The father constantly updates his scaffolding to fit the child's present knowledge and skill. In this way, developmental change occurs both inside the child and outside her—an often overlooked fact to which we will return.

In much human behavior there is indeed a collaboration between two or more individuals. Recent socially oriented analyses of development have emphasized this process. Sometimes the emphasis is on the joint contributions of collaborating individuals, and the process is called coregulation or something similar (see Feldman, 1980; Markus and Nurius, Maccoby, and Weisner, in this volume; Westerman and Fischman-Havstad, 1982). Sometimes the emphasis is on the role of the parent or older child in supporting and advancing the child's behavior, and the process is called scaffolding or something similar, as in Figure 3-2 (Brunet, 1982; Kaye, 1982; Laboratory of Comparative Human Cognition, 1983; Lock, 1980; Vygotsky, 1934/1978; Wertsch, 1979; Wood et al., 1976; Wood, 1980).

Even when a child is acting alone, collaboration can occur because the nonpersonal environment can play the role of collaborator. Because environments have structures, every environment supports some behaviors more than others. For example, a tree that has strong branches far down on its trunk provides strong support for climbing, a tree with only high branches provides less support, and a pole with no branches provides little support.

Of course, much about human environments is socially constructed. Consequently, the collaboration between child and environment often involves other people even when no other person is immediately present, because people have constructed the physical environment to correspond with mental structures that organize their activity. Good examples include a library with a spatial/topical organization of its many books and a classroom with its desks, chalkboards, and wall displays all designed to facilitate the types of interactions needed for schooling.

Implications For Research

Although the collaboration approach has not yet been fully articulated, it already seems to have straightforward implications for research practice. If child and environment are always collaborating to produce a behavior, explanations of that behavior must invoke characteristics of both. As a practical procedure to encourage such explanations, investigators can use research designs that vary important characteristics of both the child and the environment. With such designs, variations in both child and environment are likely to affect behavior (Fischer et al., in press; Hand, 1981).

A series of studies on the development of understanding social categories illustrates how this type of research design can lead to analyses of the collaboration between child and environment in cognitive development (Hand, 1982; Van Parys, 1983; Watson and Fischer, 1977, 1980). The studies were designed to test several predicted sequences for the development of social categories such as the social roles of doctor and patient and the social-interaction categories of ''nice'' and "mean." Each study was designed to include variations in both the child and the environment.

The main variable involving child characteristics was age. A wide age range was included in each study to ensure substantial variation in children's capacities to understand the social categories. Ages ranged from 1 to 12 and thus included the relevant periods for the major developmental reorganizations in preadolescent school-age children.

To determine the contribution of environmental characteristics, behavior was assessed under three different conditions, which were designed to provide varying degrees of support for advanced performance. In a structured condition—the elicited-imitation assessment—a separate task was administered to test each predicted step in the developmental sequence. The subject was shown a story embodying the skill required for that step and was asked to act out the story. Thus this condition provided high environmental support for performance at every step. The other two conditions provided less support and thus assessed more spontaneous behavior. In the free-play condition, each child played alone with the toys, acting out his or her own stories. In the best-story condition the experimenter returned to the testing room and asked the child to make up the best story he or she could.

The results showed a systematic effect of environmental support on the child's performance, but the effect varied as a function of the developmental level of the child's best performance. For the first several steps in the developmental sequence, virtually all children showed the same highest step in all three conditions. However, a major change occurred beginning with the first step testing the developmental level of simple relations of representations (which typically emerges at approximately age 4). At this step most children performed at a higher step in the structured assessment than in the two more spontaneous conditions, and that gap grew systematically in the later steps in the sequence. Figure 3-3 shows these results for the studies of the social roles of doctor and patient, and parallel results were obtained in studies of the social interaction categories of nice and mean (Hand, 1982) and the self-related categories of gender and age (Van Parys, 1983).

A systematic change in the proportion of children showing the same step in elicited imitation and free play. Adapted with permission from Watson & Fischer (1980). Copyright © American Psychological Association.

A similar design and method was used to test for an analogous phenomenon in adolescents. The developmental sequence involved the moral concepts of intention and responsibility. It was predicted that at the cognitive-developmental level of formal operations (also called "single abstractions") subjects would show the same highest step in a structured assessment and in a spontaneous condition. However, when they became capable of performing at the next developmental level, relations of abstractions, a major gap would appear between performance in the structured and spontaneous conditions. The prediction was supported. Once again, the highest developmental step that the individual demonstrated varied systematically as a function of both the individual's capacity and the environmental condition (Fischer et al., 1983).

In analyzing results of this sort a proponent of a noncollaborative approach would ask which condition provides the best assessment of the child's true competence. The collaboration theorist replies, "You've missed the point. Competence as traditionally assessed is a joint function of child and environment." The child does not have any true competence independent of particular environmental conditions. Competence varies with degree of support.

Even for an individual child research can be designed to investigate variations in both the child and the environment. Cole and Traupman (1983), for example, assessed a learning disabled child's capabilities using a range of cognitive tests and examined his performance in settings outside the classroom. They found that, in settings involving social interactions with other people, his disabilities were hardly noticeable because he used his social skills to compensate for them. Thus, the portrait of the child in a standard testing situation was vastly different from the portrait in a real-life social setting.

It is surprising how few cognitive-developmental studies have systematically varied characteristics of both child and environment. Typically, studies examine either changes with age and ability or changes resulting from environmental factors. In the infrequent studies that include variations in both child and environment, the interpretations often neglect the interaction and instead focus on the child and the environment separately. For example, many studies criticizing Piaget's work demonstrate that variations in environmental conditions produce developmental unevenness (decalage), but they seldom deal with the variations as a function of children's ages or abilities. Fortunately, there are a growing number of exceptions to this characterization—studies that seriously consider the effects of both child and environment on performance. The results of these studies are already beginning to transform explanations of cognitive development (see O'Brien and Overton, 1982; Rubin et al., 1983; Tabor and Kendler, 1981).

The Transformation Of Concepts Of Ability And Competence

As these research examples illustrate, analyzing development as a collaborative process leads to a reconceptualization of many basic cognitive-developmental concepts. Since every behavior can be seen to depend on a collaboration between child and environment, it becomes impossible to analyze any behavior without including both organismic and environmental factors.

Cognitive developmentalists and psychometricians commonly speak of children's ability, or capacity, or competence, as if a child possessed a set of static characteristics that could be defined independently of any context: One child has the ability to understand conservation of water, and another child does not. As soon as the collaborative role of the environment is introduced, these concepts must be radically changed. Competence is not a fixed characteristic of the child but an emergent characteristic of the child in a specific context. It is not enough to make a distinction between competence and performance, because in standard usage this distinction begs the question. The assumption is made that children really do possess a set of competences, but they are somehow prevented from demonstrating them in their performance (Overton and Newman, 1982). If concepts such as ability and competence are to be consonant with a collaboration approach, they must be redefined in terms of the interaction of child with environment.

Within a collaboration approach, concepts of ability and competence retain their utility, because the child is part of the analysis, too. In certain contexts, children perform up to a certain level of complexity and not beyond it, thus demonstrating a certain competence for those contexts. At times children show partial knowledge of what is needed for a particular task (Brown et al., 1983; Feffer, 1982) and so demonstrate the competence for collaboration with a more knowledgeable partner. Also, children evidence large individual differences in the facility with which they can generalize an ability to new contexts, thus demonstrating variations in the competence to generalize. Upon the emergence of formal operation, for example, very bright children seem to be able to use their new capacity quickly in a wide range of tasks, whereas children of normal intelligence take much longer to extend the capacity to many tasks (Fischer and Pipp, 1984; Webb, 1974).

The collaboration orientation poses many new questions for the study of cognitive development. It is not enough to ask questions such as: How does the child's behavior change with age, or how does the child's behavior change as a function of experience? Instead, questions like the following need to be asked: Why do children often perform below capacity? How does context support or fail to support high level performances that are known to be within the child's reach? How do specific collaborative systems support the acquisition of particular skills in different ways at different developmental levels? How is the nature of the child's experience jointly regulated by the child and by resources (human and other) available in the child's environment? Later, we examine several lines of research that show promise of contributing answers to such questions.

Integrating Across Traditional Research Categories

In the same way that scholars are coming to treat child and environment as collaborators in development they are recognizing the need to integrate the traditional categories for categorizing behavior. Cognition and emotion, for example, are not separate in the developing child. There seem to be at least three reasons for this changing orientation.

First, after decades of research, developmentalists have found that a child's behavior does not fit neatly into separate boxes labeled cognition, emotion, motivation, social skills, personality, and physical development (see, for example, Harter, 1982, 1983; Selman, 1980). Indeed, even behavior in more restricted, intuitively appealing categories such as perspective taking and conservation does not fit together coherently (see Hooper et al., 1971; Rubin, 1973; Uzgiris, 1964). Behavioral development has not proved to follow the "obvious" categories devised by developmentalists.

Second, the general movement toward integrating diverse approaches and dealing with the whole child leads not only to an emphasis on the collaboration of child and environment but also to the consideration of relations between behaviors in the traditional categories: How does emotional development relate to cognitive development? How does social development relate to cognitive development? Instead of one set of researchers studying a cognitive child, while another set studies a social child, and still another set studies an emotional child, the field is moving toward viewing the child as a whole—a cognitive, social, emotional, motivated, personal, biological child.

Third, during the last 20 years the cognitive-developmental orientation has become a dominant influence in the study of development, and it has provided a major impetus toward integration. The central questions in the study of cognitive development involve the organization of behavior and the processes underlying behavioral change. Because these questions are so general and fundamental, their applicability is not limited to the traditional domain of cognitive development—increments in knowledge about "cold" topics, such as objects, space, and scientific principles. All behavior, including that involving "hot" topics, such as emotions and social interaction, is organized in some way and undergoes developmental change.

The movement toward integration across behavioral categories has been promising, and many interesting results have come from research in this new tradition. But thus far progress has been limited by several conceptual difficulties.

Overcoming The Obstacles

One of the central conceptual problems has been the tendency to reify the traditional behavioral categories despite the lack of evidence that children's behavior fits the categories. Thus, the most common hypotheses about the relationship between, for example, cognitive development and social development have assumed the validity of cognition and social skills as separate categories. This assumption is especially clear when cognitive development is postulated as a prerequisite for social development.

One such hypothesis that has received much attention involves the relation between cognition and morality: Cognitive development is hypothesized to be a prerequisite for moral development (see Kohlberg, 1969). In practice, this proposition has been taken to mean that performance on Piagetian tasks is a prerequisite for performance on Kohlberg's moral dilemmas. Why should conservation of amount of clay, for instance, be a prerequisite for moral reasoning based on normative concepts of good and bad (Kohlberg's stage 3)? Is there any sense in which conservation is included in the concepts of good and bad? Or is there any way that conservation is more fundamental to mental functioning than concepts of good and bad? Isn't it just as reasonable (or unreasonable) to suggest that concepts of good and bad may be a prerequisite for conservation? If evidence does not support the division of behavior into separate categories of cognition about science problems and moral reasoning, it cannot be meaningful to suggest that such cognition is a prerequisite for moral reasoning (Rest, 1979, 1983).

A similar problem arises when investigators assume that the behaviors captured by the traditional categories are totally separate, showing no relation to each other at all. One of the most neglected topics for school-age children is emotional development, which is sometimes treated as if it is not related at all to cognitive development. Perhaps this assumption helps explain why cognitive developmentalists have omitted emotions from their research agenda. In a later section we suggest some guidelines for stimulating the study of emotional development in school-age children, especially as it relates to cognitive development.

A third, related conceptual problem has been the assumption that one variable can capture an entire behavioral category. Self-esteem as assessed by a questionnaire is treated as measuring the core of the developing self (Hatter, 1983; Markus and Nurius, in this volume; Wylie, 1979). The stage of moral judgment, as assessed by reasoning about a set of moral dilemmas, is believed to assess the fundamental nature of moral development (Rest, 1983).

This mistaken assumption is at the heart of a recent controversy about the nature of brain-behavior relations. Several investigators have used measurements of the growth rate of children's heads as indexes of changes in the children's ability to learn (Epstein, 1978; Toepfer, 1979). Although no measures of learning were used, conclusions were drawn from the head-growth data about what children of different ages were able to learn. The relationship between brain growth and cognitive development is an exciting topic worthy of research, as we discuss later. It is important, however, that researchers differentiate what they are measuring from other developmental changes. Relationships between developments in different domains cannot be assumed; they must be assessed.

Since the traditional categories for categorizing behavior do not seem to capture either the way behavior is organized or how its organization develops, it makes sense to analyze development across categories. More generally, the concern for explaining development in the whole child and for building a framework that emphasizes the collaboration of child and environment demands that researchers assess behavior in multiple contexts and with various methods. In doing such research, however, developmentalists need (1) to avoid allowing the categories to limit their thinking, as when cold cognition is considered to be a prerequisite for moral reasoning, and (2) to avoid assuming that a single variable will provide a valid index of overall cognitive functioning, as when head growth is treated as if it directly reflects cognitive changes.

In practice, doing research on development across traditional categories is closely related to doing research on the collaboration of child and environment in development. In both cases a number of variables must be measured in several settings, and the investigator must analyze not only each variable itself but also the relations among variables. Consider, for example, research on the effects of divorce on the school-age child. It would appear to be wise to assess (1) the child's understanding of family roles and the effects of divorce on that understanding, (2) the child's emotional reactions to the divorce, (3) the types of social interactions between parents and child and the changes in those interactions that resulted from the divorce, (4) the child's attitudes toward the parents, and so forth. On the basis of the collaboration argument, it may also be important to measure each of these factors under several different degrees of environmental support. Obviously, such research is difficult because it can quickly become unmanageably complex.

Despite this complexity it is possible to do research on patterns of development across categories without either being overwhelmed by complexity or becoming entangled in the conceptual problems that have plagued much past research. At least two helpful guidelines can be articulated: First, development should be analyzed in what promises to be a coherent domain of personal functioning. For example, an investigator might study the mastery of early skills involved in learning to read words (for example, Knight, 1982) or the relationship of divorce to a child's understanding and use of social roles in the family. Within such domains the investigator can examine development in different contexts while still keeping the project within a manageable scope. In addition, the coherence of the domain itself will often provide environmental support to guide the investigator's efforts.

Second, the researcher needs to use methods and measures appropriate to the questions being addressed. Of course, this admonition has been made often. In cognitive-developmental research, however, inadequate methods have been used repeatedly even when appropriate methods were available. In addition, recent innovations in developmental methodology have provided powerful methods for studying many fundamental developmental issues, including relationships between development in different contexts.

Methods Of Assessing Development Change And Continuity

Cognitive-developmental research has not generally been distinguished by the sophistication of its methodology. One of the primary reasons has been that the traditional methods used in the behavioral sciences are not appropriate for studying such issues as developmental change and continuity (Wohlwill, 1973). Analysis of variance, for example, was originally constructed to test whether one or more factors made a difference in the outcomes of independent, equivalent groups. It was not constructed to examine questions about cognitive-developmental issues such as changes in the organization of behavior.

Children almost invariably become smarter as they grow older, and so it has been a simple matter in cognitive-developmental research to use analysis of variance to demonstrate differences between age groups and to use correlations to demonstrate relations between development and age. By themselves, such differences and relations can be uninteresting unless they help answer important questions such as the following: Do children show a systematic developmental sequence in a given domain? Does that sequence demonstrate reorganization of behavior? Are there differences in the speed of developmental change at different times in that domain? Across domains or contexts, are there systematic relations among sequences, reorganizations, changes in speed, or other developmental patterns?

Fortunately, there has recently been substantial progress in constructing designs, measures, and statistics for asking developmental questions (Applebaum and McCall, 1983; Bart and Krus, 1973; Coombs and Smith, 1973; Fischer et al., in press; Krus, 1977; Siegler, 1981; Wohlwill, 1973). Although we do not review all these methods here, we do sketch some of the important concepts behind them.

Developmental Sequences

Systematic change is clearly one of the fundamental concerns of developmental science in general. In cognitive development the tool used most often to describe and analyze systematic change has been the developmental sequence—a series of steps, levels, or stages that portray how behavior gradually changes from some starting point to some endpoint (Flavell, 1972). As a descriptive tool the sequence has been at the center of cognitive-developmental research, providing the core set of observations on which most cognitive-developmental theories are based, ranging from classical approaches (for example, Piaget and Inhelder, 1966/1969; Werner, 1957) to more recent ones (for example, Case, 1980; Siegler, 1981).

Developmental sequences demonstrate not only developmental change but also a form of developmental continuity. They describe how one type of behavior gradually changes into another, and scales based on sequences can be used to examine when change is relatively gradual and continuous and when it is relatively abrupt and discontinuous.

Since the developmental sequence is so important to the study of cognitive development, scaling should clearly be a central concern in research. Documenting that a description of a series of steps in fact forms a scale would seem to be integral to the research enterprise, yet very few investigations of cognitive development in school-age children demonstrate a basic concern with scaling.

The most common type of study in published cognitive-developmental research fits the following description. Children from a few different age groups are tested on several tasks. For example, 5-, 8-, and 11-year-olds are tested on three tasks: one task for conservation of number of plastic chips, one for conservation of amount of clay in a ball, and one for conservation of amount of water in a beaker. Performance on each task is scored on a three-step hypothesized sequence. Step 1 reflects a clear nonconservation response, such as a statement that the amount changes when the array is transformed. Step 2 indicates a transitional or ambiguous response, as when a child states that the amount stays the same but gives no satisfactory elaboration or explanation. Step 3 indicates an answer showing full conservation. An analysis of variance is then performed on the results, which demonstrate that, for each of the three tasks, performance improved across the three age groups and that performance for one or two tasks was significantly better than that for the other tasks. For example, children had significantly more advanced scores for conservation of number than for the other two tasks.

These analyses clearly demonstrate that the older groups performed better than the younger ones—hardly a surprise. The results document little else of interest, failing even to test directly for any developmental sequences. They do not adequately test the hypothesized three-step sequence, nor do they demonstrate that the three conservation tasks form a two-step sequence, with conservation of number developing before the other two.

To test a developmental sequence an independent assessment is required of each step in the hypothesized scale (Fischer and Bullock, 1981). With such an assessment it is possible to test directly whether one step comes consistently before or after another. Performance on the independent assessments should form a Guttman (1944) scale, in which every child passes all the steps prior to his or her highest step passed (and fails all the steps after the lowest step failed). Table 3-1 shows the possible performance profiles that are consistent with a simple eight-step Guttman scale. Scales can also be more complex, with two or more tasks at a single step, as for step 2 in Table 3-2 . Indeed, methods are available for tracing highly complex scales, such as those that branch into multiple parallel paths (Bart and Krus, 1973; Coombs and Smith, 1973; Krus, 1977).

Strong Scalogram Method: Profiles for an 8-Step Developmental Sequence.

Profiles for a Measure With Two Tasks at Step 2.

The design of the hypothetical study of conservation allows only one such direct test for sequence. Because of the independent assessment of the three types of conservation, a sequence involving those types can be tested. For example, consider a two-step sequence in which the first step is full understanding of conservation of number and the second is full understanding of either conservation of clay, water, or both. With that sequence every child should show one of the profiles for steps 0, 1, and 2 in Table 3-2. However, it is not possible to test directly the hypothesized three-step developmental sequence (from nonconservation to conservation with explanation) within each type of conservation, because with the specified design of the study the steps are not assessed independently.

There is another method that provides independent assessments without requiring a separate task for each step—the longitudinal design traditionally espoused for developmental research (Wohlwill, 1973). Longitudinal testing of children on the three conservation tasks would make it possible to determine whether for each task and every child, steps always occurred in the predicted order. From one testing to the next, children should either move to a higher step or remain at the same step. This design has been used very effectively in research on moral development to demonstrate that the stages hypothesized by Kohlberg do in fact form a developmental sequence (Colby et al., 1983; Kuhn, 1976; Rest, 1983). The use of scalogram assessments in longitudinal research would provide even greater power and precision, however. With separate tasks to assess each step, individual children's development could be traced in detail. We know of no studies of cognitive development in school-age children using scalograms with a longitudinal design.

Of course, longitudinal research is not needed to test a developmental sequence. With a cross-sectional design, powerful methods are available for rigorously testing a predicted developmental sequence, as suggested by Tables 3-1 and 3-2 . Scalogram statistics can be used to test how well the data fit the predicted scale (Green, 1956), and measures approximating a developmental scale can be devised when a specific sequence cannot be predicted. A strong scalogram measure, in which a different task is constructed a priori to assess each predicted step in a sequence, can be especially useful because the theoretical interpretation of each task can be specified unambiguously. For the most part, however, researchers have not taken advantage of the obvious virtues of scalogram methods for testing sequences or other hypotheses about development.

In most published studies, scalability tests are not reported even when the design allows them. The apparent reason for the neglect of scalogram methods is that, when they were used to test some of the detailed developmental sequences inferred by Piaget from mean age differences between tasks, the scalability of the sequences was poor (Hooper et al., 1979; Kofsky, 1966; Wohlwill and Lowe, 1962). Instead of concluding that Piaget's sequences were incorrect, developmentalists seem to have shot the messenger that brought the bad news: They discarded neglect of a powerful method appears to be coming to an end. scalogram methods, for the most part. Fortunately, this unwarranted

The cognitive-developmental issues that can be addressed with scalogram methods include the following: (1) With independent assessments of each steps, the parallels and differences between developments in different contexts can be traced precisely (Corrigan, 1983). (2) Individual differences in developmental sequences can be directly tested, especially when separate assessments are used to detect hypothesized differences (Knight, 1982). (3) Changes in the speed of development can be detected.

The particular method will vary with the hypothesis, of course. For instance, to test for changes in the speed of development, such as spurts and plateaus, it is essential that subjects be sampled such that their ages are distributed evenly (Fischer et al., in press). If a developmental spurt is predicted at age 10, for example, it is necessary to sample children evenly throughout the age range between 9 and 11. If all children tested are at a few restricted ages, such as within a few months of age 9 or 11, it will be impossible to determine whether a difference between 9- and 11-year-olds reflects a developmental spurt, since the distribution of ages alone will produce a bunching of subjects at certain steps in the scale.

Several studies using appropriate designs to assess speed of development have found that speed does seem to accelerate at certain ages during the school years and to slow down at other ages (Jacques et al., 1978; Kenny, 1983; Tabor and Kendler, 1981). That is, there may be periods of discontinuity and periods of continuity as assessed by speed of development. Current data are consistent with the hypothesis that spurts are associated with the large-scale reorganizations or levels described earlier (Fischer and Pipp, 1984), although more research is necessary to fully test this hypothesis.

In general, research with infants and young children has used much more sophisticated scaling methods than has research with school-age children. For example, Seibert and Hogan (1983), Uzgiris and Hunt (1975), and others have devised a number of scales for infant cognitive development in which each step in a predicted sequence is assessed independently. These scales have been used by various investigators to examine developmental change with some precision (Hunt et al., 1976; Seibert et al., in press). Using methods that approximate a Guttman scale, McCall et al. (1977) analyzed a longitudinal study of performance on infant intelligence tests to assess both changes in the speed of development and individual differences in developmental sequences. We know of no large-scale research projects on school-age children that have used such sophisticated methods to assess developmental change.

Rule-Assessment Methods

Developmental sequences are a central concern in cognitive research, but an emphasis on the relations of behavior across contexts highlights the centrality of a second, related issue: the generality or breadth of applicability of a skill or scheme. A full analysis of the skill underlying a behavior should predict not only where that behavior will fall in a developmental sequence but also how the skill will be evident across a range of contexts.

In recent years several investigators have elaborated a set of methods for assessing the rules underlying a behavior and explaining how those rules apply across contexts (Klahr and Wallace, 1976; MacWhinney, 1978; Siegler, 1981). Siegler (1983) provides an especially clear statement of the logic of rule assessment and focuses on school-age children (as does most rule-assessment research).

Typically, ''rule'' refers to a mental procedure whose operation affects performance on many problems within a task domain. Virtually all of the various approaches to specifying rules derive from the theory of production systems (Newell and Simon, 1972), which analyzes human behavior in terms of systems of rules for generating actions. A rule is defined in terms of a condition-action pair, in which the condition for taking some action is specified abstractly. For example, in simple arithmetic tasks involving division, such as 13 divided by 3, a sequence of rules can be used to describe the division procedure. After an estimate has been made of the whole number required in the quotient, a rule applies for dealing with what is left over, the remainder: If the remainder is less than the divisor, a fraction is made, with the remainder as the numerator and the divisor as the denominator. The "if" clause specifies the condition, and the "then" clause gives the action to be followed. For 13 divided by 3 the estimated whole number is 4. Application of the rule leads to the following procedure: The remainder of 1 is less than the divisor of 3, and therefore the remainder is made into a fraction of 1/3.

To use this rule across division problems, the child must check the current situation to see whether it meets the condition specified in the rule. Such checking can be done only if the rule is represented in some general format. To start with, the child must be able to distinguish which number is currently serving as remainder and which as divisor. Neither remainder nor divisor can be specified in the rule in terms of particular numerical values, such as 1 and 3, respectively, because across problems all numbers can be in both categories.

Researchers can determine whether a child is using such a rule in some set of problems by testing him or her on a number of division problems. The child is said to be using the rule whenever the pattern of behaviors (answers or methods of solution) on some set of the problems fits the rule. The child does not have to state the rule explicitly.

Though the concept of "rule" was controversial two decades ago, today it provides a basis for one of the most promising approaches for exact specification of the cognitive structures underlying child performance. Indeed, it also promises more generally to provide a powerful tool for describing change and continuity in cognitive organization.

In practice, research based on the rule-assessment approach has been characterized by two prominent features. First, it has provided highly differentiated models of regularities in behavior across contexts, including not only correct performances but also errors. This research has articulated the Piagetian hypothesis that errors form coherent patterns that derive from developmentally immature procedures (see Roberts, 1981; Siegler, 1981, 1983). Thus, both errors and correct performances can serve as indexes of the current state of a child's rule system for a particular task domain.

Second, the rule-assessment approach has fostered what might be called a "particulate" view of the child's mind. The methods are designed to detect rules in specified, interrelated tasks, in which the rules are described in terms closely tied to the tasks. Changes in performance are typically explained in terms of modifications, additions, or deletions of particular rules. Just as the philosopher Hume was criticized for depicting the mind as a "bundle of perceptions," some researchers who use rule-assessment techniques might be criticized for depicting the mind as a bundle of rules. Although such localism avoids the postulation of global, vague cognitive metamorphoses, it is in danger of treating the child too narrowly—as merely a solver of division problems, for example.

This pull toward the particular seems to be necessary if researchers are to deal with the effects of specific environments, but there is no need to stop with the particular. In some work in this tradition, children's goals figure in the definition of every rule, and these goals can apply across situations. Moreover, the idea of a rule system seems to have within it the seeds of an approach that combines the particular with the general, because rules must articulate with one another in such a system (Anderson, 1982; Siegler and Klahr, 1982) and because the construction of rules must be determined in part by the general nature of the child's information-processing system.

What seems to be required is the construction of a framework that expressly integrates methods for examining large-scale developmental changes, such as the general developmental levels, with approaches for analyzing particular rule systems. Toward this end, a straightforward approach would combine the use of developmental scales to analyze broad-scale patterns with the use of rule-assessment methods to analyze particular sets of tasks included in those scales. Thus, developmentalists can move toward a richer, fuller portrait of the development of the child in context.

• Examples Of Promising New Directions

The three central issues in the study of cognitive development—the collaboration of child and environment, the relationship of development in traditional research categories, and the methods necessary to investigate developmental questions—lead naturally to a reorientation of research. In this emerging reorientation, as we see it, the study of knowledge defined narrowly is deemphasized, and the study of the organization of behavior in general becomes the focus of developmental inquiry. The analysis of behavioral organization requires topics and methods that directly involve the collaboration of child and environment in development. A number of topics could potentially fit this criterion, but four especially promising new directions that deal with school-age children seem to us to merit the attention of cognitive-developmental researchers: (1) emotional development and its relation to cognitive development; (2) the relation of brain changes to cognitive development; (3) the role of social interaction, especially informal teaching, in cognitive development; and (4) the nature of schooling and literacy and their effects on cognitive development.

Cognitive Development And Emotional Dynamics

Emotion is becoming a central research topic, not only in the study of development but also in behavioral science more generally. From the 1940s until the mid-1970s, so little research was done on emotional development that it was fair to say that emotions had virtually disappeared from developmental science.

In the last 10 years, interest in emotional development has clearly been stirring, and much of the resulting research has dwelt on the relationship between cognition and emotion in development. Researchers on infancy have led the way, with arguments that emotions show major developmental reorganizations that are closely related to cognitive changes (Campos et al., 1978; Erode et al., 1976), and now research on cognition-emotion relationships in childhood is beginning to appear.

Children's Conceptions Of Emotions

The research that seems to have advanced farthest involves the development of conceptions of emotions in school-age children. During the school years, several major changes take place, as children become able to understand that a person can experience two distinct emotions at the same time and then to integrate emotions into abstract categories for interpreting behavior.

To study how children think about their emotions, Hatter (1982) devised a series of interview tasks ingeniously adapted to avoid the usual problems that arise with interviewing young children. Her research demonstrated systematic changes in the organization of children's thinking about emotions in themselves and in other people. One of the central changes was that children gradually became able to conceive of themselves as experiencing two distinct emotions at the same time, as when a girl felt happy that her parents gave her a bicycle but sad that it was only a 3-speed not a 10-speed. Preschoolers were unable to think of experiencing two emotions simultaneously. The best they could do was to portray one emotion followed by another: The girl with the bicycle could first feel happy that she had been given a bicycle and later feel sad that it was not a 10-speed. The elementary school years marked the onset of the capacity to conceive of experiencing two emotions simultaneously, and not until age 9 or later was this ability fully consolidated across Harter's various interview tasks.

Hand (1982) found the same general developmental pattern with a different type of emotion category and a different methodology. The emotions dealt with social interaction categories such as "nice" and "mean." Her main measures required children to act out stories involving these categories, and the conditions for acting out the stories provided varying degrees of environmental support for advanced performance. She also employed a structured interview designed to provide a strong-scalogram test of the developmental sequences she had predicted.

Hand's findings strongly supported the conclusion that preschool children cannot conceive of two or more simultaneous emotions. One of her subjects provided a striking example of preschool children's difficulty in thinking about simultaneous emotions: A girl was shown a story in which one child acted nice and mean simultaneously to another child, and then she was asked to retell the story in her own terms. The girl changed the story, separating it into two distinct stories. First she told about the two children being mean to each other. Then she said, "And a long time later," and began an independent story about the two children being nice to each other. In the story the girl had seen, there was no separation of the nice and mean interactions; instead, they were intertwined and integrated. To understand how the child in the story could experience two emotions, the girl apparently had to distort the story by separating the emotions into two separate stories. Other preschool children showed similar distortions, altering the stories about simultaneous emotions by separating the positive and negative emotions into distinct stories.

Hand's various assessment conditions also demonstrated that the ability to understand that opposite emotions can be experienced simultaneously could appear as early as age 6-7 or as late as age 10-12, depending on the degree of environmental support provided. Thus, social conceptions of emotions seem to show the same pattern as nonsocial conceptions: Variations in both child and environment affect the child's competence.

Hand extended the developmental sequence for nice and mean interactions into the adolescent years (Hand, 1981; Hand and Fischer, 1981). Even under supportive environmental conditions, elementary school children do not seem to be able to integrate nice and mean interactions into general abstract categories, such as "Nice or mean intentions matter more than nice or mean actions."

Hand's categories did not deal with pure emotions but instead involved emotions in social interactions. Indeed, except perhaps for the few "pure" emotions proposed by researchers such as Ekman et al. (1972) and Izard (1982), most human emotions seem to be intimately connected with social situations. Categories for social interactions as well as those for personality descriptions, such as evil, kind, sincere, honest, and responsible are often heavily loaded with emotions. The development of categories for social interactions and personality descriptions appears to follow the same sequence outlined for emotions (Fischer et al., in press; Harter, 1982; Rosenberg, 1979; Selman, 1980):

Preschool children seem to be able to deal with only one concrete category at a time or with a simple relationship between closely related categories, such as that indicated in the statement, "If you are mean to me, I will be mean to you."

Elementary school children begin to be able to describe and use intersections of concrete social and personality categories. For example, by the third or fourth grade, a boy can describe how his best friend generally tries to be nice to him and to share things most of the time, even though he can be mean and stingy when he gets grumpy.

In adolescence, children begin to describe themselves and other people in terms like those of personality theories. They use trait names, such as responsible, introspective, and nonconformist, and eventually they even begin to use ideas similar to the Freudian notion of internal psychological conflict.

In general, then, substantial progress has been made toward describing the development of school-age children's conceptions of emotions and related social and personality categories. As valuable as this progress is, there is much more to emotional development than conceptions of emotions.

Emotional Reorganizations

One of the most straightforward implications of the organization approach to cognitive development is that each major reorganization or level of development should produce a significant change in emotions. This hypothesis has been pursued most explicitly in infancy, for which data and theory have suggested reliable emotional concomitants of general behavioral reorganizations (Campos et al., 1978; Emde et al., 1976; McCall et al., 1977; Papousek and Papousek, 1979; Sroufe, 1979; Zelazo and Leonard, 1983). For example, the social smile, eye-to-eye contact, and the greeting response all seem to emerge at 2-4 months, which is also a time of major cognitive reorganization. Similarly, at 7-9 months, stranger distress, separation distress, and fear of heights appear to increase dramatically just as another cognitive reorganization is occurring.

Similar emotional reorganizations can be expected to occur for every new cognitive-developmental level during the school years, although virtually no research has examined such changes. Despite the dearth of research, the psychological literature suggests many possible examples of such reorganizations involving emotions.

With the emergence of simple relations of representations at approximately age 4, there appears to be a surge of new emotions accompanying the new understanding of social roles in the family. The emotions described in Freud's (1909/1962) analysis of the Oedipus conflict may well be a part of this reorganization (Fischer and Watson, 1981). The understanding of social roles may also lead to a change in the nature of friendships, since the child will now be able to understand the role relations in friendship (see Furman, 1982; Hartup, 1983). Any such change in important social relationships would seem almost inevitably to have emotional consequences.

For the development of concrete operations at age 6-7, a number of emotional changes have been suggested by Freud and others. At this point, children appear to develop a clear-cut conscience, with an accompanying surge in guilt (Freud, 1924/1961, 1933/1965). They develop the capacity for social comparison, so they can compare and contrast their own behavior with that of other people (Ruble, 1983). Presumably, this capacity can lead to a surge in both anxiety and pride about one's relative social standing. One component of this new ability for social comparison may also be a spurt in identification with parents and other significant adults, since identification requires the comparison of self with the adult (Kagan, 1958). Any change in how children understand themselves is likely to have emotional implications.

Formal operations and the ability to understand single abstractions emerge at age 10-12 with serious emotional consequences. The confusion and turmoil of early adolescence may result in part from this new capacity (Elkind, 1974; Inhelder and Piaget, 1955/1958; Rosenberg, 1979). With formal operations, children can construct new, general concepts about themselves and other people, but they remain unable to compare one such abstraction with another. Consequently, they have difficulty thinking clearly about abstract concepts. One 16-year-old, looking back on the time when he was 12-14, described it as a fog from which he was just now emerging (Fischer et al., 1983). Erikson (1974) has suggested that the formal operations level gives the ability to form an identity—another major change in the sense of self, with inevitable emotional concomitants.

The development level that first appears at age 14-16, relations of abstractions, presumably has emotional consequences, too. The ability to relate abstractions would help the individual move out of the confusing fog of early adolescence. Likewise, it might lead to a substantial change in emotions about intimate relationships, because the person could begin to relate an abstraction about his or her own personality to an abstraction about the personality of a loved one (Fischer, 1980).

Such hypotheses about emotional reorganizations during childhood have been almost entirely unexplored. Plainly, this is a promising direction for research and one in which there is no lack of stimulating hypotheses to guide the investigator. The methods outlined above for studying developments in the organization of behavior can be used in the study of such emotional changes and will substantially enhance the usefulness of such research.

Freudian Processes

It is no accident that hypotheses suggested by Freud appear repeatedly in the section on emotional reorganizations. Psychoanalysis remains one of the most fertile sources of hypotheses about emotional development. Although researchers have generally neglected psychoanalytic ideas about emotional development, especially for the school-age child, a resurgence of interest is evident.

In fact, there are signs that a major conceptual breakthrough may be in progress. For years many scholars have been dissatisfied with Freud's model of the mind (Hartmann, 1939; Holt, 1976; Schafer, 1976). Repeatedly the suggestion has been made that the cognitive-developmental orientation might well provide the framework necessary to rebuild the psychoanalytic theory of the mind (Rapaport, 1951; Schimek 1975; Wolff, 1967). A group of neo-Freudians has been working to construct a position called "object relations" theory that makes significant steps toward integrating the cognitive-developmental and psychoanalytic orientations (for example, Kernberg, 1976; Winnicott, 1971). More recently, Feffer (1982) has suggested a recasting of the distortions of primary process in cognitive-developmental terms.

These integrations of psychoanalysis and cognitive development have already led to a large number of interesting empirical claims. For example, it has been hypothesized that mechanisms of defense follow a developmental progression (A. Freud, 1966; Fischer and Pipp, in press; Haan, 1977; Vaillant, 1977). Repression appears to first develop at age 3-4, which is the approximate age of emergence of the ability to relate representations. Several sophisticated mechanisms of defense, such as sublimation, suppression, and mature humor, do not seem to emerge until after age 11 or 12, when formal operations are beginning. These are only a few of the many interesting hypotheses in the literature about emotional development in school-age children.

Despite the easy access of such hypotheses, there have been few studies testing them. Mahler et al. (1975) assessed the development of mother-child relationships in infants and preschool children, which supported several object-relations hypotheses about the early development of self. With school-age children it is difficult to find any systematic research. Clearly, this is another promising direction.

Research On Emotions

One of the reasons for the lack of research on emotional reorganizations and Freudian processes has been that it has proved to be difficult to determine how to investigate them. Research with seriously disturbed children is particularly difficult to do, and the induction of strong emotions in children for research purposes is unethical. As a result, scholars interested in pursuing these important questions have often had to approach them indirectly—studying, for example, the development of children's conceptions of defense mechanisms in other people (Chandler et al., 1978).

A straightforward solution to this dilemma may be available. Many issues in children's everyday lives naturally evoke emotions of various degrees and types. Such issues seem to provide natural avenues for studying the organization of behavior in a way that brings together cognition and emotion.

One set of candidates includes virtually any topic involving the self—identification, identity, self-control, attributions about one's successes and failures. Kernberg (1976) has suggested that one of the primary dimensions around which the psyche is organized is whether events are perceived as threatening to the self or as supportive of the self, and much social-psycho-logical research with adults generally supports this hypothesis (Greenwald, 1980). The development of self in children and its relation to the organization of behavior is a promising avenue for studying cognition-emotion relations.

Another set of issues of special relevance to school-age children is family relations, including the emotional climate in the family. The Oedipus conflict is merely the most discussed of a wide-ranging set of family phenomena that are emotion laden.

Consider, for example, divorce. The proportion of children growing up in divorced families has risen sharply, and some projections place it at 40-50 percent in coming years. The experience of divorce is clearly emotional for many children, and systematic relations seem to exist between emotional problems in adulthood (such as loneliness and depression) and the ages of individuals when their parents were divorced (Shaver and Rubenstein, 1980). In addition, young children seem to seriously misunderstand the causes of their parents' divorce, often blaming themselves for the breakup (Longfellow, 1979; Wallerstein and Kelly, 1980). Research on how children understand and deal with divorce would seem a natural avenue for studying the development of emotion and cognition. How children understand what happened and how they conceive of the relationships in their family will probably relate in interesting ways to how they feel about themselves and their parents.

Children's reactions to illness provide another promising topic for the study of emotion-cognition relations. Virtually all children experience illnesses several times during the school years, and a substantial number of children suffer from chronic illnesses (Shonkoff, in this volume). Research on how children understand what happens during an illness and how they cope with it promises to illuminate cognition-emotion relations in development. Indeed, it would be surprising if mechanisms of defense and other emotional organizations could not be investigated in connection with divorce and illness.

A note about emotional development is in order. In our analysis we have focused on promising areas for study of how emotion relates to cognitive development. In doing so we have not differentiated the many components of emotions, including triggering, expression, suppression, interpretation, and communication. Clearly, a full analysis of emotional development will require study of these components (Campos et al., 1983).

Relations Between Brain Changes And Cognitive Development

It is a truism in developmental science that changes in the brain must be central to cognitive development, yet researchers have mostly neglected investigation of the relationship between brain and cognition in development. Recent research on development in animals has begun to illuminate relevant topics, such as the processes by which experience affects the development of the visual system in mammals (Movshon and Van Sluyters, 1981) and the mechanisms by which the brain adjusts to early damage (Goldman-Rackic et al., 1983).

Of course, the methods used to study brain development in animals cannot be applied to human beings, but the paucity of research on the relationship between brain changes and cognitive development in children is nevertheless remarkable. One reason for neglect of this topic seems to be that previous investigations searching for such relationships did not meet with much success. Another reason may be that scientists shy away from the topic because past findings have sometimes led to a simplistic form of reductionist thinking, in which any brain changes are assumed to have direct correlates in behavioral development.

A few investigators have studied the relationship between certain global changes in the brain and the cognitive-developmental levels occurring during the school years. They have uncovered evidence that brain or head growth may spurt on the average at ages 4-5, 6-7, 10-12, and 14-16 (Eichorn and Bayley, 1962; Epstein, 1974, 1980; Fischer and Pipp, 1984; Nellhaus, 1968). The primary data involve growth in head circumference and change in certain waves of the electroencephalogram. The data for head circumference tend to support the occurrence of spurts at the expected ages, but there is substantial inconsistency across studies (McQueen, 1982). Fewer studies exist on the electroencephalogram, but extant data appear to be more consistent across samples. For brain-wave characteristics that show consistent increases or decreases with age, children show spurts during the four predicted age periods.

Unfortunately, these data have been used to support unjustified conclusions about the nature of cognitive development and learning at various ages during the school years. Children can learn new skills during periods of brain growth spurts, it has been claimed, but they cannot learn during periods of slow growth (Epstein, 1978, 1980; Toepfer, 1979). Thus, for example, children between ages 12 and 14 are said to be unable to learn new skills, because brain growth shows a plateau rather than a spurt during that period. These conclusions have been based almost entirely on the brain growth data, with virtually no assessment of actual learning.

Despite the limitations of the data, some school systems have begun to base portions of their curricula on these unwarranted conclusions. Efforts are being made, for example, to build middle-school curricula around the assumption that children of middle-school age cannot learn very much because their brains are not undergoing a growth spurt. Clearly, no conclusions about learning ability or recommendations about educational practices can be supported by data on brain growth alone.

Several recent studies have tested the hypothesis that individual children undergo cognitive spurts when they show head-growth spurts and cognitive plateaus when they show head-growth plateaus (McCall et al., 1983; Petersen and Cavrell, in press). The results are clear: There was no correlation between head growth and cognitive growth. The most reasonable conclusion at this point seems to be that head growth and cognitive-developmental level are related for large samples but not for individual children.

Similar problems have arisen in research on the development of brain lateralization (Kinsbourne and Hiscock, 1983). From a few early findings on differences between the right and left hemispheres, some investigators have jumped to broad generalizations about the different natures of intelligence in the two hemispheres. Journalists and educators have gone further and drawn sweeping, unjustified conclusions about the nature of intelligence in general and cognitive development in particular. There seems to be an unfortunate tendency for people to repeatedly make the same unjustified leap from data on brain growth to conclusions about behavior.

This leap is apparently predicated on the assumption that brain developments appear before behavioral changes and then have an immediate, measurable impact on behavior. Based on research on the relationships between developments in other domains, the most reasonable hypothesis is that the relationship between brain changes and cognitive development will be highly complex. Indeed, behavioral changes are probably just as likely to precede brain changes as to follow them. For both head circumference and the electroencephalogram, for example, brain growth shows a spurt one to three years after the first cognitive changes reflecting concrete operations: Concrete-operational skills are first evident as early as age 5.5-6, but brain spurts do not usually appear until age 7-9. One reasonable hypothesis is that small behavioral changes typically precede any global brain changes of the type measured by head circumference and the electroencephalogram. Some animal research supports the argument that behavioral changes can precede major brain changes (Greenough and Schwark, in press).

The findings of correlations between brain growth and cognitive development may eventually lead researchers to examine seriously brain-behavior relationships in development. The research topic is both legitimate and important, and eventually it is likely to produce important scientific breakthroughs. However, the complexity of the topic means that legitimate applications leading to the solution of practical problems almost certainly will not be available for a long time (Shonkoff, in this volume).

Cognitive Development And Modes Of Social Interaction

A third promising direction in the study of cognitive development addresses the question of how social interaction dynamically constitutes a favorable climate for the growth of the mind. In the past, psychologists' answers to related questions have often over- or underestimated the contribution of social interaction to normal cognitive development. Recently, renewed interest in the problem has produced a burst of naturalistic and seminaturalistic studies of parent-child and teacher-student interactions.

This new research has begun to chart a middle course between two extreme views of the role of social interaction in cognitive development. The first of these extremes can be called the social learning straw man. It holds that most cognitive development is a result of imitation, which is construed as mere mimicry rather than cognitive reconstruction. The second extreme can be called the little scientist straw man. This position holds that most cognitive development is a result of autonomous inventions, cognitive reconstructions in which social interaction plays no formative role. Both of these views are caricatures of human development. A minimal task for cognitive developmentalists is to portray the role of social interaction without resorting to either caricature.

The words that best depict the middle-course alternative emerging from recent research are guided reinvention (Lock, 1980; see also Karplus, 1981; Resnick, 1976), which acknowledges the social learning theorists' insistence that social guidance is ubiquitous, both within and outside the classroom. They also acknowledge, however, the Piagetian insight that to understand is to reconstruct. Thus, the guided reinvention perspective elaborates the theme that normal cognitive development must be understood as a collaborative phenomenon.

In classical writings on cognitive development, Vygotsky (1934/1962, 1934/1978) seems to have best anticipated the guided reinvention perspective. For Vygotsky, an analysis of modes of social interaction is essential for explaining cognitive development. In addition, he argued that an explanation of guided reinvention must use the historical-reconstructive method, which is similar to what Piaget called the ''genetic'' method. For Vygotsky, Piaget's "to understand is to reconstruct" was as apt a summary of the successful theorist's efforts as it was a summary of the child's efforts. Vygotsky argued that developmentalists need to study the dynamics of the developmental process directly, rather than continuing merely to draw inferences about the process from structural analyses of the products of development.

What would a reconstructive understanding of social interaction involve? One of Vygotsky's central tenets was that social interaction is organized on a number of planes and that each successive plane is associated with greater cognitive powers. One way of conceiving these planes is schematized in Table 3-3 , adapted from a convergence rate hierarchy proposed in Bullock (1983) as a synthesis of both Vygotskyan and social learning (Bandura, 1971) principles.

A Hierarchy of Factors Affecting Convergence Rate.

The core ideas of the convergence rate hierarchy are simple. Cognitive development can be idealized as a process of converging, step by step, toward some higher plane of knowledge and skill. Such convergence must proceed at some rate, and that rate is affected by many factors. One basic factor is the plane of social interaction available to the young, e.g., whether the young participate in symbolic communication with elders. Table 3-3 presents a hypothetical ordering of some major steps along the road to the complexly layered type of social interaction available to today's children. Each step is called a level, but this terminology is not meant to imply any special connection with the levels of cognitive reorganization suggested by cognitive-developmental theorists.

By hypothesis, each new level in the hierarchy produces an increase in the average convergence rate of offspring toward higher levels of knowledge and skill (see Bullock, 1983, for details). Beyond level 3, each level involves an innovation in the form of social interaction. Thus, the hierarchy synthesizes social learning theorists' observations about the effects of modeling on learning rate (Bandura, 1971) and Vygotsky's observations about the hierarchically layered nature of social interaction (see also Dennett, 1975; Premack, 1973).

The entire hierarchy might be taken as a schematic for assembling a system for guided reinvention. In this regard, special note should be made of levels 5 and 6, because they mark the crystallization of two complementary roles, i.e., child as reinventor and parent as guide. The words constructive imitation , which describe the social innovation at level 5, are meant to be a reminder of the reconstructive nature of imitation noted by all major students of imitation since Baldwin (1895; Bandura, 1971, 1977; Guillaume, 1926/1971; Kaye, 1982; Piaget, 1946/1951). Many imitative achievements are not mere mimicry; instead, they involve persistent reconstructive efforts on the part of the imitator. These efforts are a major source of developmental reorganizations, especially when complemented by the purposive teaching spontaneously provided by parents. Also, because constructive imitation engages a wide range of cognitive resources, there is no isolable imitative faculty, as some have supposed.

By hypothesis, constructive imitation by children and purposive teaching by parents are complementary components of an evolved system for guided reinvention. Moreover, when these components are seen as parts of the entire hierachy, a further hypothesis is suggested. When cognitive development is proceeding most rapidly, it will involve guided reinvention embedded within goal-directed activity that is jointly undertaken by an apprentice (the child) and an expert, who are tied together by positive affect. This would be true if the higher social-interactive levels are built on the lower, older ones and continue to depend on them for their own optimal functioning. For example, the developmental value of practices at the high end of the hierarchy, such as formal schooling, may depend on the modes of interaction at lower levels. A corollary to this hypothesis is that the large departures from the modes of interaction that evolved to support guided reinvention will create difficulties for children. The remainder of this section surveys research relevant to these ideas and traces possible implications for education.

Guided Reinvention Within Dyadic Goal-Directed Activity

The most intensive basic research on naturally occurring social-interactive modes as vehicles for guided reinvention (outside classrooms) has occurred in the field of language development (Brown, 1980; Bruner, 1983; Bullock, 1979; Cross, 1977; Kaye, 1982; Kaye and Charney, 1980; Lock, 1980; Moerk, 1976; Snow, 1977; Swensen, 1983; Wells, 1974). Most of this research involved children younger than school age. There are, however, a few notable studies of older children in domains of cognitive development other than language (Donaldson, 1978; Heber, 1977; Karplus, 1981; Wertsch, 1979; Wood, 1980). We briefly survey available results from the language development literature and use the results from studies of older children to demonstrate the generality of basic principles.

Both logical (Bruner, 1975; Macnamara, 1972; Wittgenstein, 1953) and empirical (Bullock, 1979; Cross, 1977; Snow, 1977; Swensen, 1983) analyses indicate that normal language development depends on social-cognitive coordination between the child and someone who uses language in a contextually appropriate way while interacting with the child. Other research has shown that mere exposure to television does not result in normal language development, apparently because its dynamic linguistic stimulation is provided without social-cognitive coordination. There is now ample evidence that an extraordinarily high degree of social-cognitive coordination can accelerate language development (Cross, 1977; Swenson, 1983).

Social-cognitive coordination is always a matter of degree. The degree of coordination increases with the amount of overlap between two individuals' understanding of the situation in which they jointly find themselves (e.g., the situation of playing a game). Thus, a high degree of social-cognitive coordination requires the achievement of many moments of shared understanding.

Shared understanding is such a critical factor because normal language development is a comprehension-driven process that involves much more than the learning of syntactic patterns (Curtis, 1981; Macnamara, 1972; Nelson, 1973; Wittgenstein, 1953), even though it is sometimes discussed as a pure exercise in pattern learning (Kiss, 1972). Comprehension involves both isolating new patterns and making sense of them by finding a way to articulate them with what is already understood (Clark and Clark, 1977; Schlesinger, 1982). In guided reinvention the child and adult share an understanding of their joint situation, and the adult's speech takes that understanding as a point of departure while heeding developmental and contextual constraints. As a result of this support, the child stands a good chance of being able to comprehend the adult's utterance the first time he or she hears it, even when it contains novel components (Bullock, 1979; Cross, 1977; Wells, 1974).

How do child and adult articulate new patterns with what the child already understands? The child seeks above all to discover the relevance of the adult's contributions to his or her own purposes and goals at the moment. The adult attempts to ensure that his or her acts are relevant to the child's activity in a way that the child is prepared to discover.

How is shared understanding dynamically maintained over long bouts of interaction? Parents of children who exhibit rapid language development actively work to maintain shared understanding over long stretches of interaction. They do this in several ways. They introduce objects to serve as bases for joint activities, and they closely monitor their child's apparent goals or intentions. During most of their interactive turns, they attempt to modulate, correct, or elaborate their child's behavior rather than redirect it. And they construct an internal model of their child's current preferences, skills, and world knowledge, which they continuously update and check (Brown, 1980; Kaye, 1982; Nelson, 1973; Snow, 1977).

Embedded Teaching And Formal Schooling

It would certainly be misleading to say that language is not caught, but the type of teaching uncovered in these naturalistic studies of language development is unlike that found in most formal schooling. Under normal conditions it seems that every child receives a steady diet of what might be called embedded teaching—elaborative and corrective acts responsively embedded by parents in the flow of joint goal-directed activity. As the child spontaneously and vigorously works to master a wide range of goals, his or her constructive efforts are constantly guided by the parent's embedded teaching efforts. Although such efforts do not obviate the need for inventive and inductive efforts by the child (Maratsos, 1983), they appear to be crucial if the child's efforts are to result in a course of development that is recognizably normal.

With preschool and school-age children, research has focused not on language learning but on cognitive tasks ranging from puzzle solving to classical Piagetian tasks such as seriation and conservation. Yet the results paint much the same picture (Heber, 1977; Sonstroem, 1966; Wertsch, 1979; Wood, 1980). In his survey of this small body of research, Wood (1980) concluded that "where instruction is contingent on the child's own activities and related to what he is currently trying to do .... considerable progress may be made" (p. 290). His survey also revealed that when instructional techniques depart from the embedded teaching mode the child's progress is markedly slowed. Finally, in research on the learning cycle or guided discovery approach to the instruction of mathematical reasoning, this embedded teaching method was very successful in a domain in which many students fail with more traditional classroom techniques (Karplus, 1981).

Much more research along these lines is needed, especially with school-age children. We expect that studies of embedded teaching with older children will show it to be superior to "disembedded" teaching, especially in the promotion of lasting changes in cognitive skills. Here, disembedded teaching means any teaching that departs significantly from guided reinvention. On the basis of available research, two characteristics of guided reinvention seem particularly critical: (1) any new information provided is relevant to furthering the child's current goal-directed activity, and (2) information is provided in a way that is immediately responsive and "proportionate" (Wood, 1980) to the child's varying information needs. Note that much classroom instruction departs from guided reinvention in both respects.

Recently a number of authors have tried to explain the difficulty many children have making the transition to school or the related difficulty they have in becoming engaged in certain school subjects (Bereiter and Scardamalia, 1982; Cook-Gumperz and Gumperz, 1981; Donaldson, 1978; Papert, 1980). All these analyses support the idea that many children fail not because of inability but because they are ill prepared for the mode of social interaction encountered in many classrooms. This ill preparedness—or to see it the other way, this ill adaptedness of some schooling modes to what many children naturally expect—has two consequences. First, many children fail to progress at an acceptable rate and fall progressively further behind their peers. Second, many children become disaffected with the classroom setting.

Obviously, these two results are closely linked. Failure to progress implies continual frustration, which leads to global disaffection. But several lines of research suggest a deeper relationship. In the literature on the development of affective relationships, responsiveness seems to play a crucial role in attachment formation (Ainsworth, 1979). At every level of the convergence rate hierarchy, the child's development depends on the contributions of others in immediate social interaction. In parametric research on what makes educational computer games attractive, contingency on the child's behavior in essential (Malone, 1981). And in informal research on how to make mathematics more appealing, Papert (1980) even speaks seriously of the child's affective relationship to the world of mathematics. Given the human ability to personify, there is no reason to dismiss Papert's usage as mere metaphor.

There is ample evidence that several qualities of dyadic social interaction contribute to a positive attitude toward instructional activities, what Malone (1981) calls their holding power: in particular, goal-directedness, responsiveness, novelty, and performance-contingent shifts in problem difficulty. Indeed, a classic study by Bowman (1959) showed that disaffected delinquents will regain interest in classroom work and markedly reduce their disruptive behavior when the classroom mode is restructured around goal-directed activities. Although Bowman failed to find larger academic gains in the embedded teaching group than in a control group, the study deserves replication with more sensitive cognitive outcome measures and with a better-designed "guided reinvention" curriculum.

We would like to raise another issue, although we cannot pursue it here. We noted earlier that the disembedded teaching that children encounter in many classroom settings does not meet their expectations. However, this statement is too weak because it presents too passive a picture of the student. We believe that children actively try to structure their interactions such that the type of teaching they receive is the embedded type. Children demand involvement as performers rather than as mere observers. (See Barker and Gump, 1964, for the classic treatment of this distinction.) A common childhood plea is "I want to be included and help you do it, not just watch." In this connection it is also interesting to note a convergence with Harter's (1978) revision of the concept of competence motivation. According to her reformulation, the child with high competence motivation actively resists excessive guidance in joint-task contexts.

Collaboration Not Conservation

As noted in the introduction to this section, history shows that it has been quite difficult to maintain a balanced view of the role of social interaction in cognitive development. Many seem to think of the problem according to the scheme of a "conservation" equation: Child's Contribution + Social Contribution = A Constant Amount of Knowledge. Given this scheme, the laws of algebra demand that if the child's contribution goes up the social contribution must go down, and vice versa. Any theorist who focuses on one factor is led by the scheme to downplay the other. But the scheme itself is plainly inappropriate. Not only is the amount of knowledge not conserved, but the evidence indicates that social factors contribute most when embedded within the child's own ongoing efforts at mastery. As Bullock (1983) noted when proposing the convergence rate hierarchy, higher cognitive potentials seem to arise with specific new types of social interaction. By emphasizing the concept of guided reinvention, we hope to have made it difficult for investigators to continue thinking in terms of the conservation scheme.

Because this treatment stands on the shoulders of Vygotsky's pioneering work and because the next section is devoted to the topic of literacy, it is fitting to round off this section with Vygotsky's (1934/1978:117-118) prescient remarks about the need for embedded teaching of literacy:

Reading and writing must be something the child needs. Here we have the most vivid example of the basic contradiction that appears in the teaching of writing not only in Montessori's school but in most other schools as well, namely, that writing is taught as a motor skill and not as a complex cultural activity .... Writing should be meaningful for children .... an intrinsic need should be aroused in them, and... writing should be incorporated into a task that is necessary and relevant for life. Only then can we be certain that it will develop not as a matter of hand and finger habits but as a really new and complex form of speech.

The Effects Of Schooling And Other Literate Practices

One of the most promising new directions for cognitive-developmental research concerns the cognitive effects of literacy and formal schooling (Cole and Brunet, 1971; Cole and Griffin, 1980; Goody, 1977; Luria, 1976; Olson, 1976; Ong, 1982; Scribner and Cole, 1981; Vygotsky 1934/1978). This new area has live roots in anthropology, educational theory, historiography, philosophy, linguistics, and developmental and cross-cultural psychology. These roots give the area both a singular vitality and a special promise for promoting communication among relatively isolated academic disciplines (Ong, 1982). Moreover, literacy and schooling relate closely to the emphasis on the interaction between child and environment in cognitive development. The effects of literacy and schooling seem to arise from the environmental supports they provide for advanced cognitive functioning. To understand cognitive development in the child in school, scientists and educators need to understand how the teaching of literacy and schooling relates to the child's natural learning processes and how literacy and schooling affect the child's mind.

Our treatment of literacy effects necessarily begins with the problem of definition, because there are many literacies and each may have distinctive cognitive-developmental effects. The range of literate practices is analyzed in terms of how each functions in mental life. This analysis leads to the specification of appropriate methods for assessing the cognitive effects of literate practices. The approach presented here represents what seems to be an emerging consensus about literacy and schooling.

Defining Literacy

What are the cognitive effects of literacy? According to recent research (Goody, 1977; Scribner and Cole, 1981), answering this question in a scientifically useful manner requires careful specification of what is meant by literacy. All literacies involve both (1) one or more conventionalized systems for external representation of ideas and (2) a set of cultural practices that use the systems. Literacies include all conventionalized representational systems, not just alphabetic writing. Any cognitive consequences can be expected to be determined jointly by the specific nature of a representational system and its associated practices. As a reminder of these points, we use the words literate practices rather than literacy .

Table 3-4 presents some literate practices that span a range from simple labeling (practice 1) to scientific theory construction (practice 9). To illustrate the vastness of this span, we discuss two extreme cases of literate practices: the use of a limited writing system by some men in West Africa and the use of multiple representational systems by modem scientists. The vast differences between these two cases suggest enormous differences in their cognitive consequences.

A Range of Literate Practices.

In the first case, men belonging to the rural Vai people in West Africa are taught a native script (Scribner and Cole, 1981). (Literate practices are virtually absent among Vai women.) The Vai script is a syllabary, a system for representing speech phonetically syllable by syllable. In this system a text consists of a continuous stream of symbols without any segmentation markers such as blanks to indicate word boundaries. Also, homophonic syllables (such as boar and bore in English) are always represented by the same symbol. These characteristics make it virtually impossible to read Vai script rapidly with full comprehension. Because of this limitation as well as competition from other scripts, the Vai script is highly restricted in the range of practices it supports. The script is neither taught nor used in formal school settings, and its major use is letter writing (practice 3 in Table 3-3 ). Scribner and Cole report that letters written in Vai script are short and limited to expected themes. Because of the difficulty of reading the script, long texts on novel themes would overwhelm even the most accomplished Vai readers. Not one Vai occupation depends critically on the use of the script.

At the other extreme, consider a modem scientist working at the frontiers of the field of neural modeling of cognitive processes (Grossberg, 1982). A single paper published in this area may draw on a tool kit of conventionalized representations that includes (1) standard written English, including the modem Roman alphabet and numerous other conventions; (2) mathematical equations, including modern number systems and the Greek alphabet; (3) a biochemical symbol system; (4) labeled graphs that are a hybrid of iconic and more arbitrary representational devices; (5) a computer language used to write simulation programs; and (6) models of memory, cognitive development, and other psychological processes. All these resources are being used to compose a new formalism capable of expressing a set of critical theoretical distinctions (practice 9) for characterizing the design principles exhibited by the human brain.

Modern science has institutionalized the practice of inventing such new representational systems. This enterprise is critically dependent for its success on both the evolving representational systems already in the tool kit and the evolving tradition of scientific practices (e.g., techniques for studying nonlinear differential equations, computer simulation techniques, and so forth). Equally important, the whole enterprise would be inconceivable to anyone who was unschooled in similar literacy-based practices. Even for someone who knew some such practices but was not familiar with the specific tool kit, the enterprise would be difficult to conceive with any specificity. The scientific enterprise is thus much farther removed from the preliterate world than is the Vai practice of writing simple status reports or orders.

Consequently, it would be odd to expect the Vai male's literacy to have the same cognitive effects as the neural modeler's literacy. In fact, both persons differ in some way from nonliterates because of their shared encounter with an external, representational system in use. Yet that common difference pales in comparison with other intellectual differences arising from the distinctiveness of their literate practices.

A common question in research has been whether some specific cognitive effect should be attributed to literacy or to formal schooling. The definitional problems with such a question are similar to those with questions about the effects of literacy alone. The term formal schooling is just as ill defined as the term literacy . Moreover, posing a dichotomy between literacy and formal schooling obscures the fact that all types of formal schooling are literacy based. Though it is possible to have literate practices without formal schooling, it is not possible to have formal schooling without literate practices. In general, formal schooling and literate practices are closely linked. Many literate practices with distinctive cognitive effects were probably invented in an attempt to improve schooling (Goody, 1977), and many children encounter these practices for the first time in a school setting.

Characterizing The Range Of Literate Practices

The literate practices in Table 3-4 are divided into three groups: amplification, nonlocal integration, and systemic analysis. These labels are meant to capture qualitative differences in how literate practices seem to function in the cognitive life of individuals and to suggest directions for research on literate practices.

In amplification, some human ability already exists in some form, and the literate practices simply magnify that ability (Cole and Bruner, 1971; Cole and Griffin, 1980). For example, labeling of containers (practice 1) provides redundant cues for identifying contents and thus often increases the speed of identification. Listing donations (practice 2) duplicates a pre-literate mnemonic achievement and supports more accurate recall. The writing of orders (practice 3) substitutes for speaking them in a way that allows the orders to affect people at greater distances. Note that these are all quantitative (amplifying) effects. They leave the structure of the activity largely unchanged.

A literate practice can do more than amplify. It can induce a qualitatively different ability (Cole and Griffin, 1980). Though the distinction between quantitative and qualitative is sometimes fuzzy, it is useful. Classical writings on cognitive development describe two pervasive functions of literate practices that involve qualitative effects: nonlocal integration and systemic analysis, as shown in Table 3-3 (e.g., Inhelder and Piaget, 1955/1958; Vygotsky, 1934/1978).

Many literate practices support nonlocal integration of materials that would otherwise remain separate. Under aliterate conditions, thoughts tend to shift from one content to the next on the basis of characteristics that are relatively obvious and that have already been recognized. Contents with similarities, complementarities, or other relationships that have not yet been recognized will rarely be juxtaposed in thought. As a result, the undiscovered relationships between them will rarely be discovered.

When writing, the writer has a device that supports the juxtaposition of such apparently disparate contents and thus raises the chances of discovering a new way of integrating experience. As a result, writing can accelerate the pace of conceptual innovation, forming the core of new types of cultural practices, including the scientific method. By overcoming a systematic limit of human memory, it opens up a new range of human practices. For example, in constructing the theory of evolution, Darwin had to put together widely disparate contents. Howard Gruber (1981) wrote of Darwin: ''To understand what he had seen, and to construct a theory that would do it new justice, he had to re-examine everything incessantly from the varied perspectives of his diverse enterprises'' (p. 113, italics added). Darwin wrote down observations and thoughts in a series of logs and notebooks to facilitate this process. Indeed, the experimentalist's practice of keeping a log is a particularly clear example of how writing can overcome the limitations of memory. The log supports simultaneous consideration of experiments that are temporally and conceptually remote.

Nonlocal integration is certainly not unique to literate practices. Under aliterate conditions it would seem to occur primarily in social interactions in which communicating individuals try to reconcile disparate schemes. It is probably common in language and cognitive development, when a child is trying to reconstruct integrative schemes underlying adult usage (Feldman, 1980; Horton and Markman, 1980; Laboratory of Comparative Human Cognition, 1983; Perret-Clermont, 1980). Among adults it can occur when individuals confront each others' disparate ways of organizing experience. At the same time, literacy practices themselves support a heterogeneity of adult perspectives unheard of in aliterate cultures. After the invention of literate practices, a language's stock of terms based on nonlocal integration explodes (Slaughter, 1982). Apparently, literacies support lifelong use of a type of integration that would otherwise be rarely exploited after the early years of development.

A third function of literate practices, systemic analysis, occurs whenever the focus of a thinker's concern is the adequacy of an entire representational system. Nonlocal integration promotes the building of conventionalized representational systems, and systemic analysis involves the evaluation of those systems. It seems that literate practices provide strong support for the ability to consider such systems and to analyze and compare them.

Consider the following historical examples. The ancient Greeks compared what is now known as the Greek alphabet with various other writing systems of the time. It was seen as an improvement over its competitors because it could represent vowel sounds as well as consonantal sounds (practice 8 in Table 3-4 ). Riemannian geometry was an improvement over Euclidean geometry because it provided a better representation of physical space under relativistic conditions (practice 8). Most behavioral scientists have joined the enterprise of trying to formulate a new cognitivist theoretical system for thought and behavior because the old behaviorist system appears to be inherently unequal to the task of modeling psychological phenomena (practice 9).

Systemic analysis is fundamental to the modem scientific enterprise. Modem scientists are acutely aware that at some future date their current systems for representing reality will probably prove inadequate. They take it as their task to contribute to a better, but never final, fit between data patterns and theoretical models (representational systems) (Goody, 1977; Toulmin, 1972). Such an attitude has led to ferment on many levels. Scholars of many stripes struggle with the problems of relativism, and school-age children are confused at the apparent lack of absolute truth in modem knowledge. To understand this attitude, children seem to require many years of experience, and they may be able finally to understand it only when they reach the highest levels of cognitive development (Kitchener, 1983).

This phenomenon seems to be tightly bound up with the development of literate practices (Goody, 1977; Ong, 1982). It seems to require at least four components: (1) possession of the concept of a representational system, (2) appreciation that the belief system accepted in one's day is one of many possible systems, (3) presumption that today's belief system will prove less adequate than some alternatives that have not yet been specified, and (4) institutionalized support of practices that have a history of producing improvements in representational systems. The second, third, and fourth components require historical studies and are therefore literacy dependent in a strict sense, because historical studies do not seem to be possible without written histories. The first component, possession of the concept of a representational system, seems at least to be greatly facilitated by literate practices. The development of this concept in school-age children certainly merits study (Feldman, 1980; Gardner, 1983).

Aliterate cultures seem to provide little environmental support for the concept of a representational system (Goody, 1977), but literacy provides open and direct support for the concept. Writing is permanent, and so language becomes subject to extended scrutiny. As a result, people can conceive the nature and shortcomings of the written system for language. For example, all alphabets are small systems that can be understood as a whole and that are manifestly imperfect in their ability to represent speech. They fail to capture even many of the vocal aspects of speech, such as timing and inflection. These limitations make it relatively easy for literate peoples to abstract the concept of a representational system.

Methods For Assessing The Cognitive Effects Of Literate Practices

If this characterization of the functioning of literate practices in mental life is correct, most traditional methods for assessing literacy effects will need to be revised. Consider one assessment strategy used often in the past: The researcher constitutes a group with equal numbers of illiterates and literates and tests all of them on some cognitive task, such as recalling a long list of words. All subjects perform the task in the same way, with no access to literate tools such as pencil and paper. After statistically controlling for factors such as intelligence, age, and social background, the researcher assesses whether there is any residual effect of literacy on performance. To date, the results of such traditional studies have been disappointing, typically showing no, or only modest, effects of literacy (Scribner and Cole, 1981).

In hindsight this failure is not surprising because the studies do not assess the right skills. First, subjects performing the tasks are denied access to the literate tool kit during their performance. Unable to use the external tools of literacy, they are denied environmental support for their literate skills, which typically require operations with external representational devices.

As a result, the main effects of literacy are at best severely attenuated. Second, the research addresses basic cognitive abilities such as recall. Literacy effects that do not permanently amplify such basic abilities go undetected. Third, the major comparison treats illiterates and literates as homogeneous classes, ignoring the tremendous differentiation within the class of literates. In particular, many literates have little exposure to the literate skills most critical to the modem knowledge explosion—the practices that institutionalized nonlocal integration and systemic analysis.

Figure 3-4 shows the range of conditions needed to assess the cognitive effects of literate practices in children or adults. Subjects need to be differentiated according to their literacy status, as shown in the top row. Pre literates are members of cultures that lack any literate practices, while illiterates are aliterate members of cultures rich in literate practices. This distinction permits assessment of whether some cognitive effects of literate practices diffuse within a culture to those who have not actually learned enough to be literate. Nominal literates have learned the basics about using an external representational system but not the practices that promote non local integration and systemic analysis, while advanced literates have mastered some of those practices. This distinction allows assessment of the effects of the advanced literacy skills related to the modem knowledge explosion.

A matrix of contrasts for the assessment of literacy effects.

Individuals should be tested with or without access to the external tool kit of literacy, as shown in the second row of the figure. Testing both ways is critical so that researchers can determine whether literacy effects depend on the environmental support of the tool kit. Most past assessments of literacy effects have denied access to the tools (Scribner and Cole, 1981) and thus have tested only for the residual effects of prior engagement in literate practices. Also, subjects should be tested on a range of types of task, as shown in the left column. Many of the effects of literate practices will remain obscure if only basic cognitive abilities are assessed.

An Emerging Consensus

The approach outlined here represents an emerging consensus about the effects of literacy (Bullock, 1983; Cole and Griffin, 1980; Goody, 1977; Scribner and Cole, 1981; Slaughter, 1982; Tannen, 1982; Vygotsky, 1934/1978; Zebroski, 1982). This consensus includes an appreciation of at least four major characteristics of the functioning of literate practices:

Literate practices are highly diverse.

The diversity includes differences not only in the tools of literacy but also in the cultural practices related to the tools.

Many literacy effects depend on long exposure to organized use of literate tool kits, and the most interesting literacy effects are probably not automatic products of learning to read, write, or count. Literate practices have their effects via a long developmental process beginning in the school years and extending into adulthood.

Different literate practices play different roles in mental life, and some of the most important roles seem to involve providing support for functioning at levels of cognitive development that emerge in the late school years and beyond.

Of course, the consensus is not complete. Two of the remaining controversies are especially relevant here. First, do literate practices have a pervasive effect on thinking and consciousness, or are their effects highly specific and localized? Second, are literate practices fundamental to the most advanced forms of human thinking, as Vygotsky (1934/1978) believed, or can such advanced skills develop without literacy?

Although firm answers to these questions will not be available until more of the blank cells in Figure 3-4 are filled in, we hazard two predictions. On general theoretical grounds (Fischer, 1980; Fischer and Bullock, 1981) and on the basis of available research on literacy effects (see Scribner and Cole, 1981), we expect that some form of the specificity hypothesis will survive the test of time. But along with specificity there can also be some generality. Literacy is itself a vehicle for partially overcoming the natural tendency for skills to remain localized.

Regarding the role of literate practices in advanced forms of thought, we have already proposed that modem scientific enterprises are literally inconceivable to preliterates because they involve explicit attempts to revise entire conceptual systems. It remains to be seen whether other examples of such systemic analysis can be found among preliterates (Goody, 1977).

Literate Practices And Schooling

We noted earlier that the mode of teaching in traditional schooling departs substantially from the natural teaching mode children experience in everyday life. Instead of being embedded in the course of joint goal-directed activity, teaching is disembedded and organized around domains of knowledge (Slaughter, 1982).

This property of formal schooling appears to be a product of literate practices. In all likelihood the very idea of a domain of knowledge and the disembedded teaching it encourages are two sides of a coin that could only be minted in a literate culture. Only with literacy are words or statements disembedded from the evanescent stream of human action and given the spatial permanence of things. Only with literacy are large bodies of such statements sorted into separate places that are internally organized according to the taxonomic schemes associated with domains of knowledge.

Based on the concept of domains of knowledge, teaching can be disembedded from the world of human purposes and reconceptualized as the transfer of a body of knowledge from one depository (books) to another (children). As Ong (1982:175-177) suggested, the message transfer model of communication appears to be a distortion based in literate educational practice. Fortunately, teachers can reembed their teaching in several ways and reintroduce the natural strategy of guided reinvention. They can show children how what they learn is relevant to everyday goals, and they can introduce the new goals related to domains of knowledge. Children can learn such goals as adding newly encountered facts to the appropriate domain, trying to find and fill gaps in existing domains, trying to reorganize or reconceptualize domains of knowledge, and trying to transfer organizational schemes from one domain to another. An important topic for research is how schooling practices can be organized to help children make such practices their own.

Modem science could in some ways serve as a model for such research, since it seems to be the epitome of a collaborative, literacy-based enterprise (Toulmin, 1972). Goody, one of the most insightful theorists of literacy effects, made the following argument (1977:46-47, emphasis added):

It is not so much scépticism itself that distinguishes post-scientific thought as the accumulated scepticism that writing makes possible; it is a question of establishing a cumulative tradition of critical discussion . It is now possible to see why science, in the sense we usually think of this activity, occurred only when writing made its appearance and why it made its most striking advances when literacy became widespread.

Here, the cumulative tradition of critical discussion provides a milieu within which scientific advances can occur rapidly.

It is only within this milieu that scientists have the ability to construct new insights so rapidly. Goody noted the implication of this fact for the traditional competence-performance distinction (p. 18):

[Studies of literacy effects] can be taken to indicate... that while cognitive capacities remain the same, access to different skills can produce remarkable results. Indeed, I myself would go further and see the acquisition of [literate] means of communication as effectively transforming the nature of cognitive processes, in a manner that leads to a partial dissolution of the boundaries erected by psychologists and linguists between abilities and performance.

• Summary And Conclusions

Cognitive development in school-age children has been one of the most active areas of research in developmental science. Yet the range of issues investigated has been relatively narrow and based primarily on Piaget's theory of cognitive development, school-related concerns about the testing of intelligence and achievement, and behaviorist theories of conditioning and learning and, more recently, information-processing theories.

Today many cognitive-developmental scholars are moving toward a broader, more integrative orientation, emphasizing relationships among the traditional categories for behavior (cognition, emotion, social behavior, personality, and so forth) and constructs that highlight the interaction or collaboration of child and environment. There has also been a growing emphasis on constructing and using methods and statistics that allow direct tests of cognitive-developmental hypotheses, in place of traditional methods and statistics, which often do not allow appropriate tests.

A Portrait Of The Capacities Of The School-Age Child

The cognitive capacities that develop during the school years do not develop in stages as traditionally defined. Instead, children's abilities seem to cumulate gradually and to show wide variations as a function of environmental support. Certain components of children's capacities do show weakly stagelike characteristics, however. At specific periods a wide range of children's abilities appear to undergo rapid development. These spurts may be particularly evident in children's best performances.

When the various neo-Piagetian theories are compared, there seems to be a consensus, with substantial empirical support, that four of these large-scale reorganizations occur between ages 4 and 18. At approximately age 4, middle-class children develop the capacity to construct simple relationships of representations, coordinating two or more ideas. The capacity for concrete operations emerges at age 6-7, as children become able to deal with complex problems about concrete objects and events. The first level of formal operations appears at age 10-12, when children can build general categories based on concrete instances and when they can begin to reason hypothetically. Abilities take another leap forward at age 14-16, when children develop the capacity to relate abstractions or hypotheses.

Cognitive developmentalists have often assumed that all children move through the same general developmental sequences, but research suggests that such generality occurs at best only for the most global analytic categories, such as concrete and formal operations. With more specific analyses, it seems that children will demonstrate important differences in developmental sequences. Only with research on these differences will a full portrait of school-age children's capacities be possible.

Little consensus exists on the specific processes underlying the cognitive changes that occur during the school years. Most characterizations of these processes fall into two opposing frameworks: an emphasis on changes in organization, usually conceptualized in terms of either logic or short-term memory capacity, versus an emphasis on continuous accumulation of independent habits or production systems. Progress is not likely to arise from continuation of arguments based on this assumption of opposition. The most promising direction for resolution would seem to lie in attempts to determine when abilities show reorganization and when they show continuous accumulation.

What Is Not Known

The new integrative orientation in cognitive-developmental science has led to wide recognition of the need for framing questions in ways that avoid the traditional oppositions that have typified behavioral science. Most centrally, questions have traditionally been formulated in ways that led to answers focusing on either the child or the environment as the main locus of developmental change. What many researchers are striving for today are ways of building constructs that combine the child and the environment as joint determiners of development. A promising direction for this enterprise is a focus on the collaboration of child and environment. The child is seen as always acting in some particular context that supports his or her behavior to varying degrees. One result of this focus is that concepts of ability, capacity, and competence are radically altered. They are no longer fixed characteristics of a child but emergent characteristics of a child in a context. How to recast these concepts is a major unresolved question in cognitive development.

To do research based on the integrative, collaborative orientation, investigators need to assess behavior in multiple contexts and with various methods. It cannot be assumed that a single variable provides a valid index of overall cognitive functioning in any domain or that behavior is truly divided into neat boxes labeled cognition, social behavior, emotion, and so forth. Within this reorientation toward research, investigations naturally cross traditional category boundaries and examine variations in the child and the environment simultaneously. We have focused on four topics consistent with this reorientation that have been generally neglected in research on cognitive development in school-age children.

Emotion has traditionally been treated as distinct from cognition, but some recent research suggests that in many ways the two may develop hand in hand. Some research has shown that school-age children make major advances in their ability to conceptually integrate diverse emotions. Other major topics that demand investigation include emotional reorganizations that appear to accompany the general cognitive reorganizations of the school years and Freudian, psychodynamic processes, which seem to flower during these years. A promising approach to studying emotion-cognition relationships is to choose issues in children's daily lives that naturally evoke strong emotions, such as the self, divorce, and illness.

Brain development is a major topic in the neurosciences today, but there has been little research on the relationships between brain development and cognitive development. Such research is especially difficult to do, and it has an unfortunate history. Preliminary results have often been overgeneralized and distorted, and unjustified claims have been made about practical implications for education or other socially important endeavors. Nevertheless, research on brain growth and cognitive development promises to provide important scientific breakthroughs, even though it will be a long time before legitimate practical applications will be possible.

Social development and cognitive development have typically been treated as distinct categories, and there has been little research on the contributions of social interaction to cognitive development. The few studies in recent years on this topic suggested that social interaction plays a central role in cognitive development in the school years. Much of the course of normal cognitive development seems to involve a process of guided reinvention, in which the child constructs new skills with the help of constant support and guidance from the social environment, especially from dyadic interactions. Analysis of this process has been almost completely neglected in school-age children, despite the fact that many of the failures of school-based education seem to result from the ways that classroom procedures diverge from the norm of guided reinvention.

Schooling and the literate practices associated with it seem to produce major extensions of human intelligence. Not only are basic cognitive abilities amplified, but the scope of intelligence broadens greatly, and a new capacity arises to conceive of representational systems and to analyze them. The scientific revolution appears both to have resulted from these extensions of human intelligence and to be producing further extensions. These effects of schooling and literate practices illustrate the central role of the environment in supporting cognitive growth. Unfortunately, research has been sparse on these effects, especially in school-age children, even though the school years appear to be the period during which these new types of intelligence are built.

The present epoch is an exciting time in the history of developmental science in general and the study of cognitive development in particular. With the new emphasis on relating the parts of the child and on placing the child firmly in a context, we expect to see major advances in the understanding of cognitive development in school-age children.

• Acknowledgments

We thank Richard Canfield for his help in the preparation of this chapter. We also thank the following people for their contributions: Helen Hand, Susan Harter, Marlin. Pelot, Kathy Purcell, Phillip Shaver, Louise Silvern, Helen Strautman, and Michael Westerman. Preparation of the chapter was supported by a grant from the Carnegie Corporation of New York and from the National Institute of Mental Health, grant number 1 RO3 MH38162-01. The statements made and views expressed are solely those of the authors.

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Jennifer E. Crotty , Susanne P. Martin-Herz , Rebecca J. Scharf; Cognitive Development. Pediatr Rev February 2023; 44 (2): 58–67. https://doi.org/10.1542/pir.2021-005069

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Cognitive development in children begins with brain development. Early life exposures may both positively and negatively influence cognitive development in children. Infants, toddlers, and children learn best in secure, nurturing environments and when attachment to a consistent caregiver is present. Pediatricians can screen for both social determinants of health and developmental milestones at office visits to address barriers to care and promote positive cognitive and learning outcomes. Pediatricians may model developmental stimulation during office visits to talk with an infant/child, asking questions of a child, singing and pointing to pictures in books, and modeling responsive listening. Pediatricians may support caregivers to talk with their children, read to their children, and avoid/reduce screen time. Pediatricians can help point caregivers to resources for parent training, Head Start, and quality preschool programs. School readiness has both pre-academic and socioemotional components and can have long-term effects on a child’s school success, health, and quality of life. School readiness depends on both the child and the caregiver being ready for school, taking into account caregiver and child health and mental health and child cognitive development.

Pediatricians should be familiar with typical cognitive developmental milestones and concerns for developmental delay for all domains between birth and 5 years of age. Health supervision visits are opportunities for pediatricians to model developmental stimulation.

Recognize delays in cognitive development in patients.

Identify ways to model developmental stimulation in office visits.

Demonstrate how to connect caregivers with resources to promote healthy development in patients, such as literary resources, parent training programs, Head Start, and preschool programs.

Cognitive development occurs alongside the growth and maturation of a child’s brain, as the child learns to explore, reason, solve problems, and think. This development happens over a lifetime, as neurons develop and neural connections form and are pruned through experiences. Scientific research continues to find that early responsive relationships and safe, nurturing environments protect brain development and allow for healthy growth in children. ( 1 )( 2 ) Ideally, children and adults are in stimulating, supportive environments that allow for continual learning and development. However, challenges such as poor health, instability in housing or school, caregiver mental health challenges or addictions, poverty, or family violence can lead to disruptions in developing brain architecture, as well as longer-term health challenges. ( 3 )( 4 ) As the brain is developing and neural connections are formed, responsive caregiving can help buffer the effects of toxic stress. ( 2 )( 5 ) Over the years, psychologists have proposed a variety of perspectives on how children develop cognitively, which can add useful insights into how children learn, enabling clinicians and caregivers to optimize development by modeling and teaching to specific developmental stages. We have come a long way in our understanding of cognitive development since William James described infancy as a “blooming, buzzing confusion.” ( 6 )

Jean Piaget (1896–1980) was a Swiss psychologist who described cognitive development in 4 stages: sensorimotor (birth to <24 months), preoperational (2–7 years), concrete operational (7–11 years), and formal operational (≥12 years). ( 7 ) In the sensorimotor period, infants learn and explore through their senses and movements, and for this phase, Piaget described the “cognitive goal” as object permanence. In the preoperational stage, children learn to interact with the world through language and imagery, and they learn to pretend play. The goal of this phase is using symbols in thinking. Next, in the concrete operational phase, children work toward the goal of logical thinking. They begin to understand conservation of numbers, mass, and weight. Children also learn that some things are reversible; for example, after a sequence of events such as pouring water from a small wide beaker to a tall thin one, the step can be undone and the water will go back to the previous shape of the first beaker. Finally in the formal operational phase, individuals are able to think about ideas in the abstract world, without physical constraints. They are able to begin scientific thinking with hypotheses and theories and to reason through a variety of ideas. Dr Piaget noted that across cultures and places, people tend to follow these stages in order. Although approximate ages are given, he did not believe that everyone follows these patterns in the same time span; people move through these phases at different interval lengths over the life span, and not all will reach the abstract reasoning goal of the formal operational phase.

Lev Vygotsky (1896–1934) emphasized the importance of social interactions in the development of cognition. ( 8 ) He noted that children “make meaning” via their communities. Whereas Piaget taught that development precedes learning, Vygotsky found that learning is a universal social function that may precede development. Of Vygotsky’s many contributions to our understanding of cognitive development, his ideas of scaffolding (the concept of learning through completion of small and manageable steps with the support of someone who is already more advanced in that skill) and the zone of proximal development (the difference between a child’s current cognitive developmental level and their potential) have been particularly helpful to our understanding of promoting developmental progress. Vygotsky taught that children learn skills and concepts from trying new things, in social relationships, “on the edge of emergence.” Individuals who interact with a “more knowledgeable other” who can provide some direction (scaffolding) can learn in their “zone of proximal development,” making progress in areas that had previously been just outside their reach or would have remained so without this support. Jerome Bruner expanded on this, noting that all people can learn new concepts in a social environment, with another providing support, and that support can gradually be removed as a person becomes more independent in their abilities. ( 9 )

Erik Erikson (1902–1994) described psychosocial development in 8 stages from infancy to adulthood. ( 10 )( 11 ) Although he did not examine cognition explicitly, psychosocial development is often tied to development of reasoning skills, and children who successfully develop psychosocial skills will be better prepared for learning at home, at school, and in communities. Noting in which psychosocial stage a patient is functioning can be helpful in determining an efficacious treatment plan. ( 12 ) For example, a 1-year-old in the hospital for treatment of infection may be most worried about a caregiver leaving and may need to be allowed to stay with a caregiver at all times during treatment, whereas a teen in the hospital for treatment may be most worried about missing out on peer time and may benefit from visits or video calls with friends while undergoing a stay for treatment. When determining a plan for a patient, the pediatrician can think through ways to support cognitive and social development in line with whether that patient is currently in the “autonomy versus shame and doubt” phase or the “identity versus role confusion” phase. The developmental theory may lend perspective to allow the health-care team to provide support during medical challenges.

Finally, John Bowlby (1907–1990) highlighted the importance of early childhood relationships, particularly that between a child and the primary caregiver, and pioneered attachment theory. ( 13 ) Mary Ainsworth (1913–1999) expanded John Bowlby’s teachings on attachment. ( 14 ) Through experiments with the Strange Situation procedure, Dr Ainsworth described 3 main attachment patterns: secure, anxious-resistant, and anxious-avoidant. A fourth category was later added as disorganized. Children who experience secure attachment to a caregiver are in a better position to develop cognitive skills. ( 15 )( 16 ) Further studies are finding that children with secure attachment have greater activation in the frontal, limbic, and basal ganglia areas of the brain, which may represent more mature socioemotional processing and motivation. ( 17 ) Children with a secure base of attachment are more likely to thrive in school and to have higher executive functioning. ( 18 )( 19 )

Clinical studies demonstrate the remarkable pace and nature of cognitive and social skill acquisition in the early years. ( 20 ) In utero, the brain is developing rapidly, with both genetic and uterine environmental factors contributing to neurologic growth. After birth, cognitive development begins through sensory and motor inputs and caregiver-infant interactions. At approximately 1 to 2 months of age, infants follow a caregiver’s face ( Table 1 ). ( 21 )( 22 ) At approximately 3 months, they may follow an object in a circle, regard toys, and reach for faces. Six-month-olds may turn their head to look for a dropped object and interact with toys. Nine-month-olds will search for objects under a cloth or play peek-a-boo, demonstrating an understanding of object permanence. Twelve-month-olds enter a new world of exploration with first words, pointing for requests, and finding toys in containers. The first year of cognitive development is incredible, as infants are continually learning new communication, problem-solving, and adaptive skills.

Cognitive Milestones

Note that new milestones were published in March 2022 using not the mean or median age but the 75th percentile of children reaching the milestone. For updated lists, see Zubler JM, Wiggins LD, Macias MM, et al. Evidence-informed milestones for developmental surveillance tools. Pediatrics . 2022;149( 3 ):e2021052138.

Adapted from Scharf RJ, Scharf GJ, Stroustrup A. Developmental milestones. Pediatr Rev . 2016;37( 1 ):25–37.

During the toddler years, children continue to experience an explosion of cognitive skill growth and learning. As infants and toddlers become more mobile and curious, they are more able to explore and interact with their environment, supporting growth in problem-solving skills. Two-year-olds can learn to open doors, pull off clothing, and make requests. Toddlers are constantly busy, learning from interactions and experiences. Interacting with nature, toys, books, and peers is optimal for neural connections to be formed and pruned; screen time at this age, which limits the physical exploration of the environment and playful interactions with others, may hamper cognitive development. ( 23 ) In addition, both screen time by the child and background television exposure seem to have negative effects on behavior in young children. ( 24 )( 25 ) Three-year-old children may want to help with household tasks, begin completing puzzles, and increasingly play interactively with other children.

Preschool years are critical to set the foundation for a lifetime of learning and exploring. ( 26 ) Children who come to school healthy with regular pediatric health supervision visits, proper sleep hygiene, limited exposure to screen time, and early exposure to language, singing, games, colors, and shapes are better primed for learning. ( 27 )( 28 ) School readiness has both pre-academic and socioemotional components and can have long-term effects on a child’s school success, health, and quality of life. School readiness depends on both the child and the caregiver being ready for school, taking into account caregiver and child health and mental health and child cognitive development. A safe and nurturing environment can support the development of skills needed for participating in school. ( 1 )( 29 ) Preschool is the age of learning independence, including dressing, toilet training, telling stories, interacting with art and music, and playing with friends. When children are exposed to singing, stories, rhymes, counting, art, and outdoor play in the preschool years, they will come to school more prepared for learning. Early skills are foundational to success in the early school years ( Table 2 ). ( 26 )( 30 )( 31 )

School Readiness Skills

Developmental surveillance is encouraged at all health supervision visits. ( 22 )( 32 ) Newly published milestone tables give ages at which approximately 75% of children will meet a milestone, rather than the mean or median age, to discourage a “wait-and-see” approach and encourage more in-depth surveillance or consideration of screening. ( 22 ) Developmental screening with a standardized, validated developmental screening tool is recommended by the American Academy of Pediatrics at 9, 18, and 30 months, but tools can identify delays when used at any age. ( 32 ) Pediatricians can also use developmental screening as a means of celebrating milestones met and skills gained with families. Reviewing with families why we screen is important so that families understand that there may be resources and supports to help their children be ready for school at 5 years of age and to address any detected or suspected cognitive or attention challenges. Table 3 highlights areas of concern that may warrant further assessment or referral.

Cognitive Development Concerns

Although normal variations in achievement of cognitive development exist, pediatricians should recognize the multiple threats that negatively alter a child’s development. Chronic congenital or acquired disease, physical trauma, abuse, and neglect can all lead to devastating impacts. Adverse childhood experiences (ACEs) are potentially traumatic events that occur in a child’s life before age 18 years that can have lasting effects on lifetime health. ( 33 ) ACEs, along with sustained exposure to other stressors or acute traumatic events, can result in chronic activation of the body’s stress responses, particularly when a supportive environment is missing for the child. This state is sometimes referred to as “toxic stress.” ( 4 )( 5 ) Finally, more general social determinants of health, including insecure or inappropriate housing, food insecurity, poverty, etc, can also have an effect on cognitive development.

Pediatricians can encourage cognitive development by connecting parents and guardians with evidence-based interventions that have proved to increase cognition and decrease toxic stress. ( Table 4 ). Many practice-based and community resources exist. Not all the topics later herein can be covered at every visit, but over the course of health supervision visits in early childhood, pediatric clinicians can touch on these topics and offer supports as appropriate.

Office-Based Actions for Encouraging Cognitive Development

Prolonged and exclusive breastfeeding has a clear positive effect on a child’s cognitive development. ( 34 ) Breastfeeding may aide in attachment and bonding and may be an opportunity for mothers to promote cognitive development through talking, singing, eye contact, and social smiles as well. The American Academy of Pediatrics has ample breastfeeding education resources for the pediatrician, and its Breastfeeding Residency Curriculum has demonstrated an improvement in provider knowledge, comfort, and breastfeeding rates. ( 35 ) At the prenatal visit, mothers can be encouraged to breastfeed by providing education and practical information about what to expect as well as information about local breastfeeding groups and lactation consultants; mothers who receive breastfeeding education are 41% more likely to initiate breastfeeding. ( 36 ) At newborn visits, the office can provide access to lactation services and provide letters to insurance companies and employers to support mothers’ right to pump. Although pediatricians do not routinely otherwise provide medical care to adult patients, they should know how to diagnose and treat basic breastfeeding problems. ( 37 )

ACEs can impair early cognitive development, and poverty is 1 of the most studied negative influences on cognitive development. ( 38 ) There is no evidence-based screening for social determinants of health recommendation from the US Preventive Services Task Force, but the American Academy of Pediatrics recommends that pediatric practices screen for “basic needs, such as food, housing, and health” during patient encounters. ( 39 ) Many free, validated screens are available and can be administered by any member of the care team; examples in Table 5 show screens that address 1 need and others that evaluate multiple needs. ( 40 ) Some of these screens, such as the SEEK PQ-R and the Hunger Vital Sign, are available in multiple languages. Screening for social determinants of health has become an important component of pediatric preventive care, as noted in the Bright Futures guidelines, 4th edition. ( 41 ) ( https://brightfutures.aap.org/materials-and-tools/tool-and-resource-kit/Pages/Developmental-Behavioral-Psychosocial-Screening-and-Assessment-Forms.aspx ) When needs are discovered, pediatricians should be prepared to refer to resources; teaming up with a local social worker or care coordinator is beneficial. Online resources can help guide referrals ( Table 6 ). Pediatricians are also well-placed to encourage evidence-based resilience strategies for children, such as access to health-care and education for parent and child, access to employment resources for parent, community resources for healthy food, and encouraging social connection ( https://www.cdc.gov/violenceprevention/aces/riskprotectivefactors.html ). Advocacy at local, state, and national levels is helpful in attempting appropriate resource allocation for children, such as funding for early intervention services, child care and Head Start/preschool programs, and the Supplemental Nutrition Program for Women, Infants, and Children, which have broad reaches for overall child and family health. ( 29 )( 42 )( 43 )

Examples of Evidence-Based Screeners for Social Determinants of Health

Resources for Positive SDOH Screens

SDOH=social determinant of health.

Untreated maternal depression is strongly associated with negative health outcomes for infants, including a detrimental effect on development. ( 44 ) Universal screening for maternal depression is key in ensuring the best possible outcome for the mother and child. ( 45 ) All guardians of an infant may experience an increase in depressive symptoms, so any caregiver can complete screening and be offered resources. ( 46 ) Pediatricians with unrestricted licenses can treat maternal depression if documented in the mother’s medical record; they may also establish connections for the caregivers with social workers or mental health clinicians for supportive services.

Positive parenting affirms that all children are good and helps parents learn developmentally appropriate discipline while building a child’s self-confidence. Adaptive reasoning and consequences are taught in a loving, patient manner, promoting positive cognitive development. Pediatricians should promote this strengths-based approach to setting boundaries and celebrating strengths. Table 7 provides a list of website resources free to providers and families. Several individual companies have intensive curricula on this topic, and many states provide curricula free both online and in-person. Two such programs, Triple P and the Incredible Years, have been evaluated by multiple meta-analyses, all of which conclude that these programs have a positive effect on children’s behavior and adjustment and achieve a decrease in dysfunctional parenting. ( 24 )( 47 )

Positive Parenting Resources

AAP=American Academy of Pediatrics; CDC=Centers for Disease Control and Prevention.

Early literacy support is the role of every pediatrician, ( 48 ) and this support can be office-based and community-based. Book giveaway programs, although not strongly tied to increased cognitive skills in children, encourage word-rich homes and improve home literacy habits. ( 49 ) Provider-based programs such as Reach Out and Read encourage physicians to use reading as a starting point for emotional connection between the parent and the child. Evidence shows improvement in receptive and expressive language development for toddlers and preschool children who participate. ( 50 ) Being kindergarten ready, with age-appropriate math, literacy, and socioemotional skills, is predictive of higher academic achievement in high school; helping families seek active learning opportunities can also have long-term benefits. ( 51 ) Pediatricians can suggest ways for parents to weave reading and literacy-encouraging habits into daily routines. Using books, pediatricians can model give-and-take conversations with infants, even if the child is not conversational yet. Encouraging imaginative and creative play between parent and child builds a child’s cognition and strengthens the parent-child bond. In addition, no literacy support is complete without addressing adult literacy, which can also promote employment for parents of patients and financial stability. Having resources for parents in the office is beneficial ( Table 6 ).

Children who qualify for and participate in subsidized preschool programs or Head Start, a federally funded program that assists low-income families whose children attend and has services that support learning, health, and family well-being, ( 52 ) have significantly higher cognitive development than their age- and socioeconomically matched peers. ( 42 )( 53 )( 54 )( 55 )( 56 )( 57 ) Understanding a state’s preschool and Head Start funding is important so that a provider can encourage eligible children to enroll. For working parents, pediatricians may point to high-quality child care for children in the area while parents pursue employment. These discussions can be included during health supervision visits to let parents know the value of early education. ( 56 )

From birth to age 3 years, children whose parents have concerns for developmental delay, who have delays identified on developmental screening in the primary care office, or who have a diagnosed disability should be referred to early intervention services (EIS). Through the Individuals with Disabilities Education Act (IDEA) Part C, EIS provides therapies and services to infants and toddlers with developmental delays, whether or not they are enrolled in a formal school program. EIS can improve a child’s developmental skill attainment and have a significant influence on school and life success. ( 58 ) These programs exist in every US state and territory, although definitions for which children qualify differ. Services, which often include physical, occupational, and speech/language therapies and developmental support and education, are provided free or at a reduced cost for children who meet the state’s criteria, as outlined on the IDEA website ( https://sites.ed.gov/idea/ ). For children older than 3 years, the school systems provide learning and therapeutic treatments for children as provided for by IDEA Part B. Individualized Educational Programs are developed for children who need services for specific medical or learning needs. 504 plans provide some specific services, such as accommodations for attention-deficit/hyperactivity disorder, without full assessment. Referring for psychological testing in patients who struggle academically or who have comorbidities is fundamental to secure educational resources for academic success. Every state varies for required documentation and initiation of plans; therefore, pediatricians have the opportunity to learn how to specifically navigate the school system in their areas and share this information with parents. All states must abide by the IDEA, which includes an obligation to reassess the child every 3 years.

Cognitive development is a many-faceted and well-researched topic that has lifelong consequences for children and families. Educational success can lead to financial and health stability. The pediatric clinician has a crucial role in identifying cognitive concerns and barriers to developmental progress. There are many ways to monitor for concerns and to refer families for supports. The pediatrician may encourage families toward the best possible cognitive development for every child.

Based on level C/D evidence (recommendation based on observational data and expert opinions), cognitive development is a multifactorial, lifelong process that ideally occurs in supportive environments that optimize learning.

Based on level C evidence (recommendation based on observational data and expert opinions), pediatricians should screen for development and refer to appropriate resources during health-care and health supervision visits.

Based on level C evidence (recommendation based on observational data and expert opinions), pediatricians should offer guidance to caregivers on promoting cognitive development through reading, talking, singing.

Based on level C evidence (recommendation for benefits of early childhood programs based on longitudinal cohort studies, case reports, and expert opinions), pediatricians may connect caregivers with information for children to attend Head Start or high-quality preschool programs.

Promote breastfeeding to increase rates and duration of exclusive breastfeeding

Seek to increase caregiver depression screening rates

Have ready information for Centers for Disease Control and Prevention (CDC), American Academy of Pediatrics, Triple P, 1-2-3 Magic, and/or Incredible Years parenting programs

Consider ways to integrate “Parents as Teachers” into clinics

Increase use of appropriate standardized developmental screening tools at each health supervision visit

Increase Reach Out and Read availability

Draft talking points for pediatric clinicians to teach parents about language stimulation

Teach “Tune In, Talk More, Take Turns” to office staff to promote to caregivers

Create handouts on school readiness promotion

Show parents school readiness activities to do with their children on the American Academy of Pediatrics or CDC websites during clinic visits

Track preschool/Head Start referrals and work to ensure that families are aware of available early education resources in their community

AUTHOR DISCLOSURE:

Drs Crotty, Martin-Herz, and Scharf have disclosed no financial relationships relevant to this article. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device.

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• Published: 02 June 2021

Change by challenge: A common genetic basis behind childhood cognitive development and cognitive training

• Bruno Sauce 1 ,
• John Wiedenhoeft   ORCID: orcid.org/0000-0002-6935-1517 2 ,
• Nicholas Judd 1 &
• Torkel Klingberg   ORCID: orcid.org/0000-0002-3175-2171 1  

npj Science of Learning volume  6 , Article number:  16 ( 2021 ) Cite this article

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The interplay of genetic and environmental factors behind cognitive development has preoccupied multiple fields of science and sparked heated debates over the decades. Here we tested the hypothesis that developmental genes rely heavily on cognitive challenges—as opposed to natural maturation. Starting with a polygenic score (cogPGS) that previously explained variation in cognitive performance in adults, we estimated its effect in 344 children and adolescents (mean age of 12 years old, ranging from 6 to 25) who showed changes in working memory (WM) in two distinct samples: (1) a developmental sample showing significant WM gains after 2 years of typical, age-related development, and (2) a training sample showing significant, experimentally-induced WM gains after 25 days of an intense WM training. We found that the same genetic factor, cogPGS, significantly explained the amount of WM gain in both samples. And there was no interaction of cogPGS with sample, suggesting that those genetic factors are neutral to whether the WM gains came from development or training. These results represent evidence that cognitive challenges are a central piece in the gene-environment interplay during cognitive development. We believe our study sheds new light on previous findings of interindividual differences in education (rich-get-richer and compensation effects), brain plasticity in children, and the heritability increase of intelligence across the lifespan.

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Introduction.

During childhood, cognitive abilities dramatically improve to make us who we are: persons capable of multiple academic, social, and professional activities. That process is incredibly complex—the interplay of genetic and environmental factors has preoccupied multiple fields of science and sparked heated debates over the decades 1 , 2 . Studies on the development of cognition have been surrounded by difficulties, and new advances are just now shedding light on mechanisms 3 .

It’s useful to start with the seemingly obvious fact that cognitive development differs from child to child. Some children develop their general cognition, such as reasoning, attention, and working memory (WM), at a rapid rate, while some others struggle far behind 4 , 5 , 6 . What explains these interindividual differences? We know that genetic inheritance must play a substantial role—decades of twin studies have shown cognition to be highly heritable in a multitude of populations and environmental contexts 7 . And recent studies show that genes can have powerful and complex interplays with the environment during development 4 , 8 .

How does gene-environment interplay come about? Many researchers favor the idea of natural maturation—during childhood, genes are the main drivers of development and need only a supporting role of common experiences available to almost every child, such as visual stimuli and social interactions (for reviews, see refs. 2 , 9 , 10 , 11 ). Like a train track, genes set the “default course” of development, while most environments serve as the coal to keep the train going (or, in the case of extremes environments, obstacles that can lag development). Under this maturation hypothesis (also known as experience-expectant), variation in cognitive change exists because some sets of genes code for brains with faster default maturation than others. In other words, children differ mostly because they have different default courses. In contrast, the cognitive challenge hypothesis (also known as skill learning or experience-dependent) proposes that the effect of developmental genes relies heavily on cognitive experiences 12 , 13 , 14 . Under that alternative explanation, variation in cognitive change exists because genes interplay with the many distinct (and idiosyncratic) cognitive challenges in children’s environments.

These two hypotheses have been well-studied for simpler traits in nonhuman species. There are traits that can be mostly explained by natural maturation, such a binocular vision in cats and heat resistance in fruit flies. In those traits, experience-expectant genes set a default course, and the environmental interplay with genes is expected and strictly planned by evolution 15 . For other traits, such as singing in songbirds and body growth in fish, there are large genetic variations that interplay with evolutionarily new and unexpected environments 15 . Those studies show there are multiple genes in nature that evolved to be flexible (or open-ended) towards the effect of particular experiences 16 .

However, for general cognitive abilities in humans, the picture is still inconclusive. We do not know if the interindividual differences in cognitive improvement in human children emerge mostly from experience-expectant genes (maturation hypothesis) or with flexible genes (cognitive challenges hypothesis). Due to practical reasons, it is extremely elusive to understand the interplay between genes and environments 4 , 17 . Two big problems have hindered progress: The first problem is that experiences are difficult to control or even measure accurately over years of development. So, studies on typical childhood development will always confound the cognitive challenge hypothesis with the maturation hypothesis—at first glance, any measured interindividual differences in cognitive development are as likely to arise from (flexible) genes interplaying with unknown cognitive experiences as they are to arise from (experience-expectant) genes via natural maturation. To contrast these hypotheses, we believe that developmental studies should be combined with intervention designs that create deliberate and trackable cognitive experiences. The second problem is that genetic studies with cognitive interventions have all looked at only a few candidate genes or DNA markers (e.g., refs. 18 , 19 , 20 , 21 ). General cognition is a complex polygenic trait, influenced by thousands of genetic regions, so those past studies cannot give us a full and reliable genetic picture.

Here we tested the cognitive challenge hypothesis for cognitive ability. To accomplish this, we combined genetic data from a longitudinal sample of typical childhood development and an intervention sample with intense, short-term (25-day-long) cognitive training. No study to date had such a design, partly due to conceptual quagmires (to our knowledge, we are the first to propose a study on gene-environment interplay in development by contrasting the role of a genetic set on the gains from cognitive training and on the changes in typical development) and partly due to lack of power. In the past, studies able to account for multiple genes needed to have hundreds of thousands of participants—an unrealistically large sample for experimental, cognitive interventions. Recently, that problem was partly overcome, thanks to the existence of reliable polygenic scores—indexes that put together thousands of genetic regions that alone would have extremely small predictive value. A large, genome-wide association study with 1.1 million people was able to estimate the effect of genetic regions on differences in cognitive performance, educational attainment, and mathematical ability 22 . With that information available, we were able to sum the reported effect sizes of all available genetic markers to create polygenic scores for cognitive performance (cogPGS) for each individual in our sample—providing our study with a much greater statistical power than typical in the past.

We focused on WM, a trait central to other general cognitive abilities, and responsible for the active maintenance and manipulation of information. High WM is associated with competence in reasoning and learning 23 , and benefit future school performance 24 , while low WM is associated with the inattentive symptoms of ADHD 25 . Recent studies have shown that structured WM training programs can have beneficial effects on an individual’s WM 26 , 27 , 28 , 29 , and suggests WM is, to some extent, malleable to experiences.

Our study rests on the following reasoning: if cogPGS can explain the variation in WM changes in both typical development and training, it suggests that both have genetic variation the interplays with cognitive experiences. This, we believe, represents a test of the cognitive challenge hypothesis—more specifically, the prediction that cogPGS explains the change of both development and training over time of cognition.

Our study included 344 children, adolescents, and young adults by combining a developmental and a training sample. The developmental sample ( n  = 160) was recruited to represent the general population, participants were between 6 and 25 years old and had their WM assessed twice with a 2-year interval. In the training sample ( n  = 184), participants were between 7 and 19 years old and completed an average of 24.7 days of WM training (SD = 1.06). In both samples, we measured WM performance by averaging standardized scores on a visuospatial and a verbal WM task.

We created polygenic scores for cognitive performance (cogPGS) using the SNP effect sizes from a multi-trait analysis that focused on a GWAS of cognitive performance and complemented by information from a GWAS on educational attainment and a GWAS on mathematical ability 22 .

Change in WM

Figure 1A shows baseline WM performance in the developmental sample, showing an increase in capacity with age, with gradual flattening of development at older age-range. Baseline WM performance was subtracted from follow-up performance. This showed that, regardless of starting age, participants had a significant longitudinal increase in their WM after 2 years (β = 0.60, p  < 0.001), with a mean increase of 14% (±2.5).

A Baseline WM performance in the different age groups from the developmental sample. That variable is a combination of visuospatial and verbal working memory tasks and is total the number of correct trials given at the start of the study and averaged between the two tasks. Shades represent standard error. B WM performance on different days during cognitive training in the training sample. This daily WM performance is a combination of visuospatial and a verbal working memory tasks and represents the average level of the three successful trials with the highest level on each day and averaged between the two tasks. Shades represent the standard error of the mean. C , D Distribution of standardized change in WM per count of individuals in the developmental sample (after 2 years) and the training sample, respectively. The WM change variable is the subtracted baseline WM from the follow-up WM in each sample and then separately standardized (mean of zero and standard deviation of 1). Values of zero represent the mean change in each sample.

Figure 1B shows the average daily values in WM performance over the training duration in the training sample. Training led to a large average improvement in the performance of the trained WM tasks at the end of the intervention (β = 2.10, p  < 0.001), with a mean increase of 34% (±1.5). Importantly, both samples showed large interindividual differences in the amount of WM change (Fig. 1C, D ), which is the subtraction of baseline WM from the follow-up WM in each sample.

Effect of age and gender on WM

To understand the effects on initial levels of WM, we created a model (here called “baseline WM model”) with baseline WM as the outcome and the predictors: sample (developmental and training), baseline age, gender, and cogPGS. Baseline WM was the performance on the first visit in the developmental sample, and maximum performance during days 2 and 3 of the training sample. As expected, we found that age affected the baseline levels of WM (β = 0.54, p  < 0.001). There was no effect of gender ( p  = 0.44).

To understand the effects on the change in WM after 2 years (developmental sample) and after training (training sample), we subtracted baseline WM from the follow-up WM to obtain the variable WM change. We then used a general linear model (here called “WM change model”) with WM change as the outcome and the predictors: sample (developmental or training), baseline age, gender, cogPGS, age x sample, and cogPGS x sample. The model showed no effect of gender on the change in WM ( p  = 0.09). We also found that there was no main effect of age on the change in WM ( p  = 0.16), but an interaction of age with the type of sample ( p  = 0.04). For the developmental sample, younger participants changed more than older participants. This pattern was inversed in the training sample, with older participants improving more from the training.

Effect of polygenic scores for cognitive performance on WM

Using the baseline WM model, we found that cogPGS significantly explained the baseline variation in WM (β = 0.10, p  = 0.03). That translates into 1.0% of variance explained by cogPGS after accounting for the effect of gender and age differences. As described in Methods, note that this polygenic prediction is already controlled for population stratification, genotyping chip, and batch type (done during quality control of genotype data before obtaining the cogPGS), as well as gender and age (in the baseline WM model for prediction).

Finally, we tested our main hypothesis: if cogPGS is related to interindividual differences in change in development and training and, if so, whether there are significant differences between training and development. The change in WM in both developmental and training samples can be seen in Fig. 2 . The WM change model showed that cogPGS significantly explained interindividual differences in WM change (β = 0.15, p  = 0.04), with 2.2% of variance explained after accounting for the effect of gender, age, sample, and the interactions. Importantly, there was no interaction of cogPGS with sample ( p  = 0.46), which suggests that the effect of cogPGS does not depend on the sample being developmental or training.

The WM change variable is the subtracted baseline WM from the follow-up WM in each sample and then separately standardized (mean of zero and standard deviation of 1). Values of zero represent the mean change in each sample. Values of cogPGS are also standardized to have a mean of zero and a standard deviation of one. Shades represent 95% confidence intervals.

To check the consistency of our results, we reanalyzed our data with the following modifications: (1) Removing outliers with a WM change score beyond three standard deviations from the mean (five participants in total); (2) Using standardized age in each of the samples separately. In the original analyses, we used actual age—even though the two samples have around the same mean age, differences in standard deviation could have mattered. Reanalyses with these modifications (separately and combined) confirmed our main finding: that PGS has a significant association to change in WM (all p  < 0.01), but there was no interaction between PGS and group.

The role of genetic factors in modulating the effect of experiences in cognitive development is a key question in developmental psychology, behavior genetics, and cognitive neuroscience. Here we showed that the amount of WM change in both a longitudinal sample of typical development and an intervention sample of cognitive training is in part explained by the same polygenic score (a set of genetic markers known to explain variation in cognitive performance at a single time point 22 ). This genetic variation in plasticity, or malleability, is a pattern that in evolutionary biology has been considered a hallmark of flexible genes interplaying with new, unexpected environments 30 , 31 .

Our results here are evidence for the cognitive challenge hypothesis in cognitive development—more specifically, it supports the prediction that the genetic mechanisms of development should be partly shared with those of cognitive training. This implies that children’s cognitive abilities develop partly as a result of the cognitive challenges that they experience, much like a skill 12 , 32 , 33 . Our results are also evidence against purely natural maturation, in which cognitive development is genetically coded and with minimal influence of normal environmental variability. However, the two processes are not mutually exclusive and could coexist or dominate at different points during childhood.

One of the influential cognitive challenges during development might be schooling. Our finding here could thus explain the mechanism of why years of schooling, rather than chronological age, drives the development of WM 34 , as well as why education affects IQ 35 , and why twin studies show how environmental effects can be responsible for the change in cognitive function over time 36 , 37 .

For the developmental sample, younger participants exhibited more change (during the 2-year interval) than did older participants. This was expected and is in line with the literature on cognitive development, which increases with a rate that is inversely proportional to age, and approaches an asymptote in the early 20s (Fig. 1A ) 38 , 39 . However, for the training sample, older participants improved more (during the 25-day interval) than younger participants. At first glance, this will look surprising. How could older children, whose brains are presumably less plastic, show more improvement? In the cognitive training literature, studies frequently find a Matthew effect (also known as the rich-get-richer effect or the magnification effect), where the participants with the highest initial cognitive values tend to be the ones getting the most gains from training 40 , 41 , 42 . There are a few potential reasons for the Matthew effect: (a) A child with higher WM will also focus better and therefore get more effective training and improve more; (b) The underlying mechanism why a child is high performing at baseline and why the child improves during an intervention might be partly identical. One such mechanism is highlighted in the present study: the same set of genes affect both development and response to an intervention; (c) Cognitive training programs often adapt to the performance of the user, so a high-performing child will get more challenged—resulting in a beneficial feedback loop. Important to our point here, this beneficial loop is not as strong and pronounced during many of the typical experiences/challenges in development, like a classroom (in fact, schools might plausibly show the opposite pattern, where teachers adapt and give more attention to the struggling students). This distinction in how “adaptive” the challenges are could be at the core of our contrasting finding in the effect of age in development versus training (at least when it comes to the “training” portion of developmental gains).

We believe our study also sheds light on the known increase in the heritability of intelligence during childhood development. Unlike many other traits, as we interact more with our environment over childhood, genetic effects seem to become more relevant to intelligence—heritability increases from 0.2 at 5 years of age to 0.6 by 16 years. Attempts to explain these results include models of gene-environment interplay—genetically endowed cognition influences one’s proximal environment and that environment in turn influences one’s cognition in continuous, reciprocal interactions, such as the multiplier theory 13 and the transactional model 4 . Our finding here adds another line of evidence for these proposed models.

To our knowledge, only three other studies have measured the effect of any cognitive polygenic score on longitudinal change during development, and zero studies on cognitive training changes. Of those, two studies have failed to predict development from the cogPGS using as outcome measure either long-term memory 43 or a broad cognitive measure (including decision making, pattern recognition, rapid visual processing, and WM) 44 . What could explain these contrasting findings? We believe this could be due to these polygenic scores for cognition having some degree of specificity. Our group recently looked at the cognitive change in typical development in a different sample 45 and we identified the neural correlates with a polygenic score similar to our current cogPGS and using the same GWASs. That polygenic score was found to correlate with global surface area, and, even after correction for global effects, it was also associated with surface area in a single region located in the intraparietal cortex. This region is known to be linked to nonverbal, spatial cognitive abilities, including spatial attention, visuospatial WM, reasoning, and mathematics. This might explain why a cognitive polygenic score has a stronger association with the spatial abilities measured in our present study but not as strong with long-term memory (more strongly related to the medial temporal lobe) or with broad cognition (which includes a wider range of areas, including frontal cortical regions on decision making and possibly occipital areas involved in pattern recognition and rapid visual processing).

As was the case for the three studies mentioned above, our measure of cognitive change also has an important limitation: it is made of only two time points. Ideally, change should be estimated by three or more time points 46 , 47 . Two time points are still a viable way to measure change, but it comes with problems. In our analyses, we used the difference method to estimate change in WM (in other words, a subtraction between baseline and follow-up values), as it is likely the best method for our purposes here 48 , 49 . This method, however, always suffers from at least some regression to the mean—the size of this problem depends on how well the tasks are tapping into true performance (in other words, how much measurement error there is in each of the two time points). Because our WM measures were a composite of two tasks in both samples and were based on multiple trials (as described in more detail in Methods), that minimized the bias from regression to the mean.

All things considered—our results are of great theoretical importance to understand the genetics of flexible responses in cognitive development. We consider it a valuable piece in the elusive puzzle of gene-environment interplay in general cognition. In addition, given the critical role of WM and other general cognitive abilities in individual lives and societies, studies on the development of these traits could contribute to shape new avenues of research on training and plasticity, as well as help informing public policies on education and addressing at-risk groups with targeted interventions.

Our study included a total of 344 children, adolescents, and young adults from the combination of two samples: a developmental sample and a training sample.

Developmental sample

The developmental sample had 160 participants who were recruited using random sampling from a registry in Sweden and part of a longitudinal study of typical development. These individuals were in nine age groups (6, 8, 10, 12, 14, 16, 18, 20, and 25 years; mean age = 12.55, SD = 4.62), and have an equal gender distribution (78 females). More details about this sample in ref. 21 . For the developmental sample, informed written consent to participate in the study was obtained from all participants over 18 years old and from the legal guardians of participants under 18.

Cognitive training sample

In the training sample, we had 184 participants from Sweden who underwent cognitive training (described below) These individuals were between 7 and 19 years old at the time of training (mean age = 12.32, SD = 2.19), and have an equal gender distribution (86 females). More details in ref. 19 . For the training sample, informed written consent to participate in the study was given by all participants older than 15 years and by all legal guardians for participants younger than 15 years.

The cognitive training in our training sample used the software Cogmed RM (Cogmed Systems, https://www.cogmed.com ) developed by Torkel Klingberg 26 , 50 . Prior studies using exactly this method have shown significant improvements in WM when compared the improvement to passive control groups 51 , 52 , 53 and also to active control groups 26 , 54 , 55 . Furthermore, studies with this method of training have related improvement to genetic polymorphism in candidate genes 19 , 20 , 56 .

The Cogmed training program consists of 12 different WM demanding tasks covering mostly visuospatial but also some verbal domains. Some of the tasks are changed during the training period to increase variability so that 8 of the 12 tasks are trained in each session.

As described in Holmes 2009 51 , each training task involved the temporary storage and manipulation of sequential visuospatial or verbal information or both. Three of the tasks involved the temporary storage of sequences of spoken verbal items, such as letters. These tasks tapped verbal short-term memory, although the simultaneous presentation of verbal information on the computer screen as it was spoken aloud in two of the tasks likely also tapped visuospatial short-term memory and WM. Two tasks involved the immediate serial recall of visuospatial information, such as a series of lamps that illuminated successively and, which the child attempted to recall in the correct order by clicking the appropriate location with the computer mouse. Verbal WM was tapped by two tasks, which involved the immediate recall of a sequence of digits in backward order. In one task the digits were spoken aloud at the same time as the corresponding numbers lit up on a keypad. Participants attempted to recall the sequence of digits in a backward sequence by clicking on the keypad. In a second task, the numbers were not displayed as they were spoken aloud. Three tasks required the processing and immediate serial recall of visuospatial information that was either moving around the screen during presentation and recall (e.g., asteroids that were continuously moving around the screen lit up one at a time and had to be remembered and recalled in the correct order) or moved spatial location between presentation and recall (e.g., lamps light up one at a time in a grid, the entire grid then rotates 90° and participants recall the order in which the lamps lit up, even though these are now in new positions). Motivational features in the program included positive verbal feedback, a display of the user’s best scores, and the accumulation of “energy” based on performance levels that was spent on a racing game completed after training each day. The racing game was included as a reward and did not tax WM.

All the training tasks had their difficulty level (number of items to be remembered) adapted on a trial-by-trial basis for each task. This was done according to a built-in algorithm that takes an individual’s previous performance into consideration. The adaptation allows for training to be performed at a level that is close to the capacity limit for each user. Participants completed an average of 24.7 training sessions (SD = 1.06) where each session lasted for an average of 36.5 min (SD = 8.8).

The study was approved by the regional ethical committees at Karolinska Institutet and the Karolinska University Hospital in Stockholm, Sweden.

Measuring cognitive variables

We measured WM of all participants to create measures of baseline performance as well as the performance after training (in the case of the training sample) and after development (in the case of the developmental sample). WM in both samples was a combination of visuospatial WM and verbal WM.

In both samples, visuospatial WM was assessed using a similar task: a visuospatial grid task 57 requiring remembering the location and order of dots displayed sequentially in a four-by-four grid on a computer screen. Verbal WM was also assessed in both samples using a similar task: the backward digit recall test, where numbers were read aloud to the participant who had to repeat them verbally in the reverse order. In the experimenter-led testing of the tasks, difficulty was increased by one level (number of items to be remembered) after at least two trials were correctly answered on one level. For the training group, difficulty was adjusted based on performance. Tasks terminated after three errors were committed on one level. The score used was the total number of correct trials.

For the developmental sample, we set the WM baseline as total the number of correct trials given at the start of the study and averaged between the visuospatial grid task and the backward digit recall test. WM performance after development was, again, the same measure but now between the two tasks given to the same participants after 2 years. For the Training sample, we set the WM baseline performance as the mean level of the three successful trials with the highest level on the visuospatial grid task and a verbal backward digit span task during days 2 and 3. WM performance after training was set as the mean level of the three successful trials with the highest level on a visuospatial grid task and a verbal backward digit span task during the two best training days. We used these measures for the training sample to keep the same standard used in a previous study 19 . As reported then, these two measures during training are also related to the WM performances at day 1 and day 20 (last day with complete data from all participants), with a correlation between mean day 1 WM performance and WM baseline of r  = 0.875 and a correlation between day 20 WM performance and WM performance after training of r  = 0.912 19 .

Genotyping, quality control, and imputation

Blood and saliva samples were collected for genetic analyses in both samples, developmental and training. For the developmental samples, genomic DNA was extracted in a 96-wells format using the PureLink 96 genomic DNA kit K182104 (Invitrogen, United Kingdom). For the training sample, genomic DNA was extracted using OraGene OG-500 (DNA Genotek, Canada).

Genotyping was done in two batches. Batch 1 was genotyped on an Affymetrix Genome-Wide Human SNP Array 6.0. Liftover to human reference genome version hg19 was performed using liftOverPlink [Scott Ritchie: https://github.com/sritchie73/liftOverPlink ]. Batch 2 was genotyped on an Illumina Infinium OmniExpressExome-8 v1.6 SNP array by SciLifeLab at Uppsala University.

After genotyping, quality control for individuals and markers was performed on both batches using the R package plinkQC [Meyer HV (2018) plinkQC: Genotype quality control in genetic association studies. https://doi.org/10.5281/zenodo.3373798 ] with PLINK v1.9b6 58 . This procedure also controlled for population stratification, genotyping chip, and batch type. In batch 1, there were 76 individuals and 424,323 variants that passed QC. In batch 2, that was true for 250 people and 543,103 variants. After quality control, we performed imputation of the remaining SNPs with IMPUTE2 v2.3.2 59 using the 1000 Genome Project Phase 3 reference panel, at a window size of 5,000,000 bp, which yielded high concordance (Batch 1: 97.7%, Batch 2: 98.1%). Markers were filtered for existing RSIDs, and both datasets overlapped in 17,331,954 SNPs.

Creating polygenic scores

We created polygenic scores for cognitive performance (here called “cogPGS”) for each participant using PRSice-2 60 . This was calculated by the sum of effect sizes of thousands of SNPs (weighted by how many of the effect alleles were present in each individual) that were discovered by a large genome-wide association study on educational attainment, mathematical ability, and general cognitive ability 22 . That study has available all effects sizes and p values of their SNPs on the website of the Social Science Genetics Association Consortium ( https://www.thessgac.org/data ).

We used the data available from a multi-trait analysis of GWAS 61 , which, in our case, represents a joint polygenic score focused on a GWAS of cognitive performance and complemented by information from a GWAS on educational attainment, a GWAS on the highest-level math class completed, and a GWAS on self-reported math ability (data called MTAG_CP by the consortium). This joint analysis is ideal because pairwise genetic correlations of these traits were high 22 . Furthermore, these GWAS had hundreds of thousands of individuals, and such a large sample size allows new studies to detect effects in samples of 100 individuals with 80% statistical power 22 .

For the creation of cogPGS in our samples, we performed clumping and pruning to remove nearby SNPs that are correlated with one another. The clumping sliding window was 250 kb, with the LD clumping set to r 2  > 0.25. We included the weightings of all SNPs, regardless of their p value from the GWAS ( p  = 1.00 threshold). At the end of this process, we had 5255 SNPs included. We standardized the cogPGS to have a mean of zero and a standard deviation of one.

Statistical analyses

To understand the effects on baseline levels of WM, we created a general linear model (here called “baseline WM model”) with baseline WM as the outcome and the predictors: sample (developmental and training), baseline age, gender, cogPGS. The model was: WM baseline  = Sample + Age baseline  + Gender + cogPGS. Baseline WM was standardized separately in each sample to have a mean of zero and a standard deviation of one.

Our main goal in the study was to test the independent influence of cogPGS on the change of WM. For that, we first subtracted baseline WM from the follow-up WM in each sample and then separately standardized them (mean of zero and standard deviation of 1) to obtain the variable WM change. We then used a general linear model (here called “WM change model”) with WM change as the outcome and the predictors: sample (developmental and training), baseline age, gender, cogPGS, age x sample, cogPGS x sample. The model was: WM change  = Sample + Age baseline  + Gender + cogPGS + cogPGS*Sample + Age baseline *Sample. For all analyses, we used univariate general linear models in SPSS version 26.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work received support from the following sources: Vetenskapsradet (Swedish Research Council; 2015-02850), Wenner-Gren Foundation (UPD2018-0295), and NIH (Meaningful Data Compression and Reduction of High-Throughput Sequencing Data) (1 U01 CA198952-01).

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Sauce, B., Wiedenhoeft, J., Judd, N. et al. Change by challenge: A common genetic basis behind childhood cognitive development and cognitive training. npj Sci. Learn. 6 , 16 (2021). https://doi.org/10.1038/s41539-021-00096-6

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InBrief: The Science of Early Childhood Development

This brief is part of a series that summarizes essential scientific findings from Center publications.

Content in This Guide

Step 1: why is early childhood important.

• : Brain Hero
• : The Science of ECD (Video)
• You Are Here: The Science of ECD (Text)

Step 2: How Does Early Child Development Happen?

• : 3 Core Concepts in Early Development
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The science of early brain development can inform investments in early childhood. These basic concepts, established over decades of neuroscience and behavioral research, help illustrate why child development—particularly from birth to five years—is a foundation for a prosperous and sustainable society.

Brains are built over time, from the bottom up.

The basic architecture of the brain is constructed through an ongoing process that begins before birth and continues into adulthood. Early experiences affect the quality of that architecture by establishing either a sturdy or a fragile foundation for all of the learning, health and behavior that follow. In the first few years of life, more than 1 million new neural connections are formed every second . After this period of rapid proliferation, connections are reduced through a process called pruning, so that brain circuits become more efficient. Sensory pathways like those for basic vision and hearing are the first to develop, followed by early language skills and higher cognitive functions. Connections proliferate and prune in a prescribed order, with later, more complex brain circuits built upon earlier, simpler circuits.

The interactive influences of genes and experience shape the developing brain.

Scientists now know a major ingredient in this developmental process is the “ serve and return ” relationship between children and their parents and other caregivers in the family or community. Young children naturally reach out for interaction through babbling, facial expressions, and gestures, and adults respond with the same kind of vocalizing and gesturing back at them. In the absence of such responses—or if the responses are unreliable or inappropriate—the brain’s architecture does not form as expected, which can lead to disparities in learning and behavior.

The brain’s capacity for change decreases with age.

The brain is most flexible, or “plastic,” early in life to accommodate a wide range of environments and interactions, but as the maturing brain becomes more specialized to assume more complex functions, it is less capable of reorganizing and adapting to new or unexpected challenges. For example, by the first year, the parts of the brain that differentiate sound are becoming specialized to the language the baby has been exposed to; at the same time, the brain is already starting to lose the ability to recognize different sounds found in other languages. Although the “windows” for language learning and other skills remain open, these brain circuits become increasingly difficult to alter over time. Early plasticity means it’s easier and more effective to influence a baby’s developing brain architecture than to rewire parts of its circuitry in the adult years.

Cognitive, emotional, and social capacities are inextricably intertwined throughout the life course.

The brain is a highly interrelated organ, and its multiple functions operate in a richly coordinated fashion. Emotional well-being and social competence provide a strong foundation for emerging cognitive abilities, and together they are the bricks and mortar that comprise the foundation of human development. The emotional and physical health, social skills, and cognitive-linguistic capacities that emerge in the early years are all important prerequisites for success in school and later in the workplace and community.

Toxic stress damages developing brain architecture, which can lead to lifelong problems in learning, behavior, and physical and mental health.

Scientists now know that chronic, unrelenting stress in early childhood, caused by extreme poverty, repeated abuse, or severe maternal depression, for example, can be toxic to the developing brain. While positive stress (moderate, short-lived physiological responses to uncomfortable experiences) is an important and necessary aspect of healthy development, toxic stress is the strong, unrelieved activation of the body’s stress management system. In the absence of the buffering protection of adult support, toxic stress becomes built into the body by processes that shape the architecture of the developing brain.

Policy Implications

• The basic principles of neuroscience indicate that early preventive intervention will be more efficient and produce more favorable outcomes than remediation later in life.
• A balanced approach to emotional, social, cognitive, and language development will best prepare all children for success in school and later in the workplace and community.
• Supportive relationships and positive learning experiences begin at home but can also be provided through a range of services with proven effectiveness factors. Babies’ brains require stable, caring, interactive relationships with adults — any way or any place they can be provided will benefit healthy brain development.
• Science clearly demonstrates that, in situations where toxic stress is likely, intervening as early as possible is critical to achieving the best outcomes. For children experiencing toxic stress, specialized early interventions are needed to target the cause of the stress and protect the child from its consequences.

Suggested citation: Center on the Developing Child (2007). The Science of Early Childhood Development (InBrief). Retrieved from www.developingchild.harvard.edu .

Related Topics: toxic stress , brain architecture , serve and return

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Child cognitive development is a fascinating and complex process that entails the growth of a child’s mental abilities, including their ability to think, learn, and solve problems. This development occurs through a series of stages that can vary among individuals. As children progress through these stages, their cognitive abilities and skills are continuously shaped by a myriad of factors such as genetics, environment, and experiences. Understanding the nuances of child cognitive development is essential for parents, educators, and professionals alike, as it provides valuable insight into supporting the growth of the child’s intellect and overall well-being.

Throughout the developmental process, language and communication play a vital role in fostering a child’s cognitive abilities . As children acquire language skills, they also develop their capacity for abstract thought, reasoning, and problem-solving. It is crucial for parents and caregivers to be mindful of potential developmental delays, as early intervention can greatly benefit the child’s cognitive development. By providing stimulating environments, nurturing relationships, and embracing diverse learning opportunities, adults can actively foster healthy cognitive development in children.

Key Takeaways

• Child cognitive development involves the growth of mental abilities and occurs through various stages.
• Language and communication are significant factors in cognitive development , shaping a child’s ability for abstract thought and problem-solving.
• Early intervention and supportive environments can play a crucial role in fostering healthy cognitive development in children.

Child Cognitive Development Stages

Child cognitive development is a crucial aspect of a child’s growth and involves the progression of their thinking, learning, and problem-solving abilities. Swiss psychologist Jean Piaget developed a widely recognized theory that identifies four major stages of cognitive development in children.

Sensorimotor Stage

The Sensorimotor Stage occurs from birth to about 2 years old. During this stage, infants and newborns learn to coordinate their senses (sight, sound, touch, etc.) with their motor abilities. Their understanding of the world begins to develop through their physical interactions and experiences. Some key milestones in this stage include object permanence, which is the understanding that an object still exists even when it’s not visible, and the development of intentional actions.

Preoperational Stage

The Preoperational Stage takes place between the ages of 2 and 7 years old. In this stage, children start to think symbolically, and their language capabilities rapidly expand. They also develop the ability to use mental images, words, and gestures to represent the world around them. However, their thinking is largely egocentric, which means they struggle to see things from other people’s perspectives. During this stage, children start to engage in pretend play and begin to grasp the concept of conservation, recognizing that certain properties of objects (such as quantity or volume) remain the same even if their appearance changes.

Concrete Operational Stage

The Concrete Operational Stage occurs between the ages of 7 and 12 years old. At this stage, children’s cognitive development progresses to more logical and organized ways of thinking. They can now consider multiple aspects of a problem and better understand the relationship between cause and effect . Furthermore, children become more adept at understanding other people’s viewpoints, and they can perform basic mathematical operations and understand the principles of classification and seriation.

Formal Operational Stage

Lastly, the Formal Operational Stage typically begins around 12 years old and extends into adulthood. In this stage, children develop the capacity for abstract thinking and can consider hypothetical situations and complex reasoning. They can also perform advanced problem-solving and engage in systematic scientific inquiry. This stage allows individuals to think about abstract concepts, their own thought processes, and understand the world in deeper, more nuanced ways.

By understanding these stages of cognitive development, you can better appreciate the complex growth process that children undergo as their cognitive abilities transform and expand throughout their childhood.

Key Factors in Cognitive Development

Genetics and brain development.

Genetics play a crucial role in determining a child’s cognitive development. A child’s brain development is heavily influenced by genetic factors, which also determine their cognitive potential , abilities, and skills. It is important to understand that a child’s genes do not solely dictate their cognitive development – various environmental and experiential factors contribute to shaping their cognitive abilities as they grow and learn.

Environmental Influences

The environment in which a child grows up has a significant impact on their cognitive development. Exposure to various experiences is essential for a child to develop essential cognitive skills such as problem-solving, communication, and critical thinking. Factors that can have a negative impact on cognitive development include exposure to toxins, extreme stress, trauma, abuse, and addiction issues, such as alcoholism in the family.

Nutrition and Health

Maintaining good nutrition and health is vital for a child’s cognitive development. Adequate nutrition is essential for the proper growth and functioning of the brain . Key micronutrients that contribute to cognitive development include iron, zinc, and vitamins A, C, D, and B-complex vitamins. Additionally, a child’s overall health, including physical fitness and immunity, ensures they have the energy and resources to engage in learning activities and achieve cognitive milestones effectively .

Emotional and Social Factors

Emotional well-being and social relationships can also greatly impact a child’s cognitive development. A supportive, nurturing, and emotionally healthy environment allows children to focus on learning and building cognitive skills. Children’s emotions and stress levels can impact their ability to learn and process new information. Additionally, positive social interactions help children develop important cognitive skills such as empathy, communication, and collaboration.

In summary, cognitive development in children is influenced by various factors, including genetics, environmental influences, nutrition, health, and emotional and social factors. Considering these factors can help parents, educators, and policymakers create suitable environments and interventions for promoting optimal child development.

Language and Communication Development

Language skills and milestones.

Children’s language development is a crucial aspect of their cognitive growth. They begin to acquire language skills by listening and imitating sounds they hear from their environment. As they grow, they start to understand words and form simple sentences.

• Infants (0-12 months): Babbling, cooing, and imitating sounds are common during this stage. They can also identify their name by the end of their first year. Facial expressions play a vital role during this period, as babies learn to respond to emotions.
• Toddlers (1-3 years): They rapidly learn new words and form simple sentences. They engage more in spoken communication, constantly exploring their language environment.
• Preschoolers (3-5 years): Children expand their vocabulary, improve grammar, and begin participating in more complex conversations.

It’s essential to monitor children’s language development and inform their pediatrician if any delays or concerns arise.

Nonverbal Communication

Nonverbal communication contributes significantly to children’s cognitive development. They learn to interpret body language, facial expressions, and gestures long before they can speak. Examples of nonverbal communication in children include:

• Eye contact: Maintaining eye contact while interacting helps children understand emotions and enhances communication.
• Gestures: Pointing, waving goodbye, or using hand signs provide alternative ways for children to communicate their needs and feelings.
• Body language: Posture, body orientation, and movement give clues about a child’s emotions and intentions.

Teaching children to understand and use nonverbal communication supports their cognitive and social development.

Parent and Caregiver Interaction

Supportive interaction from parents and caregivers plays a crucial role in children’s language and communication development. These interactions can improve children’s language skills and overall cognitive abilities . Some ways parents and caregivers can foster language development are:

• Reading together: From an early age, reading books to children enhance their vocabulary and listening skills.
• Encouraging communication: Ask open-ended questions and engage them in conversations to build their speaking skills.
• Using rich vocabulary: Expose children to a variety of words and phrases, promoting language growth and understanding.

By actively engaging in children’s language and communication development, parents and caregivers can nurture cognitive, emotional, and social growth.

Cognitive Abilities and Skills

Cognitive abilities are the mental skills that children develop as they grow. These skills are essential for learning, adapting, and thriving in modern society. In this section, we will discuss various aspects of cognitive development, including reasoning and problem-solving, attention and memory, decision-making and executive function, as well as academic and cognitive milestones.

Reasoning and Problem Solving

Reasoning is the ability to think logically and make sense of the world around us. It’s essential for a child’s cognitive development, as it enables them to understand the concept of object permanence , recognize patterns, and classify objects. Problem-solving skills involve using these reasoning abilities to find solutions to challenges they encounter in daily life .

Children develop essential skills like:

• Logical reasoning : The ability to deduce conclusions from available information.
• Perception: Understanding how objects relate to one another in their environment.
• Schemes: Organizing thoughts and experiences into mental categories.

Attention and Memory

Attention refers to a child’s ability to focus on specific tasks, objects, or information, while memory involves retaining and recalling information. These cognitive abilities play a critical role in children’s learning and academic performance . Working memory is a vital component of learning, as it allows children to hold and manipulate information in their minds while solving problems and engaging with new tasks.

• Attention: Focuses on relevant tasks and information while ignoring distractions.
• Memory: Retains and retrieves information when needed.

Decision-Making and Executive Function

Decision-making is the process of making choices among various alternatives, while executive function refers to the higher-order cognitive processes that enable children to plan, organize, and adapt in complex situations. Executive function encompasses components such as:

• Inhibition: Self-control and the ability to resist impulses.
• Cognitive flexibility: Adapting to new information or changing circumstances.
• Planning: Setting goals and devising strategies to achieve them.

Academic and Cognitive Milestones

Children’s cognitive development is closely linked to their academic achievement. As they grow, they achieve milestones in various cognitive domains that form the foundation for their future learning. Some of these milestones include:

• Language skills: Developing vocabulary, grammar, and sentence structure.
• Reading and mathematics: Acquiring the ability to read and comprehend text, as well as understanding basic mathematical concepts and operations.
• Scientific thinking: Developing an understanding of cause-and-effect relationships and forming hypotheses.

Healthy cognitive development is essential for a child’s success in school and life. By understanding and supporting the development of their cognitive abilities, we can help children unlock their full potential and prepare them for a lifetime of learning and growth.

Developmental Delays and Early Intervention

Identifying developmental delays.

Developmental delays in children can be identified by monitoring their progress in reaching cognitive, linguistic, physical, and social milestones. Parents and caregivers should be aware of developmental milestones that are generally expected to be achieved by children at different ages, such as 2 months, 4 months, 6 months, 9 months, 18 months, 1 year, 2 years, 3 years, 4 years, and 5 years. Utilizing resources such as the “Learn the Signs. Act Early.” program can help parents and caregivers recognize signs of delay early in a child’s life.

Resources and Support for Parents

There are numerous resources available for parents and caregivers to find information on developmental milestones and to learn about potential developmental delays, including:

• Learn the Signs. Act Early : A CDC initiative that provides pdf checklists of milestones and resources for identifying delays.
• Parental support groups : Local and online communities dedicated to providing resources and fostering connections between families experiencing similar challenges.

Professional Evaluations and Intervention Strategies

If parents or caregivers suspect a developmental delay, it is crucial to consult with healthcare professionals or specialists who can conduct validated assessments of the child’s cognitive and developmental abilities. Early intervention strategies, such as the ones used in broad-based early intervention programs , have shown significant positive impacts on children with developmental delays to improve cognitive development and outcomes.

Professional evaluations may include:

• Pediatricians : Primary healthcare providers who can monitor a child’s development and recommend further assessments when needed.
• Speech and language therapists : Professionals who assist children with language and communication deficits.
• Occupational therapists : Experts in helping children develop or improve on physical and motor skills, as well as social and cognitive abilities.

Depending on the severity and nature of the delays, interventions may involve:

• Individualized support : Tailored programs or therapy sessions specifically developed for the child’s needs.
• Group sessions : Opportunities for children to learn from and interact with other children experiencing similar challenges.
• Family involvement : Parents and caregivers learning support strategies to help the child in their daily life.

Fostering Healthy Cognitive Development

Play and learning opportunities.

Encouraging play is crucial for fostering healthy cognitive development in children . Provide a variety of age-appropriate games, puzzles, and creative activities that engage their senses and stimulate curiosity. For example, introduce building blocks and math games for problem-solving skills, and crossword puzzles to improve vocabulary and reasoning abilities.

Playing with others also helps children develop social skills and better understand facial expressions and emotions. Provide opportunities for cooperative play, where kids can work together to achieve a common goal, and open-ended play with no specific rules to boost creativity.

Supportive Home Environment

A nurturing and secure home environment encourages healthy cognitive growth. Be responsive to your child’s needs and interests, involving them in everyday activities and providing positive reinforcement. Pay attention to their emotional well-being and create a space where they feel safe to ask questions and explore their surroundings.

Promoting Independence and Decision-Making

Support independence by allowing children to make decisions about their playtime, activities, and daily routines. Encourage them to take age-appropriate responsibilities and make choices that contribute to self-confidence and autonomy. Model problem-solving strategies and give them opportunities to practice these skills during play, while also guiding them when necessary.

Healthy Lifestyle Habits

Promote a well-rounded lifestyle, including:

• Sleep : Ensure children get adequate and quality sleep by establishing a consistent bedtime routine.
• Hydration : Teach the importance of staying hydrated by offering water frequently, especially during play and physical activities.
• Screen time : Limit exposure to electronic devices and promote alternative activities for toddlers and older kids.
• Physical activity : Encourage children to engage in active play and exercise to support neural development and overall health .

Frequently Asked Questions

What are the key stages of child cognitive development.

Child cognitive development can be divided into several key stages based on Piaget’s theory of cognitive development . These stages include the sensorimotor stage (birth to 2 years), preoperational stage (2-7 years), concrete operational stage (7-11 years), and formal operational stage (11 years and beyond). Every stage represents a unique period of cognitive growth, marked by the development of new skills, thought processes, and understanding of the world.

What factors influence cognitive development in children?

Several factors contribute to individual differences in child cognitive development, such as genetic and environmental factors. Socioeconomic status, access to quality education, early home environment, and parental involvement all play a significant role in determining cognitive growth. In addition, children’s exposure to diverse learning experiences, adequate nutrition, and mental health also influence overall cognitive performance .

How do cognitive skills vary during early childhood?

Cognitive skills in early childhood evolve as children progress through various stages . During the sensorimotor stage, infants develop fundamental skills such as object permanence. The preoperational stage is characterized by the development of symbolic thought, language, and imaginative play. Children then enter the concrete operational stage, acquiring the ability to think logically and solve problems. Finally, in the formal operational stage, children develop abstract reasoning abilities, complex problem-solving skills and metacognitive awareness.

What are common examples of cognitive development?

Examples of cognitive development include the acquisition of language and vocabulary, the development of problem-solving skills, and the ability to engage in logical reasoning. Additionally, memory, attention, and spatial awareness are essential aspects of cognitive development. Children may demonstrate these skills through activities like puzzle-solving, reading, and mathematics.

How do cognitive development theories explain children’s learning?

Piaget’s cognitive development theory suggests that children learn through active exploration, constructing knowledge based on their experiences and interactions with the world. In contrast, Vygotsky’s sociocultural theory emphasizes the role of social interaction and cultural context in learning. Both theories imply that cognitive development is a dynamic and evolving process, influenced by various environmental and psychological factors.

Why is it essential to support cognitive development in early childhood?

Supporting cognitive development in early childhood is critical because it lays a strong foundation for future academic achievement, social-emotional development, and lifelong learning. By providing children with diverse and enriching experiences, caregivers and educators can optimize cognitive growth and prepare children to face the challenges of today’s complex world. Fostering cognitive development early on helps children develop resilience, adaptability, and critical thinking skills essential for personal and professional success.

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Piaget’s Theory and Stages of Cognitive Development

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Key Takeaways

• Jean Piaget is famous for his theories regarding changes in cognitive development that occur as we move from infancy to adulthood.
• Cognitive development results from the interplay between innate capabilities (nature) and environmental influences (nurture).
• Children progress through four distinct stages , each representing varying cognitive abilities and world comprehension: the sensorimotor stage (birth to 2 years), the preoperational stage (2 to 7 years), the concrete operational stage (7 to 11 years), and the formal operational stage (11 years and beyond).
• A child’s cognitive development is not just about acquiring knowledge, the child has to develop or construct a mental model of the world, which is referred to as a schema .
• Piaget emphasized the role of active exploration and interaction with the environment in shaping cognitive development, highlighting the importance of assimilation and accommodation in constructing mental schemas.

Stages of Development

Jean Piaget’s theory of cognitive development suggests that children move through four different stages of intellectual development which reflect the increasing sophistication of children’s thought

Each child goes through the stages in the same order (but not all at the same rate), and child development is determined by biological maturation and interaction with the environment.

At each stage of development, the child’s thinking is qualitatively different from the other stages, that is, each stage involves a different type of intelligence.

Although no stage can be missed out, there are individual differences in the rate at which children progress through stages, and some individuals may never attain the later stages.

Piaget did not claim that a particular stage was reached at a certain age – although descriptions of the stages often include an indication of the age at which the average child would reach each stage.

The Sensorimotor Stage

Ages: Birth to 2 Years

The first stage is the sensorimotor stage , during which the infant focuses on physical sensations and learning to coordinate its body.

Major Characteristics and Developmental Changes:

• The infant learns about the world through their senses and through their actions (moving around and exploring their environment).
• During the sensorimotor stage, a range of cognitive abilities develop. These include: object permanence; self-recognition (the child realizes that other people are separate from them); deferred imitation; and representational play.
• They relate to the emergence of the general symbolic function, which is the capacity to represent the world mentally
• At about 8 months, the infant will understand the permanence of objects and that they will still exist even if they can’t see them and the infant will search for them when they disappear.

During the beginning of this stage, the infant lives in the present. It does not yet have a mental picture of the world stored in its memory therefore it does not have a sense of object permanence.

If it cannot see something, then it does not exist. This is why you can hide a toy from an infant, while it watches, but it will not search for the object once it has gone out of sight.

The main achievement during this stage is object permanence – knowing that an object still exists, even if it is hidden. It requires the ability to form a mental representation (i.e., a schema) of the object.

Towards the end of this stage the general symbolic function begins to appear where children show in their play that they can use one object to stand for another. Language starts to appear because they realise that words can be used to represent objects and feelings.

The child begins to be able to store information that it knows about the world, recall it, and label it.

Individual Differences

• Cultural Practices : In some cultures, babies are carried on their mothers’ backs throughout the day. This constant physical contact and varied stimuli can influence how a child perceives their environment and their sense of object permanence.
• Gender Norms : Toys assigned to babies can differ based on gender expectations. A boy might be given more cars or action figures, while a girl might receive dolls or kitchen sets. This can influence early interactions and sensory explorations.

Learn More: The Sensorimotor Stage of Cognitive Development

The Preoperational Stage

Ages: 2 – 7 Years

Piaget’s second stage of intellectual development is the preoperational stage . It takes place between 2 and 7 years. At the beginning of this stage, the child does not use operations, so the thinking is influenced by the way things appear rather than logical reasoning.

A child cannot conserve which means that the child does not understand that quantity remains the same even if the appearance changes.

Furthermore, the child is egocentric; he assumes that other people see the world as he does. This has been shown in the three mountains study.

As the preoperational stage develops, egocentrism declines, and children begin to enjoy the participation of another child in their games, and let’s pretend play becomes more important.

Toddlers often pretend to be people they are not (e.g. superheroes, policemen), and may play these roles with props that symbolize real-life objects. Children may also invent an imaginary playmate.

• Toddlers and young children acquire the ability to internally represent the world through language and mental imagery.
• During this stage, young children can think about things symbolically. This is the ability to make one thing, such as a word or an object, stand for something other than itself.
• A child’s thinking is dominated by how the world looks, not how the world is. It is not yet capable of logical (problem-solving) type of thought.
• Moreover, the child has difficulties with class inclusion; he can classify objects but cannot include objects in sub-sets, which involves classifying objects as belonging to two or more categories simultaneously.
• Infants at this stage also demonstrate animism. This is the tendency for the child to think that non-living objects (such as toys) have life and feelings like a person’s.

By 2 years, children have made some progress toward detaching their thoughts from the physical world. However, have not yet developed logical (or “operational”) thought characteristics of later stages.

Thinking is still intuitive (based on subjective judgments about situations) and egocentric (centered on the child’s own view of the world).

• Cultural Storytelling : Different cultures have unique stories, myths, and folklore. Children from diverse backgrounds might understand and interpret symbolic elements differently based on their cultural narratives.
• Race & Representation : A child’s racial identity can influence how they engage in pretend play. For instance, a lack of diverse representation in media and toys might lead children of color to recreate scenarios that don’t reflect their experiences or background.

Learn More: The Preoperational Stage of Cognitive Development

The Concrete Operational Stage

Ages: 7 – 11 Years

By the beginning of the concrete operational stage , the child can use operations (a set of logical rules) so they can conserve quantities, realize that people see the world in a different way (decentring), and demonstrate improvement in inclusion tasks. Children still have difficulties with abstract thinking.

• During this stage, children begin to think logically about concrete events.
• Children begin to understand the concept of conservation; understanding that, although things may change in appearance, certain properties remain the same.
• During this stage, children can mentally reverse things (e.g., picture a ball of plasticine returning to its original shape).
• During this stage, children also become less egocentric and begin to think about how other people might think and feel.

The stage is called concrete because children can think logically much more successfully if they can manipulate real (concrete) materials or pictures of them.

Piaget considered the concrete stage a major turning point in the child’s cognitive development because it marks the beginning of logical or operational thought. This means the child can work things out internally in their head (rather than physically try things out in the real world).

Children can conserve number (age 6), mass (age 7), and weight (age 9). Conservation is the understanding that something stays the same in quantity even though its appearance changes.

But operational thought is only effective here if the child is asked to reason about materials that are physically present. Children at this stage will tend to make mistakes or be overwhelmed when asked to reason about abstract or hypothetical problems.

• Cultural Context in Conservation Tasks : In a society where resources are scarce, children might demonstrate conservation skills earlier due to the cultural emphasis on preserving and reusing materials.
• Gender & Learning : Stereotypes about gender abilities, like “boys are better at math,” can influence how children approach logical problems or classify objects based on perceived gender norms.

Learn More: The Concrete Operational Stage of Development

The Formal Operational Stage

Ages: 12 and Over

The formal operational period begins at about age 11. As adolescents enter this stage, they gain the ability to think in an abstract manner, the ability to combine and classify items in a more sophisticated way, and the capacity for higher-order reasoning.

Adolescents can think systematically and reason about what might be as well as what is (not everyone achieves this stage). This allows them to understand politics, ethics, and science fiction, as well as to engage in scientific reasoning.

Adolescents can deal with abstract ideas: e.g. they can understand division and fractions without having to actually divide things up, and solve hypothetical (imaginary) problems.

• Concrete operations are carried out on things whereas formal operations are carried out on ideas. Formal operational thought is entirely freed from physical and perceptual constraints.
• During this stage, adolescents can deal with abstract ideas (e.g. no longer needing to think about slicing up cakes or sharing sweets to understand division and fractions).
• They can follow the form of an argument without having to think in terms of specific examples.
• Adolescents can deal with hypothetical problems with many possible solutions. E.g. if asked ‘What would happen if money were abolished in one hour’s time? they could speculate about many possible consequences.

From about 12 years children can follow the form of a logical argument without reference to its content. During this time, people develop the ability to think about abstract concepts, and logically test hypotheses.

This stage sees the emergence of scientific thinking, formulating abstract theories and hypotheses when faced with a problem.

• Culture & Abstract Thinking : Cultures emphasize different kinds of logical or abstract thinking. For example, in societies with a strong oral tradition, the ability to hold complex narratives might develop prominently.
• Gender & Ethics : Discussions about morality and ethics can be influenced by gender norms. For instance, in some cultures, girls might be encouraged to prioritize community harmony, while boys might be encouraged to prioritize individual rights.

Learn More: The Formal Operational Stage of Development

Piaget’s Theory

• Piaget’s theory places a strong emphasis on the active role that children play in their own cognitive development.
• According to Piaget, children are not passive recipients of information; instead, they actively explore and interact with their surroundings.
• This active engagement with the environment is crucial because it allows them to gradually build their understanding of the world.

1. How Piaget Developed the Theory

Piaget was employed at the Binet Institute in the 1920s, where his job was to develop French versions of questions on English intelligence tests. He became intrigued with the reasons children gave for their wrong answers to the questions that required logical thinking.

He believed that these incorrect answers revealed important differences between the thinking of adults and children.

Piaget branched out on his own with a new set of assumptions about children’s intelligence:

• Children’s intelligence differs from an adult’s in quality rather than in quantity. This means that children reason (think) differently from adults and see the world in different ways.
• Children actively build up their knowledge about the world . They are not passive creatures waiting for someone to fill their heads with knowledge.
• The best way to understand children’s reasoning is to see things from their point of view.

Piaget did not want to measure how well children could count, spell or solve problems as a way of grading their I.Q. What he was more interested in was the way in which fundamental concepts like the very idea of number , time, quantity, causality , justice , and so on emerged.

Piaget studied children from infancy to adolescence using naturalistic observation of his own three babies and sometimes controlled observation too. From these, he wrote diary descriptions charting their development.

He also used clinical interviews and observations of older children who were able to understand questions and hold conversations.

2. Piaget’s Theory Differs From Others In Several Ways:

Piaget’s (1936, 1950) theory of cognitive development explains how a child constructs a mental model of the world. He disagreed with the idea that intelligence was a fixed trait, and regarded cognitive development as a process that occurs due to biological maturation and interaction with the environment.

Children’s ability to understand, think about, and solve problems in the world develops in a stop-start, discontinuous manner (rather than gradual changes over time).

• It is concerned with children, rather than all learners.
• It focuses on development, rather than learning per se, so it does not address learning of information or specific behaviors.
• It proposes discrete stages of development, marked by qualitative differences, rather than a gradual increase in number and complexity of behaviors, concepts, ideas, etc.

The goal of the theory is to explain the mechanisms and processes by which the infant, and then the child, develops into an individual who can reason and think using hypotheses.

To Piaget, cognitive development was a progressive reorganization of mental processes as a result of biological maturation and environmental experience.

Children construct an understanding of the world around them, then experience discrepancies between what they already know and what they discover in their environment.

Piaget claimed that knowledge cannot simply emerge from sensory experience; some initial structure is necessary to make sense of the world.

According to Piaget, children are born with a very basic mental structure (genetically inherited and evolved) on which all subsequent learning and knowledge are based.

Schemas are the basic building blocks of such cognitive models, and enable us to form a mental representation of the world.

Piaget (1952, p. 7) defined a schema as: “a cohesive, repeatable action sequence possessing component actions that are tightly interconnected and governed by a core meaning.”

In more simple terms, Piaget called the schema the basic building block of intelligent behavior – a way of organizing knowledge. Indeed, it is useful to think of schemas as “units” of knowledge, each relating to one aspect of the world, including objects, actions, and abstract (i.e., theoretical) concepts.

Wadsworth (2004) suggests that schemata (the plural of schema) be thought of as “index cards” filed in the brain, each one telling an individual how to react to incoming stimuli or information.

When Piaget talked about the development of a person’s mental processes, he was referring to increases in the number and complexity of the schemata that a person had learned.

When a child’s existing schemas are capable of explaining what it can perceive around it, it is said to be in a state of equilibrium, i.e., a state of cognitive (i.e., mental) balance.

Operations are more sophisticated mental structures which allow us to combine schemas in a logical (reasonable) way.

As children grow they can carry out more complex operations and begin to imagine hypothetical (imaginary) situations.

Apart from the schemas we are born with schemas and operations are learned through interaction with other people and the environment.

Piaget emphasized the importance of schemas in cognitive development and described how they were developed or acquired.

A schema can be defined as a set of linked mental representations of the world, which we use both to understand and to respond to situations. The assumption is that we store these mental representations and apply them when needed.

Examples of Schemas

A person might have a schema about buying a meal in a restaurant. The schema is a stored form of the pattern of behavior which includes looking at a menu, ordering food, eating it and paying the bill.

This is an example of a schema called a “script.” Whenever they are in a restaurant, they retrieve this schema from memory and apply it to the situation.

The schemas Piaget described tend to be simpler than this – especially those used by infants. He described how – as a child gets older – his or her schemas become more numerous and elaborate.

Piaget believed that newborn babies have a small number of innate schemas – even before they have had many opportunities to experience the world. These neonatal schemas are the cognitive structures underlying innate reflexes. These reflexes are genetically programmed into us.

For example, babies have a sucking reflex, which is triggered by something touching the baby’s lips. A baby will suck a nipple, a comforter (dummy), or a person’s finger. Piaget, therefore, assumed that the baby has a “sucking schema.”

Similarly, the grasping reflex which is elicited when something touches the palm of a baby’s hand, or the rooting reflex, in which a baby will turn its head towards something which touches its cheek, are innate schemas. Shaking a rattle would be the combination of two schemas, grasping and shaking.

4. The Process of Adaptation

Piaget also believed that a child developed as a result of two different influences: maturation, and interaction with the environment. The child develops mental structures (schemata) which enables him to solve problems in the environment.

Adaptation is the process by which the child changes its mental models of the world to match more closely how the world actually is.

Adaptation is brought about by the processes of assimilation (solving new experiences using existing schemata) and accommodation (changing existing schemata in order to solve new experiences).

The importance of this viewpoint is that the child is seen as an active participant in its own development rather than a passive recipient of either biological influences (maturation) or environmental stimulation.

When our existing schemas can explain what we perceive around us, we are in a state of equilibration . However, when we meet a new situation that we cannot explain it creates disequilibrium, this is an unpleasant sensation which we try to escape, and this gives us the motivation to learn.

According to Piaget, reorganization to higher levels of thinking is not accomplished easily. The child must “rethink” his or her view of the world. An important step in the process is the experience of cognitive conflict.

In other words, the child becomes aware that he or she holds two contradictory views about a situation and they both cannot be true. This step is referred to as disequilibrium .

Jean Piaget (1952; see also Wadsworth, 2004) viewed intellectual growth as a process of adaptation (adjustment) to the world. This happens through assimilation, accommodation, and equilibration.

To get back to a state of equilibration, we need to modify our existing schemas to learn and adapt to the new situation.

This is done through the processes of accommodation and assimilation . This is how our schemas evolve and become more sophisticated. The processes of assimilation and accommodation are continuous and interactive.

5. Assimilation

Piaget defined assimilation as the cognitive process of fitting new information into existing cognitive schemas, perceptions, and understanding. Overall beliefs and understanding of the world do not change as a result of the new information.

Assimilation occurs when the new experience is not very different from previous experiences of a particular object or situation we assimilate the new situation by adding information to a previous schema.

This means that when you are faced with new information, you make sense of this information by referring to information you already have (information processed and learned previously) and trying to fit the new information into the information you already have.

• Imagine a young child who has only ever seen small, domesticated dogs. When the child sees a cat for the first time, they might refer to it as a “dog” because it has four legs, fur, and a tail – features that fit their existing schema of a dog.
• A person who has always believed that all birds can fly might label penguins as birds that can fly. This is because their existing schema or understanding of birds includes the ability to fly.
• A 2-year-old child sees a man who is bald on top of his head and has long frizzy hair on the sides. To his father’s horror, the toddler shouts “Clown, clown” (Siegler et al., 2003).
• If a baby learns to pick up a rattle he or she will then use the same schema (grasping) to pick up other objects.

6. Accommodation

Accommodation: when the new experience is very different from what we have encountered before we need to change our schemas in a very radical way or create a whole new schema.

Psychologist Jean Piaget defined accommodation as the cognitive process of revising existing cognitive schemas, perceptions, and understanding so that new information can be incorporated.

This happens when the existing schema (knowledge) does not work, and needs to be changed to deal with a new object or situation.

In order to make sense of some new information, you actually adjust information you already have (schemas you already have, etc.) to make room for this new information.

• A baby tries to use the same schema for grasping to pick up a very small object. It doesn’t work. The baby then changes the schema by now using the forefinger and thumb to pick up the object.
• A child may have a schema for birds (feathers, flying, etc.) and then they see a plane, which also flies, but would not fit into their bird schema.
• In the “clown” incident, the boy’s father explained to his son that the man was not a clown and that even though his hair was like a clown’s, he wasn’t wearing a funny costume and wasn’t doing silly things to make people laugh. With this new knowledge, the boy was able to change his schema of “clown” and make this idea fit better to a standard concept of “clown”.
• A person who grew up thinking all snakes are dangerous might move to an area where garden snakes are common and harmless. Over time, after observing and learning, they might accommodate their previous belief to understand that not all snakes are harmful.

7. Equilibration

Piaget believed that all human thought seeks order and is uncomfortable with contradictions and inconsistencies in knowledge structures. In other words, we seek “equilibrium” in our cognitive structures.

Equilibrium occurs when a child’s schemas can deal with most new information through assimilation. However, an unpleasant state of disequilibrium occurs when new information cannot be fitted into existing schemas (assimilation).

Piaget believed that cognitive development did not progress at a steady rate, but rather in leaps and bounds. Equilibration is the force which drives the learning process as we do not like to be frustrated and will seek to restore balance by mastering the new challenge (accommodation).

Once the new information is acquired the process of assimilation with the new schema will continue until the next time we need to make an adjustment to it.

Equilibration is a regulatory process that maintains a balance between assimilation and accommodation to facilitate cognitive growth. Think of it this way: We can’t merely assimilate all the time; if we did, we would never learn any new concepts or principles.

Everything new we encountered would just get put in the same few “slots” we already had. Neither can we accommodate all the time; if we did, everything we encountered would seem new; there would be no recurring regularities in our world. We’d be exhausted by the mental effort!

Applications to Education

Think of old black and white films that you’ve seen in which children sat in rows at desks, with ink wells, would learn by rote, all chanting in unison in response to questions set by an authoritarian old biddy like Matilda!

Children who were unable to keep up were seen as slacking and would be punished by variations on the theme of corporal punishment. Yes, it really did happen and in some parts of the world still does today.

Piaget is partly responsible for the change that occurred in the 1960s and for your relatively pleasurable and pain-free school days!

“Children should be able to do their own experimenting and their own research. Teachers, of course, can guide them by providing appropriate materials, but the essential thing is that in order for a child to understand something, he must construct it himself, he must re-invent it. Every time we teach a child something, we keep him from inventing it himself. On the other hand that which we allow him to discover by himself will remain with him visibly”. Piaget (1972, p. 27)

Plowden Report

Piaget (1952) did not explicitly relate his theory to education, although later researchers have explained how features of Piaget’s theory can be applied to teaching and learning.

Piaget has been extremely influential in developing educational policy and teaching practice. For example, a review of primary education by the UK government in 1966 was based strongly on Piaget’s theory. The result of this review led to the publication of the Plowden Report (1967).

In the 1960s the Plowden Committee investigated the deficiencies in education and decided to incorporate many of Piaget’s ideas into its final report published in 1967, even though Piaget’s work was not really designed for education.

The report makes three Piaget-associated recommendations:

• Children should be given individual attention and it should be realized that they need to be treated differently.
• Children should only be taught things that they are capable of learning
• Children mature at different rates and the teacher needs to be aware of the stage of development of each child so teaching can be tailored to their individual needs.

“The report’s recurring themes are individual learning, flexibility in the curriculum, the centrality of play in children’s learning, the use of the environment, learning by discovery and the importance of the evaluation of children’s progress – teachers should “not assume that only what is measurable is valuable.”

Discovery learning – the idea that children learn best through doing and actively exploring – was seen as central to the transformation of the primary school curriculum.

How to teach

Within the classroom learning should be student-centered and accomplished through active discovery learning. The role of the teacher is to facilitate learning, rather than direct tuition.

Because Piaget’s theory is based upon biological maturation and stages, the notion of “readiness” is important. Readiness concerns when certain information or concepts should be taught.

According to Piaget’s theory, children should not be taught certain concepts until they have reached the appropriate stage of cognitive development.

According to Piaget (1958), assimilation and accommodation require an active learner, not a passive one, because problem-solving skills cannot be taught, they must be discovered.

Therefore, teachers should encourage the following within the classroom:

• Educational programs should be designed to correspond to Piaget’s stages of development. Children in the concrete operational stage should be given concrete means to learn new concepts e.g. tokens for counting.
• Devising situations that present useful problems, and create disequilibrium in the child.
• Focus on the process of learning, rather than the end product of it. Instead of checking if children have the right answer, the teacher should focus on the student’s understanding and the processes they used to get to the answer.
• Child-centered approach. Learning must be active (discovery learning). Children should be encouraged to discover for themselves and to interact with the material instead of being given ready-made knowledge.
• Accepting that children develop at different rates so arrange activities for individual children or small groups rather than assume that all the children can cope with a particular activity.
• Using active methods that require rediscovering or reconstructing “truths.”
• Using collaborative, as well as individual activities (so children can learn from each other).
• Evaluate the level of the child’s development so suitable tasks can be set.
• Adapt lessons to suit the needs of the individual child (i.e. differentiated teaching).
• Be aware of the child’s stage of development (testing).
• Teach only when the child is ready. i.e. has the child reached the appropriate stage.
• Providing support for the “spontaneous research” of the child.
• Using collaborative, as well as individual activities.
• Educators may use Piaget’s stages to design age-appropriate assessment tools and strategies.

Classroom Activities

Sensorimotor stage (0-2 years):.

Although most kids in this age range are not in a traditional classroom setting, they can still benefit from games that stimulate their senses and motor skills.

• Object Permanence Games : Play peek-a-boo or hide toys under a blanket to help babies understand that objects still exist even when they can’t see them.
• Sensory Play : Activities like water play, sand play, or playdough encourage exploration through touch.
• Imitation : Children at this age love to imitate adults. Use imitation as a way to teach new skills.

Preoperational Stage (2-7 years):

• Role Playing : Set up pretend play areas where children can act out different scenarios, such as a kitchen, hospital, or market.
• Use of Symbols : Encourage drawing, building, and using props to represent other things.
• Hands-on Activities : Children should interact physically with their environment, so provide plenty of opportunities for hands-on learning.
• Egocentrism Activities : Use exercises that highlight different perspectives. For instance, having two children sit across from each other with an object in between and asking them what the other sees.

Concrete Operational Stage (7-11 years):

• Classification Tasks : Provide objects or pictures to group, based on various characteristics.
• Hands-on Experiments : Introduce basic science experiments where they can observe cause and effect, like a simple volcano with baking soda and vinegar.
• Logical Games : Board games, puzzles, and logic problems help develop their thinking skills.
• Conservation Tasks : Use experiments to showcase that quantity doesn’t change with alterations in shape, such as the classic liquid conservation task using different shaped glasses.

Formal Operational Stage (11 years and older):

• Hypothesis Testing : Encourage students to make predictions and test them out.
• Abstract Thinking : Introduce topics that require abstract reasoning, such as algebra or ethical dilemmas.
• Problem Solving : Provide complex problems and have students work on solutions, integrating various subjects and concepts.
• Debate and Discussion : Encourage group discussions and debates on abstract topics, highlighting the importance of logic and evidence.
• Feedback and Questioning : Use open-ended questions to challenge students and promote higher-order thinking. For instance, rather than asking, “Is this the right answer?”, ask, “How did you arrive at this conclusion?”

While Piaget’s stages offer a foundational framework, they are not universally experienced in the same way by all children.

Social identities play a critical role in shaping cognitive development, necessitating a more nuanced and culturally responsive approach to understanding child development.

Piaget’s stages may manifest differently based on social identities like race, gender, and culture:

• Race & Teacher Interactions : A child’s race can influence teacher expectations and interactions. For example, racial biases can lead to children of color being perceived as less capable or more disruptive, influencing their cognitive challenges and supports.
• Racial and Cultural Stereotypes : These can affect a child’s self-perception and self-efficacy . For instance, stereotypes about which racial or cultural groups are “better” at certain subjects can influence a child’s self-confidence and, subsequently, their engagement in that subject.
• Gender & Peer Interactions : Children learn gender roles from their peers. Boys might be mocked for playing “girl games,” and girls might be excluded from certain activities, influencing their cognitive engagements.
• Language : Multilingual children might navigate the stages differently, especially if their home language differs from their school language. The way concepts are framed in different languages can influence cognitive processing. Cultural idioms and metaphors can shape a child’s understanding of concepts and their ability to use symbolic representation, especially in the pre-operational stage.

Curriculum Development

According to Piaget, children’s cognitive development is determined by a process of maturation which cannot be altered by tuition so education should be stage-specific.

For example, a child in the concrete operational stage should not be taught abstract concepts and should be given concrete aid such as tokens to count with.

According to Piaget children learn through the process of accommodation and assimilation so the role of the teacher should be to provide opportunities for these processes to occur such as new material and experiences that challenge the children’s existing schemas.

Furthermore, according to this theory, children should be encouraged to discover for themselves and to interact with the material instead of being given ready-made knowledge.

Curricula need to be developed that take into account the age and stage of thinking of the child. For example there is no point in teaching abstract concepts such as algebra or atomic structure to children in primary school.

Curricula also need to be sufficiently flexible to allow for variations in the ability of different students of the same age. In Britain, the National Curriculum and Key Stages broadly reflect the stages that Piaget laid down.

For example, egocentrism dominates a child’s thinking in the sensorimotor and preoperational stages. Piaget would therefore predict that using group activities would not be appropriate since children are not capable of understanding the views of others.

However, Smith et al. (1998), point out that some children develop earlier than Piaget predicted and that by using group work children can learn to appreciate the views of others in preparation for the concrete operational stage.

The national curriculum emphasizes the need to use concrete examples in the primary classroom.

Shayer (1997), reported that abstract thought was necessary for success in secondary school (and co-developed the CASE system of teaching science). Recently the National curriculum has been updated to encourage the teaching of some abstract concepts towards the end of primary education, in preparation for secondary courses. (DfEE, 1999).

Child-centered teaching is regarded by some as a child of the ‘liberal sixties.’ In the 1980s the Thatcher government introduced the National Curriculum in an attempt to move away from this and bring more central government control into the teaching of children.

So, although the British National Curriculum in some ways supports the work of Piaget, (in that it dictates the order of teaching), it can also be seen as prescriptive to the point where it counters Piaget’s child-oriented approach.

However, it does still allow for flexibility in teaching methods, allowing teachers to tailor lessons to the needs of their students.

Social Media (Digital Learning)

Jean Piaget could not have anticipated the expansive digital age we now live in.

Today, knowledge dissemination and creation are democratized by the Internet, with platforms like blogs, wikis, and social media allowing for vast collaboration and shared knowledge. This development has prompted a reimagining of the future of education.

Classrooms, traditionally seen as primary sites of learning, are being overshadowed by the rise of mobile technologies and platforms like MOOCs (Passey, 2013).

The millennial generation, defined as the first to grow up with cable TV, the internet, and cell phones, relies heavily on technology.

They view it as an integral part of their identity, with most using it extensively in their daily lives, from keeping in touch with loved ones to consuming news and entertainment (Nielsen, 2014).

Social media platforms offer a dynamic environment conducive to Piaget’s principles. These platforms allow for interactions that nurture knowledge evolution through cognitive processes like assimilation and accommodation.

They emphasize communal interaction and shared activity, fostering both cognitive and socio-cultural constructivism. This shared activity promotes understanding and exploration beyond individual perspectives, enhancing social-emotional learning (Gehlbach, 2010).

A standout advantage of social media in an educational context is its capacity to extend beyond traditional classroom confines. As the material indicates, these platforms can foster more inclusive learning, bridging diverse learner groups.

This inclusivity can equalize learning opportunities, potentially diminishing biases based on factors like race or socio-economic status, resonating with Kegan’s (1982) concept of “recruitability.”

However, there are challenges. While the potential of social media in learning is vast, its practical application necessitates intention and guidance. Cuban, Kirkpatrick, and Peck (2001) note that certain educators and students are hesitant about integrating social media into educational contexts.

This hesitancy can stem from technological complexities or potential distractions. Yet, when harnessed effectively, social media can provide a rich environment for collaborative learning and interpersonal development, fostering a deeper understanding of content.

In essence, the rise of social media aligns seamlessly with constructivist philosophies. Social media platforms act as tools for everyday cognition, merging daily social interactions with the academic world, and providing avenues for diverse, interactive, and engaging learning experiences.

Applications to Parenting

Parents can use Piaget’s stages to have realistic developmental expectations of their children’s behavior and cognitive capabilities.

For instance, understanding that a toddler is in the pre-operational stage can help parents be patient when the child is egocentric.

Play Activities

Recognizing the importance of play in cognitive development, many parents provide toys and games suited for their child’s developmental stage.

Parents can offer activities that are slightly beyond their child’s current abilities, leveraging Vygotsky’s concept of the “Zone of Proximal Development,” which complements Piaget’s ideas.

• Peek-a-boo : Helps with object permanence.
• Texture Touch : Provide different textured materials (soft, rough, bumpy, smooth) for babies to touch and feel.
• Sound Bottles : Fill small bottles with different items like rice, beans, bells, and have children shake and listen to the different sounds.
• Memory Games : Using cards with pictures, place them face down, and ask students to find matching pairs.
• Role Playing and Pretend Play : Let children act out roles or stories that enhance symbolic thinking. Encourage symbolic play with dress-up clothes, playsets, or toy cash registers. Provide prompts or scenarios to extend their imagination.
• Story Sequencing : Give children cards with parts of a story and have them arranged in the correct order.
• Number Line Jumps : Create a number line on the floor with tape. Ask students to jump to the correct answer for math problems.
• Classification Games : Provide a mix of objects and ask students to classify them based on different criteria (e.g., color, size, shape).
• Logical Puzzle Games : Games that involve problem-solving using logic, such as simple Sudoku puzzles or logic grid puzzles.
• Debate and Discussion : Provide a topic and let students debate on pros and cons. This promotes abstract thinking and logical reasoning.
• Hypothesis Testing Games : Present a scenario and have students come up with hypotheses and ways to test them.
• Strategy Board Games : Games like chess, checkers, or Settlers of Catan can help in developing strategic and forward-thinking skills.

Critical Evaluation

• The influence of Piaget’s ideas on developmental psychology has been enormous. He changed how people viewed the child’s world and their methods of studying children.

He was an inspiration to many who came after and took up his ideas. Piaget’s ideas have generated a huge amount of research which has increased our understanding of cognitive development.

• Piaget (1936) was one of the first psychologists to make a systematic study of cognitive development. His contributions include a stage theory of child cognitive development, detailed observational studies of cognition in children, and a series of simple but ingenious tests to reveal different cognitive abilities.
• His ideas have been of practical use in understanding and communicating with children, particularly in the field of education (re: Discovery Learning). Piaget’s theory has been applied across education.
• According to Piaget’s theory, educational programs should be designed to correspond to the stages of development.
• Are the stages real? Vygotsky and Bruner would rather not talk about stages at all, preferring to see development as a continuous process. Others have queried the age ranges of the stages. Some studies have shown that progress to the formal operational stage is not guaranteed.

For example, Keating (1979) reported that 40-60% of college students fail at formal operation tasks, and Dasen (1994) states that only one-third of adults ever reach the formal operational stage.

The fact that the formal operational stage is not reached in all cultures and not all individuals within cultures suggests that it might not be biologically based.

• According to Piaget, the rate of cognitive development cannot be accelerated as it is based on biological processes however, direct tuition can speed up the development which suggests that it is not entirely based on biological factors.
• Because Piaget concentrated on the universal stages of cognitive development and biological maturation, he failed to consider the effect that the social setting and culture may have on cognitive development.

Cross-cultural studies show that the stages of development (except the formal operational stage) occur in the same order in all cultures suggesting that cognitive development is a product of a biological process of maturation.

However, the age at which the stages are reached varies between cultures and individuals which suggests that social and cultural factors and individual differences influence cognitive development.

Dasen (1994) cites studies he conducted in remote parts of the central Australian desert with 8-14-year-old Indigenous Australians. He gave them conservation of liquid tasks and spatial awareness tasks. He found that the ability to conserve came later in the Aboriginal children, between ages of 10 and 13 (as opposed to between 5 and 7, with Piaget’s Swiss sample).

However, he found that spatial awareness abilities developed earlier amongst the Aboriginal children than the Swiss children. Such a study demonstrates cognitive development is not purely dependent on maturation but on cultural factors too – spatial awareness is crucial for nomadic groups of people.

Vygotsky , a contemporary of Piaget, argued that social interaction is crucial for cognitive development. According to Vygotsky the child’s learning always occurs in a social context in cooperation with someone more skillful (MKO). This social interaction provides language opportunities and Vygotsky considered language the foundation of thought.

• Piaget’s methods (observation and clinical interviews) are more open to biased interpretation than other methods. Piaget made careful, detailed naturalistic observations of children, and from these, he wrote diary descriptions charting their development. He also used clinical interviews and observations of older children who were able to understand questions and hold conversations.

Because Piaget conducted the observations alone the data collected are based on his own subjective interpretation of events. It would have been more reliable if Piaget conducted the observations with another researcher and compared the results afterward to check if they are similar (i.e., have inter-rater reliability).

Although clinical interviews allow the researcher to explore data in more depth, the interpretation of the interviewer may be biased.

For example, children may not understand the question/s, they have short attention spans, they cannot express themselves very well, and may be trying to please the experimenter. Such methods meant that Piaget may have formed inaccurate conclusions.

• As several studies have shown Piaget underestimated the abilities of children because his tests were sometimes confusing or difficult to understand (e.g., Hughes , 1975).

Piaget failed to distinguish between competence (what a child is capable of doing) and performance (what a child can show when given a particular task). When tasks were altered, performance (and therefore competence) was affected. Therefore, Piaget might have underestimated children’s cognitive abilities.

For example, a child might have object permanence (competence) but still not be able to search for objects (performance). When Piaget hid objects from babies he found that it wasn’t till after nine months that they looked for it.

However, Piaget relied on manual search methods – whether the child was looking for the object or not.

Later, researchers such as Baillargeon and Devos (1991) reported that infants as young as four months looked longer at a moving carrot that didn’t do what it expected, suggesting they had some sense of permanence, otherwise they wouldn’t have had any expectation of what it should or shouldn’t do.

• The concept of schema is incompatible with the theories of Bruner (1966) and Vygotsky (1978). Behaviorism would also refute Piaget’s schema theory because is cannot be directly observed as it is an internal process. Therefore, they would claim it cannot be objectively measured.
• Piaget studied his own children and the children of his colleagues in Geneva to deduce general principles about the intellectual development of all children. His sample was very small and composed solely of European children from families of high socio-economic status. Researchers have, therefore, questioned the generalisability of his data.
• For Piaget, language is considered secondary to action, i.e., thought precedes language. The Russian psychologist Lev Vygotsky (1978) argues that the development of language and thought go together and that the origin of reasoning has more to do with our ability to communicate with others than with our interaction with the material world.

Piaget’s Theory vs Vygotsky

Piaget maintains that cognitive development stems largely from independent explorations in which children construct knowledge of their own.

Whereas Vygotsky argues that children learn through social interactions, building knowledge by learning from more knowledgeable others such as peers and adults. In other words, Vygotsky believed that culture affects cognitive development.

These factors lead to differences in the education style they recommend: Piaget would argue for the teacher to provide opportunities that challenge the children’s existing schemas and for children to be encouraged to discover for themselves.

Alternatively, Vygotsky would recommend that teachers assist the child to progress through the zone of proximal development by using scaffolding.

However, both theories view children as actively constructing their own knowledge of the world; they are not seen as just passively absorbing knowledge.

They also agree that cognitive development involves qualitative changes in thinking, not only a matter of learning more things.

What is cognitive development?

Cognitive development is how a person’s ability to think, learn, remember, problem-solve, and make decisions changes over time.

This includes the growth and maturation of the brain, as well as the acquisition and refinement of various mental skills and abilities.

Cognitive development is a major aspect of human development, and both genetic and environmental factors heavily influence it. Key domains of cognitive development include attention, memory, language skills, logical reasoning, and problem-solving.

Various theories, such as those proposed by Jean Piaget and Lev Vygotsky, provide different perspectives on how this complex process unfolds from infancy through adulthood.

What are the 4 stages of Piaget’s theory?

Piaget divided children’s cognitive development into four stages; each of the stages represents a new way of thinking and understanding the world.

He called them (1) sensorimotor intelligence , (2) preoperational thinking , (3) concrete operational thinking , and (4) formal operational thinking . Each stage is correlated with an age period of childhood, but only approximately.

According to Piaget, intellectual development takes place through stages that occur in a fixed order and which are universal (all children pass through these stages regardless of social or cultural background).

Development can only occur when the brain has matured to a point of “readiness”.

What are some of the weaknesses of Piaget’s theory?

Cross-cultural studies show that the stages of development (except the formal operational stage) occur in the same order in all cultures suggesting that cognitive development is a product of a biological maturation process.

However, the age at which the stages are reached varies between cultures and individuals, suggesting that social and cultural factors and individual differences influence cognitive development.

What are Piaget’s concepts of schemas?

Schemas are mental structures that contain all of the information relating to one aspect of the world around us.

According to Piaget, we are born with a few primitive schemas, such as sucking, which give us the means to interact with the world.

These are physical, but as the child develops, they become mental schemas. These schemas become more complex with experience.

Baillargeon, R., & DeVos, J. (1991). Object permanence in young infants: Further evidence . Child development , 1227-1246.

Bruner, J. S. (1966). Toward a theory of instruction. Cambridge, Mass.: Belkapp Press.

Cuban, L., Kirkpatrick, H., & Peck, C. (2001). High access and low use of technologies in high school classrooms: Explaining an apparent paradox.  American Educational Research Journal ,  38 (4), 813-834.

Dasen, P. (1994). Culture and cognitive development from a Piagetian perspective. In W .J. Lonner & R.S. Malpass (Eds.), Psychology and culture (pp. 145–149). Boston, MA: Allyn and Bacon.

Gehlbach, H. (2010). The social side of school: Why teachers need social psychology.  Educational Psychology Review ,  22 , 349-362.

Hughes, M. (1975). Egocentrism in preschool children . Unpublished doctoral dissertation. Edinburgh University.

Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence . New York: Basic Books.

Keating, D. (1979). Adolescent thinking. In J. Adelson (Ed.), Handbook of adolescent psychology (pp. 211-246). New York: Wiley.

Kegan, R. (1982).  The evolving self: Problem and process in human development . Harvard University Press.

Nielsen. 2014. “Millennials: Technology = Social Connection.” http://www.nielsen.com/content/corporate/us/en/insights/news/2014/millennials-technology-social-connecti on.html.

Passey, D. (2013).  Inclusive technology enhanced learning: Overcoming cognitive, physical, emotional, and geographic challenges . Routledge.

Piaget, J. (1932). The moral judgment of the child . London: Routledge & Kegan Paul.

Piaget, J. (1936). Origins of intelligence in the child. London: Routledge & Kegan Paul.

Piaget, J. (1945). Play, dreams and imitation in childhood . London: Heinemann.

Piaget, J. (1957). Construction of reality in the child. London: Routledge & Kegan Paul.

Piaget, J., & Cook, M. T. (1952). The origins of intelligence in children . New York, NY: International University Press.

Piaget, J. (1981).  Intelligence and affectivity: Their relationship during child development.(Trans & Ed TA Brown & CE Kaegi) . Annual Reviews.

Plowden, B. H. P. (1967). Children and their primary schools: A report (Research and Surveys). London, England: HM Stationery Office.

Siegler, R. S., DeLoache, J. S., & Eisenberg, N. (2003). How children develop . New York: Worth.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes . Cambridge, MA: Harvard University Press.

Wadsworth, B. J. (2004). Piaget’s theory of cognitive and affective development: Foundations of constructivism . New York: Longman.

Further Reading

• BBC Radio Broadcast about the Three Mountains Study
• Piagetian stages: A critical review
• Bronfenbrenner’s Ecological Systems Theory

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Vygotsky vs. Piaget: A Paradigm Shift

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Cognitive Developmental Milestones

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• Birth to 3 Months
• 3 to 6 Months
• 6 to 9 Months
• 9 to 12 Months
• 1 to 2 Years
• 2 to 3 Years
• 3 to 4 Years
• 4 to 5 Years
• Reaching Cognitive Milestones

Cognitive milestones represent important steps forward in a child's development. Cognitive development refers to how children think, learn, explore, remember, and solve problems.

Historically, babies were often thought of as simple, passive beings. Prior to the 20th-century, children were often seen simply as miniature versions of adults.

It wasn't until psychologists like Jean Piaget proposed that children think differently than adults do that people began to view childhood and adolescence as unique periods of growth and development.

In the past, adults often dismissed the remarkable intellectual skills of infants and very young children, but modern thinkers and researchers have discovered that babies are, in fact, always learning, thinking, and exploring the world around them.

Even newborn infants are actively taking in information and learning new things. In addition to gathering new information about the people and the world around them, babies constantly discover new things about themselves.

This article discusses cognitive milestones that occur between the ages of birth and five years. It also explores what you can do to help encourage your child's cognitive development.

From Birth to 3 Months

The first three months of a child's life are a time of wonder. Major developmental milestones at this age focus on exploring the basic senses and learning more about the body and the environment.

During this period, most infants begin to:

• Demonstrate anticipatory behaviors, like rooting and sucking at the site of a nipple or bottle
• Detect sound differences in pitch and volume
• Discern objects more clearly within a distance of 13 inches
• Focus on moving objects, including the faces of caregivers
• See all colors of the human visual spectrum
• Tell between tastes, from sweet, salty, bitter, and sour
• Use facial expressions to respond to their environment

From 3 to 6 Months

In early infancy, perceptual abilities are still developing. From the age of 3–6 months, infants begin to develop a stronger sense of perception . At this age, most babies begin to:

• Imitate facial expressions
• React to familiar sounds
• Recognize familiar faces
• Respond to the facial expressions of other people

From 6 to 9 Months

Looking inside the mind of an infant is no easy task. After all, researchers cannot just ask a baby what he or she is thinking at any given moment. To learn more about the mental processes of infants, researchers have come up with many creative tasks that reveal the inner workings of the baby's brain.

From the age of 6–9 months, researchers have found that most infants begin to:

• Gaze longer at "impossible" things, such as an object suspended in midair
• Tell the differences between pictures depicting different numbers of objects
• Understand the differences between animate and inanimate objects
• Utilize the relative size of an object to determine how far away it is

From 9 to 12 Months

As infants become more physically adept, they can explore the world around them in greater depth. Sitting up, crawling, and walking are just a few physical milestones that allow babies to gain a greater mental understanding of the world around them.

As they approach one year of age, most infants can:

• Enjoy looking at picture books
• Imitate gestures and some basic actions
• Manipulate objects by turning them over, trying to put one object into another, etc.
• Respond with gestures and sounds
• Understand the concept of object permanence , the idea that an object continues to exist even though it cannot be seen

From 1 to 2 Years

After reaching a year of age, children's physical, social, and cognitive development seems to grow by leaps and bounds. Children at this age spend a tremendous amount of time observing the actions of adults, so it is important for parents and caregivers to set good examples of behavior.

Most one-year-olds begin to:

• Identify objects that are similar
• Imitate the actions and language of adults
• Learn through exploration
• Point out familiar objects and people in picture books
• Tell the difference between "Me" and "You"
• Understand and respond to words

From 2 to 3 Years

At 2 years of age, children are becoming increasingly independent . Since they are now able to better explore the world, a great deal of learning during this stage is the result of their own experiences.

Most two-year-olds are able to:

• Identify their own reflection in the mirror by name
• Imitate more complex adult actions (playing house, pretending to do laundry, etc.)
• Match objects with their uses
• Name objects in a picture book
• Respond to simple directions from parents and caregivers
• Sort objects by category (i.e., animals, flowers, trees, etc.)
• Stack rings on a peg from largest to smallest

From 3 to 4 Years

Children become increasingly capable of analyzing the world around them more complexly. As they observe things, they begin to sort and categorize them into different categories, often referred to as schemas .

Since children are becoming much more active in the learning process, they also begin to pose questions about the world around them. "Why?" becomes a very common question around this age.

At the age of three, most kids are able to:

• Ask "why" questions to gain information
• Demonstrate awareness of the past and present
• Learn by observing and listening to instructions
• Maintain a longer attention span of around 5 to 15 minutes
• Organize objects by size and shape
• Seek answers to questions
• Understand how to group and match objects according to color

From 4 to 5 Years

As they near school age, children become better at using words, imitating adult actions, counting, and other basic activities that are important for school preparedness.

Most four-year-olds are able to:

• Create pictures that they often name and describe
• Count to five
• Draw the shape of a person
• Name and identify many colors
• Tell where they live

Help Kids Reach Cognitive Milestones

Encouraging children's intellectual development is a concern for many parents. Fortunately, children are eager to learn right from the very beginning.

• Cultivate learning experiences at home : While education will soon become an enormous part of a growing child's life, those earliest years are influenced mainly by close family relationships, particularly those with parents and other caregivers. This means that parents are uniquely positioned to help shape how their children learn, think, and develop.
• Encourage children's interest in the world : Parents can encourage their children's intellectual abilities by helping kids make sense of the world around them. When an infant shows interest in an object, parents can help the child touch and explore the object and say what the object is.
• Demonstrate information : For example, when a baby looks intently at a toy rattle, the parent might pick up the item and place it in the infant's hand, saying, "Does Gracie want the rattle?" and then shake the rattle to demonstrate what it does.
• Encourage exploration : Parents should encourage their children to explore the world as they grow older. Try to have patience with young children who seem to have an endless array of questions about each and everything around them. Parents can also pose their own questions to help kids become more creative problem solvers.
• Ask questions : When facing a dilemma, ask questions such as "What do you think would happen if we…?" or "What might happen if we….?" By allowing kids to come up with original solutions to problems, parents can help encourage both intellectual development and self-confidence.

A Word From Verywell

Developmental milestones provide guideposts so that children can better understand whether their child is developing similarly to other children their age. However, it is important for parents to remember that all kids develop at their own pace. Some cognitive milestones may emerge earlier and others later. Talk to your child's doctor if you are concerned about your child's development.

Larcher V.  Children are not small adults: Significance of biological and cognitive development in medical practice .  Handbook Philos Med.  2015. doi:10.1007/978-94-017-8706-2_16-1

Centers for Disease Control and Prevention. Developmental milestones .

Unicef. Your toddler's developmental milestones at 2 years .

Children's Hospital of Philidelphia. Developmental milestones .

Child Mind Institute. Complete guide to developmental milestones .

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• Second Opinion

Cognitive Development in the Teen Years

What is cognitive development.

Cognitive development means the growth of a child’s ability to think and reason. This growth happens differently from ages 6 to 12, and from ages 12 to 18.

Children ages 6 to 12 years old develop the ability to think in concrete ways. These are called concrete operations. These things are called concrete because they’re done around objects and events. This includes knowing how to:

Combine (add)

Separate (subtract or divide)

Order (alphabetize and sort)

Transform objects and actions (change things, such as 5 pennies = 1 nickel)

Ages 12 to 18 is called adolescence. Kids and teens in this age group do more complex thinking. This type of thinking is also known as formal logical operations. This includes the ability to:

Do abstract thinking. This means thinking about possibilities.

Reason from known principles. This means forming own new ideas or questions.

Consider many points of view. This means to compare or debate ideas or opinions.

Think about the process of thinking. This means being aware of the act of thought processes.

How cognitive growth happens during the teen years

From ages 12 to 18, children grow in the way they think. They move from concrete thinking to formal logical operations. It’s important to note that:

Each child moves ahead at their own rate in their ability to think in more complex ways.

Each child develops their own view of the world.

Some children may be able to use logical operations in schoolwork long before they can use them for personal problems.

When emotional issues come up, they can cause problems with a child’s ability to think in complex ways.

The ability to consider possibilities and facts may affect decision-making. This can happen in either positive or negative ways.

Types of cognitive growth through the years

A child in early adolescence:

Uses more complex thinking focused on personal decision-making in school and at home

Begins to show use of formal logical operations in schoolwork

Begins to question authority and society's standards

Begins to form and speak his or her own thoughts and views on many topics. You may hear your child talk about which sports or groups he or she prefers, what kinds of personal appearance is attractive, and what parental rules should be changed.

A child in middle adolescence:

Has some experience in using more complex thinking processes

Expands thinking to include more philosophical and futuristic concerns

Often questions more extensively

Often analyzes more extensively

Thinks about and begins to form his or her own code of ethics (for example, What do I think is right?)

Thinks about different possibilities and begins to develop own identity (for example, Who am I? )

Thinks about and begins to systematically consider possible future goals (for example, What do I want? )

Thinks about and begins to make his or her own plans

Begins to think long-term

Uses systematic thinking and begins to influence relationships with others

A child in late adolescence:

Uses complex thinking to focus on less self-centered concepts and personal decision-making

Has increased thoughts about more global concepts, such as justice, history, politics, and patriotism

Often develops idealistic views on specific topics or concerns

May debate and develop intolerance of opposing views

Begins to focus thinking on making career decisions

Begins to focus thinking on their emerging role in adult society

How you can encourage healthy cognitive growth

To help encourage positive and healthy cognitive growth in your teen, you can:

Include him or her in discussions about a variety of topics, issues, and current events.

Encourage your child to share ideas and thoughts with you.

Encourage your teen to think independently and develop his or her own ideas.

Help your child in setting goals.

Challenge him or her to think about possibilities for the future.

Compliment and praise your teen for well-thought-out decisions.

Help him or her in re-evaluating poorly made decisions.

If you have concerns about your child's cognitive development, talk with your child's healthcare provider. 

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Cognitive Development of Preschool Children with Hearing Impairments: Results of an Experimental Study

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• Tatiana V. Nikolaeva   ORCID: orcid.org/0000-0003-3269-8119 4  

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The paper presents the results of the approbation of a diagnostic set of tasks aimed at determining the level of cognitive development of children with hearing impairments at the final stage of preschool education. The scope of examination of cognitive development of preschool children with hearing impairments included the tasks for determining the level of development of intellectual operations, the level of comprehension of elementary mathematical notions, and tasks for evaluating voluntary attention and activity organization. The research presents diagnostic possibilities of the practical application of the developed diagnostic complex. The author shows that the proposed set of tasks makes it possible to determine the individual abilities of children with hearing impairments in the studied development area. The research tentatively distinguishes different groups of children with hearing impairments, differing in the level of cognitive development. The obtained data are summarized and systematized for each selected group.

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The research was carried out under State Assignment No. 073-00028-22-00 of the Ministry of Education of the Russian Federation.

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Tatiana A. Solovyova

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Anna A. Arinushkina

Ekaterina A. Kochetova

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Nikolaeva, T.V. (2024). Cognitive Development of Preschool Children with Hearing Impairments: Results of an Experimental Study. In: Solovyova, T.A., Arinushkina, A.A., Kochetova, E.A. (eds) Educational Management and Special Educational Needs. Springer, Cham. https://doi.org/10.1007/978-3-031-57970-7_4

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A QUANTITATIVE ASSESSMENT OF VISUAL FUNCTION FOR YOUNG AND MEDICALLY COMPLEX CHILDREN WITH CEREBRAL VISUAL IMPAIRMENT: DEVELOPMENT AND INTER-RATER RELIABILITY

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Background Cerebral Visual Impairment (CVI) is the most common cause of low vision in children. Standardized, quantifiable measures of visual function are needed.

Objective This study developed and evaluated a new method for quantifying visual function in young and medically complex children with CVI using remote videoconferencing.

Methods Children diagnosed with CVI who had been unable to complete clinic-based recognition acuity tests were recruited from a low-vision rehabilitation clinic(n=22)Video-based Visual Function Assessment (VFA) was implemented using videoconference technology. Three low-vision rehabilitation clinicians independently scored recordings of each child’s VFA. Interclass correlations for inter-rater reliability was analyzed using intraclass correlations (ICC). Correlations were estimated between the video-based VFA scores and both clinically obtained acuity measures and children’s cognitive age equivalence.

Results Inter-rater reliability was analyzed using intraclass correlations (ICC). Correlations were estimated between the VFA scores, clinically obtained acuity measures, and cognitive age equivalence. ICCs showed good agreement (ICC and 95% CI 0.835 (0.701-0.916)) on VFA scores across raters and agreement was comparable to that from previous, similar studies. VFA scores strongly correlated (r= -0.706, p=0.002) with clinically obtained acuity measures. VFA scores and the cognitive age equivalence were moderately correlated (r= 0.518, p=0.005), with notable variation in VFA scores for participants below a ten month cognitive age-equivalence. The variability in VFA scores among children with lowest cognitive age-equivalence may have been an artifact of the study’s scoring method, or may represent existent variability in visual function for children with the lowest cognitive age-equivalence.

Conclusions Our new VFA is a reliable, quantitative measure of visual function for young and medically complex children with CVI. Future study of the VFA intrarater reliability and validity is warranted.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported by the EyeSight Foundation of Alabama, Alie B. Gorrie Low Vision Research Fund and Research to Prevent Blindness. Additional support came from the National Institutes of Health [UL1 TR003096 to R.O.] and Grant T32 HS013852.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRB of the University of Alabama at Birmingham gave ethical approval for this work

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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All data produced in the present study are available upon reasonable request to the authors

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