thesis about cognitive development

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16 Cognitive Development: An Overview

David F. Bjorklund, Developmental Evolutionary Psychology Lab, Department of Psychology, Florida Atlantic University.

• Published: 16 December 2013
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In this overview, I focus on contemporary research and theory related to five “truths” of cognitive development: (1) cognitive development proceeds as a result of the dynamic and reciprocal transaction of endogenous and exogenous factors; (2) cognitive development involves both stability and plasticity over time; (3) cognitive development involves changes in the way information is represented, although children of every age possess a variety of ways to represent experiences; (4) children develop increasing intentional control over their behavior and cognition; and (5) cognitive development occurs within a social context. Cognitive development happens at a variety of levels, and developmental scientists are becoming increasingly aware of the need to be cognizant of this and the interactions among the various levels to produce a true developmental science.

Cognitive development proceeds as a result of the dynamic and reciprocal transaction of endogenous and exogenous factors.

Cognitive development involves both stability and plasticity over time.

Cognitive development involves changes in the way information is represented, although children of every age possess a variety of ways to represent experiences.

Children develop increasing intentional control over their behavior and cognition.

Cognitive development occurs within a social context.

Human infants and children have strong dispositions/intuitive information-processing biases, but our species’ thinking is highly sensitive to context and highly plastic, and this is particularly true early in life, when developmental trajectories are put in motion.

The ability to represent the intentions and goals of other people allows children to learn through observation and direct teaching, permitting the acquisition of knowledge and skills that were foreign to our ancestors.

The development of executive function involves age-related changes in working memory, inhibition, and cognitive flexibility and plays a central role in the development of higher-level cognition and the regulation of one’s emotions and behaviors.

Background knowledge, or knowledge base, has a significant influence on how children think.

Cultural “explanations” for cognitive development do not provide alternative interpretations to those based on biology (e.g., neurological factors, evolutionary explanations) or specific experience (e.g., how mothers talk to their babies); rather, cognitive development must be seen as the result of interacting factors at multiple levels of organization, with the social environment being a critical ingredient in this mix.

Although there are many characteristics of human beings that make us distinct from our simian cousins, our cognition is high among them. Humans’ abilities to represent relationships, contemplate the past, anticipate the future, and adapt to a broader range of environments than any other mammal make us intellectually distinct in the animal kingdom. We are not the only “thinking” animal, of course, and our impressive suite of cognitive abilities has deep evolutionary roots, some of which can be inferred by studying our close genetic relatives, the great apes. But Homo sapiens ’ intellectual wherewithal has resulted in our species attaining ecological dominance over the globe, for better or worse, making the study of cognition perhaps the most central topic in attaining an understanding of humankind. Most critical for the current handbook, human cognition develops, emerging over infancy and childhood as a result of a continuous interaction of species-typical abilities and environment, broadly defined, and becoming adapted to the specific cultural environment in which children grow up. An understanding of cognitive development is not only of great theoretical importance but also has some obvious practical implications, especially with respect to the education of children and the modification of intellectual deficits attributed to deleterious early environments.

The field of cognitive development is a vast and varied one, and, on the surface, some of the topics classified under the rubric of “cognitive development” seem quite disparate and unrelated. For instance, many psychologists focus on lower-level mechanisms, such as developmental differences in speed of processing or memory span, which can seem light years away from topics such as theory of mind, metacognition, and scientific reasoning. The disparity is due, in part, to the exceptional range over which human cognition extends. Human cognition is affected by basic-level processes that influence how information is encoded, stored, and processed, much as the cognition of other animals with complex brains is. However, these basic-level abilities also develop in conjunction with a representational system that is far different from those of other animals, permitting the development of symbolic thought and forms of thinking and problem solving that require explanations beyond those afforded solely via basic-level analyses. Yet, despite the difference in levels of analysis (and other differences, such as examining developmental function vs. individual differences), the field of cognitive development is unified by some basic beliefs and themes. Some of the themes represent points of controversies as opposed to areas of agreement (e.g., the extent to which cognitive development is influenced by endogenous vs. exogenous factors), and each scientist will have his or her own pet issues that may not be shared with the same level of enthusiasm by others in the field.

I have not attempted in this chapter to provide a complete description of all issues, controversies, or topics of modern cognitive development; any overview chapter by necessity must be incomplete. Rather, I have organized the chapter around what I see as five general “truths” about cognitive development. These truths are actually generalizations, and I make no pretense that they have the authority of scientific law. Other researchers may have a different set of “truths,” and I might (and in fact have; Bjorklund, 1997 , 2005 ) generate a different list depending on the audience or points I wish to address. In the process of discussing these truths, I have slipped in other issues that I believe are important to understanding cognitive development (my set of pet issues), including the importance of taking an evolutionary perspective, the use of comparative animal data, and the distinction between domain-general and domain-specific mechanisms. The five “truths” are as follows:

I believe these “truths” will be familiar to most cognitive developmental psychologists and at least some of the topics will be central to the theoretical and research questions that stimulate all developmental scientists’ quest for knowledge.

Cognitive Development Proceeds as a Result of the Dynamic and Reciprocal Transaction of Endogenous and Exogenous Factors

One issue central to all of psychology is that of nature versus nurture. Traditionally, this has been posed as a dichotomy: Is human thought and behavior genetically/biologically determined or is it shaped by learning/experience/culture? This is dealt with in a more sophisticated way today, in that everyone is an interactionist, with the issue being better expressed as “how do biological/endogenous factors interact with environmental/exogenous factors to produce the adult phenotype?” From this perspective, cognitive development does not simply mature, or bloom, over time, nor is it solely a product of a child’s culture; rather, it emerges over the course of ontogeny as a result of the dynamic and reciprocal transaction between a child’s biological constitution, including genetics, and his or her physical and social environment ( Bjorklund, Ellis, & Rosenberg, 2007 ; Gottlieb, 2007 ). This can be seen in a wide range of research in cognitive development, from the ontogeny of the brain ( Greenough, Black, & Wallace, 1987 ) and the development of perceptual systems ( Lickliter, 1990 ), to the interaction between specific genes associated with intelligence and whether a child is breastfed or bottle-fed ( Caspi et al., 2007 ).

Developmental Systems and Cognitive Development

At the crux of cognitive development (in fact, of development in general) is the idea that development is not simply “produced” by genes, nor constructed by the environment, but emerges from the continuous, bidirectional interaction between all levels of biological and environmental factors ( Gottlieb, 2007 ; Gottlieb, Wahlsten, & Lickliter, 2006 ; Oyama, 2000 ; see chapters by Lickliter and by Moore in this handbook). From this perspective, even phenomena usually identified as innate, such as imprinting in precocial birds, result from the interaction of genetic and environmental factors. For example, research by Gottlieb (1992) demonstrated that ducklings required auditory experience prior to hatching—hearing their mother’s call, the call of brood mates, or even their own vocalization—in order to approach the appropriate (i.e., same-species) maternal call hours after hatching. In other research, birds that received visual experience prior to hatching showed enhanced visual discrimination abilities shortly after hatching, but species-atypical experiences interfered with auditory attachment behaviors ( Lickliter, 1990 ). Bobwhite quail that were exposed to patterned light days before hatching generally failed to approach the species-typical maternal call in a subsequent test, with some approaching the call of a chicken! In other words, even for usually reliably developing phenomena, experience necessarily interacts with genes to affect their expression.

Such interactions are seen in the development of individual differences in intelligence in children (see the chapter by Flynn & Blair in this handbook). For example, it is well established that children growing up in emotionally supportive homes and receiving cognitively rich experiences tend to have higher IQs than do children growing up in high-risk homes who receive less intellectual stimulation ( NICHD Early Child Care Research Network, 2005 ). However, the adverse effects of a nonstimulating environment are often exacerbated for children with medical problems. For example, classic research by Zeskind and Ramey (1978 , 1981 ) revealed that children from impoverished homes who were given educational daycare beginning in their first year of life showed enhanced IQs relative to control children. However, the effects of the intervention were moderated by the biological constitution of the infants at birth. By chance, approximately half of the infants in their rural, poverty sample were fetally malnourished. Fetal malnourishment is associated with slower development, more aversive cries, and less responsiveness in infants. Whereas fetally malnourished babies in the educational daycare group displayed normal IQs comparable to nonfetally malnourished infants in the educational group by 18 months, the fetally malnourished infants in the control group showed the lowest IQ (71 at 36 months of age), 14 points less than the IQs of the biologically normal children in the control group. Some of the differences in the cognitive outcomes of the fetally malnourished children were attributed to ways mothers interacted with their children and how this changed over time. The general lethargy shown by fetally malnourished infants in the control condition did not evoke much in the way of social interaction from their impoverished, highly stressed mothers, which set the stage for future interactions. Mothers tended to initiate little in the way of interaction with their infants, and their infants, in turn, reciprocated. This pattern of less attention and social give-and-take between infant and mother persisted long after children had “recovered” from the poor prenatal diet. In contrast, the social interaction received by the fetally malnourished children in the educational daycare resulted in increased responsiveness, behaviors that they brought home with them. These more outgoing children affected their mothers and set the stage for a more positive interactional style, which, by 18 months of age, was associated with significantly higher IQs.

Gene–Environment Interactions and the Development of Intelligence

Genetic versus environmental effects on the development of intelligence have been the topic of controversy for nearly 100 years (see Gould, 1981 ). Most behavioral genetic accounts put the heritability of intelligence as measured by IQ between 0.50 and 0.60 (i.e., between 50% and 60% of differences in IQ among people can be attributed to differences in genetics), with shared environmental effects (mainly home environment) being significantly less ( Plomin et al., 2008 ). However, estimates of heritability and shared environmental effects vary as a function of the family in which children grow up ( Rowe, Jacobson, & der Oord, 1999 ; Turkheimer et al., 2003 ). For example, in one study of 3,139 adolescent sibling pairs, Rowe and his colleagues reported a heritability of IQ of 0.57 and an effect of shared environment of 0.13. When the sample was divided into adolescents who came from homes where parents had greater than a high-school education versus those with a high-school education or less, the pattern changed substantially. For the high-education group the heritability of IQ was now 0.74 and the effect of shared environment was 0; in contrast, for children from the low-education families, heritability of IQ was reduced to 0.26 and the effect of shared environment was 0.23 (see also Turkheimer et al., 2003 ). Consistent with earlier theorizing ( Bronfenbrenner & Ceci, 1994 ; Scarr, 1993 ), these findings indicate that heritability of IQ varies with environmental conditions. When the environment is “good enough” to support intellectual accomplishments, as presumably the high-education homes were, individual differences in genes presumably contribute more to IQ level than individual differences in environment; when environmental conditions are less than optimal for supporting IQ, however, individual differences in genes are less predictive of IQ, with shared-environment effects increasing in significance.

More straightforward gene × environment interactions are found in contemporary behavioral genetics studies that have identified specific genes associated with intelligence, but only under certain environments. For example, Caspi and his colleagues (2007) identified a variant of a gene associated with higher IQ, but only for children who were breastfed. The gene, located on chromosome 11, is associated with the processing of fatty acids. In two large-scale samples, one from New Zealand and the other from Great Britain, people who had either of two variants of the gene, and were breastfed as infants, had significantly higher IQs (between about 5 and 10 points) than people with the gene who were not breastfed, and people with a third variant of the gene. For this latter group of people, adult IQ did not vary as a function of whether they were breastfed as infants or not. This is a typical type of finding from recent behavioral genetics literature; individual genes have small effects that are usually mediated by the environment, with likely many genes being associated with complex psychological characteristics, such as the development of intelligence ( Plomin, Kennedy, & Craig, 2006 ).

Fleshing Out of Skeletal Competencies

Debates among contemporary researchers often revolve around the extent to which infants enter the world “prepared” by natural selection to encounter a species-typical environment and are constrained to process some information more efficiently than others, with some arguing that infants and young children inherit skeletal competencies ( Geary, 2005 ) or core knowledge ( Baillargeon, 2008 ; Carey, 2009 , 2011 ; Spelke & Kinzler, 2007 ) in specific domains (folk physics, folk biology, and folk psychology), with these competencies being fleshed out over the course of development as children explore, play, and engage in social interactions. Consider the case of processing human faces. In adults, portions of the right frontal cortex appear to be specialized for processing human faces, and adults are especially skilled at processing upright faces, although these special face-processing skills do not apply to upside-down faces or extend to faces of animals from other species—monkeys, for instance. This general pattern is evident by 9 months of age, with infants displaying an upright-face advantage for human faces but not for monkey faces. However, 6-month-old infants process both human and monkey upright faces more efficiently than upside-down faces, displaying a more general “face-processing” bias. This is consistent with the suggestion that infants’ brains are biased to process faces, but that the processing of human faces becomes more specialized with age and experience (e.g., de Haan, Oliver, & Johnson, 1998 ; Johnson & de Haan, 2001 ; Pascalis, de Haan, & Nelson, 2002 ). According to Pascalis and his colleagues (2002 , p. 1321), “the ability to perceive faces narrows with development, due in large measure to the cortical specialization that occurs with experience viewing faces. In this view, the sensitivity of the face recognition system to differences in identity among the faces of one’s own species will increase with age and with experience in processing those faces.”

Even perspectives that have been labeled as neo-nativism (e.g., Spelke, 1991 ; Spelke & Kinzler, 2007 ) do not attribute fully formed “innate ideas” to infants and children, but argue instead that infants inherit a small set of knowledge systems, shaped by natural selection, that serve as the basis for the development of flexible skills and belief systems (e.g., mathematics, knowledge of the properties of objects, reasoning about other people’s thoughts). For example, Geary (1995) proposed that children possess sets of universal biologically primary abilities that have been shaped by natural selection over our species’ phylogeny that children use spontaneously and that will emerge in a species-typical fashion if children experience a species-typical environment. Language and simple quantitative abilities are examples of biologically primary abilities. These are contrasted with culturally determined biologically secondary abilities that do not have an evolutionary history, often require external motivation for their mastery, and are based on biologically primary abilities. Reading and more advanced forms of mathematics are examples of biologically secondary abilities. Although children may be prepared by natural selection to acquire language, for instance, appropriate environmental input is necessary (social interaction in a language-using culture), and when learning to read children require substantial adult support and instruction in applying a series of biologically primary abilities to achieve mastery.

Intuitive mathematics . As an example of biologically primary abilities, consider those Geary proposed for mathematics: numerosity, ordinality, simple arithmetic, and counting. Numerosity refers to the ability to determine quickly the number of items in a set without counting. Using looking-time procedures, 6-month-old infants have been shown to be able to make discriminations between arrays of three versus four items ( Starkey, Spelke, & Gelman, 1990 ; van Loosbroek & Smitsman, 1990 ), as have many mammal and bird species (see Davis & Pérusse, 1988 ), including cats, chimpanzees, and an African grey parrot. Ordinality refers to a basic understanding of more than and less than relationships, and there is evidence for this late in infancy. In one study, Strauss and Curtis (1981) conditioned infants to point to either the larger or smaller array of dots. For instance, infants may have been shown arrays of three and four dots and trained to point to the smaller array. After training, infants were shown two new arrays, in this case two versus three dots. If they had learned merely to point to the array with three dots, they should continue to point to the three-dot array on the new trials. However, if they had learned an ordinal relation (i.e., point to the smaller array), they should point to the two-dot array on the new trial. Infants did the latter, suggesting they had learned an ordinal relationship.

With respect to simple arithmetic, some researchers have interpreted patterns of infants’ attention to unexpected events (using the violation-of-expectation procedure ; see the chapter by Rakison & Lawson in this handbook) as evidence that they can add and subtract small quantities (e.g., 1 + 1 = 2; 2 − 1 = 1). In an experiment by Wynn (1992) , on one set of trials, 5-month-old infants saw a doll placed on a stage, and a screen was raised to hide the object. Infants watched as a hand holding a second doll moved behind the screen and then exited the stage, empty-handed. If infants have some notion of simple arithmetic, they should infer that there are now two dolls behind the screen. When the screen was then lowered, the possible outcome revealed exactly this, two dolls; for the impossible outcome, only one doll was behind the screen. Infants increased their looking time to the impossible condition, consistent with the idea that they expected two dolls to be behind the screen, and they expressed surprise (reflected by increased looking time) when their expectation was violated when only one doll appeared. This phenomenon has been replicated numerous times (e.g., Simon, Hespos, & Rochat, 1995 ; Walden et al., 2007 ), although some question whether this finding reflects not simple addition but rather a more perceptually based phenomenon (e.g., Clearfield & Westfahl, 2006 ).

Counting is a later-emerging ability, with children acquiring the various principles of counting (e.g., each item in an array is associated with one and only one number name; number names must be in a stable, repeatable order; the final number in a series represents the quantity of the set; the order in which things are counted is irrelevant) over the preschool years ( Gelman & Gallistel, 1978 ). Preschool children spontaneously count things, gradually acquiring the principles of counting and the number names used in their culture before they enter school.

Young children’s tool use . Infants and young children also seem prepared to assume that tools are designed for an intended function, referred to as the design stance ( Dennett, 1990 ). That is, once children see a tool being used, or use a tool themselves, for a specific purpose, they assume the tool is “for” that purpose. This is illustrated in a study in which 12- and 18-month-old children watched an experimenter use the straight end of a spoon or a novel spoonlike object to insert into a hole in a box to turn on a light ( Barrett, David, & Needham, 2007 ). When infants were given the opportunity to turn on the light, they used the novel tool appropriately (i.e., grabbed the spoonlike end and inserted the straight end) most of the time, but did so less than 25% of the time when the familiar spoon was used as a tool. By 12 months of age, infants had apparently formed the category “spoon” and knew how this tool should be used. Although such a design stance can lead to less effective problem solving, it also functions to constrain learning in a way that, on average, likely results in infants and children learning the utility of tools from watching other people use them, greatly facilitating their understanding and use of tools, something that is ubiquitous in human cultures. This is something that other tool-using primates seem not to realize. For example, when selecting a tool to solve a problem, tool-using monkeys are not influenced by having used a tool before, as human children are, but will use any equally useful but novel tool ( Cummins-Sebree & Fragaszy, 2005 ; see also Buttelmann et al., 2008 , for similar studies with great apes).

There has always been debate among developmentalists about the extent to which ontogeny is governed by biological versus environmental factors. Contemporary research and theory has changed substantially the nature of this debate, however. The nativists and empiricists of the old days are gone. Advances in genetics and brain research make it clear that biological development always occurs in an environmental context, and this extends to the expression of genes. The cognition of infants and children is constrained by biological factors, yet there is sufficient neuronal plasticity for the considerable influence of experience, broadly defined. Development is a transaction between endogenous and exogenous factors, with hormones and the firing of neighboring neurons being microenvironmental factors for other neurons, and thus for cognition and its development. Debates related to the old nature/nurture issue persist among cognitive developmentalists, but they are framed differently than in the past, and today’s “extremists” share far more ground than their arguments often seem to suggest.

Cognitive Development Involves Both Stability and Plasticity over Time

Cognitive development is about change over time—yet once a level of cognitive competence is established, will it remain stable over time? Will infants with good visual memories grow up to be children and adults with superior memory abilities? Will high-IQ 4-year olds retain their intellectual advantage relative to their peers by high-school graduation? To what extent can patterns or levels of cognition be changed once established? That is, how plastic, or modifiable, is cognition?

There is evidence that some basic-level processes are relatively stable over development, beginning in infancy. For example, in one study, measures of visual reaction times (the time it takes infants to begin an eye movement toward a picture after it appeared) at 3.5 months of age correlated significantly with visual reaction times 4 years later ( r = 0.51; Dougherty & Haith, 1997 ). In other research, measures of visual recognition memory at 7 months of age were significantly correlated with perceptual speed at age 11 years ( Rose & Feldman, 1995 ). Perhaps more compelling, measures of basic information processing in infancy, as assessed by visual recognition memory (usually determined by infants showing a preference for novel pictures) and rate of habituation (how quickly infants tire of attending to a repeated stimulus), have been found to correlate significantly with childhood IQ (e.g., Bornstein et al., 2006 ; Dougherty & Haith, 1997 ; Rose & Feldman, 1995 ; Rose, Feldman, & Wallace, 1992 ; see Bornstein, 1989 ; McCall & Carriger, 1993 ; Fagan & Singer, 1983 , for reviews), which tends to remain highly stable across childhood and into adulthood ( Bayley, 1949 ; Honzik, MacFarlane, & Allen, 1948 ).

The significant relation between mechanisms for basic information processing in infancy and childhood IQ has caused some theorists to propose these infant abilities, as tapped by recognition memory and habituation tasks, are the basis for intelligence, arguing that cognitive development can be best expressed as reflecting continuity of cognitive function with stability ( Fagan, 1992 ). That is, developmental changes in cognitive abilities are quantitative in nature (e.g., increases in speed of processing, working memory), with individual differences being stable over time. The origins of this stability seem to lie both within children themselves and their environments, as measures of both the home environment (e.g., aspects of mother–child interaction) and habituation rate independently predict childhood IQ (e.g., Bornstein et al., 2006 ; Tamis-LeMonda & Bornstein, 1989 ).

But cognition is multifaceted, and other aspects of children’s thinking do not show levels of stability over time. For example, although some aspects of memory, such as memory span and story recall, show moderate to high degrees of stability over childhood (between 4 and 10 years), the cross-age correlations for other aspects of memory, such as free recall and use of memory strategies, are quite low and usually nonsignificant ( Schneider & Weinert, 1995 ). In other research, cross-age correlations of performance on psychometric tests in infancy tended to be high when infants were within a Piagetian-defined stage (e.g., between 8 and 12 months, corresponding to Piaget’s substage of the coordination of secondary circular reactions) but low when measures were taken between stages ( McCall, Eichorn, & Hogarty, 1977 ). This suggests that when there is discontinuity of cognitive change (as reflected by qualitative changes in cognition as in stage theories such as Piaget’s), there is instability of individual differences.

Although some aspects of cognition show high levels of stability over childhood into adulthood, this does not mean that once some level of cognitive accomplishment has been established it is “permanent.” Rather, intellectual functioning once established must be maintained and in some circumstances can be drastically modified, either for the better or worse. The plasticity of cognition is perhaps best exemplified by research examining changes in IQ levels of children originally reared in stultifying institutions and later placed in intellectually stimulating foster or adoptive homes. Research dating back to the 1930s has demonstrated significant and long-lasting enhancements of IQs for such children (e.g., Beckett et al., 2006 ; Nelson et al., 2007 ; O’Connor et al., 2000 ; Skeels, 1966 ; Skeels & Dye, 1939 ; St. Petersburg-USA Orphanage Research Team, 2008 ; Windsor et al., 2011 ). Not surprisingly, the degree of recovery is related to the age at which children are removed from the deleterious environment and placed in supportive homes. For example, recent research examining the IQs of children removed from Romanian orphanages and placed in British adoptive homes revealed no deficits in IQ at ages 6 or 11 years for children adopted within their first 6 months ( Beckett et al., 2006 ). IQs were lower for children adopted at later ages, particularly those adopted after 24 months. However, the 11-year IQs (83) were higher than the 6-year IQs (77) for these late-adopted children, suggesting a catch-up effect for the children who experienced the longest deprivation.

One methodological problem has plagued all of these “natural experiments,” in that children are not randomly assigned to “institution” and “adoptive” conditions. Perhaps the brighter or more maturationally advanced children are more likely to be selected for adoption than less-advanced children, for example. This problem was overcome in a recent study by randomly assigning Romanian infants who had been abandoned at birth to either foster care or to continued institutional care. These infants were followed to 54 months of age and also compared to a group of never-institutionalized infants who were being reared by their biological families in Bucharest, Romania ( Nelson et al., 2007 ). Similar to other studies, Nelson and colleagues reported higher IQs for children in foster care than for those who remained institutionalized, with IQ levels of the foster children being higher the earlier they were removed from the institution (IQs at 0 to 18 months = 85.8; 18 to 24 months = 86.7; 24 to 30 months = 78.1; 30-plus months = 71.5). In fact, children placed in foster care after 30 months of age had IQs similar to those of children in the institutionalized group (72 vs. 73).

It is not surprising that the brains of once-institutionalized children show signs of dysfunction in structure and processing in several areas ( Chugani et al., 2001 ; Eluvathingal et al., 2006 ). Nelson (2007) proposed that the stimulus-poor environments in which these children spend their early lives fail to provide the species-typical experiences human infants have evolved to expect, including sensory stimulation, social stimulation from a caregiver, and language, among others. Nelson suggested that the normal process of selective cell death may go awry in these children, resulting in excess neurons and synapses being lost, most of which can never be replaced.

Institutionalization studies indicate that patterns of cognitive growth can be facilitated when children experience a change from a nonstimulating to a stimulating intellectual environment. Similar changes can also occur in the opposite direction, however, when the supportive environments responsible for the establishment of intellectual accomplishment are changed. For instance, infant and preschool enrichment programs provided intellectually stimulating environments for children at risk for mental retardation, usually through kindergarten. These programs typically resulted in significant gains in IQ and academic performance relative to control children who did not experience educational enrichment (e.g., Bradley, Burchinal, & Casey, 2001 ; Klaus & Gray, 1968 ; Ramey et al., 2000 ). However, with only a handful of exceptions ( Campbell et al., 2002 ; Reynolds et al., 1996 , 2011 ), the gains shown by children in these enrichment preschool programs dwindled with time, with average IQs and school achievement of children attending these programs being comparable to those of control children by fourth grade (see Barnett, 1995 ; Lazar et al., 1982 , for reviews).

The children who attended preschool enrichment programs did, of course, get smarter with age (i.e., showed gains in cognitive development); however, as they returned to their homes and schools, they lost the supportive environment responsible for establishing intellectual accomplishments, and thus lost their intellectual edge relative to control children. Not surprisingly, at-risk children who stay in compensatory education programs once they begin formal school continue to maintain an academic advantage over their peers, but these gains, too, diminish after the completion of the program (e.g., Becker & Gersten, 1982 ).

Human intellectual plasticity is one of our species’ greatest claims to fame (see the chapters by Markant & Thomas in this handbook, and the chapter by Maurer and Lewis on sensitive periods). It permits us to adapt to a broad range of environments and to perform complex cognitive tasks, such as reading and calculus, that our ancestors never faced. Homo sapiens ’ cognitive flexibility is as much a part of our evolved nature as is our upright stance. Human infants and children have strong dispositions/intuitive information-processing biases, but our species’ thinking is highly sensitive to context, and this is particularly true early in life, when developmental trajectories are put in motion. This plasticity early in life is afforded by humans’ slow-developing brain that permits children to adjust to a wide range of circumstances. From this perspective, cognition is always expressed in an environment (usually a social environment, see discussion below), and when the conditions supporting the expression of those intellectual abilities change, one can expect corresponding changes in patterns of cognitive development. This makes humans the most educable of animals—that is, able to learn through experience.

Cognitive Development Involves Changes in the Way Information is Represented, Although Children of Every Age Possess a Variety of Ways to Represent Experiences

Central to all major theories of cognitive development are age-related changes in how objects, people, and experiences are represented (e.g., Brainerd & Reyna, 2002 ; Bruner, 1966 ; Case, 1992 ; Fischer, 1980 ; Karmiloff-Smith, 1991 ; Piaget, 1983 ). Piaget’s stage theory is the classic example in which major changes in how children represent the world reflect qualitative changes in cognition. According to Piaget, infants during their first 18 months or so represent objects and events by means of self-produced action (including sensory “action” such as looking at things), termed sensorimotor intelligence. Beginning around their second birthdays, children are able to represent objects and events symbolically, as reflected by their use of language, mental imagery, deferred imitation, and symbolic play, among other expressions of the symbolic (or semiotic) function. Although symbolic, the thinking of children in this preoperational stage (ranging from about 2 to 7 years) is intuitive and lacks logical operations, such as reversibility (e.g., a cognitive operation can be reversed, as in the case of subtraction, the effects of which can be reversed by addition). The thinking of children in the next state, concrete operations (ranging in age from about 7 to 11 years), although logical, is limited, as the stage name denotes, to concrete entities; abstract reasoning comes on line beginning around 11 or 12 years of age with the advent of formal operations .

Piaget’s stage theory has served as the jumping-off point for other theories proposing developmental differences in representational abilities (e.g., Case, 1992 ; Fischer, 1980 ; Fischer & Bidell, 1998 ; Pascual-Leone, 1970 , 2000 ). It has been critiqued widely (e.g., Brainerd, 1978 ; see papers in Brainerd, 1996 ), and I will not provide a detailed examination of this influential theory here. Rather, I devote most space to what is perhaps the most studied and controversial transition reflected in Piaget’s theory, the change from sensorimotor to symbolic representation. The advent and widespread use of symbolic representation marks a major milestone in cognitive development, and although humans may not be the only species capable of representational thought (see, e.g., Parker & McKinney, 1999 ), the extent to which humans apply such thinking differentiates us from all other species.

As I noted, Piaget believed that the symbolic function was expressed via children’s language, mental imagery, deferred imitation, and symbolic play, among others, each emerging around 18 to 24 months of age. For example, although children typically speak their first words around 10 months of age, they usually don’t put them into sentences until around 18 months, and Piaget (1962) observed, and others confirmed (e.g., Kaye & Marcus, 1981 ), that children display deferred imitation (copying the actions of a model some significant time after observing the behavior) late in the second year of life. However, more recent research indicates that infants show signs of symbolic representation much earlier than Piaget proposed when simplified and age-appropriate tasks are used.

Perhaps the best-documented case of infants displaying symbolic representational abilities much earlier than Piaget proposed is for deferred imitation (see Bauer, 2007 , and the chapters by Bauer and Meltzoff & Williamson in this handbook for reviews). Although infants’ ability to imitate multistep actions increases with age, infants as young as 9 months old will imitate simple actions for up to 5 weeks (e.g., Carver & Bauer, 1999 ); 6-month-olds have been shown to imitate simple behaviors after a 24-hour delay ( Collie & Hayne, 1999 ); and preverbal toddlers have shown evidence of deferred imitation for as long as 1 year (e.g., Bauer, 2002 , 2007 ; Bauer et al., 2000 ). Other research indicates that infants in their first year of life may be able to add and subtract small quantities (e.g., Wynn, 1992 , discussed earlier) and may possess some precocious problem-solving strategies based on analogical reasoning ( Chen, Sanchez, & Campbell, 1997 ; Willatts, 1990 ), and newborns have been shown to copy facial expressions (e.g., Meltzoff & Moore, 1977 ) and integrate information from multiple senses (e.g., Meltzoff & Borton, 1979 ). These and other findings lead Meltzoff (1990 , p. 20) to conclude that “ in a very real sense, there may be no such thing as an exclusively ‘sensorimotor period’ in the normal human infant ” (italics in the original).

Although there are alternate interpretations of some of the findings purported to reflect infant representational abilities (e.g., infant “addition” may actually be the result of perceptual, not conceptual, processes, Clearfield & Westfahl, 2006 ; neonatal imitation may have a communicative and/or affiliative function and is not related to the imitation observed later in infancy, Bjorklund, 1987 ; Byrne, 2005 ), most contemporary theorists concur that representational cognition does not suddenly appear around children’s second birthdays, but rudimentary abilities are seen late in the first and early in the second year of life.

Representing Others as Intentional Agents

In addition to evidence from studies of deferred imitation in infancy (see Bauer, 2007 ), representational competency in infancy is supported by research examining children’s understanding of seeing both themselves and other people as intentional agents —as beings whose behavior is based on what they know and what they want, and who act deliberately to achieve their goals (i.e., they do things “on purpose”; see Bandura, 2006 ; Tomasello & Carpenter, 2007 ).

On the surface, viewing others as intentional agents may not appear to be a major intellectual accomplishment, but it serves as the basis for human social cognition, which includes social learning and teaching, the foundation for culture—the nongenetic transmission of information between generations. Although the first signs of intentional representation appear late in the first year, children’s understanding of others as intentional agents develops over childhood, culminating in the ability to pass false-belief tasks around 4 years of age, the benchmark for attaining theory of mind (see the chapter by Astington & Hughes in this volume 2).

The earliest sign of infants’ understanding of others as intentional agents is seen in shared (or joint ) attention , which involves a triadic interaction between the child, another person, and an object ( Tomasello & Carpenter, 2007 ; Tomasello et al., 2005 ). For example, parents often draw children’s attention to an object by pointing or gazing at the object, a form of referential communication , which indicates that the “pointer” understands that he or she sees something that the observer does not. Despite parents’ actions, infants do not engage in shared attention until about 9 months of age, although they do display some biases toward social stimuli from birth. For example, newborns orient to human faces and learn to seek their mothers’ faces ( Feldman & Eidelman, 2004 ), and by 3 or 5 months infants can recognize self-produced biological motion ( Bertenthal, Proffitt, & Cutting, 1984 ) and turn to look in the same direction of another person ( Tomasello et al., 2005 ).

Beginning around 9 months of age, infants will gaze in the direction adults are looking or pointing, engage in repetitive interaction with an adult and an object, imitate an adult’s action, and point or hold up objects to another person (see Carpenter et al., 1998 ; Tomasello, 1999 ). Shared attention and related abilities increase over the next year. For example, 12-month-olds will point to objects and events that others are unaware of ( Liszkowski, Carpenter, & Tomasello, 2007 ); between 12 and 18 months infants learn to use where others are looking to inform their own attention ( Brooks & Meltzoff, 2002 ) and to point to objects to direct an adult’s attention to something he or she is searching for ( Liszkowski et al., 2006 ).

Although shared attention may seem to reflect a low-level form of representation, it may be unique to humans. For example, although chimpanzees and even monkeys will follow the gaze of another individual in some contexts (e.g., Bering & Povinelli, 2003 ; Bräuer, Call, & Tomasello, 2005 ) and point out things to other individuals (e.g., Leavens, Hopkins, & Bard, 2005 ), there is little evidence that chimpanzees engage in shared attention (e.g., Herrmann et al., 2007 ; Tomasello & Carpenter, 2005 ).

The importance of seeing others as intentional agents can be seen in social learning. The most sophisticated forms of social learning, including teaching, require that the observer not only copy significant aspects of a model’s behavior, but also understand that the model has a specific goal, or intention, in mind. That is, behavior is not copied just for the sake of reproducing the actions of another individual, but to achieve some specific outcome. This is seen early in the second year of life. For example, 14- and 18-month-old infants will copy the behavior an adult intended to perform (e.g., pulling the ends off a dumbbell), even if the adult failed to complete the action (e.g., Meltzoff, 1995 ; see also Carpenter, Akhtar, & Tomasello, 1998 ). In fact, preschool children will generally reproduce most of an adult model’s actions even if all the actions are not necessary to achieve a goal ( Gardiner, Greif, & Bjorklund, 2011 ; Horner & Whiten, 2005 ; Nagell, Olguin, & Tomasello, 1993 ; Nielsen & Tomaselli, 2010 ). For instance, in one study 3- and 4-year-old children were shown a transparent puzzle box and an adult demonstrated a series of three actions, two of which were necessary and one of which was not, to retrieve a gummy bear from inside the box ( Horner & Whiten, 2005 ). Children copied all of the adult’s actions, even those that were obviously irrelevant for attaining the goal. One interpretation of findings such as these is that young children may believe that all of an adult’s actions are goal-directed, making imitation of those actions a reasonable course to take ( Lyons, Young, & Keil, 2007 ).

Although chimpanzees and the other great apes clearly engage in sophisticated forms of social learning, passing information from one generation to the next, the minimal criterion for culture (e.g., van Schaik et al., 2003 ; Whiten, 2007 ; Whiten et al., 1999 ), they tend not to engage in true imitation (understanding the model’s goal and copying most behaviors to achieve that goal) as young children do. Rather, they are more apt to engage in emulation , attaining the same goal as the model but using different, and sometimes more effective, actions in doing so (e.g., Call, Carpenter, & Tomasello, 2004 ; Horner & Whiten, 2005 ; Nagell et al., 1993 ). Thus, despite being apparently able to represent the goals of a model, chimpanzees seem not possess the same degree of recognition of other beings as intentional agents as human preschoolers do, perhaps accounting for the greater effectiveness of social learning in humans than in great apes.

Another major representational change in understanding others as intentional agents seems to occur around 4 years of age when children can pass false-belief tasks. Much before this time, children have great difficulty attributing a false belief to others. For example, if a 3-year-old knows that a cookie, originally hidden in a cupboard, has been moved to a jar, he or she believes that another person, although not privy to the change in location, will also know the correct whereabouts of the cookie (e.g., Baron-Cohen et al., 1985 ; Wimmer & Perner, 1983 ). Although performance on false-belief tasks is affected by task characteristics and by basic-level processes such as executive function (e.g., Flynn, O’Malley, & Wood, 2004 ; Henning, Spinath, & Aschersleben, 2011 ; Hughes & Ensor, 2007 ; see the chapter by Carlson, Zelazo, and Faja in this handbook), 3-year-olds seem to truly lack the conceptual/representational competence to solve such tasks that most 4-year olds possess ( Wellman, Cross, & Watson, 2001 ).

Representational Insight

Most aspects of mental representation and symbolic functioning would seem to require the knowledge that one entity can stand for something other than itself, termed representational insight ( DeLoache, 1987 ; DeLoache & Marzolf, 1992 ). This can be seen in how children interpret pictures or photographs. In one study, children between 9 and 19 months of age in the United States and the Ivory Coast were given photographs of objects to inspect ( DeLoache et al., 1998 ). Most of the youngest children treated the photos as if they were real objects, sometimes even trying to pick them off the page. In contrast, most of the older children pointed at the depicted objects rather than trying to manipulate them, realizing they were representations of things.

In other studies, researchers showed children scale models or photographs of rooms, including the location of a hidden toy. Children were then given the opportunity to find the toy in a “real” room (e.g., DeLoache, 1987 ; DeLoache & Marzolf, 1992 ; Kuhlmeier, 2005 ; Suddendorf, 2003 ). Somewhat surprisingly, children were first able to use the photograph as a cue to where the toy was hidden in the real room (at around 2.5 years of age), but only later were able to find the object when a scale model was used (about 3 years of age). One explanation for this pattern was that the scale model was an interesting object itself, making it difficult for children to treat it as a representation for something else, or what DeLoache (2000) referred to as dual representation . In support of this explanation, when the model was made less interesting (e.g., by having children look at it through a window), 2.5-year-old children were able to use it to find the toy in the real room ( DeLoache, 1991 ).

Implicit/Explicit Representation

One distinction frequently made in cognitive psychology is that between implicit and explicit cognition. Implicit cognition refers to cognition without awareness, whereas explicit cognition refers to cognition with conscious awareness. Generally, human infants and all nonhuman animals may be limited to implicit cognition (but see Bjorklund & Rosenberg, 2005 , for discussion of possible explicit cognition in chimpanzees), and the evolution of conscious awareness, with a well-developed sense of self, has been proposed to be essential for evaluating the causes of one’s behavior and the behavior of others—that is, treating other people as intentional agents ( Bering & Bjorklund, 2007 ). Although implicit cognition may lack the important ingredient of self-awareness, it can be quite sophisticated, as reflected by the knowledge spiders have for building webs, birds have for building nests, or people have for complicated motor tasks, such as skiing down a twisting slope.

Karmiloff-Smith (1991 , 1992 ) developed a theory of representational redescription in which implicit representations are transformed, or redescribed, into various forms of explicit cognition. According to Karmiloff-Smith, redescription permits children to use their representations more flexibly, including taking one piece of information (watching mother as she points in the distance) and making some inferences (perhaps she wants me to look at the object she’s pointing at). With redescription , knowledge that was once implicit becomes explicit, allowing children to generate new insights by reflecting on what they already know.

As with other aspects of cognitive development, there seems not to be a definitive point in time before which self-awareness is not present and after which it is. Perhaps the classic demonstration of self-awareness is mirror self-recognition , in which children realize that it is themselves and not another child that they see in the mirror. Children “pass” this task, usually by pointing to a mark on their face that was surreptitiously placed there rather than pointing at the mirror, around 18 months of age (e.g., Brooks-Gunn & Lewis, 1984 ; Nielsen, Suddendorf, & Slaughter, 2006 ), as do chimpanzees, orangutans, and a few gorillas ( Gallup, 1979 ; Suddendorf & Whiten, 2001 ), dolphins ( Reiss & Marino, 2001 ), elephants ( Plotnik, de Waal, & Reiss, 2006 ), and magpies ( Prior, Schwarz, & Güntürkün, 2008 ). However, when researchers placed stickers on children’s heads, most 2- and 3-year-old children failed to reach for the stickers when shown photographs or videos of themselves (e.g., Povinelli, Landau, & Perilloux, 1996 ; Povinelli & Simon, 1998 ), suggesting that children’s sense of self develops gradually over the preschool years, as their ability to deal with different modes of representation (mirrors, photos, videos) develops (see also Skouteris, Spataro, & Lazaridis, 2006 ; Zelazo, Sommerville, & Nichols, 1999 ).

Other research suggests that some aspects of self-awareness and explicit cognition develop much earlier. For example, as I mentioned previously, infants as young as 9 months old display deferred imitation (see Bauer, 2007 ), which has been proposed to be a nonverbal form of explicit memory. This is seen in studies of adults with hippocampal damage, who are unable to acquire new explicit knowledge but can learn new implicit knowledge. For instance, when given a mirror-drawing task (trace figures while watching one’s hand in a mirror), patients with hippocampal damage don’t remember performing the task from day to day (explicit memory) but nonetheless improve their performance as a result of practice (implicit memory) ( Milner, 1964 ). When these patients are given deferred-imitation tasks similar to those used with infants (observe a novel behavior and then reproduce it a day later), they behave much as they do on verbal explicit memory tasks—they are unable to remember seeing the task performed and fail to reproduce the modeled behavior ( McDonough et al., 1995 ).

Children of all ages beyond infancy (and perhaps during) have both implicit and explicit representations available to them, and operations involving both systems are used in processing information. However, tasks that tap mostly explicit representations show larger development differences than tasks that tap mostly implicit representations (e.g., Billingsley, Smith, & McAndrews, 2002 ; Newcombe et al., 1998 ). For example, in one study, 4-, 5-, and 6-year-old children saw a series of pictures and were asked to identify them or to answer some questions about them (for example, “What would you use an X for?”) ( Hayes & Hennessy, 1996 ). Two days later children were shown a series of fragmented pictures, some of which they had seen earlier and some of which were new. The initial picture in each series was substantially degraded and gradually more detail was provided until children identified the picture. Children were also asked if they remembered each picture from 2 days ago. Recognition memory, a measure of explicit cognition, improved with age; however, children of all ages identified the fragmented “old” pictures (i.e., those they had seen with less detail provided) earlier than the fragmented “new” pictures, a measure of implicit memory. This effect held regardless of whether children remembered seeing the pictures 2 days earlier or not.

Infants and children often display greater cognitive competence on tasks when their knowledge is assessed by implicit rather than explicit measures ( Keen, 2003 ). For example, in a false-belief task, after a piece of cheese is moved from its original container to a new one, children were asked where Sam, who saw where the object was hidden initially but did not see it moved, will look. Most 3-year-olds stated, erroneously, that Sam will look for the cheese in the new location. This is a measure of explicit representation. However, when 3-year-olds were asked this question, they first gazed at the original location, where Sam saw the cheese being hidden ( Clements & Perner, 1994 ; Clements, Ruffman, & McCallum, 2000 ). Looking behavior is a nonverbal and implicit measure, and when it is used as an indication of children’s knowledge, it appears that even 3-year-olds understand (at least implicitly) the possibility that others can hold a false belief.

Other research using infants’ implicit looking behavior (e.g., increasing looking time to an unexpected event, such as a screen that continues to descend when its trajectory should be stopped by an object) indicates that babies possess knowledge of physical objects, such as object permanence ( Baillargeon, 1987 ), months earlier than observed by Piaget using more explicit reaching behaviors as measures (e.g., reaching and retrieving a covered object). Other research using similar looking-time measures has shown that 5- and 6-month-old infants realize that items that are unsupported will fall ( Baillargeon, 1994 ; see Baillargeon, 2008 ; Spelke & Kinzler, 2007 , for reviews). In contrast, 2-year-old children fail to show this knowledge when explicit searching behavior is used as a measure ( Berthier et al., 2000 ; Hood, Carey, & Prasada, 2000 ). For example, after watching a ball dropped onto a stage behind a screen and seeing the resting ball on the floor, 2- and 2.5-year-old children watched as the experimenter placed a cup on the floor of the stage, a shelf over the cup, and then a second cup on that shelf ( Hood et al., 2000 ). The screen was then replaced and the ball dropped again. If the children understood the solidity of objects, as 6-month-old infants presumably do, they should search in the cup on the top shelf. Most 2.5-year-old children did so (93%), but only 40% of the 2-year-old children searched in the top cup, suggesting that, when using explicit measures, their understanding of solidity was tenuous. These and other findings (see Keen, 2003 ) suggest that implicit knowledge develops before explicit knowledge, and we must be cautious when we state that infants or children either possess, as reflected by implicit knowledge, or don’t possess, as reflected by explicit knowledge, a particular concept.

Dual-Process/Representation Theories of Cognitive Development

The implicit/explicit distinction just discussed suggests that children have multiple ways of representing information. Such theories are often referred to as dual-process theories , and most theorists postulate that people have (at least) two basic ways of representing information (e.g., implicit vs. explicit; experiential vs. analytic; exact, verbatim traces vs. inexact, “fuzzy” traces) and that there are developmental differences in how children use these various forms of representation (e.g., Barrouillet, 2011 ; Brainerd & Reyna, 2002 , 2005 ; Klaczynski, 2009 ). One dual-process theory that has been widely applied to children’s cognition is fuzzy-trace theory (e.g., Brainerd & Reyna, 1993 , 2002 , 2005 ). Brainerd and Reyna propose that people represent experiences on a fuzzy-to-verbatim continuum . At one extreme are verbatim traces , which are elaborated, exact representations of recently encoded information. At the other extreme are fuzzy traces , or gist, which are vague, degenerated representations that maintain only the sense or pattern of recent experiences.

Although people of all age process information along the entire continuum, young children are biased to represent experiences in terms of verbatim traces, with this bias shifting in middle childhood. This has implications for children’s performance on a host of tasks, because verbatim and fuzzy traces are processed differently. For example, verbatim traces are more likely to be forgotten and are more susceptible to output interference than fuzzy traces. Although space prevents me from providing a detailed description of research performed following fuzzy-trace theory, it has been applied to a wide range of domains within cognitive development, including memory (e.g., Brainerd & Reyna, 2005 ), arithmetic (e.g., Brainerd & Gordon, 1994 ), and reasoning (e.g., Reyna & Farley, 2006 ), and has generated a number of counterintuitive predictions that have been confirmed by research. For example, under some circumstances, children’s false memories (e.g., remembering an event that didn’t happen) are more resistant to forgetting than true memories (e.g., Brainerd & Mojardin, 1999 ; see Brainerd & Reyna, 2005 ). This was predicted premised on the fact that correct recognition is based, in part, on literal, or verbatim, memory traces. Because there are no verbatim memory traces for falsely remembered events, they are based solely on the more durable fuzzy traces. As a result, true memories are more likely to be forgotten than false memories.

Representation has been one of the most investigated and theorized-about aspects of cognitive development. Counter to Piaget’s original proposal, children, beginning in infancy, have multiple ways of representing information, although their ability to mentally represent people, objects, and events increases in sophistication over infancy and childhood. The ability to represent the intentions of other people, a form of social cognition, may be of special significance, for with it children can represent the goals of other people and are able to learn through observation and direct teaching, permitting the acquisition of knowledge and skills that were foreign to our ancestors. In fact, many theorists propose that humans’ exceptional intelligence, which affords scientific, artistic, and technological accomplishments, is derived from our social intelligence, evolved for cooperating and competing with fellow conspecifics (e.g., Alexander, 1989 ; Dunbar, 1995 , 2010 ; Geary & Ward, 2005 ; Humphrey, 1976 ), and the result of the confluence of a big brain, an extended juvenile period, and living in socially complex groups (e.g., Bjorklund, Cormier, & Rosenberg, 2005 ; Dunbar, 1995 ). Human representational ability is seemingly unique in the animal world. Although hints of representational thought can be seen in other big-brained animals, including the great apes (e.g., Herrmann et al., 2007 ; Whiten, 2007 ), dolphins (e.g., Bender, Herzing, & Bjorklund, 2009 ; Krützen et al., 2005 ), and elephants (e.g., Plotnik et al., 2006 ), no other species makes use of symbolic representation to the extent that humans do. Although I’ve emphasized that there does not seem to be a single point in development when we can say children “have” representational thought versus when they do not, the change of thinking between the mainly sensorimotor infant and the child who possesses language and theory of mind is substantial, giving the appearance, if not the reality, of a stagelike transformation in cognition.

Children Develop Increasing Intentional Control over Their Behavior and Cognition

The purpose of cognition is to solve problems. Although adult human minds can ponder esoteric questions concerning the meaning of existence, cognition evolved to help animals solve the problems they encounter in everyday life. The ability to solve problems increases with age, and one important issue for developmental psychologists concerns the degree to which children of different ages can intentionally guide their problem solving. Much research on this topic has addressed the use of strategies , usually defined as deliberate, goal-directed mental operations that are aimed at solving a problem (e.g., Harnishfeger & Bjorklund, 1990 ; Pressley & Hilden, 2006 ). However, central to using strategies and intentional control of behavior is self-regulation , the ability to guide not only one’s problem solving but also one’s emotions (e.g., Cole, Martin, & Dennis, 2004 ; Posner, Rothbart, & Sheese, 2007 ).

Several basic-level cognitive abilities are involved in self-regulation, which collectively are referred to as executive function ( Jones, Rothbart, & Posner, 2003 ; Wiebe, Espy, & Charak, 2008 ; Zelazo, Carlson, & Kesek, 2008 ). Executive function refers to the processes involved in regulating attention and in determining what to do with information just gathered or retrieved from long-term memory. It plays a central role in planning and behaving flexibly, particularly when dealing with novel information. It involves a related set of basic information-processing abilities, including working memory , the structures and processes used for temporarily storing and manipulating information; selectively attending to relevant information; inhibiting responding and resisting interference; and cognitive flexibility, as reflected by how easily individuals can switch between different sets of rules or different tasks (see Garon, Bryson, & Smith, 2008 ; McAuley & White, 2011 ; Zelazo et al., 2008 ). In this section, I review the development of various aspects of executive function and then look briefly at children’s development of strategies, topics that will both be examined in more detail later in this handbook (see the chapters by Rueda & Posner; and Carlson, Zelazo, & Faja).

The Development of Executive Function

Executive function seems to include at least three factors—working memory, inhibition, and cognitive flexibility—each of which develops. Working memory is measured by performance on working-memory span tasks. Working-memory span can be contrasted with the more familiar measure of memory span , found on the Stanford-Binet and Wechsler IQ tests. Memory span is typically measured by asking children to recall in exact order a list of items that are presented at a rate of about 1 per second. In contrast, in working-memory span tasks, children are asked to perform simple cognitive operations in addition to remembering the items. For example, in a counting-span task children may see arrays of blue circles and yellow triangles and be asked to count the number of circles. Children must then recall the number of circles in that array and in each prior array. Both memory and working-memory span show regular increases with age, with working-memory span usually being about two items less than a child’s memory span (e.g., Alloway, Gathercole, & Pickering, 2006 ; Case, 1985 ; Dempster, 1981 ).

One popular account of working memory and its development was presented by Baddeley and Hitch ( Baddeley, 1986 ; Baddeley & Hitch, 1974 ), who proposed that working memory consists of a central executive that stores information and two temporary systems, one for coding verbal information called the articulatory , or phonological , loop , and another for coding visual information, referred to as the visuospatial scratch pad , or visuospatial working memory . Developmental differences in verbal memory span are primarily due to age differences in the articulatory loop. Age differences in the rate of decay of verbal representations held in the articulatory loop and/or the rate that that information can be rehearsed contribute to developmental differences in memory and working-memory span (see Cowan & Alloway, 2009 ). Support for this contention comes from research reporting a relationship between the speed with which individual words can be articulated and memory span. Researchers have found reliable age differences in speed of processing , with younger children taking more time to process information and make decisions than older children ( Kail & Ferrer, 2007 ; Miller & Vernon, 1997 ). With age, children are able to read or say words at a faster rate, and memory span increases accordingly (e.g., Chuah & Maybery, 1999 ; Hulme et al., 1984 ). When adults’ speed of processing is slowed down to be comparable to that of 6-year-olds (e.g., by making them remember digits using a foreign language), their memory and working-memory spans are similarly reduced to be comparable to those of 6-year-olds (e.g., Case, Kurland, & Goldberg, 1982 ).

The relationship between speed of enunciating individual items and memory span is nicely illustrated by some cross-cultural research. For example, Chinese-speaking children have longer memory spans than English-speaking children ( Chen & Stevenson, 1988 ; Geary et al., 1993 ), and this is related to the fact that the digits 1 through 9 can be articulated more rapidly in Chinese than in English. A similar effect has been found for bilingual Welsh children, who have longer digit spans in English, their second language, than in Welsh, their first language. This counterintuitive effect is attributed to the fact that number words can be articulated more rapidly in English than in Welsh ( Ellis & Hennelley, 1980 ).

Children’s familiarity with the to-be-remembered items also affects span length (e.g., Dempster, 1981 , 1985 ). For example, in a much-cited study, Chi (1978) reported that a group of 10-year-old chess experts had longer memory spans for game-possible positions on a chessboard than a group of adults who knew how to play chess but were not experts. However, their greater memory span was limited to their area of expertise; the adults had longer memory spans than the children for digits (see also Schneider et al., 1993 ). Memory and working-memory span, then, should not be viewed as absolute limits of children’s information-processing abilities, but rather they are influenced by factors including the speed with which individual items can be processed and children’s knowledge for the to-be-remembered information.

Yet there is evidence that there may be some absolute limits in how much information children of different ages can hold in working memory ( Cowan et al., 1999 , 2011 ). For example, Cowan and his colleagues (1999) evaluated age differences in span of apprehension ( Sperling, 1960 ), which refers to the amount of information that people can attend to at a single time. The span of apprehension of adults is about four items, compared to memory span, which is 7 ± 2 items. In the study by Cowan and colleagues, first- and fourth-grade children and adults heard series of digits over headphones, which they were to ignore, while simultaneously playing a video game. Occasionally and unexpectedly, however, they were asked to recall, in exact order, the most recently presented set of digits they had heard. The average span of apprehension increased with age: about 2.5 digits for first graders, about 3.0 for fourth graders, and about 3.5 digits for adults. Cowan and his colleagues interpreted these significant age differences as reflecting a true developmental difference in the capacity of the short-term store that serves as the foundation for age differences on memory-span tasks.

Individual differences in children’s working memory are related to a host of higher-order cognitive abilities. For example, working memory correlates moderately with IQ (see Fry & Hale, 2000 ) and is significantly associated with the speed and accuracy of arithmetic computation (e.g., Adams & Hitch, 1997 ; Zheng, Swanson, & Marcoulides, 2011 ), reading comprehension (e.g., Daneman & Blennerhassett, 1984 ; Daneman & Green, 1986 ), writing ability (e.g., Swanson & Beringer, 1996 ), and the use of arithmetic (e.g., Berg, 2008 ) and memory strategies (e.g., Lehmann & Hasselhorn, 2007 ; Woody-Dorning & Miller, 2001 ). Children with math (e.g., Geary et al., 1991 ) and reading (e.g., Gathercole et al., 2006 ; Swanson & Jerman, 2007 ) disabilities have smaller working memories than nondisabled children, and children with precocious mathematical skills have higher levels of executive function (e.g., working memory, inhibition) than typically devleoping children ( Johnson, Im-Bolter, & Pascual-Leone, 2003 ; Swanson, 2006 ).

A second basic-level ability included in executive function is inhibition , which refers to the ability to prevent making some cognitive or behavioral response. Researchers have proposed that children’s abilities to inhibit preferred or well-established responses plays an important role in cognitive development (e.g., Bjorklund & Harnishfeger, 1990 ; Dempster, 1992 ; Diamond & Taylor, 1996 ; Harnishfeger, 1995 ). Related to inhibition is resistance to interference ( Dempster, 1993 ), which refers to “susceptibility to performance decrements under conditions of multiple distracting stimuli” ( Harnishfeger, 1995 , pp. 188–189). Resistance to interference is seen in dual tasks, when performing one task (watching television) interferes with performance on a second task (comprehending a story one is reading), or in selective attention, when one must focus on “central” information (reading a story) and ignore “peripheral” information (the plot of a television sitcom).

Inhibition and the ability to resist interference increase with age. For example, in Piaget’s A-not-B object permanence tasks, infants much younger than about 12 months continue to search at location A, where they had retrieved a hidden object several times previously, despite seeing it hidden at a new location (B). One factor hypothesized to be related to performance on this task is infants’ ability to inhibit their previous correct responses, which improves over the latter part of the first year ( Diamond, 1985 ; Holmboe et al., 2008 ).

Inhibition abilities continue to develop over childhood and adolescence (see, e.g., Kochanska et al., 1996 ; Luria, 1961 ) and are assessed by a variety of simple tests. For instance, in the tapping task children must tap once each time the examiner taps twice and tap twice each time the examiner taps once; in the day-night task , children must say “day” each time they see a picture of the moon and “night” each time they see a picture of the sun; and in Simon Says , children must perform an action only when Simon says so (“Simon says, touch your nose”). These tasks require children to inhibit a prepotent response and execute another, and individual and development differences are found on these and other tasks (e.g., Baker, Friedman, & Leslie, 2010 ; Diamond & Taylor, 1996 ; Sabbagh et al., 2006 ). More complicated tasks, appropriate for older children and adolescents, include variants of the Wisconsin Card Sorting Task, in which participants sort cards by one dimension (e.g., shape of object), which is then switched to another dimension (e.g., color). The number of perseverative errors (i.e., continuing to sort by the previously correct category) is used as a measure of inhibition (e.g., Chelune & Baer, 1986 ).

Children’s performance on inhibition and resistance to interference tasks is associated with a host of higher-level cognitive abilities, including false-belief tasks assessing theory of mind (e.g., Sabbagh et al., 2006 ), selective attention (e.g., Ridderinkhof, van der Molen, & Band, 1997 ), selective forgetting (e.g., Harnishfeger & Pope, 1996 ; Lehman et al., 1997 ), incidental learning (e.g., Schiff & Knopf, 1985 ), and intelligence (see Harnishfeger & Bjorklund, 1994 ; McCall & Carriger, 1993 ). Behavioral inhibition has also been identified as the principal cause of attention-deficit/hyperactivity disorder (ADHD; Barkley, 1997 ).

A third basic-level component hypothesized to be involved in the development of executive function is cognitive flexibility , as reflected by the ability to shift between sets of tasks or rules (e.g., Garon et al., 2008 ; Zelazo et al., 2003 ). Many of the tasks used to assess inhibition abilities also require children to shift, or change, between a set of rules. For example, in the Wisconsin Card Sorting Task participants must switch from following one rule (it’s the shape of the object that’s important) to another (it’s the number of objects on a card that’s important). Relatedly, Zelazo and his colleagues have argued that the development of executive function involves the increasing ability to formulate and maintain rules, as illustrated on simplified “shifting tasks,” in which children must change from following one criteria (sort by color) to another (sort by shape) ( Zelazo et al., 2003 , 2008 ).

Developmental differences in executive function have been related to age-related differences in brain development, particularly the frontal lobes (see Zelazo et al., 2008 ). For example, myelination of neurons, which promotes a faster rate of neuronal processing, is not fully developed in the frontal cortex until adolescence or young adulthood (see Lenroot & Giedd, 2007 ; Yakovlev & Lecours, 1967 ). There is substantial research with infants (e.g., Bell, Wolfe, & Adkins, 2007 ) and older children and adults (e.g., Luna et al., 2001 ) pointing to the frontal lobes as the locus of inhibitory control. For example, neuroimaging studies reveal relations between infants’ performance on A-not-B object-permanence tasks and frontal lobe activity (e.g., Baird et al., 2002 ; Segalowitz & Hiscock, 2002 ), and young children’s performance on the Wisconsin Card Sorting Task is similar to that of adults with frontal lesions ( Chelune & Baer, 1986 ). Research by Luna and colleagues (2001) found that inhibition abilities develop gradually between the ages of 8 and 30 years and are associated with activity in the frontal cortex. However, rather than showing a linear relation between age and brain functioning on inhibition tasks, they reported that the prefrontal cortex was more active on these inhibition tasks in adolescents than in either children or adults.

Most accounts of executive function hold that the various processes are domain-general in nature. That is, developmental and individual differences in working memory and inhibition influence children’s performance on a host of tasks in a similar way (e.g., Case, 1992 ). Despite this, one should not expect executive function to be uniform across all tasks, as differences in motivation and knowledge base will influence levels of children’s performance on different tasks, and some aspects of executive function are likely domain-specific in nature. For example, although reading comprehension seems to be associated with a domain-general set of processing resources, the relation between working memory and writing ability appears to be specific to that domain only ( Swanson & Berninger, 1996 ).

Developmental changes in working memory, inhibition, and cognitive flexibility are all related to one another and to changes in neurological development, particularly the frontal cortex. Few higher-level cognitive tasks can be performed without adequate control of one’s attention, and it is difficult to emphasize too much the importance of executive function to the development of higher-level cognition and to the regulation of one’s emotions and behaviors. Some have even speculated that the evolution of executive function may have been an important component in the emergence of the modern human mind (e.g., Geary, 2005 ). The abilities to inhibit inappropriate behavior, resist distraction, and control one’s actions in general are critical to effective function in any social group, as well as for activities such as hunting, preparing meals, or constructing tools, among many others. These abilities are better developed in humans than in other primates and in older children than in younger children, and may be a key to understanding both human cognitive development and evolution.

Becoming Self-Directed Learners: Strategy Development

Once children have sufficient cognitive and behavioral self-control, they can reflect on the problems they face and approach them strategically. Strategies are usually defined as deliberately implemented, nonobligatory (one doesn’t have to use them to perform a task), mentally effortful operations that are aimed at solving a problem and are potentially available to consciousness ( Harnishfeger & Bjorklund, 1990 ; Pressley & Hilden, 2006 ). Children become self-directed learners by using deliberate information-processing operations to achieve specific goals that could not be achieved “without thinking” (i.e., automatically or with implicit cognition).

It should not be surprising that children become better problem solvers with age. What is important is the way in which they become better problem solvers. As I mentioned in the previous section , children’s strategy use is affected by processes such as working memory and inhibition (e.g., Lehmann & Hasselhorn, 2007 ; Woody-Dorning & Miller, 2001 ). However, even young preschool children use simple strategies in some contexts. For example, 18- and 24-month-old children playing a modified game of hide-and-seek looked at the hiding location of a toy or repeated the toy’s name during a delay period between the time the toy was hidden and they were permitted to search for it ( DeLoache & Brown, 1983 ; DeLoache, Cassidy, & Brown, 1985 ). With age, the sophistication of children’s strategies increases. For example, preschool children perform simple addition problems by counting on their fingers; they use counting strategies that involve enunciating all numbers in each addend (e.g., for 5 + 3 = ?, saying “one, two, three, four, five…six, seven, eight,” called the sum strategy ), later using counting strategies in which only the numbers in the smaller addend are enumerated (e.g., for 5 + 3 = ?, saying “five…six, seven, eight,” called the min strategy , because the minimum number of counts is made), to retrieving the answer directly from memory (e.g., for 5 + 3 = ?, saying “eight” immediately after the problem is posed) (see Ashcraft, 1990 ). Use of increasingly sophisticated strategies with age is observed for most other complex cognitive tasks, including memory (see Bjorklund, Dukes, & Brown, 2009 ), problem solving and reasoning (see DeLoache, Miller, & Pierroutsakos, 1998 ), attention (see Miller, 1990 ), and reading (see Garner, 1990 ), among others.

However, it would be misleading to believe that strategies develop in a stagelike fashion, with a less sophisticated strategy being replaced by a more sophisticated one. Rather, children of all ages have multiple strategies available to them at any one time. The number of strategies available to a child increases with age, as does the effectiveness of the modal strategy that is used on any particular task. This is best reflected by Robert Siegler’s (1996 , 2006 ) adaptive strategy choice model . Using Darwin’s metaphor of natural selection as a guide, Siegler proposes that children generate a broad range of strategies to solve a particular class of problem and then select among those strategies. Depending on the child’s goals and the nature of the task, some strategies are selected and used frequently, whereas others that are less effective are used less often and eventually decrease in frequency (and may eventually go “extinct”). Early in development or when a child is first learning a new task, relatively simple strategies “win” most of the time. With practice and maturation, children use other, more effortful (i.e., requiring more mental effort and greater executive control) and effective strategies.

Siegler conceives of development as occurring via as a series of overlapping waves, with the pattern of those waves changing over time. Thus, extending the example of the development of addition strategies, individual preschool and early school-age children actually use multiple strategies that vary with age (older children make greater use of the more sophisticated strategies; see Siegler, 1996 ), specific problems (fact retrieval is more apt to be used on doubles, e.g., 5 + 5 = ?, Siegler & Shrager, 1984 ), and context (e.g., children used less sophisticated strategies in the context of a game using dice than when given problems in a standard format; e.g., Bjorklund & Rosenblum, 2002 ). Multiple and variable strategy use has been found for children for a wide range of tasks, including arithmetic (e.g., Alibali, 1999 ), memory (e.g., Coyle & Bjorklund, 1997 ), spelling (e.g., Kwong & Varnhagen, 2005 ), scientific reasoning ( Schauble, 1990 ), and conservation ( Church & Goldin-Meadow, 1986 ), among others (see Siegler, 1996 , 2006 ).

Children’s strategy use is influenced by a host of factors in addition to executive function, two important ones being knowledge base and metacognition . Knowledge base refers to how much children know about the problems they’re trying to solve. Children over a broad age range for a wide range of tasks use strategies more effectively when they have detailed knowledge for the to-be-processed information. The principal reason for this relationship seems to be that having an extensive knowledge base results in faster processing of information within that domain (e.g., the domain of chess, soccer, or developmental psychology), which in turn results in more efficient processing (see Bjorklund, Muir–Broaddus, & Schneider, 1990 ; Kee, 1994 ). As an example, consider a free-recall task in which children are given lists of words from different categories to remember. Some category members are typical exemplars of their category (e.g., orange, banana, pear for FRUIT), whereas others are atypical category members (e.g., raisin, melon, grapefruit ). Children are more likely to use one or more strategies and to remember more words when recalling the more familiar and more categorically integrated sets of typical items than atypical items (e.g., Best, 1993 ; Schneider, 1986 ; Schwenck, Bjorklund, & Schneider, 2007 ). In general, children’s world knowledge increases with age, and as it does their strategic performance on a host of tasks increases with it.

Metacognition refers to one’s knowledge of one’s cognitive abilities. For each type of cognition there is a corresponding type of metacognition—for example, meta-attention, metamemory, and metalinguistics. Both cognition and metacognition increase with age and are usually (but not always) correlated with one another ( Schneider & Lockl, 2002 ). When problem solving is governed by the use of goal-directed strategies, task performance is considerably enhanced by knowing how well one is doing (i.e., monitoring task performance, procedural metacognition ) and by assessing which strategies will be most effective and when (i.e., declarative metacognition ) ( Schneider & Lockl, 2002 ). This has been found for a host of cognitive domains, including scientific reasoning (e.g., Kuhn et al., 1988 ), arithmetic (e.g., Carr & Jessup, 1995 ), attention (e.g., Miller & Weiss, 1981 ), and memory (e.g., DeMarie et al., 2004 ), among others, although positive relations between cognitive and metacognitive performance are often not found until late childhood (e.g., Hasselhorn, 1990 ; Lange et al., 1990 ), unless simple tasks that involve metamemory questions that are highly related to task performance are used ( Schneider & Sodian, 1988 ). When children are provided metacognitive training, their use of strategies tends to increase, particularly for older children (e.g., Ghatala et al., 1986 ; Ringel & Springer, 1980 ).

The relationship between cognitive performance, strategies, and metacognition is a multidirectional one (e.g., Schneider & Bjorklund, 1998 ). Children’s tendency to use and be aware of the availability and effectiveness of cognitive strategies is related to their level of conceptual development, executive functioning, and familiarity with the materials and tasks. Even very young children use strategies effectively in some situations, but will fail to use them, or use them and fail to enhance task’s performance, in other situations. The latter phenomenon has been referred to as utilization deficiency ( Miller, 1990 ) and has been found for a variety of strategies, including selective attention (e.g., DeMarie-Dreblow & Miller, 1988 ), memory (e.g., Bjorklund et al., 1994 ), reading (e.g., Gaultney, 1995 ), and analogical reasoning (e.g., Muir-Broaddus, 1995 ), among many others. Children who do not use a strategy spontaneously can often be trained to do so, often with increases in task performance (e.g., Flavell, 1970 ; Gelman, 1969 ; see Harnishfeger & Bjorklund, 1990 , for review).

Like most aspects of cognitive development, it is not possible to specify a time in development when children are astrategic and a time when they become strategic. A child who fails to use a strategy on one task may do so given a slightly different context or set of instructions, or a different set of materials. Children’s cognitive functioning is influenced by a host of both endogenous and exogenous factors, and, depending on the amount and type of support children receive for performing a given task, they may display substantial cognitive competence or incompetence. Despite this variability, one can conclude with confidence that children’s problem solving becomes increasingly strategic with age; they have a broad selection of strategies to choose from, and they become more effective with age in their selection and monitoring of problem-solving strategies.

Cognitive Development Is Constructed Within a Social Context

Humans are a social species, and human development can be properly understood only when the influence of social relations and the broader social/cultural environment are considered. Development always occurs within a social context, culturally shaped and historically conditioned, although the specific details of a child’s social environment can vary widely.

This is no less true of cognitive development as it is of social and emotional development. Although the great bulk of cognitive-development research has been conducted in laboratories or quiet rooms in children’s schools, and the topics of study have often been divorced from children’s everyday lives, children’s developing cognitive skills are used to solve everyday problems, and they “learn” to think by interacting with their social environment (see Gauvain, Beebe, & Zhao, 2011 ; Cole, 2006 ; Rogoff, 2003 ; Vygotsky, 1978 ; see the chapter by Gauvain in this volume 2). Acquiring a full understanding of cognitive development requires examining both distal (e.g., evolutionary) and proximal, or immediate (e.g., role of parents, peers, neuronal development) influences. Included in both the distal and the proximal levels of causation is the social environment. First, the opportunities and tools that a culture provides will obviously have an immediate impact on children’s thinking (e.g., learning to read). But many of these tools are products of the sociohistorical context in which a culture developed. The traditions, tools, and languages spoken have deep cultural roots that can influence a child’s intellectual development.

Cultural Contexts for Learning

In all cultures, parents, teachers, siblings, and peers influence children’s cognitive development both by serving as a source of problems (much of humans’ considerable intelligence is used to deal with conspecifics) and by guiding their problem solving. Vygotsky’s (1978) concept of parents and other more cognitively sophisticated people working with children in the zone of proximal development is well known to developmental psychologists and reflects the routine interactions parents and others have with children that fosters cognitive change. Learning is most apt to occur when parents provide children with the appropriate degree of scaffolding ( Wood, Bruner, & Ross, 1976 ), giving neither too little nor too much help with a particular problem. There are cultural and individual differences in the assistance adults give children in solving daily problems, and children learn much about “how to think,” not through explicit teaching by adults, but through what Rogoff (1998 ; Rogoff et al., 1993 ) calls guided participation , “the process and system of involvement of individuals with others, as they communicate and engage in shared activities” ( Rogoff et al., 1993 , p. 6).

Different cultures (and subcultures) construct different experiences for their children, and this has consequences for both what and how children learn. For example, children living in traditional societies are more attentive to what adults do as opposed to what adults say to them, and thus develop a keener ability to learn through observation than children from schooled societies (e.g., Mejia-Arauz, Rogoff, & Paradise, 2005 ; Morelli, Rogoff, & Angelillo, 2003 ). In one study, children of traditional Mexican heritage whose mothers had only basic schooling (on average, a seventh-grade education) and children of Mexican or European background whose mothers had a high-school education or more observed a woman creating origami figures ( Mejia-Arauz et al., 2005 ). When they were later asked to make their own figures, children of traditional Mexican heritage were less likely to request information from the “Origami Lady” than children of the more educated mothers. These findings are consistent with the observations that these “traditional” children pay more attention to the actions of adults and learn more through observation, rather than seeking instructions from adults or learning through verbal instructions (see Cole, 2006 ).

Other research suggests that general cultural perspectives influence some basic aspects of cognitive development. For example, East Asian cultures are proposed to promote a holistic approach to reasoning, whereas Western cultures are hypothesized to promote a more analytic style ( Nisbett et al., 2001 ). Such differences have been hypothesized to affect how children learn to allocate their attention, with East Asians socialized to divide their attention between objects and events in their environments and Westerners socialized to focus their attention on key features of objects ( Duffy & Kitayama, 2007 ). For example, when Japanese and American adults were shown a picture of a box with a line drawn in it and then asked to draw a line in a larger box that is either of the same absolute length or the same relative length as the line in the smaller box, robust cultural differences were found: Japanese adults were more accurate performing the relative task, whereas American adults were more accurate performing the absolute task ( Kitayama et al., 2003 ). This cultural pattern is found as early as age 6 ( Duffy et al., 2009 ; Vasilyeva, Duffy, & Huttenlocher, 2007 ); however, both American and Japanese 4- and 5-year-olds made more errors on the absolute task ( Duffy et al., 2009 ), suggesting that young children from both cultures initially have an easier time dealing with relative information, but, depending on cultural practices, sometime around 6 years of age, some children (in this case, Americans) become socialized to focus their attention, whereas others (in this case, Japanese) become socialized to divide their attention.

Some everyday practices of parents in Western culture serve to prepare their children for life in a schooled and literate society. For example, children attending Western schools are frequently asked questions for which adults already know the answers. They also are asked to learn and discuss things that have no immediate relevance—knowledge for knowledge’s sake. We take such practices for granted, but such context-independent learning is foreign to many cultures, and we are mostly unaware of how evolutionarily novel formal education practices are for our species. Despite the novelty of such practices, children do not enter school totally unprepared for such experiences. For example, Western parents of young children frequently prompt them to name objects or to recall recent events (e.g., “What did we do today? Who did we see? Did you cry? Yes? Who else was there?”) ( Gauvain, 2001 ; Rogoff, 1990 ). In addition to preparing children for the type of discourse they will experience in school, such shared remembering helps children learn how to remember and communicate memories; learn about themselves, which contributes to the development of the self-concept; learn about their own social and cultural history; and learn what is worth remembering. It also promotes social solidarity ( Gauvain, 2001 ).

Sociohistorical Influences

According to Vygotsky (1978) , cultures provide the tools of intellectual adaptation that children learn to use to think and solve problems. These tools include such things as computers, alphabets, abacuses, books, number systems, music, art, and other cultural inventions specifically designed to foster learning and communication, but also more implicit devices that can influence thinking, such as the language spoken and how it represents concepts. Concerning language, something as simple as how a language expresses its numbers can affect important aspects of quantitative development. For instance, we saw earlier that differences in the time it takes to articulate the digit words (one, two, three, etc.) influences digit span (e.g., Geary et al., 1993 ). Differences in how languages name number words have also been shown to be related to aspects of mathematical development. For example, the first 10 digit words have to be memorized in all languages (e.g., one, two, three; eins, zwei, drei ; yee, uhr, shan , in English, German, and Chinese, respectively), but once the teen decade is reached languages differ in terms of how much new vocabulary must be learned and the extent to which one uses the base-10 number system for enunciating number words. For instance, in Chinese numbers from 11 to 19 follow a simple rule: the Chinese word for 10 is shi and the numbers 11 through 19 are made by taking shi and adding the appropriate digit (11 = shi yee , or “ten one”; 12 = shi uhr , or “ten two”; 13 = shi shan , or “ten three,” and so on). In contrast, many of the words denoting the numbers from 11 to 19 in English are arbitrary (e.g., eleven, twelve, thirteen, fifteen), and even for “regular” numbers, the decade name is stated second (nine teen ), unlike numbers beginning with twenty, in which the decade term is stated first (e.g., twenty-one; thirty-two). As a result of these differences, Chinese children learn to count to 20 before English-speaking children, although there are no cultural differences in learning to count to 10 and in counting to 100 once children learn to count to 20 ( Miller et al., 1995 ). Similarly, German children have difficulty when learning how to convert spoken numbers to numerals, because, in German, the decade term follows the unit term (for instance, 42 is said zweiundvierzig , or “two-and-forty”); as a result they frequently invert the order of the numerals (for example, writing “24” instead of “42”) ( Zuber et al., 2009 ).

Other cultures have a limited way of expressing quantities. For instance, the Amazonia languages of the Pirahã and Mundurukú have no number words for quantities larger than five ( Gordon, 2004 ; Pica et al., 2004 ). Adults from these cultures can perform tasks involving small quantities easily, but their performance deteriorates rapidly when attempting tasks with larger quantities. In contrast, Pirahã children who learn Portuguese are able to perform arithmetic calculations with larger quantities, bolstering the interpretation that it is the language’s ability to represent numbers that is responsible for the pattern of numerical thinking in these cultures ( Gordon, 2004 ).

Natural selection has provided humans with a unique nervous system that develops in a species-typical way in all but the most deprived environments. As such, it is easy to think of cognitive development as something that “just happens,” pretty much the same way for children worldwide. Yet intelligence is also rooted in culture, and understanding how cultural practices and technological tools influence cognitive development helps us better comprehend the process of development and our role as adults in fostering that process. Cultural “explanations” for cognitive development do not provide alternative interpretations to those based on biology (e.g., neurological factors) or specific experience (e.g., how mothers talk to their babies); rather, cognitive development must be seen as the result of interacting factors, with the social environment being a critical ingredient in this mix.

Cognitive Development: A Mature and Developing Science

Nearly 20 years ago, a colleague specializing in social development asked me what had happened to cognitive development. It used to “lead the field,” he said, providing a framework for researchers in other areas of development, but now it seemed fragmented. The field was once united behind Piaget’s theory (or united in trying to refute Piaget’s theory), and this provided a framework nearly all psychologists could use to interpret children’s behavior and development. Information-processing approaches replaced Piaget’s account as the dominant metaphor for development, but shortcomings left the field without an overarching metatheory ( Bjorklund, 1997 ).

In the years since I had this conversation, I believe the field of cognitive development has gotten back on track. In place of the theoretical hegemony afforded by Piagetian or information-processing approaches, cognitive developmentalists adopted some principles, many of them based in developmental biology, that served to unify the field. Advances in brain research make it necessary for cognitive developmentalists to provide accounts that are at least not contradictory to what is known about how the brain works and develops (e.g., Lenroot & Giedd, 2007 ; Nelson, Thomas, & de Haan, 2006 ), and in some cases that are usefully informed by neuroscience, as in the case of executive function (e.g., Zelazo et al., 2008 ); new research in genetics points to the complex and bidirectional interactions between genes, environment, and development (e.g., Caspi et al., 2007 ; Rutter, 2007 ); and research and theory in evolutionary developmental biology and psychology (e.g., Bjorklund & Pellegrini, 2002 ; Gardiner & Bjorklund, 2009 ; Ploeger, van der Maas, & Rajimakers, 2008 ; West-Eberhard, 2003 ) make it clear that our ancestors also developed and that an appreciation of human phylogeny can help us acquire a better understanding of human ontogeny without the taint of genetic determinism that was once associated with evolutionary accounts of human behavior. Some researchers are fully aware of these influences on their science, whereas for others they serve as barely noticed background, but influence their thinking nonetheless.

Cognitive developmentalists are not of one mind about development, but they never have been, even when Piaget reigned. Contemporary cognitive-developmental science recognizes the significance of both lower-level and higher-level processes to children’s thinking, that the ontogeny of cognition has both biological and social origins, and that individual differences in cognition and its development are often associated with normative, age-related changes in thought. In brief, cognitive development happens at a variety of levels, and developmental scientists are becoming increasingly aware that we need to be cognizant of this and the interactions among the various levels to produce a true developmental science.

Questions for Future Research

How are patterns of neurological and behavioral/cognitive development coordinated and related?

How does the implicit knowledge/cognition of the infant and young child relate to the subsequent development of explicit knowledge/cognition?

Why do some forms of thinking come easily to children and others do not?

How do children gain intentional control over their thinking and problem solving?

What is the nature of representational change over infancy and childhood?

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Cognitive Development Essay

Cognitive development is concerned with how thinking processes flow from childhood through adolescence to adulthood by involving mental processes such as remembrance, problem solving, and decision-making. It therefore focuses on how people perceive, think, and evaluate their world by invoking the integration of genetic and learned factors.

Hence, cognitive development mainly concentrates on “areas of information processing, intelligence, reasoning, language development, and memory” (Kendler, 1995, p.164). In essence, cognitive development theory reveals how people think and how thinking changes over time.

The basic premises of cognitive development theory

The premises of cognitive development theory largely allow future investigation to amplify, specify, and modify them according to data trends. These premises frame the theory in a way that it addresses the structure, working, and progress of the system that governs discrimination learning.

Primarily, the theory is based on observable behaviors and indirectly defined theoretical constructs. These constructs assume that psychological and neurological theorizing about cognitive development will gradually coalesce (Kendler, 1995). The premises take form of two different approaches that have been developed over the years.

The first approach postulates that thinking is a universal sequence of stages, while the second approach postulates that people process information in a similar manner computers do (Kail & Cavanaugh, 2008, p.13). One of the best-known examples of the first approach is Piaget’s theory of development that explains how children construct their knowledge, and how the format of their knowledge changes over time.

The second approach is exemplified by Information processing theory that focuses on how computers work to explain thinking and its development through childhood and adolescence.

The cognitive development theory has application in various areas such as works of Aaron Beck and Albert Ellis with the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI), both being very popular quick assessments of an individual’s functioning (Kail & Cavanaugh, 2008).

Discussion of Piaget Theory and Vygotsky Theory on Intelligence Development

The next part of this paper will be a discussion of the works of Piaget and Vygotsky, including comparison and contrast of their views on various aspects of cognitive development theory.

Jean Piaget was one of the most influential developmental psychologists of the 20 th century, who believed that children naturally make sense of their world.

Lev Vygotsky, a Russian psychologist, was one of the first theorists to emphasize that children’s thinking develops through influence of the socio-cultural context in which children grow up rather than developing in a void. Piaget observed children’s past and potential interaction with their environment as being determined by their schemas, which are modified by the processes of assimilation and accommodation.

According to Kail & Cavanaugh (2008), assimilation may be described as a process that allows a child to add “new information by incorporating it into an existing schema.” For Piaget, enhancing a balance or truce between assimilation and accommodation in the schemas definitely leads to cognitive development.

This unlike Vygotsky, whose view is that cognitive growth occurs in a socio-cultural context that influences the form it takes, for instance, a child’s most remarkable cognitive skills are shaped by social interactions with parents, teachers, and other competent partners (Shaffer & Kipp, 2009).

Thus, cognitive development is more of an apprenticeship in which children develop through working with skilled adult assistants. Both Piaget and Vygotsky held the view that children’s thinking becomes more complex as they develop, highlighting that this change is influenced by the more complex knowledge that children construct from the more complex thinking.

Stages of development in both theories

Both theorists explain cognitive development in four distinct stages, but each of them explains these stages in different aspects and perspectives. According to Piaget, cognitive development takes place in “four distinct, universal stages, each characterized by increasingly sophisticated and abstract levels of thought” (Kendler, 1995).

These stages include sensorimotor stage (infancy) that begins from birth to 2 years and is characterized infant’s knowledge being demonstrated in six sub-stages through sensory and motor skills. The second stage is pre-operational stage (2 to 6 years) during which a child learns how to use symbols such as words and numbers to represent various aspects of the world but relates to the world only through his or her perspective.

Additionally, “concrete operational stage is characterized by seven types of conservation,” with “intelligence being demonstrated through logical and systematical manipulation of symbols related to concrete objects” (Kail & Cavanaugh, 2008).

In this third stage, operational thinking develops while the egocentric thinking diminishes. Lastly, formal operational stage, which occurs in late stages of human development or old age, involves “logical use of symbols related to abstract concepts” signifying a more complex and mature way of thinking (Kail & Cavanaugh, 2008).

A departure from Piaget, Vygotsky proposed that we should evaluate development from perspective of four interrelated levels in interaction with children’s environment. These stages include ontogenetic development, which refers to development of the individual over his or her lifetime.

Secondly, Microgenetic development refers to changes that occur over brief periods such as minutes, a few days, or seconds. In addition, Phylogenetic development refers to changes over evolutionally time. Lastly, sociohistorical development refers to changes that have occurred in one’s culture and the values, norms, and technology, such as a history has generated (Shaffer & Kipp, 2009).

Classroom Application of Both Theorists’ Views

Both theorists’ views can find classroom application in trying to explain educational process. For Piaget, children learn because naturally, all children want to understand their world. According to Piaget, early children’s life up to adolescence stage presents them with an urge to explore and try to “understand the workings of both the physical and the social world” (Kail & Cavanaugh, 2008).

Whereas, Vygotsky would explain education as being shaped by cultural transmission, since the fundamental aim of all societies is to impart on their children, the basic cultural values, and skills. For example, most parents in western nations want their children to do well in their studies and obtain a college degree, as this may lead to a good job.

However, parents in African countries such as Mali want their children to learn activities such as farming, herding animals, hunting, and gathering of food, as these skills may enhance their survival in their environment. Thus, each culture provides its children with tools of intellectual adaptation that permit them to use their basic mental functions more adaptively (Shaffer & Kipp, 2009).

Piaget theory would be limited in explaining academic excellence, since it views education as a natural process, while Vygotsky would explains that as a product of cultural environment that influences a student to excel. Educationally, Piaget provided an accurate overview of how children of different ages think and asked crucial questions that drew literally, thousands of scholars to the study of cognitive development.

According to Vygotsky, children are active participants in their education, with teachers in Vygotsky’s classroom favoring a guided participation, in which they structure learning activity, as well as guiding, monitoring, and promoting cooperative learning process.

Piaget’s theory would be limited in explaining academic excellence, since it views education as a natural process, while Vygotsky would explain that as a product of cultural environment that influences a student to excel.

Educationally, Piaget provided an accurate overview of how children of different ages think, and asked crucial questions that drew literally, thousands of scholars to the study of cognitive development. In essence, these theories laid grounds for other developmental theorists to further their views or critique them, leading to other cognitive development theories.

Kail, R.V. & Cavanaugh, J.C. (2008). Human Development: A Life-Span View . OH: Cengage Learning.

Kendler, T.S. (1995). Levels of cognitive development. NJ: Routledge.

Shaffer, D.R. & Kipp, K. (2009). Developmental Psychology: Childhood and Adolescence. Eighth edition. OH: Cengage Learning.

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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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Introduction to Cognitive Development

In addition to rapid physical growth, young children also exhibit significant development of their cognitive abilities, particularly in language acquisition and in the ability to think and reason. In this section, we’ll apply Piaget’s model of development to cognitive tasks during infancy through adolescence. The ability to use language is an innately and uniquely human phenomenon, and we will examine how language develops during the early years. We will also examine bilingualism and why it is important for children to be exposed to as much language (and if the opportunity is available, multiple languages) as possible in their early life.

Early childhood is a time of pretending, blending fact and fiction, and learning to think of the world using language. As young children move away from needing to touch, feel, and hear about the world toward learning basic principles about how the world works, they hold some pretty interesting initial ideas. For example, how many of you are afraid that you are going to go down the bathtub drain? Hopefully, none of you! But a child of three might really worry about this as they sit at the front of the bathtub. A child might protest if told that something will happen “tomorrow” but be willing to accept an explanation that an event will occur “today after we sleep.” Or the young child may ask, “How long are we staying? From here to here?” while pointing to two points on a table. Concepts such as tomorrow, time, size and distance are not easy to grasp at this young age. Understanding size, time, distance, fact, and fiction are all tasks that are part of cognitive development in the preschool years.

Children in middle childhood are beginning a new experience—that of formal education. In the United States, formal education begins at a time when children are beginning to think in new and more sophisticated ways. According to Piaget, the child is entering a new stage of cognitive development where they are improving their logical skills. Educational opportunities do not end with graduation from high school, and we will discuss how the goals and experience of education change from childhood mandatory education to returning to college among middle or older adults.

In adolescence, changes in the brain interact with experience, knowledge, and social demands and produce rapid cognitive growth. The changes in how adolescents think, reason, and understand can be even more dramatic than their obvious physical changes. This stage of cognitive development, termed by Piaget as the formal operational stage, marks a movement from the ability to think and reason logically only about concrete, visible events to an ability to also think logically about abstract concepts. Adolescents are now able to analyze situations logically in terms of cause and effect and to entertain hypothetical situations and entertain what-if possibilities about the world. This higher-level thinking allows them to think about the future, evaluate alternatives, and set personal goals. Although there are marked individual differences in cognitive development among teens, these new capacities allow adolescents to engage in the kind of introspection and mature decision making that was previously beyond their cognitive capacity. For example, while moral development begins far before adolescence, these new cognitive  skills allow adolescents to consider the implications of moral decision-making and morality.

We will also discuss changes in adult cognition. Does cognitive development end after adolescence? According to Piaget, it does. However, other theories and research suggest otherwise, and we will explore some changes that might occur beyond the formal operational stage. Adults experience changes in their memories as they age, but not all these changes are undesirable; older adults have some cognitive advantages when it comes to memory and decision-making in everyday tasks.

One of the ways in which individual define themselves in through their occupation. This section will also discuss some of the factors that influence our decisions about work and the role that work plays in many adults’ lives. Our work is the product of many factors, including societal and cultural expectations, educational opportunities, and sense of identity. This discussion of work will encompass many of the cognitive concepts discussed in this section and will prepare us to move into the realm of psychosocial development in the following section.

• Lifespan Development  by Nicole Arduini-Van Hoose is licensed under a  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Lifespan Human Development: A Topical Approach Copyright © by Meredith Palm is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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ORIGINAL RESEARCH article

Determinants of cognitive development in the early life of children in bhaktapur, nepal.

• 1 Child Health Research Project, Department of Pediatrics, Tribhuvan University Teaching Hospital, Kathmandu, Nepal
• 2 Department of Psychosocial Science, Faculty of Psychology, University of Bergen, Bergen, Norway
• 3 Regional Center for Child and Youth Mental Health and Child Welfare, NORCE Norwegian Research Centre AS, Bergen, Norway
• 4 Department of Community Medicine, Kathmandu Medical College, Kathmandu, Nepal
• 5 Department of Research, Innlandet Hospital Trust, Lillehammer, Norway
• 6 Centre for International Health, University of Bergen, Bergen, Norway

Background: Children in low and middle income countries may have many risk factors for poor cognitive development, and are accordingly at a high risk of not reaching their developmental potential. Determinants for cognitive development in early life can be found among biological and socioeconomic factors, as well as in stimulation and learning opportunities.

Objective: The present study aimed to identify determinants of cognitive, language and motor development in 6–11 months old Nepalese infants.

Methods: Six hundred infants with a length-for-age z -score <-1 were assessed with the Bayley Scales of Infant and Toddler development, 3rd edition (Bayley-III). Information on socioeconomic factors, child and maternal demographics, clinical and biological factors, and the home environment were collected. In a manual stepwise variable selection procedure, we examined the association between selected biological, socioeconomic and stimulation and learning opportunity variables and the Bayley-III cognitive, language and motor development subscale scores in multiple linear regression models.

Results: The length-for-age z -scores was positively associated with the cognitive composite score [standardized beta (ß): 0.22, p < 0.001] and the motor composite score [(ß): 0.14, p = 0.001]. Children born with low birth weight (<2500 g) scored significantly lower on all subscale scores. Diarrheal history was associated with poor language composite scores, and females had higher language composite scores than boys [(ß): 0.11, p = 0.015]. Children who had been hospitalized during the first month of life had also lower cognitive and motor composite scores than those who had not been hospitalized. Parental reports of physical punishment and lack of spontaneous vocalization were associated with poor cognitive and language composite scores, respectively. The statistical models with the various subscale scores as dependent variables explained between 8 to 16 percent of the variability in the cognitive developmental outcomes.

Conclusion: Our findings reveal important determinants for developmental scores in infancy, and underline the role of biological risk factors faced by marginalized children in low and middle income countries such as in Nepal.

Introduction

Children in low and middle income countries (LMIC) are at risk of not developing according to their potential, and this represents a major public health problem ( McDonald and Rennie, 2011 ). The South Asian and sub-Saharan African regions have multiple poverty related risks such as malnutrition, poor health and poor quality of stimulation and learning environment for many children ( Grantham-McGregor et al., 2007 ). In these settings, identifying the predictors for early child development will help in initiating early intervention plans to prevent developmental delays ( Persha et al., 2007 ). Known biological risk factors for poor cognitive function that are common in LMICs include short gestational duration ( Gutbrod et al., 2000 ; Espel et al., 2014 ), low birth weight ( Tong et al., 2006 ; Gill et al., 2013 ; Donald et al., 2019 ; Sania et al., 2019 ; Upadhyay et al., 2019 ), anemia ( Sungthong et al., 2002 ) and stunting ( Haile et al., 2016 ; Woldehanna et al., 2017 ). Poor nutrition, one of the causes of stunting ( De Onis and Branca, 2016 ), has crucial impact on the growth and development of the brain and later cognitive functioning ( Georgieff, 2007 ; Georgieff et al., 2018 ). Early childhood illnesses like diarrhea have also shown to predict development in high risk children ( Niehaus et al., 2002 ; Lorntz et al., 2006 ; Kvestad et al., 2015 ). There is also evidence that longer duration of breastfeeding enhances cognitive and language development in infants ( Lee et al., 2016 ).

Indicators of socioeconomic status including economic conditions ( Duc, 2009 ; Ribe et al., 2018 ) and parental education ( Roberts et al., 1999 ; Duc, 2009 ) has consistently been associated with cognitive functioning ( Christensen et al., 2014 ; Ribe et al., 2018 ). Adequate responsive stimulation during the first years of life is also crucial for children to reach their developmental potential ( Yousafzai et al., 2016 ; Nguyen et al., 2018 ).

The first 1,000 days, lasting from conception to the end of the second year of early childhood, is a particularly important period for cognitive development ( Bellieni, 2016 ). During this period, minor impairments of brain because of biological and psychosocial factors can affect the structural and functional development of the brain ( Walker et al., 2011 ).

The current study is conducted in a low and middle income setting with multiple risks factors that might affect child development. We assessed the development of the children and collected information of potential predictors for child development such as biological, socioeconomic, stimulation, and learning opportunities for the children. The main aim of this paper is to identify the determinants of cognitive, motor and language development assessed with the Bayley-III in these Nepalese infants at 6–11 months old.

Materials and Methods

Study design, setting, and population.

The children were participants in a doubled blinded clinical trial entitled “The effect of Vitamin B12 supplementation in Nepali Infants on Growth and Development” ( Strand et al., 2017 ) ( ClinicalTrials.gov : NCT02272842). The study site is the Bhaktapur municipality and surrounding areas of Bhaktapur district in Nepal. We included 600 children aged 6–11 months who were at increased risk of stunting [length for age z -score (LAZ)<-1SD], who plan to reside in the area for the next 12 months and whose parents consented to participate. Children with severe illness requiring hospitalization, severe malnutrition (weight-for-length z -score<-3SD) and with severe anemia (Hb<7 g/dL) were excluded from the study. Those with ongoing acute infections such as fever or infection that required medical treatment were temporarily excluded and enrolled after recovery.

Enrollment and baseline assessments including Bayley test were done from April 2015 to February 2017. The children were identified by field staff from immunization clinics or through door-to-door home visits, and enrolled when their length was confirmed by a supervisor or a physician at the field office. Enrollment procedures included collection of demographic information of the families, length and weight taking, blood sampling and developmental assessments at the same day. After enrollment, the date of the home visit for the home inventory assessment was scheduled with the mother within 1-week. Ethical clearances was obtained from the National Health and Research Council (NHRC; No. 233/2014) in Nepal and from the Regional Committee for Medical and Health Research Ethics (REC; No. 2014/1528) in Norway.

Cognitive, Language and Motor Development

The cognitive, language and motor development at baseline were assessed using the Bayley-III ( Bayley, 2006a ) This is a comprehensive assessment tool of developmental functioning in infants and toddlers aged 1–42 months, takes 40 to 60 min to administer and includes three main subscales; cognitive, language (receptive and expressive communication) and motor (fine and gross motor). The Bayley-III represents the gold standard in developmental assessment of this age group and is widely used for research purposes worldwide. We have used the American norms from a representative American sample ( Bayley, 2006b ). The raw scores of each subscales were converted into scaled scores with a mean of 10 (SD: 3) and a range from 1 to 19 and again converted to the three composite scores with a mean of 100 and standard deviation of 15. The scales for the current study was initially adopted for a study in the same population for children 6 to 24 months ( Murray-Kolb et al., 2014 ), and found to be reliable and feasible in children between 6–11 months in the same study setting ( Ranjitkar et al., 2018 ).

Determinants

Baseline information.

Baseline information was collected within a week from the date of enrollment. The information included family socioeconomic factors, child and maternal demographics, clinical and biological factors and the stimulation and learning opportunities in home environment. At enrollment, length and weight of the child and the mother along with head circumference of the child, was measured by well trained field staffs at the clinic following standard guidelines. Birth weight was recorded according to the parental report. Similarly, blood samples were collected from all the children. Details of the study procedure have been published elsewhere ( Strand et al., 2017 ).

Home Environment

The Home Observation for Measurement of the Environment (HOME Inventory) ( Caldwell and Bradley, 1984 ) is a structured assessment of the home environment that are indicators of stimulation and learning environment of the children. It is performed by a combination of direct observation and an interview with the mother or caregiver of the child at home by trained field staffs. We used selected items from a Bangladeshi adapted version of the Home Inventory that have been found to be a feasible tool in the same population in Nepal ( Jones et al., 2017 ). In the current study, the structured assessment took approximately 20 min to complete with altogether 16 selected items from the original version of HOME Inventory including two items from the “Emotional and verbal responsivity” factor, two items from the ‘Avoidance of restriction and punishment’ factor, four items from the “Caregiver promotes child development” factor, two items from the “Organization of physical and temporal environment”, three items from the “Provision of appropriate play materials”, and three items from the “Opportunities for variety in daily stimulation” ( Table 1 ).

Table 1. Variables assessed in multivariable regression models that measured the association with cognitive, language and motor composite scores of Bayley-III in 600 Nepalese children aged 6–11 months.

Training and Quality Control

Before the start of the study, psychologists responsible for assessing children in the study were trained and standardized in the use of the Bayley-III. A well experienced local psychologist served as “gold standard” during training and throughout the study period, and the study psychologists were required to achieve a high inter-rater agreement (ICC > 0.90) before testing study children. Seven percent of all sessions were scored by two examiners for quality assurances for Bayley with ICC’s ranging from 0.97–1.00 showing excellent inter-rater agreement. All the assessments were video recorded for further check ups when required, and for feedback from the supervising psychologists to the assessors. Any particular issues or challenges with the testing were discussed on weekly Skype-meetings with the supervising team from Norway (IK, MH) during the study period.

For the HOME Inventory, the field staff were trained and validated against a “gold standard” before the study start and they were required to achieve a good inter-rater agreement (ICC = 0.74). Seven percent of all assessments were double scored by the psychologist for quality assurance giving an ICC of 0.88.

Growth measurements and other major activities were also standardized before the study and 5% double scoring were carried out by supervisors during the study period.

Statistical Analyses

Demographic characteristics are presented as numbers (N) and percentages (%), and by means and standard deviations (SD). We used multiple linear regression models to identify determinants of the Bayley-III scores. In these models, the composite scores of the cognitive, motor and language scales of the Bayley-III were used as dependent variables and the variables listed in Table 1 were considered for inclusion in the statistical models.

For the caste variable, we set Newar caste as the reference group, and categorized the remaining in three groups; the Brahmin/Chhetri, Tamang and “Others”. Hospitalizations during the first month of life and history of diarrheal episodes prior to enrolment were dichotomized as “Yes” or “No”. Alcohol consumption by father was dichotomized as “Yes” or “No”. We introduced all the items of the HOME Inventory separately as independent variables.

Variables for the statistical analyses were carefully selected in a manual, stepwise forward procedure as suggested by Hosmer and Lemeshow (Applied logistic regression, Second Edition). In short, the association between each candidate independent variable ( Table 1 ) with the selected outcomes were initially assessed in unadjusted models. Variables that were significant at a P < 0.2 level were kept in multiple models while those that were non-significant in the initial crude assessment were re-introduced one at a time into these multiple models. Only variables that remained significant after this process were kept in the final models that are presented in the paper. This manual stepwise procedure was repeated for each composite score; cognitive, language and motor development. The statistical analyses were performed in STATA version 15 (STATA, College Station, TX, United States).

The mean age of the children was 8 months (SD: 1.7), 309 (51.5%) were male and 62 (10.4%) were born preterm. Approximately 28% of the study children had a history of diarrhea 1 month prior to enrollment and 9% were hospitalized mainly related to their low birth weight or because of jaundice during the first month of life. The mean age of the mothers was 27 (SD: 4) years. Of the mothers, 37% were illiterate or had an educational level up to grade 5. Nearly 70% of the children belonged to the Newar ethnic group. Approximately 52% of the families resided in their own house, and 47% of the families had their own land ( Table 2 ). The mean composite scores of cognitive and motor subscales were close to the American norms while the language scale was 1 SD lower than the norms ( Table 3 ).

Table 2. Baseline information of 600 participant Nepalese infants.

Table 3. Bayley-III composite scores from children 6–11 months of age residing in Bhaktapur, Nepal.

Determinants of the Cognitive Composite Score

The cognitive composite score was positively associated with the length-for-age z -score. Children who were born with low birth weight (<2500 gm) had 5 points lower cognitive composite scores compared to children with birth weight in the normal range. Those who had been hospitalized during the first month of life had an average 4.7 points lower scores compared to those with no such history. A history of alcohol consumption in the father and reports of physical punishment during the past week were also predictors for the cognitive score ( Table 4 ).

Table 4. Linear regression analysis to identify determinants of the Bayley-III cognitive composite score in Nepalese children 6–11 months.

Determinants of the Language Composite Score

Female children had significantly higher language composite scores than male children. Low birth weight and head circumference were significantly associated with lower language scores. Those who had a history of diarrhea 1 month prior to enrollment had 2 points lower language composite scores than those who did not. Tamang and other castes had lower language scores than those belonging to the Newar caste. Children whose mother or caregiver did not show spontaneous vocalization to the child during the home observation had significantly lower scores than those who had mothers or caregivers that showed such stimulation ( Table 5 ).

Table 5. Linear regression analysis to identify determinants of the Bayley-III language composite score in Nepalese children 6–11 months.

Determinants of the Motor Composite Score

The motor composite score was associated with the length-for-age z -score. Other predictors for the motor composite scores were being born with low birth weight, hospitalization during the first month of life and family ownership of house ( Table 6 ).

Table 6. Linear regression analysis to identify determinants of the Bayley-III motor composite score in Nepalese children 6–11 months.

In a high risk sample of young Nepalese children, biological, socioeconomic and home environment factors were associated with cognitive, language and motor development. The assessed risk factors are common in most LMIC. Approximately one third of our participating children were stunted (length-for-age z -score ≤ 2) and approximately 10 percent were born preterm. Length-for-age z -score and low birth weight were the strongest predictors of the cognitive subscale. For the motor subscale, hospitalizations at 1 month of life and length-for-age z -score were the strongest determinants. Newar caste was set as reference group among the castes and caste was the strongest determinant for the language subscale. The cognitive score was associated mainly with the biological factors including growth and hospitalization along with a slightly significant effect of alcohol consumption by the father and reported physical punishment during the past week, while the language domain was associated with both biological and language stimulation and the motor composite was associated with both biological and socioeconomic factors. All models explained 8 to 16 percent variability for the Bayley-III subscales.

Biological Determinants of the Developmental Outcomes

Biological risk factors were consistently associated with all the assessed developmental domains. For instance linear growth was associated with both cognitive and motor development in line with studies that have shown that stunting is one of the main factors of poor child development ( Sudfeld et al., 2015 ; Miller et al., 2016 ). Stunting is one of the most used indicators for denoting malnutrition in early childhood ( Perumal et al., 2018 ) and there are several evidences of cognitive impairment because of malnutrition ( Nyaradi et al., 2013 ). Our findings supports the frequent use of length-for-age as a proxy for neurodevelopment.

Low birth weight was significantly associated with all subscales of the Bayley-III, in line with the known risk of low birth weight for neurodevelopmental outcomes ( Aarnoudse-Moens et al., 2009 ; Oudgenoeg-Paz et al., 2017 ). Our study children showed lower scores on language development with decrease in head circumference. Head growth is consistently associated with cognitive development in previous studies ( Gale et al., 2004 ; Silva et al., 2006 ). For instance, head circumference was a strong predictor of the Bayley scores at all eight sites of Mal-Ed study in which Nepal was one of the site ( Scharf et al., 2018 ), however, some studies contradict to the consistencies of results associated with head circumference and cognitive development ( Martyn et al., 1996 ; Dupont et al., 2018 ). We expected head circumference to show a more overall association across domains in the current study, there was, however, a lack of such associations. The slight differences between studies may be due to differences in study design, sample sizes, age at testing as well as inclusion of other variables.

Twenty-eight percent of the study children has a diarrheal history 1 month prior to enrollment. Both hospitalization and diarrheal history was associated with the cognitive and motor subscales which is in line with previous study results in North India showing decreased neurodevelopmental scores in children with diarrheal history ( Kvestad et al., 2015 ). There may be both direct and indirect pathways from these biological risks and the adverse development. For instance infants who are infected with enteric diseases such as diarrhea during the golden thousand days are affected in the absorptive function of a healthy intestinal tract that is critical for the optimal growth and development of the body and brain ( Petri et al., 2008 ). The consequences of diarrhea and hospitalization may also be mediated by what has been refered to as “Functional isolation” ( Lozoff et al., 1998 ). As a result from the biological conditions, infants may be characterized by irritable and apathetic behavior, and are at risk for receiving less quality responsive care from its caregiver ( Kvestad et al., 2015 ). Thus, biological risks may indirectly limit the stimulation received from the physical and social environment.

Female children have significantly higher language scores than males in the present study. Others have described that female children had better language development than males, especially in communication gestures and vocabulary development ( Eriksson et al., 2012 ). A meta analysis on parent child language interactions showed that mothers talk more to their daughters than their sons ( Leaper et al., 1998 ). Thus, gender differences in communication between parents and children might explain this result.

Socioeconomic Determinants of the Developmental Outcomes

In our results, ownership of house is a predictor for motor development. Houses can be a reflection of economic status of families in a Nepalese context ( Subba et al., 2014 ), and hence, to own a house is one of the most important indicators of socioeconomic status in this setting.

Compared to children in the Newar ethnic group, children from the Tamang group had lower scores on language development. The low score on language development in the Tamang and other castes compared to the Newar can also be verified in relation to differences in the socioeconomic status between these groups. In the study area, the Tamang group are mainly migrated people from neighboring districts. The economic basis for this group is mainly agriculture and other labor work like work in carpet factories ( Ghimire, 2014 ). A study of socioeconomic status of indigenous people in Nepal revealed that Newars have relatively better socioeconomic conditions than other indigenous group including Tamangs ( Subba et al., 2014 ) which may explain the differences we see in language development in our study. The observed differences between the ethnic groups can also be due to variability in communication habits between the groups ( Leonard et al., 2009 ).

The locality of our study setting is rich in cultural activities, and alcohol consumption is very common in this setting ( Maharjan and Magar, 2017 ). In our analysis, father’s alcohol consumption is associated with lower scores on the cognitive subscale. Alcohol consumption in parents have been shown to be related to lower cognitive achievements in children ( Bennett et al., 1988 ; Nordberg et al., 1994 ).

Stimulation and Learning Opportunities as Determinants of the Developmental Outcomes

Of the 16 items from the HOME Inventory that were included in the analyses, only two were significantly related to the Bayley-III scores in these young Nepalese children. Children whose caregiver reported physical punishment during the past week had lower scores on the cognitive subscales. This is in line with studies conducted in the United States, where physical punishment such as spanking predicted lower cognitive scores ( Straus and Paschall, 2009 ; MacKenzie et al., 2013 ). The children whose caregiver did not vocalize spontaneously to the child scored significantly lower on the language subscale. This may be understood in light of the findings in a previous study in the same setting, that showed lack of awareness amongst Nepalese mothers about the importance of interacting with their children ( Shrestha et al., 2019 ). Our results thus confirms the importance of early parent-child communication for early language development especially in vocabulary development ( Topping et al., 2013 ).

A wide range of stimulation and learning opportunities were included, and with two exceptions, physical punishment and caregiver vocalization, none of them were associated with cognitive development. It may be that in this high-risk group at this early age biological risk factors have the largest immediate impact on the childrens development.

The large sample of 600 children is one of the main strengths of this study. The Bayley-III with cultural adaptations have already been tested in the same population and found to be a promising tool in this setting ( Murray-Kolb et al., 2014 ; Pendergast et al., 2018 ; Ranjitkar et al., 2018 ). The study was further strengthened by standardization practices before the assessments ( Ranjitkar et al., 2018 ), and double scorings with the gold standard during the study period to maintain the quality of the data and prevent the examiners drift. The standardized and reliable measurement of predictors including stimulation and learning opportunities and LAZ is also a strength.

Limitations

The sample is a high-risk sample that is part of a clinical trial, and thus, it is not a population-based sample, and care should be taken before generalizing to the population as a whole. A comparison with a typically developing group of Nepalese children would have given additional insight into predictors of development, but was beyond the scope of the present study. One of our inclusion criteria was a LAZ<-1, which reduces the variability and may potentially alter the association between LAZ scores and Bayley scores. We believe, however, that we captured the LAZ-range where there is a linear relation between LAZ and Bayley-scores. This assumption is supported by findings from a study in young children in India where there was a linear association between LAZ and Ages and Stages Questionnaire (ASQ) scores up to -1 LAZ but not beyond that ( Kvestad et al., 2015 ). Birth weight of the children and diahreal episodes were recorded based on the parental reports.

Although our result showed that both biological and social factors were associated with developmental scores of these children, our study underline the role of biological factors faced by marginalized children in low and middle income countries such as in Nepal. Early intervention programs should be encouraged for overall development of children in LMIC setting.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the National Health and Research Council (NHRC; No. 233/2014) in Nepal and Regional Committee for Medical and Health Research Ethics (REC; No. 2014/1528) in Norway. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

TS, MH, IK, and RC designed the study. RC, MU, SR, JS, RS, MS, and LS conducted the research and were responsible for the field implementation and data collection. TS and SR analyzed the data and interpreted the results. SR, MH, IK, and TS had primary responsibility for the final content. All the authors read and approved the final version of the manuscript.

This work was supported by the Thrasher Research Fund (award 11512) and GC Rieber Funds.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We acknowledge the valuable work of all the staff at the Child Health Research Project. We also thank Ravi Prakash Upadhyaya for valuable suggestion in the analysis of the data, Siddhi Memorial Foundation for the collaboration in the conduct of the study, and all the families and their children in our study for their valuable participation.

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Keywords : cognitive development, Bayley scales of infant and toddler development, biological factors, socioeconomic factors, environmental stimulation, manual stepwise procedure

Citation: Ranjitkar S, Hysing M, Kvestad I, Shrestha M, Ulak M, Shilpakar JS, Sintakala R, Chandyo RK, Shrestha L and Strand TA (2019) Determinants of Cognitive Development in the Early Life of Children in Bhaktapur, Nepal. Front. Psychol. 10:2739. doi: 10.3389/fpsyg.2019.02739

Received: 11 September 2019; Accepted: 20 November 2019; Published: 06 December 2019.

Reviewed by:

Copyright © 2019 Ranjitkar, Hysing, Kvestad, Shrestha, Ulak, Shilpakar, Sintakala, Chandyo, Shrestha and Strand. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tor A. Strand, [email protected]

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• Research article
• Open access
• Published: 16 April 2020

Psychosocial and environmental determinants of child cognitive development in rural south africa and tanzania: findings from the mal-ed cohort

• Fabrizio Drago 1 ,
• Rebecca J. Scharf 2 ,
• Angelina Maphula 3 ,
• Emanuel Nyathi 4 ,
• Tjale C. Mahopo 5 ,
• Erling Svensen 6 ,
• Estomih Mduma 7 ,
• Pascal Bessong 8 &
• Elizabeth T. Rogawski McQuade 9  

BMC Public Health volume  20 , Article number:  505 ( 2020 ) Cite this article

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Approximately 66% of children under the age of 5 in Sub-Saharan African countries do not reach their full cognitive potential, the highest percentage in the world. Because the majority of studies investigating child cognitive development have been conducted in high-income countries (HICs), there is limited knowledge regarding the determinants of child development in low- and middle-income countries (LMICs).

This analysis includes 401 mother-child dyads from the South Africa and Tanzania sites of the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) longitudinal birth cohort study. We investigated the effect of psychosocial and environmental determinants on child cognitive development measured by the Wechsler Preschool Primary Scales of Intelligence (WPPSI) at 5 years of age using multivariable linear regression.

Socioeconomic status was most strongly associated with child cognitive development (WPSSI Score Difference (SD):14.27, 95% CI:1.96, 26.59). Modest associations between the organization of the home environment and its opportunities for cognitive stimulation and child cognitive development were also found (SD: 3.08, 95% CI: 0.65, 5.52 and SD: 3.18, 95% CI: 0.59, 5.76, respectively).

This study shows a stronger association with child cognitive development at 5 years of age for socioeconomic status compared to more proximal measures of psychosocial and environmental determinants. A better understanding of the role of these factors is needed to inform interventions aiming to alleviate the burden of compromised cognitive development for children in LMICs.

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Approximately 66% of children under the age of 5 in Sub-Saharan African countries do not reach their full cognitive potential, the highest percentage in the world [ 1 ]. Children who do not fully develop to the level of cognitive development that would be expected in an optimal environment are less likely to enroll in and complete primary school [ 2 , 3 , 4 ]. These educational disadvantages can have lasting effects and are associated with adverse outcomes in adult life, e.g., lower incomes, high fertility rates, and suboptimal care for their own children [ 2 , 5 ]. Because of its lifelong ramifications, delayed cognitive development contributes to intergenerational transmission of poverty and can thus have broader consequences for the economic development of low- and middle-income countries (LMICs) [ 2 ].

Children’s cognitive development is affected by several types of factors including: (1) biological (e.g., child birth weight, nutrition, and infectious diseases) [ 6 , 7 ], (2) socio-economic (e.g., parental assets, income, and education) [ 8 ], (3) environmental (e.g., home environment, provision of appropriate play material, and access to healthcare) [ 6 ], and (4) psychosocial (e.g., parental mental health, parent-child interactions, cognitive stimulation, and learning opportunities) [ 9 , 10 , 11 ]. Household environments are the context within which a significant part of children’s development occurs. Studies show that there is a positive association between a nurturing home and optimal learning environment and children’s health and development [ 12 , 13 , 14 ].

Trials among children exposed to adverse household conditions have shown that early childhood parenting interventions can improve children’s cognitive development, educational achievements, and mental health outcomes [ 15 ]. Similar studies also have shown that cognitive development is associated with adult wage earning and financial growth in the subsequent generation [ 3 , 16 ]. For example, a study conducted in Uganda by Singla et al. showed that children of parents who were given a parenting intervention presented higher cognitive and language scores (measured through the Bayley Scales of Infant and Toddler Development) compared to the control group [ 14 ]. The study also showed that mothers in the intervention group reported significantly lower depressive symptoms post-intervention [ 14 ]. This is relevant because maternal psychosocial problems can have an effect on neonatal outcomes, including cognitive development [ 17 ]. These findings provide evidence that interventions in early childhood to develop a nurturing household environment can attenuate the negative long-term effects of delayed cognitive development.

Determinants of developmental delay (e.g., maternal depression, lower socioeconomic status, and malnutrition) are more prevalent in LMICs than in high-income countries (HICs) [ 18 , 19 ]. Despite the higher prevalence of these determinants in LMICs, the ramifications of some of these factors have not been well studied in LMIC settings and findings from HICs may not be generalizable to LMIC populations [ 18 , 19 ]. Studies that have explored the determinants of early child development in LMICs have mostly focused on biological factors, enteropathogen infections [ 20 ], the validity of measuring scales [ 21 ], and child growth [ 22 ]. The limited research on the effects of non-biological determinants of child cognitive development has explored early infant cognitive outcomes at two or three years of age [ 3 , 14 , 23 ]. Trials investigating cognitive outcomes at later stages of childhood have focused on either fluid reasoning or verbal development and have used data from several different LMICs [ 24 , 25 ]. Therefore, although studies have hypothesized long lasting effects of environmental and psychosocial factors on child outcomes, few have measured the site-specific impact of such factors at older ages [ 26 ].

This study investigates the effect of psychosocial and environmental determinants on child cognitive development at 5 years of age in rural South Africa and Tanzania. Understanding which geographically specific psychosocial and environmental factors have an impact on child cognitive development can inform further interventions aiming to alleviate the burden of compromised cognitive development for children in LMICs.

Study Design and Data

This analysis includes data from the Venda, South Africa and Haydom, Tanzania sites of the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study [ 27 , 28 , 29 ]. This study was a multi-disciplinary prospective community-based birth cohort study in eight global sites (Bangladesh, Brazil, India, Nepal, Peru, Pakistan, South Africa, and Tanzania). From November 2009 to February 2017, mother and child dyads were enrolled shortly after birth and followed until 5 years of child age. The MAL-ED study design and description of the study sites has been extensively described elsewhere [ 27 , 28 , 29 , 30 ].

Participants

A total of 576 pregnant women over a period of two years were enrolled in the South African (SA) and Tanzanian (TZ) sites. Each site was responsible for enrolling and following the cohort of children. Exclusion criteria were (1) family’s intention to move outside the area in the next 6 months, (2) mother’s age (< 16 years), (3) twin pregnancy, (4) underweight infant (< 1.5 kg), (5) presence of diagnosable congenital disease or severe neonatal disease, and (6) sibling’s enrollment in the study. For the present analysis, only children with cognitive development scores at 5 years of age were included in the analysis (N = 230 for SA; N = 171 for TZ).

Data and definitions

The main outcome of interest was child cognitive development at 5 years (±30 days) of age. Cognitive development was assessed using the Wechsler Preschool Primary Scales of Intelligence (WPPSI). This clinical tool assesses cognitive function by testing children on six subscales (Block Design, Information, Matrix Reasoning, Picture Concepts, Word Reasoning, and Vocabulary). The WPPSI measures progress and functioning in areas such as problem-solving, thinking processes, and decision-making skills. Some items in the WPPSI were adapted to account for cultural differences and to reduce the potential for the test to be culturally bias (e.g., in the information subscale, shower was changed to bath or bucket) [ 31 ].

Because the WPPSI provides both subtest and composite scores, the outcomes of interest were treated as three continuous scores representing the children’s: (1) general cognitive development and functioning (Full Scale IQ), (2) verbal reasoning and comprehension and attention to verbal stimuli (Verbal IQ), and (3) fluid reasoning, spatial processing, and visual-motor integration (Performance IQ). In comparing these three outcomes, we assessed the role that psychosocial and environmental factors play not only on the overall child development but also in specific functioning domains (i.e., verbal and performance).

Maternal depression was assessed using the self-reporting questionnaire (SRQ-20) at 1, 6, 12, 24, 36, and 60 (±30 days) months of child age. The SRQ-20 consists of 20 dichotomously coded items. We used a reduced version of SRQ-20 (SRQ-16) for this analysis because it excludes items reflecting somatic symptoms and has been used previously in the MAL-ED cohort [ 32 ]. To distinguish between the effects of exposure to postpartum depression and prolonged exposure to depressive symptoms, we assessed (1) a measure of post-partum depressive symptoms defined by the average SRQ-16 scores at 1, 6, and 12 months of child age, (2) one measure of maternal depressive symptoms defined by the average SRQ-16 scores at 24 and 36 months of child age, and (3) one measure of maternal depressive symptoms defined by the SRQ-16 score at 60 months, or 5 years, of child age.

Socioeconomic status was assessed through the WAMI index (Water, Assets, Maternal Education and Income) [ 33 ]. This measure of household socioeconomic status includes: (1) access to improved water and sanitation, (2) wealth measured by ownership of a set of eight assets, (3) maternal education, and (4) monthly household income. This index has been standardized and validated across the eight MAL-ED study sites [ 33 ].

This study assessed environmental factors that may impact child development (i.e., organization of the environment, provision of play material, opportunities for stimulation, and cleanliness of the child) through the Home Observation for the Measurement of the Environment (HOME) tool [ 34 ]. This tool was also used to measure some psychosocial factors (i.e., responsivity of the caregiver, avoidance of restrictions and punishment, and promotion of child development). This assessment tool has been used in studies worldwide [ 35 , 36 ]. Furthermore, it was adapted and validated across the eight international sites of the MAL-ED study [ 21 ]. The HOME variable was measured at 6, 24, and 36 (±15 days) months of child age. HOME assessments at each of the three points in time were averaged and coded dichotomously at the overall median (i.e., for both sites together). The organization of the environment (SA median [IQR]: 11.0 [10.3,11.5]; TZ median [IQR]: 4.3 [3.3, 5.5] and maternal education (SA median: [IQR]: 10.5 [9.0, 12.0]; TZ median [IQR]: 7.0, [3.0, 7.0] were coded dichotomously at the site-specific medians due to non-overlapping distributions of these variables across the two sites.

Following MAL-ED procedures, children were weighed and measured at enrollment. Weight at enrollment was converted to weight-for-age Z-scores (WAZ) following the WHO 2006 growth standards [ 37 ]. We used enrollment WAZ as a proxy for birthweight in the analysis because weight at birth was missing for some children and because age at enrollment varied from 0-17 days. Additionally, we conducted homogeneity tests to identify significant differences in associations between the two sites.

Data analysis

We selected covariates based on a directed acyclic graph [ 38 ]. We used multivariable linear regression for the continuous WPPSI outcomes using SAS version 9.4. The model included (1) environmental factors (organization of the environment, provision of play material, opportunities for stimulation, cleanliness of the child, and WAMI index for socioeconomic status), (2) psychosocial factors (responsivity of the caregiver, avoidance of punishment, maternal depressive symptoms, and maternal education), (3) child birthweight, and (4) indicators for the fieldworker who collected the data on the home environment (HOME field assessors). We included the HOME field assessor as a covariate because the assessor was significantly associated with both the HOME inventory scale measurements and the WPSSI outcomes.

Ethical approval

We obtained ethical approval from the Institutional Review Boards for the original and follow-up studies at the University of Venda (Limpopo, South Africa), at the Haydom Lutheran Hospital (Haydom, Tanzania), and the University of Virginia School of Medicine (Charlottesville, United States).

In this analysis, we included 401 (69.6%) children who had WPSSI scores at 5 years of age. Children were 50.6% female, had an average weight of 3.18 kg at birth, and 7.7% of them had a bodyweight of 2.50 kg or less at enrollment. Approximately 60% of mothers were married and 53.1% of them had fewer than 8.5 years of education. Almost a quarter of mothers presented with depressive symptoms postpartum (n= 91, 22.8%), at 24 and 36 months of child age (n=95, 24.8%), or at 60 months of child age (n=68, 17.3%) (Table 1 ). Women who presented depressive symptoms had relatively few symptoms, with only approximately 1% of them presenting 8 or more depressive symptoms on a 0-16 point scale. Children who did not have WPPSI measured and were not included in the analysis presented similar baseline characteristics. Baseline characteristics were also similar between the two sites with the exception of maternal education and opportunities for stimulation from the HOME index (Table 1 ).

In the multivariable regression analysis including both sites, the WAMI index had the largest effect on cognitive development and was strongly associated with full scale IQ (Score Difference (SD):14.27, 95% CI:1.96, 26.59) and performance IQ. Opportunities for stimulation in the home environment were also associated with full scale IQ (SD: 3.18, 95% CI: 0.59, 5.76) and performance IQ. However, the WAMI index and opportunities for stimulation had smaller associations with verbal IQ in this cohort. The organization of the home environment was associated with full scale IQ (SD: 3.08, 95% CI: 0.65, 5.52) and was more associated with verbal IQ compared to performance IQ. Provision of appropriate play materials was associated with performance IQ. No maternal factors or other environmental factors measured by the HOME assessment in this study were associated with any of the three WPSSI outcomes (Table 2 ).

In the South African site, organization of the environment, opportunities for stimulation, and the WAMI index were associated with at least one WPSSI outcome. Similar to the analysis for both sites, the WAMI index had the strongest effects on cognitive development. Unique to South Africa, avoidance of punishment was associated with full scale IQ (SD: 4.05, 95% CI: 0.69, 7.42; p = 0.004 for homogeneity test to assess differences between sites) and performance IQ (SD: 2.31, 95% CI: 0.41, 4.20; p=0.006 for homogeneity test) (Table 3 ).

In the Tanzania site, the provision of appropriate play material was inversely associated with full scale IQ (SD: -3.55, 95% CI: -6.91, -0.18; p=0.01 for homogeneity test) and performance IQ (SD: -2.75, 95% CI: -4.63, -0.87; p=0.03 for homogeneity test). Furthermore, the presence of depressive symptoms in the post-partum period (until one year after child birth) was associated with higher full scale IQ (SD: 3.93, 95% CI: 0.12, 7.74; p=0.03 for homogeneity test) and verbal IQ (SD: 3.11, 95% CI: 0.84, 5.39; p=0.02 for homogeneity test). Aside from these exceptions, other associations were consistent between sites (Table 4 ).

Socioeconomic status, the organization of the home environment, and opportunities for cognitive stimulation were associated with child cognitive development at 5 years of age among children in the South African and Tanzanian sites of the MAL-ED study. The strongest association with child cognitive development at 5 years of age was found for socioeconomic status (measured using the Water, Assets, Maternal Education and Income index). The WAMI index was previously shown to be associated with children’s cognitive development scores, measured with the Bayley Scale of Infant Development, at 15 months of age in the Tanzanian site of the MAL-ED cohort [ 23 ]. These results demonstrate not only the large effect of socioeconomic status on cognitive development but also its long-lasting impact up to 5 years of child age.

Intergovernmental organizations have recognized that poverty is related to suboptimal health and increased mortality [ 39 ]. However, there is less recognition of the role that poverty plays in children’s cognitive development in LMICs due to the lack of national statistics on children’s cognitive development. This study’s finding contributes to the growing body of literature showing the association between socioeconomic status and children’s cognitive development [ 3 , 40 ]. Reducing income inequalities and increasing opportunities for social mobility in LMICs can help diminish the economic divide between HICs and LMICs and contribute to the goal of achieving global health equity.

This study also found a modest association of the organization of the home environment and opportunities for cognitive stimulation on child cognitive development at 5 years of age. The results of these analyses show that an appropriate home environment (e.g., with clean, organized, hazard-free areas for children to play) where caregivers provide adequate stimulation (e.g., promoting recreational and learning materials and activities) may positively impact children’s cognitive development. In this context, we also found opportunities for stimulation and learning (e.g., presence of toys, books, and interactions with relatives) to be positively associated with children’s cognitive development.

Consistent with this perspective, other studies have found cognitive stimulation to be associated with children’s cognitive ability and academic achievement [ 40 ]. For example, Cooper et al. randomized women in South Africa to an intervention aimed to educate mothers about sensitive and responsive parenting. The researchers found that the intervention had a significant impact on mother-child relationships and predicted child development. The role that cognitively stimulating materials and experiences play in cognitive development is not only recognized in the academic literature but is also well established in global policy practices. In the 2007 Lancet series on Child Development in Developing Countries, the International Child Development Steering Group (ICDSG) identified factors with sufficient evidence to recommend implementing prevention strategies [ 2 , 40 ]. These factors include inadequate provision of cognitively stimulating materials, growth retardation and low birth weight, and illnesses.

Limitations

Because these data were from two specific rural sites in South Africa and Tanzania, the generalizability of the present findings may be limited. Future research should replicate and expand this work in rural and urban settings as well as in other LMICs. The findings of this study are also limited by the age of the children in the cohort as cognitive development was assessed at 5 years of age, when cognitive abilities may not have stabilized. Future research should investigate the role that psychosocial and environmental factors play at other stages of child development.

In this study, cognitive development was assessed using the WPPSI-III assessment tool, which assesses cognitive function by testing children on eight subscales. However, the adapted assessments did not include the word reasoning score. This limits the generalizability of these results to other assessments using the WPPSI and may have had an impact in the estimates presented in this analysis as we were not able to capture score differences within this domain. We adjusted for field assessors in the analysis to account for potential confounding by field assessor, but the limited associations found between the HOME index and the WPSSI may be in part due to measurement error.

In this analysis, maternal depressive symptoms were not significantly associated with any of the WPSSI outcomes. These findings are in contrast with past studies in LMICs that have shown that suboptimal maternal mental health is associated with poor child growth and development [ 41 ]. The lack of significant association can potentially be attributed to the use of the self-reported questionnaire to assess maternal mental health and the relatively low frequency of reports of depressive symptoms among women in this cohort, which could have underpowered the study to detect this relationship. Similarly, site-specific inconsistent effects on child cognitive development may in part reflect cultural differences in how women report depressive symptoms and the difficulty of assessing child development across diverse low-resource settings.

This study shows a stronger association with child cognitive development at 5 years of age for socioeconomic status compared to other psychosocial and environmental factors which, we had hypothesized, were more proxy determinants of child cognitive development. This demonstrates the large and long-lasting effect that socioeconomic status has on child cognitive development which contributes to the economic and health divide between HICs and LMICs. Because the limited associations between the HOME index and the WPSSI may be in part due to difficulty in measuring these constructs in low-resource settings, future studies should further investigate measures of psychosocial and environmental factors that may affect child cognitive development. A more comprehensive understanding of the context in which children grow and develop cognitively is necessary to inform interventions aiming to alleviate the burden of compromised cognitive development for children in LMICs.

Availability of data and materials

The MAL-ED dataset supporting the conclusion of this article can be accessed publicly through the ClinEpiDB platform (at ClinEpiDB.org ).

Abbreviations

High-income Country (ies)

Low- and Middle-income Country (ies)

Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development

Wechsler Preschool Primary Scales of Intelligence

Self-reporting Questionnaire

Water, Assets, Maternal Education and Income Index

Home Observation for the Measurement of the Environment

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Acknowledgements

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The Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project (MAL-ED) was carried out as a collaborative project supported by the Bill and Melinda Gates Foundation (OPP1131125), the Foundation for the National Institutes of Health, and the National Institutes of Health, Fogarty International Center. This work was supported by the National Institutes of Health, National Institute of Allergy and Infectious Diseases (grant K01AI130326 to ETRM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Fabrizio Drago

Department of Pediatrics, University of Virginia, Charlottesville, USA

Rebecca J. Scharf

Department of Psychology, University of Venda, Thohoyandou, South Africa

Angelina Maphula

Department of Animal Science, University of Venda, Thohoyandou, South Africa

Emanuel Nyathi

Department of Nutrition, University of Venda, Thohoyandou, South Africa

Tjale C. Mahopo

Centre for International Health, University of Bergen, Bergen, Norway

Erling Svensen

Haydom Global Health Research Centre, Haydom, Tanzania

Estomih Mduma

Department of Microbiology, University of Venda, Thohoyandou, South Africa

Pascal Bessong

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Contributions

FD, RJS, AM, and ETRM conceived the hypothesis and analysis plan. FD analyzed the data with the support of ETRM and RJS. AM, EN, and TCM conducted data collection. ES, EM, and PB conceived the parent study and supervised all aspects of the study. All authors have contributed substantially to the interpretation of the results. FD wrote the first draft of the manuscript and all authors critically reviewed the draft and final manuscript.

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The study was approved by the Institutional Review Board for Health Sciences Research, University of Virginia, USA (#14595) as well as the respective governmental, local institutional, and collaborating institutional ethical review boards at each site: Health, Safety and Research Ethics Committee, University of Venda; Department of Health and Social Development, Limpopo Provincial Government (South Africa); Medical Research Coordinating Committee, National Institute for Medical Research; Chief Medical Officer, Ministry of Health and Social Welfare (Tanzania). Informed written consent forms were obtained from all participants prior to study enrollment. Along with child assent, informed written parental or guardian consent was obtained for each participating child on their behalf.

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Drago, F., Scharf, R.J., Maphula, A. et al. Psychosocial and environmental determinants of child cognitive development in rural south africa and tanzania: findings from the mal-ed cohort. BMC Public Health 20 , 505 (2020). https://doi.org/10.1186/s12889-020-08598-5

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The Playing Brain. The Impact of Video Games on Cognition and Behavior in Pediatric Age at the Time of Lockdown: A Systematic Review

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A growing number of children and adolescents play video games (VGs) for long amounts of time. The current outbreak of the Coronavirus pandemic has significantly reduced outdoor activities and direct interpersonal relationships. Therefore, a higher use of VGs can become the response to stress and fear of illness. VGs and their practical, academic, vocational and educational implications have become an issue of increasing interest for scholars, parents, teachers, pediatricians and youth public policy makers. The current systematic review aims to identify, in recent literature, the most relevant problems of the complex issue of playing VGs in children and adolescents in order to provide suggestions for the correct management of VG practice. The method used searches through standardized search operators using keywords related to video games and the link with cognition, cognitive control and behaviors adopted during the pandemic. Ninety-nine studies were reviewed and included, whereas twelve studies were excluded because they were educationally irrelevant. Any debate on the effectiveness of VGs cannot refer to a dichotomous approach, according to which VGs are rigidly ‘good’ or ‘bad’. VGs should be approached in terms of complexity and differentiated by multiple dimensions interacting with each other.

1. Introduction

In the last decades, a very large body of literature has shown an increasing interest in video games (VGs) and their impact on the brain, cognition and behavior, especially in children and adolescents [ 1 ]. Indeed, a widely growing number of children and adolescents play VGs for a long time, often developing real addictive behaviors [ 2 , 3 ]. In addition, the current outbreak of the COVID-19 pandemic and the following lockdown have significantly reduced outdoor activities and direct interpersonal relationships [ 4 , 5 ]. However, literature data are still inconsistent. For example, according to some meta-analytic reviews [ 6 , 7 , 8 ], exposure to violent VGs is a causal risk factor for increased aggressive behavior, cognition and affection in children and adolescents. Conversely, many cross-sectional and intervention studies have shown that the intensive use of some types of VGs leads to significant improvements in many cognitive domains and behaviours [ 1 , 9 , 10 , 11 ]. Video games are even considered as ‘virtual teachers’ and effective and ‘exemplary teachers’ [ 12 , 13 ].

The current systematic review focuses on some crucial outstanding issues within the debate on the effects of VGs on cognition and behavior in order to provide suggestions for parents, pediatricians, health providers and educators dealing with pediatric ages, especially in the complex pandemic period. Namely, it analyzes the most debated and educationally relevant problems on the relationship between video games, cognition and behavior: 1. video games’ effects on cognitive function; 2. video games’ effects on attention and addictive behaviors; 3. video games and prosocial or aggressive behavior. Therefore, the current analysis may be accounted as an original contribution to the practical dimension in the educational and rehabilitation field for parents and educators.

Early common predominant opinions mainly focused on VGs according to dichotomous thinking, as enjoyable entertainment or harmful tools [ 14 ]. The recent literature instead provided evidence on the impact of VGs on the brain and its functional modifications while playing [ 15 , 16 , 17 , 18 , 19 ], showing that video games involve different cortical and subcortical structures, with cognitive and emotional competence, such as frontal and prefrontal regions, the posterior and superior parietal lobe, the anterior and posterior cingulate cortices, limbic areas, the amygdala, the entorhinal cortex and basal nuclei [ 1 , 20 , 21 , 22 ].

Mondéjar and colleagues [ 15 ], in a group of twelve healthy preadolescents between 8 and 12 years old, evaluated the frontal lobe activity and the different types of cognitive processing during five platform-based action videogame mechanics: 1. accurate action, related to processes such as concentration, attention, impulse control and information comprehension; 2. timely action, related to working memory, selective attention, decision-making, problem solving and perception; 3. mimic sequence, related to working memory, focalized attention and inhibition control; 4. pattern learning, as selective attention, planning, inhibition control and spatial orientation; 5. logical puzzles related to attention, working memory, the capacity for abstraction, information processing, problem solving, or resistance to interference. They found prominent bioelectrical prefrontal activity during the performance related to executive functions (timely action, pattern learning, logical puzzles) and more global brain activity and a higher presence of alpha waves, or a greater activation of the temporal lobe, in the accurate action and mimic sequence. Similarly, they correlated higher magnitudes on frequency bands with five game mechanics in ten healthy children, who played with a VG platform for an average of about 20 min [ 16 ]. Theta waves, related to memory and emotions, were more significant in the five mechanics, while beta waves, related to concentration, were more prominent in only two. Moreover, activation was more significant in the intermediate and occipital areas for all the mechanics, while recurrent magnitude patterns were identified in three mechanics.

Similarly, Lee et al. [ 17 ], found a thinner cortex and a smaller gray matter volume in critical areas for evaluating reward values, error processing and adjusting behavior, namely, the anterior cingulate cortex, the orbitofrontal cortex and the frontoparietal areas, in young male adults with internet gaming disorders, compared to age-matched healthy male controls. A neuroimaging study examined in individuals affected by gaming disorders the differences during the playing of a violence-related vs. a non-violence-related version of the same VG [ 18 ]. While functional connectivity of the reward-related network and the behavioral inhibition system was altered, the orbitofrontal cortex and anterior cingulate cerebral area were overstimulated, similarly to smart drug addiction [ 17 , 23 ].

Recently, Kwak et al. [ 19 ] longitudinally compared 14 adolescents with internet gaming disorder to 12 professional internet gaming students who practiced for about ten hours a day, within a defined support system that included practice, physical exercise, lectures on team strategy, rest and mealtimes. After one year, both groups showed increased brain activity within the attention system of the parietal lobe. However, professional gamers improved problematic behaviors, impulsivity, aggression, depression and anxiety, while adolescents with internet gaming disorder showed no behavioral improvement and a dysfunctional brain activity within the impulse control network in the left orbitofrontal cortex.

The current systematic review was structured according to the guidelines and recommendations contained in the PRISMA statement [ 24 ].

Eligibility Criteria

Both experimental and correlational studies and meta-analyses between the years of 2000 and 2020 that investigated outcomes of VG exposure were included. They were considered children and adolescents. Studies employing different methodologies were included: studies in which naive participants were trained to use a VG versus a control group and studies comparing experienced versus non-gamers, or inexperienced players. Primary outcome measures were any type of structural and functional data obtained using neuroimaging techniques and behavioral testing.

Information Sources

One hundred and twenty-two studies were identified through electronic database searching in Ovid MEDLINE, Embase, PsycINFO, PubMed, Scopus (Elsevier) and Web of Sciences. The final database search was run on January 2021 using the following keywords: video games; video games and cognition; video games and epidemic; cognitive control; behavior control; brain and video games; spatial cognition; prosocial behavior; violence in video games; aggressive behavior; addictions in adolescents; children and video games.

Study Selection

Inclusion criteria: written in English; published since 2000; deals in depth with cognitive skills, attention, executive functions, or cognitive control; follows a high methodological rigor.

Exclusion criteria: does not refer to key topics directly; the full text could not be obtained; lack of transparency due to missing methodology information. Ninety-nine studies were reviewed and included, whereas twelve studies were excluded because they were irrelevant to the topic or because the full text was not obtained. General communication materials, such as pamphlets, posters and infographics, were excluded as they do not provide evidence about their effectiveness.

Figure 1 shows the selection of studies flowchart.

Selection of studies flowchart.

3.1. Effect of Video Games on Cognitive Functions

Any modern VG requires an extensive repertoire of attentional, perceptual and executive abilities, such as a deep perceptual analysis of complex unfamiliar environments, detecting relevant or irrelevant stimuli, interference control, speed of information processing, planning and decision making, cognitive flexibility and working memory.

Literature data in the last years have proven that VGs may improve a variety of cognitive domains [ 1 , 25 ] as, for example, even just 10 hours of VG could improve spatial attention and mental rotation [ 26 , 27 ]. A large variety of design studies reported in habitual players better performance in multiple cognitive domains, including selective attention [ 3 , 21 , 26 , 28 ], speed of processing [ 21 , 28 ], executive functions [ 29 , 30 ] and working memory [ 31 ]. Similarly, a large body of intervention studies have shown improvements in the same cognitive domains in non-players following training in action VGs [ 27 , 32 , 33 , 34 , 35 , 36 , 37 ]. Recently, Benoit et al. [ 38 ] examined in 14 professional VG players and 16 casual VG players various cognitive abilities, such as processing speed, attention, memory, executive functions, manual dexterity and tracking multiple objects in three dimensions [ 39 ]. Professional players showed a very large advantage in visual–spatial short-term memory and visual attention, and less in selective and sustained attention and auditory working memory. Moreover, they showed better speed thresholds in tracking multiple objects in three dimensions overall, though the rate of improvement did not differ in the two groups. In two previous meta-analyses, Bediou et al. [ 40 ] focused on the long-term effects of action VGs on various cognitive domains using both cross-sectional and intervention studies. Overall, the results documented a positive impact of action video gaming on cognition. In cross-sectional studies, a main effect of about half a standard deviation was found. The habitual action game players showed better performance than non-players. Likewise, intervention studies showed about a third of a standard deviation advantage in cognition domains in action VG trainees. Perception, spatial cognition and top-down attention were the three cognitive domains with the most robust impact [ 40 ].

Homer et al. [ 41 ] examined the effectiveness of a custom-designed VG (‘alien game’) in a group of 82 healthy adolescents (age range 14–18 years; average = 15.5 years) trained to play for 20 min per week for 6 consecutive weeks. Such a digital game was devised to target, in a fun way, the specific executive ability of shifting, as the ability to shift between tasks or mental sets, hypothesizing that after playing the ‘alien game’ over a period of several weeks, adolescents would show significant improvements in the targeted ability. Pre- and post-test measures of another executive ability, inhibition, as the ability to control a prepotent response, were also recorded in order to examine the extent to which training would transfer from one executive ability to another. Significant advantages both in shifting and in inhibition abilities were found, providing evidence that VGs can be effective tools for training executive abilities [ 42 , 43 ].

Similarly, Oei and Patterson [ 44 ] examined the effect of action and non-action VGs on executive functions. Fifty-two non-VG gamers played one of four different games for 20 h. Pre- and post-training tests of executive function were administered. The group that trained on the physics-based puzzle game, demanding high level planning, problem solving, reframing, strategizing and new strategies from level to level, improved in several aspects of executive function. In a previous study, the same authors [ 45 ] instructed 75 non-gamers, (average age 21.07 ± 2.12) to play for 20 h, one hour a day/five days a week over four weeks. They compared effects of action and non-action games to examine whether non-action games also improve cognition. Four tests pre- and post-training were administered. The results showed that cognitive improvements were not limited to training with action games and that different games improved different aspects of cognition. Action VGs have even been used to treat dyslexic children [ 46 , 47 ]. Only 12 h of action VGs, for nine sessions of 80 min per day, significantly improved reading and attentional skills [ 48 ].

Moreover, several meta-analytic studies provide evidence that action VG training may become an efficient way to improve the cognitive performance of healthy adults. Wang et al. [ 49 ], in a meta-analysis, found that healthy adults achieve moderate benefits from action VG training in overall cognitive ability and moderate to small benefits in specific cognitive domains. In contrast, young adults gain more benefits than older adults in both overall cognition and specific cognitive domains.

In summation, the studies on VG effects, by different methodologies, document both in adults and in children significant positive outcomes in different cognitive domains. Such performance improvements may be paralleled by functional brain remodelling [ 14 ].

3.2. Video Games Effect on Attention and Addictive Behaviors

Attentional problems are accounted as a crucial area of focus on outcomes of intensive game-play practices in children and adolescents. However, literature on the topic appears inconsistent. While some research has found mixed results [ 50 ] or a positive effect [ 51 , 52 , 53 ], or no relationship between VG practice and attention, other studies have linked VG playing with greater attention problems, such as impulsiveness, self-control, executive functioning, and cognitive control [ 53 , 54 , 55 ].

Gentile et al. [ 56 ], examining longitudinally, over 3 years, a large sample of child and adolescent VG players aged 8–17 (mean = 11.2 ± 2.1), suggested a bidirectional causality: children who spend more time playing VGs have more attention problems; in turn, subjects who have more attention problems spend more time playing VGs. Therefore, children and adolescents with attention problems are more attracted to VGs (excitement hypothesis), and, in turn, they find it less engaging to focus on activities requiring more control and sustained attention, such as educational activities, homework or household chores (displacement hypothesis). According to such hypotheses, and to the operant conditioning model [ 57 , 58 ], VGs, providing strong motivational cues, become more rewarding for impulsive children and teenagers [ 51 ] who, in such contexts, experience a sense of value and feelings of mastery that they do not experience in their daily relationships [ 59 ].

Actually, any modern VG is a highly engaging activity with a variety of attractive cues, such as, for example, violence, rapid movement, fast pacing and flashing lights [ 60 , 61 ]. According to the attractive hypothesis [ 56 ], it may provide a strong motivation and support for attention and even become addictive, especially in subjects with problems maintaining attention in usual, monotonous and poorly engaging tasks. Therefore, paradoxically, a greater VG exposure may improve visual attention skills involved in such engaging play [ 26 ], but it may impair the ability to selectively focus on a target for lasting time, without external exciting cues.

Probably, in line with the bidirectional causality framework [ 56 ], such rewarding conditions could become the psychological context for the structuring of addictive behaviors, such as a sense of euphoria while playing, feeling depressed away from the game, an uncontrollable and persistent craving to play, neglect of family and friends, problems with school or jobs, alteration of sleeping routines, irregular meals and poor hygiene [ 14 ]. The most psychologically fragile subjects may be most attracted to an engaging and rewarding activity, ensuring an effective compensation to their fragility [ 14 ]. However, the topic of video game addiction continues to present today many outstanding issues. There is a large consensus that ‘pathological use’ is more debilitating than ‘excessive use’ of VGs alone [ 62 , 63 , 64 ]. Addictive behavior appears associated with an actual lowering in academic, social, occupational, developmental and behavioral dimensions, while excessive use may simply be an excessive amount of time gaming. According to Griffiths’ suggestions, ‘healthy excessive enthusiasms add to life, whereas addiction takes away from it’ [ 65 ]. However, it is sometimes difficult to identify the clear line between unproblematic overuse of gaming and the pathological and compulsive overuse that compromises one’s lifestyle and psychosocial adjustment [ 66 , 67 , 68 ]. Therefore, there may be a risk of stigmatizing an enjoyable practice, which, for a minority of excessive users, may be associated with addiction-related behaviors [ 69 , 70 ]. Przybylski and colleagues, in four survey studies with large international cohorts (N = 18,932), found that the percentage of the general population who could qualify for internet gaming disorders was extremely small (less than one percent) [ 71 ].

In such a discussion of the pathological nature of VGs, another outstanding question is whether pathological play is a major problem, or if it is the phenomenological manifestation of another pathological condition. Several studies have suggested that video game play can become harmful enough to be categorized as a psychiatric disorder, or it could be a symptom of an underlying psychopathological condition, such as depression or anxiety. Moreover, the functional impairments observed in individuals with game addictions are also thought to be similar to the impairments observed in other addictions. Neuroimaging studies have shown that the brain reward pathways which are activated during video game playing are also activated during cue-induced cravings of drug, alcohol or other type of substances abuse [ 72 , 73 , 74 ].

Some longitudinal studies [ 14 , 75 , 76 ] proved that pathological addictive behaviors, such as depression, are likely to be outcomes of pathological gaming rather than predictors of it [ 77 , 78 ]. Lam and Peng [ 79 ], in a prospective study with a randomly generated cohort of 881 healthy adolescents aged between 13 and 16 years, found that the pathological use of the internet results in later depression. Similarly, Liau et al. [ 80 ], in a 2-year longitudinal study involving 3034 children and adolescents aged 8 to 14 years, found that pathological video gaming has potentially serious mental health consequences, in particular of depression.

In summary, attention problems and addictive behaviors in the context of VGs should be addressed in a circular and bidirectional way in which each variable can influence the others.

3.3. Video Games Effect and Prosocial and Aggressive Behaviors

The positive impact of video games also concerns the social and relational dimension, as occurs in the VG training of prosocial or educational skills. Several studies have reported that playing prosocial VGs, even for a short time, increases prosocial cognition [ 81 ], positive affect [ 82 ] and helping behaviors [ 13 , 81 , 82 , 83 , 84 , 85 ], whereas it decreases antisocial thoughts and the hostile expectation bias, such as the tendency to perceive any provocative actions of other people as hostile even when they are accidental [ 13 , 86 ]. Such findings have been found in correlational, longitudinal and experimental investigations [ 82 , 85 , 87 ].

In four different experiments [ 13 ], playing VGs with prosocial content was positively related to increased prosocial behavior, even though participants played the VGs for a relatively short time, suggesting that VGs with prosocial content could be used to improve social interactions, increase prosocial behavior, reduce aggression and encourage tolerance.

Following experimental, correlational, longitudinal and meta-analytic studies provided further evidence that playing a prosocial VG results in greater interpersonal empathy, cooperation and sharing and subsequently in prosocial behavior [ 87 , 88 , 89 , 90 ].

Such literature’s data are consistent with the General Learning Model [ 91 , 92 ], according to which the positive or negative content of the game impacts on the player’s cognition, emotions and physiological arousal, which, in turn, leads to positive or negative learning and behavioral responses [ 12 , 93 , 94 , 95 ]. Therefore, repeated prosocial behavioral scripts can be translated into long-term effects in cognitive, emotional and affective constructs related to prosocial actions, cognition, feelings, and physiological arousal, such as perceptual and expectation schemata, beliefs, scripts, attitudes and stereotypes, empathy and personality structure [ 83 , 91 ].

In the same conceptual framework, educational video games have been found to positively affect behaviors in a wide range of domains [ 12 ], school subjects [ 96 ] and health conditions [ 97 , 98 ]. In randomized clinical trials, for example, diabetic or asthmatic children and adolescents improved their self-care and reduced their emergency clinical utilization after playing health education and disease management VGs. After six months of playing, diabetic patients decreased their emergency visits by 77 percent [ 99 ]. Therefore, well-designed games can provide powerful interactive experiences that can foster young children’s learning, skill building, self-care and healthy development [ 100 ].

Violence in VGs is a matter of intense debate, both in public opinion and in the scientific context [ 101 , 102 ]. A vast majority of common opinions, parents and educators consider the violence of VGs as the most negatively impacting feature to emotional and relational development of youth and children. Actually, studies agree on the negative impact of violent video games on aggressive behavior. Several meta-analyses have examined violent VGs [ 6 , 7 , 8 , 103 ] and, although they vary greatly in terms of how many studies they include, they seem to agree with each other. The most comprehensive [ 8 ] showed that violent VGs, gradually and unconsciously, as a result of repeated exposure to justified and fun violence, would increase aggressive thoughts, affect and behavior, physiological persistent alertnes, and would desensitize players to violence and to the pain and suffering of others, supporting a perceptual and cognitive bias to attribute hostile intentions to others.

Similarly, experimental, correlational and longitudinal studies supported the causal relationship between violent VGs and aggression, in the short- and long-term, both in a laboratory and in a real-life context. A greater amount of violent VGs, or even a brief exposure, were significantly associated with more positive attitudes toward violence [ 104 ], higher trait hostility [ 105 ] and with increased aggressive behaviors [ 106 ], physical fights [ 107 ] and aggressive thoughts [ 108 ] and affect [ 109 ]. In a two-year longitudinal study, children and adolescents who played a lot of violent VGs showed over time more aggressive behaviors, including fights and delinquency [ 110 ]. Saleem, Anderson and Gentile [ 82 ] examined the effects of short-term exposure to prosocial, neutral and violent VGs in a sample of 191 children of 9–14 years old. Results indicated that while playing prosocial games increased helpful and decreased hurtful behaviour, the violent games had the opposite effect.

In summation, the overall literature data support the opinion that violent video games, over time, affect the brain and activate a greater availability to aggressive behavior patterns, although some researchers have pointed out that the negative effects of violent VGs are small and may be a publication bias [ 14 , 111 ].

4. Discussion

The focus of the current overview was to identify, from a functional point of view, the most significant issues in the debate on the impact of VGs on cognition and behavior in children and adolescents, in order to provide suggestions for a proper management of VG practice.

Overall, the reviewed literature agrees in considering the practice of VGs as much more than just entertainment or a leisure activity. Moreover, research agrees that any debate on the effectiveness of VGs cannot refer to a unitary construct [ 14 ], nor to a rigidly dichotomous approach, according to which VGs are ‘good’ or ‘bad’ [ 1 , 12 , 112 , 113 ].

The term ‘video game’ should be viewed as an ‘umbrella term’ that covers different meanings, far from a single unitary construct [ 14 , 114 ]. Furthermore, VGs and their effects should be approached in terms of complexity and differentiated by multiple dimensions interacting with each other and with a set of other variables, such as, for example, the player’s age and personality traits, the amount of time spent playing, the presence of an adult, the game alone or together with others and so on [ 115 ].

Gentile and colleagues [ 116 , 117 , 118 , 119 ] have identified five main features of VGs that can affect players: 1. amount of play; 2. content; 3. context; 4. structure; and 5. mechanics. Each of these aspects can produce or increase different thoughts, feelings and behaviors.

However, the content effects, individually focused, are frequently overemphasized. According to the General Learning Model, children would learn the contents of the specific games and apply them to their lives. Nevertheless, a violent game using a team-based game modality may have different impacts than a violent game using a ‘free for all’ game modality. Although both are equally violent games, the former could suggest teamwork and collaborative behaviors, while playing in an ‘everyone for oneself’ mode could foster less empathy and more aggressive thoughts and behaviors [ 8 , 88 ].

Likewise, the outside social context can have different effects and it may even mitigate or reinforce the effects of the content. Playing violent games together with others could increase aggression outcomes if players reinforce each other in aggressive behavior. Instead, it could have a prosocial effect if the motivations to play together are to help each other [ 120 ].

According to the dominant literature, the psychological appeal of video games may be related to an operant conditioning that reinforces multiple psychological instances, including the need for belonging and social interaction [ 57 , 58 ]. On such drives and reinforcements, the playing time can expand, and it may become endless in addicted subjects. However, the amount of play, regardless of the content, can become harmful when it displaces beneficial activities, affects academic performance or social dimensions [ 52 , 121 ], or supports health problems, such as, for instance, obesity [ 122 , 123 , 124 ], repetitive strain disorder and video game addiction [ 76 , 83 ]. However, a greater amount of time inevitably implies increased repetition of other game dimensions. Therefore, it is likely that some associations between time spent and negative outcomes result from other dimensions, and not from amount of time per se. Moreover, children who perform poorly at school are likely to spend more time playing games, according to the displacement hypothesis, but over time, the excessive amount of play may further damage academic performance in a vicious circle [ 116 ].

VGs can also have a different psychological appeal in relation to their structural organization and the way they are displayed. Many structural features can affect playing behavior, regardless of the individual’s psychological, physiological, or socioeconomic status [ 125 ], such as, for instance, the degree of realism of the graphics, sound and back-ground, the game duration, the advancement rate, the game dynamics such as exploring new areas, elements of surprise, fulfilling a request, the control options of the sound, graphics, the character development over time and character customization options, the winning and losing features as the potential to lose or accumulate points, finding bonuses, having to start a level again, the ability to save regularly, the multi-player option building alliances and beating other players [ 125 ].

The more or less realistic mechanics can also configure the game differently and affect fine or gross motor skills, hand-eye coordination or even balance skills, depending on the type of controller, such as a mouse and keyboard, a game control pad, a balance board, or a joystick.

Therefore, VGs may differ widely in multiple dimensions and, as a result, in their effects on cognitive skills and behavior [ 3 , 33 ]. Moreover, the different dimensions may interact with each other and with the psychological, emotional and personality characteristics of the individual player and context. Even the same game can have both positive and negative effects in different contexts and for different subjects.

The current analysis of the literature, therefore, supports the need for further experimental and longitudinal research on the role of multiple characteristics of video games and their interactions. A wide-ranging approach dynamically focused on the multiple dimensions will allow a deeper theoretical understanding of the different aspects of video games.

Nevertheless, according to common opinion, the violence would always have a negative impact on behavior, especially in pediatric subjects. However, a strictly causal relationship between violent VGs and aggressive behavior appears rather reductive [ 126 , 127 ]. Aggressive behavior is a complex one and arises from the interaction of a lot of factors. Therefore, violent VGs, with no other risk factors, should not be considered ‘per se’ the linear cause and single source of aggressive or violent behavior. Antisocial outcomes can be influenced by personality variables, such as trait aggression, or by a number of the ‘third variables’ such as gender, parental education, exposure to family violence and delinquency history [ 83 ]. According to social learning theories [ 128 ], aggressive behavior would arise from repeated exposure to violence patterns [ 129 ]. Therefore, children who have other risk factors for violent or aggressive behavior, such as violent family patterns, excessive amount of time spent playing, playing alone, and so on are more likely to have negative consequences from playing violent video games.

An alternative theoretical framework [ 126 , 127 ] assumes that violent behavior would result from the interaction of genetically predisposed personality traits and stressful situations. In such a model, violent VGs would act as ‘stylistic catalysts’ [ 127 ], providing an individual predisposed to violence with the various models of violent behavior. Therefore, an aggressive child temperament would derive from a biological pathway, while the violent VG, as a ‘stylistic catalyst’, may suggest the specific violent behavior to enact.

Conversely, playing prosocial VGs, even for a short time, increases prosocial cognition, affect and behaviors in children and adolescents [ 13 , 81 , 82 , 83 , 84 , 85 , 89 ]. Several intervention or training studies showed that a prosocial VG should activate experiences, knowledge, feelings and patterns of behavior relating to prosocial actions, cognition, feelings and physiological arousal. In turn, in line with the General Learning Model, [ 91 , 130 ], recurrent prosocial behavioral scripts produce new learning, new behavioral patterns and emotional and affective cognitive constructs [ 83 ].

Moreover, several studies emphasize the educational and academic potential of VGs that may become effective and ‘exemplary teachers’ [ 12 , 82 ] providing fun and motivating contexts for deep learning in a wide range of content [ 12 ], such as school learning [ 96 ], rehabilitation activities [ 46 , 47 ], new health care and protection behavior development and the enhancement of specific skills [ 97 , 99 , 100 ]. Similarly, the literature data document that the intensive use of VGs results in generalized improvements in cognitive functions or specific cognitive domains, and in behavioral changes [ 1 ]. Actually, VGs involve a wide range of cognitive functions, and attentional, perceptual, executive, planning and problem solving skills. They can, therefore, be expected to improve different perceptual and cognitive domains. However, on a methodological level, the impact on behavior and cognition cannot be simplistically viewed as the linear result of a causal relationship between VG and performance. For instance, subjects with better perceptual abilities are likely to choose to play and, as a result, their increase in performance may reflect their baseline level rather than the effects of the game.

Studies focused on the attentional functions in VG playing reported inconsistent data. Playing action games may improve attention skills implied in a specific game. However, according to the attractive hypothesis [ 56 ] and operant conditioning theory, children and adolescents with attentional problems may be attracted by the motivating and engaging VG activities. On the other hand, children and adolescents with a wider VG exposure show greater attention problems [ 53 ]. The relationship between VGs and attention, then, seem to be approached in terms of bidirectional causality [ 56 ].

Similarly, since VGs and their cues appear more pleasant and desirable, a large amount of attractive VG exposure can lead to addiction and impair ability to focus on effortful goal oriented behavior [ 131 ]. However, the literature does not yet appear to agree on the objective diagnostic criteria for classifying behavioral game addiction [ 132 ].

In the fifth edition appendix of the Diagnostic and Statistical Manual of Mental Disorders [ 133 ], the diagnostic criteria for Internet Gaming Disorder included both specific internet games and offline games. However, this has led to some confusion as to whether excessive video games must necessarily occur online [ 134 , 135 ]. According to some authors, since ‘Internet addiction’ includes heterogeneous behaviors and etiological mechanisms, the term ‘video game disorder’ or simply ‘gaming disorder’ would be more suitable [ 136 , 137 ], while the term ‘Internet addiction’ appears inappropriate. Individuals rarely become addicted to the medium of the internet itself [ 137 , 138 ]. Moreover, it has also been supported theoretically [ 135 ] and empirically proven [ 139 ] that problematic internet use and problematic online gaming are not the same.

The debate on the relationship between pure game addiction behaviors and game addiction in comorbidity with other psychiatric disorders appears still on. Some researchers have argued that game addiction, as a standalone clinical entity, does not exist [ 140 ], but it is simply a symptom of psychiatric illnesses such as major depressive disorder or Attention Deficit Hyperactivity Disorder. Equally poorly defined is the question of genetic predisposition and vulnerability to game addiction.

Likewise, the relationship between clinical symptoms and changes in brain activity and the dynamics by which video games triggers such widespread brain plasticity needs to be more clearly defined.

5. Conclusions

The current analysis of the literature provides strong evidence on the power of video games as highly motivating and engaging tools in the broader context of cognitive, emotional and relational development of children and adolescents. However, the effectiveness of such tools does not arise exclusively from their content, but it results from a set of variables interacting each other.

Video games, beyond their content, can favor pathological aggression, withdrawal, escape from reality and reduction of interests. Virtual reality becomes more attractive than the real one and can become the ‘non-place’ to escape from the complexity of everyday life. Recently, to contain the spread of the COVID-19 pandemic, health authorities have forced populations to stay home and children and adolescents may experience an exacerbation of exposure to video games.

Parents, educators and teachers should ensure an educational presence, monitoring times and modalities of VG practice in a broader context in which children and adolescents live with a wider repertoire of interests, without losing social and relational engagement. Moreover, pediatric health care visits may be a great opportunity to support parents helping children to deal with media and video games.

On these assumptions, as practical suggestions to prevent or mitigate addictive behaviors, parents and educators should enforce the golden rule as the educational presence of the adult.

Moreover, in line with the literature, the core values to prevent a negative impact of video games should be focused on a few rules to be proposed with assertiveness and authority: 1. set a clear time limit to play, 2. prefer games that can also be played with family, 3. alternate video games with other games and activities, 4. avoid highly addictive games, 5. keep a social life in the real world.

Author Contributions

Conceptualization, D.S., L.D.F. and G.L.; methodology, D.S., E.G.; formal analysis, D.S., E.G. and L.D.F.; data curation, E.G. and L.D.F.; writing—original draft preparation, D.S., E.G. and L.D.F.; writing—review and editing, D.S.; supervision, D.S. and G.L.; funding acquisition, D.S. and G.L. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Cognitive Development Theory: What Are the Stages?

Sensorimotor stage, preoperational stage, concrete operational stage, formal operational stage.

Cognitive development is the process by which we come to acquire, understand, organize, and learn to use information in various ways. Cognitive development helps a child obtain the skills needed to live a productive life and function as an independent adult.

The late Swiss psychologist Jean Piaget was a major figure in the study of cognitive development theory in children. He believed that it occurs in four stages—sensorimotor, preoperational, concrete operational, and formal operational.

This article discusses Piaget’s stages of cognitive development, including important concepts and principles.

FatCamera / Getty Images

History of Cognitive Development

During the 1920s, the psychologist Jean Piaget was given the task of translating English intelligence tests into French. During this process, he observed that children think differently than adults do and have a different view of the world. He began to study children from birth through the teenage years—observing children who were too young to talk, and interviewing older children while he also observed their development.

Piaget published his theory of cognitive development in 1936. This theory is based on the idea that a child’s intelligence changes throughout childhood and cognitive skills—including memory, attention, thinking, problem-solving, logical reasoning, reading, listening, and more—are learned as a child grows and interacts with their environment.

Stages of Cognitive Development

Piaget’s theory suggests that cognitive development occurs in four stages as a child ages. These stages are always completed in order, but last longer for some children than others. Each stage builds on the skills learned in the previous stage.

The four stages of cognitive development include:

• Sensorimotor
• Preoperational
• Concrete operational
• Formal operational

The sensorimotor stage begins at birth and lasts until 18 to 24 months of age. During the sensorimotor stage, children are physically exploring their environment and absorbing information through their senses of smell, sight, touch, taste, and sound.

The most important skill gained in the sensorimotor stage is object permanence, which means that the child knows that an object still exists even when they can't see it anymore. For example, if a toy is covered up by a blanket, the child will know the toy is still there and will look for it. Without this skill, the child thinks that the toy has simply disappeared.

Language skills also begin to develop during the sensorimotor stage.

Activities to Try During the Sensorimotor Stage

Appropriate activities to do during the sensorimotor stage include:

• Playing peek-a-boo
• Reading books
• Providing toys with a variety of textures
• Singing songs
• Playing with musical instruments
• Rolling a ball back and forth

The preoperational stage of Piaget's theory of cognitive development occurs between ages 2 and 7 years. Early on in this stage, children learn the skill of symbolic representation. This means that an object or word can stand for something else. For example, a child might play "house" with a cardboard box.

At this stage, children assume that other people see the world and experience emotions the same way they do, and their main focus is on themselves. This is called egocentrism .

Centrism is another characteristic of the preoperational stage. This means that a child is only able to focus on one aspect of a problem or situation. For example, a child might become upset that a friend has more pieces of candy than they do, even if their pieces are bigger.

During this stage, children will often play next to each other—called parallel play—but not with each other. They also believe that inanimate objects, such as toys, have human lives and feelings.

Activities to Try During the Preoperational Stage

Appropriate activities to do during the preoperational stage include:

• Playing "house" or "school"
• Building a fort
• Playing with Play-Doh
• Building with blocks
• Playing charades

The concrete operational stage occurs between the ages of 7 and 11 years. During this stage, a child develops the ability to think logically and problem-solve but can only apply these skills to objects they can physically see—things that are "concrete."

Six main concrete operations develop in this stage. These include:

• Conservation : This skill means that a child understands that the amount of something or the number of a particular object stays the same, even when it looks different. For example, a cup of milk in a tall glass looks different than the same amount of milk in a short glass—but the amount did not change.
• Classification : This skill is the ability to sort items by specific classes, such as color, shape, or size.
• Seriation : This skill involves arranging objects in a series, or a logical order. For example, the child could arrange blocks in order from smallest to largest.
• Reversibility : This skill is the understanding that a process can be reversed. For example, a balloon can be blown up with air and then deflated back to the way it started.
• Decentering : This skill allows a child to focus on more than one aspect of a problem or situation at the same time. For example, two candy bars might look the same on the outside, but the child knows that they have different flavors on the inside.
• Transitivity : This skill provides an understanding of how things relate to each other. For example, if John is older than Susan, and Susan is older than Joey, then John is older than Joey.

Activities to Try During the Concrete Operational Stage

Appropriate activities to do during the concrete operational stage include:

• Using measuring cups (for example, demonstrate how one cup of water fills two half-cups)
• Solving simple logic problems
• Practicing basic math
• Doing crossword puzzles
• Playing board games

The last stage in Piaget's theory of cognitive development occurs during the teenage years into adulthood. During this stage, a person learns abstract thinking and hypothetical problem-solving skills.

Deductive reasoning—or the ability to make a conclusion based on information gained from a person's environment—is also learned in this stage. This means, for example, that a person can identify the differences between dogs of various breeds, instead of putting them all in a general category of "dogs."

Activities to Try During the Formal Operational Stage

Appropriate activities to do during the formal operational stage include:

• Learning to cook
• Solving crossword and logic puzzles
• Exploring hobbies
• Playing a musical instrument

Piaget's theory of cognitive development is based on the belief that a child gains thinking skills in four stages: sensorimotor, preoperational, concrete operational, and formal operational. These stages roughly correspond to specific ages, from birth to adulthood. Children progress through these stages at different paces, but according to Piaget, they are always completed in order.

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Börnert-Ringleb M, Wilbert J. The association of strategy use and concrete-operational thinking in primary school . Front Educ. 2018;0. doi:10.3389/feduc.2018.00038

By Aubrey Bailey, PT, DPT, CHT Dr, Bailey is a Virginia-based physical therapist and professor of anatomy and physiology with over a decade of experience.