cognitive development of preschoolers essay

Cognitive Development In Preschool Children

Your three-year-old will spend most of her waking hours questioning everything that happens around her. She loves to ask “Why do I have to . . . ?” and she’ll pay close attention to your answers as long as they’re simple and to the point. Don’t feel that you have to explain your rules fully; she can’t yet understand such reasoning and isn’t interested in it anyway. If you try to have this kind of “serious” conversation, you’ll see her stare into space or turn her attention to more entertaining matters, such as a toy across the room or a truck passing outside the window. Instead, telling her to do something “because it’s good for you” or “so you don’t get hurt” will make more sense to her than a detailed explanation.

Your child’s more abstract “why” questions may be more difficult, partly because there may be hundreds of them each day and also because some of them have no answers—or none that you know. If the question is “Why does the sun shine?” or “Why can’t the dog talk to me?” you can answer that you don’t know, or invite her to look into the question further by finding a book about the sun or about dogs. Be sure to take these questions seriously. As you do, you help broaden your child’s knowledge, feed her curiosity, and teach her to think more clearly.

When your three-year-old is faced with specific learning challenges, you’ll find her reasoning still rather one-sided. She can’t yet see an issue from two angles, nor can she solve problems that require her to look at more than one factor at the same time. For example, if you take two equal cups of water and pour one into a short, fat container and the other into a tall, skinny one, she’ll probably say the tall container holds more water than the short. Even if she sees the two equal cups to start with and watches you pour, she’ll come up with the same answer. By her logic, the taller container is “bigger” and therefore must hold more. At around age seven, children finally understand that they have to look at multiple aspects of a problem before arriving at an answer.

At about three years of age, your child’s sense of time will become much clearer. Now she’ll know her own daily routine and will try hard to figure out the routines of others. For example, she may eagerly watch for the mail carrier who arrives every day, but be perplexed that trash is picked up only one day out of seven. She’ll understand that certain special events, such as holidays and birthdays, occur every once in a while, but even if she can tell you how old she is, she’ll have no real sense of the length of a year.

But if you have any questions or concerns about your three-year-old’s development, you should discuss it with your pediatrician. If he agrees that there is reason for concern, he will refer your child for further testing.

By age four, your child is beginning to explore many basic concepts that will be taught in greater detail in school. For example, he now understands that the day is divided into morning, afternoon, and night, and that there are different seasons. By the time he’s five and entering kindergarten, he may know some days of the week and that each day is measured in hours and minutes. He also may comprehend the essential ideas of counting, the alphabet, size relationships (big versus small), and the names of geometric shapes.

There are many good children’s books that illustrate these concepts, but don’t feel compelled to rush things. There’s no advantage to him learning them this early, and if he feels pressured to perform now, he actually may resist learning when he gets to school.

The best approach is to offer your child a wide range of learning opportunities. For instance, this is the perfect age to introduce him to zoos and museums, if you haven’t done so already. Many museums have special sections designed for children, where he can actively experience the learning process. At the same time, you should respect his special interests and talents. If your child seems very artistic, take him to art museums and galleries, or let him try a preschool art class. Also, if you know an artist, take him for a visit so he can see what a studio is like. If he’s most interested in machines and dinosaurs, take him to the natural history museum, help him learn to build models, and provide him with construction kits that allow him to create his own machines. Whatever his interests, you can use books to help answer his questions and open his horizons even further. At this age, then, your child should be discovering the joy of learning so that he will be self- motivated when his formal education begins.

You’ll also find that, in addition to exploring practical ideas, your four-year-old probably will ask many “universal” questions about subjects such as the origin of the world, death and dying, and the composition of the sun and the sky. Now, for example, is when you’ll hear the classic question “Why is the sky blue?” Like so many other parents, you may have trouble answering these questions, particularly in simple language your child will understand. As you grapple with these issues, don’t make up answers; rely instead on children’s books that deal with them. Your local library should be able to recommend age-appropriate books to help you.

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8 Chapter 8: Cognitive Development in Early Childhood

After reading this chapter, you should be able to:

• Compare and contrast Piaget and Vygotsky’s beliefs about cognitive development.
• Explain the role of information processing in cognitive development.
• Discuss how preschool-aged children understand their worlds.
• Put cognitive and language milestones into the order in which they appear in typically developing children.
• Discuss how early child education supports development and how our understanding of development influence education.
• Describe autism spectrum disorder, including characteristics and possible interventions.

Introduction

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 some basic principles about how the world works, they hold some pretty interesting initial ideas. For example, while adults have no concerns with taking a bath, a child of three might genuinely worry about being sucked down the drain. 1

Figure 8.1 – A child in a bathtub. 2

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. 3

Piaget’s Preoperational Intelligence

Piaget’s stage that coincides with early childhood is the  preoperational stage.  The word operational means logical, so these children were thought to be illogical. However, they were learning to use language or to think of the world symbolically. Let’s examine some of Piaget’s assertions about children’s cognitive abilities at this age.

Pretend Play

Pretending is a favorite activity at this time. A toy has qualities beyond the way it was designed to function and can now be used to stand for a character or object unlike anything originally intended. A teddy bear, for example, can be a baby or the queen of a faraway land!

Figure 8.2 – A child pretending to buy items at a toy grocery store. 4

According to Piaget, children’s pretend play helps them solidify new schemes they were developing cognitively. This play, then, reflects changes in their conceptions or thoughts. However, children also learn as they pretend and experiment. Their play does not simply represent what they have learned (Berk, 2007).

Egocentrism

Egocentrism in early childhood refers to the tendency of young children to think that everyone sees things in the same way as the child. Piaget’s classic experiment on egocentrism involved showing children a 3-dimensional model of a mountain and asking them to describe what a doll that is looking at the mountain from a different angle might see. Children tend to choose a picture that represents their own view, rather than that of the doll. However, children tend to use different sentence structures and vocabulary when addressing a younger child or an older adult. This indicates some awareness of the views of others.

Figure 8.3 – Piaget’s egocentrism experiment. 5

Syncretism refers to a tendency to think that if two events occur simultaneously, one caused the other.  An example of this is a child putting on their bathing suit to turn it to summertime.

Attributing lifelike qualities to objects is referred to as animism . The cup is alive, the chair that falls down and hits the child’s ankle is mean, and the toys need to stay home because they are tired.  Cartoons frequently show objects that appear alive and take on lifelike qualities. Young children do seem to think that objects that move may be alive but after age 3, they seldom refer to objects as being alive (Berk, 2007).

Classification Errors

Preoperational children have difficulty understanding that an object can be classified in more than one way. For example, if shown three white buttons and four black buttons and asked whether there are more black buttons or buttons, the child is likely to respond that there are more black buttons. As the child’s vocabulary improves and more schemes are developed, the ability to classify objects improves. 6

Conservation Errors

Conservation refers to the ability to recognize that moving or rearranging matter does not change the quantity . Let’s look at an example. A father gave a slice of pizza to 10-year-old Keiko and another slice to 3-year-old Kenny. Kenny’s pizza slice was cut into five pieces, so Kenny told his sister that he got more pizza than she did. Kenny did not understand that cutting the pizza into smaller pieces did not increase the overall amount. This was because Kenny exhibited Centration, or focused on only one characteristic of an object to the exclusion of others.

Kenny focused on the five pieces of pizza to his sister’s one piece even though the total amount was the same. Keiko was able to consider several characteristics of an object than just one. Because children have not developed this understanding of conservation, they cannot perform mental operations.

The classic Piagetian experiment associated with conservation involves liquid (Crain, 2005). As seen below, the child is shown two glasses (as shown in a) which are filled to the same level and asked if they have the same amount. Usually the child agrees they have the same amount. The researcher then pours the liquid from one glass to a taller and thinner glass (as shown in b). The child is again asked if the two glasses have the same amount of liquid. The preoperational child will typically say the taller glass now has more liquid because it is taller. The child has concentrated on the height of the glass and fails to conserve. 7

Figure 8.4 – Piagetian liquid conservation experiments. 8

Cognitive Schemas

As introduced in the first chapter, Piaget believed that in a quest for cognitive equilibrium, we use schemas (categories of knowledge) to make sense of the world. And when new experiences fit into existing schemas, we use assimilation to add that new knowledge to the schema. But when new experiences do not match an existing schema, we use accommodation to add a new schema. During early childhood, children use accommodation often as they build their understanding of the world around them.

Vygotsky’s Sociocultural Theory of Cognitive Development

As introduced in Chapter 1, Lev Vygotsky was a Russian psychologist who argued that culture has a major impact on a child’s cognitive development. He believed that the social interactions with adults and more knowledgeable peers can facilitate a child’s potential for learning. Without this interpersonal instruction, he believed children’s minds would not advance very far as their knowledge would be based only on their own discoveries. Let’s review some of Vygotsky’s key concepts.

Zone of Proximal Development and Scaffolding

Vygotsky’s best known concept is the zone of proximal development (ZPD). Vygotsky stated that children should be taught in the ZPD, which occurs when they can perform a task with assistance, but not quite yet on their own. With the right kind of teaching, however, they can accomplish it successfully. A good teacher identifies a child’s ZPD and helps the child stretch beyond it. Then the adult (teacher) gradually withdraws support until the child can then perform the task unaided. Researchers have applied the metaphor of scaffolds (the temporary platforms on which construction workers stand) to this way of teaching. Scaffolding is the temporary support that parents or teachers give a child to do a task.

Figure 8.5 – Zone of proximal development. 9

Private Speech

Do you ever talk to yourself? Why? Chances are, this occurs when you are struggling with a problem, trying to remember something, or feel very emotional about a situation. Children talk to themselves too. Piaget interpreted this as e gocentric s peech or a practice engaged in because of a child’s inability to see things from another’s point of view. Vygotsky, however, believed that children talk to themselves in order to solve problems or clarify thoughts. As children learn to think in words, they do so aloud before eventually closing their lips and engaging in p rivate s peech or inner speech.

Thinking out loud eventually becomes thought accompanied by internal speech, and talking to oneself becomes a practice only engaged in when we are trying to learn something or remember something. This inner speech is not as elaborate as the speech we use when communicating with others (Vygotsky, 1962). 10

Contrast with Piaget

Piaget was highly critical of teacher-directed instruction, believing that teachers who take control of the child’s learning place the child into a passive role (Crain, 2005). Further, teachers may present abstract ideas without the child’s true understanding, and instead they just repeat back what they heard. Piaget believed children must be given opportunities to discover concepts on their own. As previously stated, Vygotsky did not believe children could reach a higher cognitive level without instruction from more learned individuals. Who is correct? Both theories certainly contribute to our understanding of how children learn.

Information Processing

Information processing researchers have focused on several issues in cognitive development for this age group, including improvements in attention skills, changes in the capacity, and the emergence of executive functions in working memory. Additionally, in early childhood memory strategies, memory accuracy, and autobiographical memory emerge. Early childhood is seen by many researchers as a crucial time period in memory development (Posner & Rothbart, 2007).

Figure 8.6 – How information is processed. 11

Changes in attention have been described by many as the key to changes in human memory (Nelson & Fivush, 2004; Posner & Rothbart, 2007). However, attention is not a unified function; it is comprised of sub-processes. The ability to switch our focus between tasks or external stimuli is called divided attention or multitasking. This is separate from our ability to focus on a single task or stimulus, while ignoring distracting information, called selective attention . Different from these is sustained attention , or the ability to stay on task for long periods of time. Moreover, we also have attention processes that influence our behavior and enable us to inhibit a habitual or dominant response, and others that enable us to distract ourselves when upset or frustrated.

Divided Attention

Young children (age 3-4) have considerable difficulties in dividing their attention between two tasks, and often perform at levels equivalent to our closest relative, the chimpanzee, but by age five they have surpassed the chimp (Hermann, Misch, Hernandez-Lloreda & Tomasello, 2015; Hermann & Tomasello, 2015). Despite these improvements, 5-year-olds continue to perform below the level of school-age children, adolescents, and adults.

Selective Attention

Children’s ability with selective attention tasks improve as they age. However, this ability is also greatly influenced by the child’s temperament (Rothbart & Rueda, 2005), the complexity of the stimulus or task (Porporino, Shore, Iarocci & Burack, 2004), and along with whether the stimuli are visual or auditory (Guy, Rogers & Cornish, 2013). Guy et al. (2013) found that children’s ability to selectively attend to visual information outpaced that of auditory stimuli. This may explain why young children are not able to hear the voice of the teacher over the cacophony of sounds in the typical preschool classroom (Jones, Moore & Amitay, 2015). Jones and his colleagues found that 4 to 7 year-olds could not filter out background noise, especially when its frequencies were close in sound to the target sound. In comparison, 8- to 11-year-old children often performed similar to adults.

Figure 8.7 – A group of children making crafts. 12

Sustained Attention

Most measures of sustained attention typically ask children to spend several minutes focusing on one task, while waiting for an infrequent event, while there are multiple distractors for several minutes. Berwid, Curko-Kera, Marks & Halperin (2005) asked children between the ages of 3 and 7 to push a button whenever a “target” image was displayed, but they had to refrain from pushing the button when a non-target image was shown. The younger the child, the more difficulty he or she had maintaining their attention.

Figure 8.8 – A child playing a game that measures her sustained attention. 13

Based on studies of adults, people with amnesia, and neurological research on memory, researchers have proposed several “types” of memory (see Figure 4.14). Sensory memory (also called the sensory register) is the first stage of the memory system, and it stores sensory input in its raw form for a very brief duration; essentially long enough for the brain to register and start processing the information. Studies of auditory sensory memory show that it lasts about one second in 2 year-olds, two seconds in 3-year-olds, more than two seconds in 4-year-olds, and three to five seconds in 6-year-olds (Glass, Sachse, & von Suchodoletz, 2008). Other researchers have also found that young children hold sounds for a shorter duration than do older children and adults, and that this deficit is not due to attentional differences between these age groups, but reflects differences in the performance of the sensory memory system (Gomes et al., 1999). The second stage of the memory system is called short-term or working memory . Working memory is the component of memory in which current conscious mental activity occurs.

Working memory often requires conscious effort and adequate use of attention to function effectively. As you read earlier, children in this age group struggle with many aspects of attention and this greatly diminishes their ability to consciously juggle several pieces of information in memory. The capacity of working memory, that is the amount of information someone can hold in consciousness, is smaller in young children than in older children and adults. The typical adult and teenager can hold a 7 digit number active in their short-term memory. The typical 5-year-old can hold only a 4 digit number active. This means that the more complex a mental task is, the less efficient a younger child will be in paying attention to, and actively processing, information in order to complete the task.

Figure 8.8 – A child thinking. 14

Changes in attention and the working memory system also involve changes in executive function. Executive function (EF) refers to self-regulatory processes, such as the ability to inhibit a behavior or cognitive flexibility, that enable adaptive responses to new situations or to reach a specific goal. Executive function skills gradually emerge during early childhood and continue to develop throughout childhood and adolescence. Like many cognitive changes, brain maturation, especially the prefrontal cortex, along with experience influence the development of executive function skills.

A child shows higher executive functioning skills when the parents are more warm and responsive, use scaffolding when the child is trying to solve a problem, and provide cognitively stimulating environments for the child (Fay-Stammbach, Hawes & Meredith, 2014). For instance, scaffolding was positively correlated with greater cognitive flexibility at age two and inhibitory control at age four (Bibok, Carpendale & Müller, 2009). In Schneider, Kron-Sperl and Hunnerkopf’s (2009) longitudinal study of 102 kindergarten children, the majority of children used no strategy to remember information, a finding that was consistent with previous research. As a result, their memory performance was poor when compared to their abilities as they aged and started to use more effective memory strategies.

The third component in memory is long-term memory , which is also known as permanent memory. A basic division of long-term memory is between declarative and non-declarative memory.

• Declarative memories , sometimes referred to as explicit memories , are memories for facts or events that we can consciously recollect. Declarative memory is further divided into semantic and episodic memory.
• Semantic memories are memories for facts and knowledge that are not tied to a timeline,
• Episodic memories are tied to specific events in time.
• Non- declarative memories , sometimes referred to as implicit memories , are typically automated skills that do not require conscious recollection.

A utobiographical memory is our personal narrative. Adults rarely remember events from the first few years of life. In other words, we lack autobiographical memories from our experiences as an infant, toddler and very young preschooler. Several factors contribute to the emergence of autobiographical memory including brain maturation, improvements in language, opportunities to talk about experiences with parents and others, the development of theory of mind, and a representation of “self” (Nelson & Fivush, 2004). Two-year-olds do remember fragments of personal experiences, but these are rarely coherent accounts of past events (Nelson & Ross, 1980). Between 2 and 2 1⁄2 years of age children can provide more information about past experiences. However, these recollections require considerable prodding by adults (Nelson & Fivush, 2004). Over the next few years children will form more detailed autobiographical memories and engage in more reflection of the past.

Neo-Piagetians

As previously discussed, Piaget’s theory has been criticized on many fronts, and updates to reflect more current research have been provided by the Neo- Piagetians , or those theorists who provide “new” interpretations of Piaget’s theory. Morra, Gobbo, Marini and Sheese (2008) reviewed Neo-Piagetian theories, which were first presented in the 1970s, and identified how these “new” theories combined Piagetian concepts with those found in Information Processing. Similar to Piaget’s theory, Neo-Piagetian theories believe in constructivism, assume cognitive development can be separated into different stages with qualitatively different characteristics, and advocate that children’s thinking becomes more complex in advanced stages. Unlike Piaget, Neo-Piagetians believe that aspects of information processing change the complexity of each stage, not logic as determined by Piaget.

Neo-Piagetians propose that working memory capacity is affected by biological maturation, and therefore restricts young children’s ability to acquire complex thinking and reasoning skills. Increases in working memory performance and cognitive skills development coincide with the timing of several neurodevelopmental processes. These include myelination, axonal and synaptic pruning, changes in cerebral metabolism, and changes in brain activity (Morra et al., 2008).

Myelination especially occurs in waves between birth and adolescence, and the degree of myelination in particular areas explains the increasing efficiency of certain skills. Therefore, brain maturation, which occurs in spurts, affects how and when cognitive skills develop. Additionally, all Neo-Piagetian theories support that experience and learning interact with biological maturation in shaping cognitive development. 15

Children’s Understanding of the World

Both Piaget and Vygotsky believed that children actively try to understand the world around them. More recently developmentalists have added to this understanding by examining how children organize information and develop their own theories about the world.

Theory-Theory

The tendency of children to generate theories to explain everything they encounter is called theory-theory . This concept implies that humans are naturally inclined to find reasons and generate explanations for why things occur. Children frequently ask question about what they see or hear around them. When the answers provided do not satisfy their curiosity or are too complicated for them to understand, they generate their own theories. In much the same way that scientists construct and revise their theories, children do the same with their intuitions about the world as they encounter new experiences (Gopnik & Wellman, 2012). One of the theories they start to generate in early childhood centers on the mental states; both their own and those of others.

Figure 8.9 – What theories might this boy be creating? 16

Theory of Mind

Theory of mind refers to the ability to think about other people’s thoughts. This mental mind reading helps humans to understand and predict the reactions of others, thus playing a crucial role in social development. One common method for determining if a child has reached this mental milestone is the false belief task, described below.

The research began with a clever experiment by Wimmer and Perner (1983), who tested whether children can pass a false-belief test (see Figure 4.17). The child is shown a picture story of Sally, who puts her ball in a basket and leaves the room. While Sally is out of the room, Anne comes along and takes the ball from the basket and puts it inside a box. The child is then asked where Sally thinks the ball is located when she comes back to the room. Is she going to look first in the box or in the basket? The right answer is that she will look in the basket, because that’s where she put it and thinks it is; but we have to infer this false belief against our own better knowledge that the ball is in the box.

This is very difficult for children before the age of four because of the cognitive effort it takes. Three-year-olds have difficulty distinguishing between what they once thought was true and what they now know to be true. They feel confident that what they know now is what they have always known (Birch & Bloom, 2003). Even adults need to think through this task (Epley, Morewedge, & Keysar, 2004).

To be successful at solving this type of task the child must separate what he or she “knows” to be true from what someone else might “think” is true. In Piagetian terms, they must give up a tendency toward egocentrism. The child must also understand that what guides people’s actions and responses are what they “believe” rather than what is reality. In other words, people can mistakenly believe things that are false and will act based on this false knowledge. Consequently, prior to age four children are rarely successful at solving such a task (Wellman, Cross & Watson, 2001).

Researchers examining the development of theory of mind have been concerned by the overemphasis on the mastery of false belief as the primary measure of whether a child has attained theory of mind. Wellman and his colleagues (Wellman, Fang, Liu, Zhu & Liu, 2006) suggest that theory of mind is comprised of a number of components, each with its own developmental timeline (see Table 4.2).

Two-year-olds understand the diversity of desires, yet as noted earlier it is not until age four or five that children grasp false belief, and often not until middle childhood do they understand that people may hide how they really feel. In part, because children in early childhood have difficulty hiding how they really feel.

Cultural Differences in Theory of Mind

Those in early childhood in the US, Australia, and Germany develop theory of mind in the sequence outlined above. Yet, Chinese and Iranian preschoolers acquire knowledge access before diverse beliefs (Shahaeian, Peterson, Slaughter & Wellman, 2011). Shahaeian and colleagues suggested that cultural differences in childrearing may account for this reversal.

Parents in collectivistic cultures, such as China and Iran, emphasize conformity to the family and cultural values, greater respect for elders, and the acquisition of knowledge and academic skills more than they do autonomy and social skills (Frank, Plunkett & Otten, 2010). This could reduce the degree of familial conflict of opinions expressed in the family. In contrast, individualistic cultures encourage children to think for themselves and assert their own opinion, and this could increase the risk of conflict in beliefs being expressed by family members.

As a result, children in individualistic cultures would acquire insight into the question of diversity of belief earlier, while children in collectivistic cultures would acquire knowledge access earlier in the sequence. The role of conflict in aiding the development of theory of mind may account for the earlier age of onset of an understanding of false belief in children with siblings, especially older siblings (McAlister & Petersen, 2007; Perner, Ruffman & Leekman, 1994).

This awareness of the existence of theory of mind is part of social intelligence, such as recognizing that others can think differently about situations. It helps us to be self-conscious or aware that others can think of us in different ways and it helps us to be able to be understanding or be empathetic toward others. Moreover, this mind reading ability helps us to anticipate and predict people’s actions. The awareness of the mental states of others is important for communication and social skills. 21

Milestones of Cognitive Development

The many theories of cognitive development and the different research that has been done about how children understand the world, has allowed researchers to study the milestones that children who are typically developing experience in early childhood. Here is a table that summarizes those.

Table 8.1 – Cognitive Milestones 22

Language Development

Vocabulary growth.

A child’s vocabulary expands between the ages of 2 to 6 from about 200 words to over 10,000 words through a process called fast-mapping. Words are easily learned by making connections between new words and concepts already known. The parts of speech that are learned depend on the language and what is emphasized. Children speaking verb-friendly languages such as Chinese and Japanese, tend to learn nouns more readily. But, those learning less verb-friendly languages such as English, seem to need assistance in grammar to master the use of verbs (Imai, et al, 2008).

Figure 8.14 – A woman instructing a girl on vocabulary. 23

Literal Meanings

Children can repeat words and phrases after having heard them only once or twice. But they do not always understand the meaning of the words or phrases. This is especially true of expressions or figures of speech which are taken literally. For example, two preschool-aged girls began to laugh loudly while listening to a tape-recording of Disney’s “Sleeping Beauty” when the narrator reports, “Prince Phillip lost his head!” They imagine his head popping off and rolling down the hill as he runs and searches for it. Or a classroom full of preschoolers hears the teacher say, “Wow! That was a piece of cake!” The children began asking “Cake? Where is my cake? I want cake!”

Overregularization

Children learn rules of grammar as they learn language but may apply these rules inappropriately at first. For instance, a child learns to add “ed” to the end of a word to indicate past tense. Then form a sentence such as “I goed there. I doed that.” This is typical at ages 2 and 3. They will soon learn new words such as “went” and “did” to be used in those situations.

The Impact of Training

Remember Vygotsky and the zone of proximal development? Children can be assisted in learning language by others who listen attentively, model more accurate pronunciations and encourage elaboration. The child exclaims, “I goed there!” and the adult responds, “You went there? Say, ‘I went there.’ Where did you go?” Children may be ripe for language as Chomsky suggests, but active participation in helping them learn is important for language development as well. The process of scaffolding is one in which the adult (or more skilled peer) provides needed assistance to the child as a new skill is learned.

Language Milestones

The prior aspects of language development in early childhood can also be summarized into the progression of milestones children typically experience from ages 3 to 5. Here is a table of those.

Table 8.2 – Language Milestones 24

Now that we have addressed some of the cognitive areas of growth in early childhood, let’s take a look at the topic of school and its various applications.

Early Childhood Education

Providing universal preschool has become an important lobbying point for federal, state, and local leaders throughout our country. In his 2013 State of the Union address, President Obama called upon congress to provide high quality preschool for all children. He continued to support universal preschool in his legislative agenda, and in December 2014 the President convened state and local policymakers for the White House Summit on Early Education (White House Press Secretary, 2014).

However, universal preschool covering all four-year olds in the country would require significant funding. Further, how effective preschools are in preparing children for elementary school, and what constitutes high quality early childhood education have been debated.

To set criteria for designation as a high quality preschool, the National Association for the Education of Young Children (NAEYC) identifies 10 standards (NAEYC, 2016). These include:

• Positive relationships among all children and adults are promoted.
• A curriculum that supports learning and development in social, emotional, physical, language, and cognitive areas.
• Teaching approaches that are developmentally, culturally and linguistically appropriate.
• Assessment of children’s progress to provide information on learning and development.
• The health and nutrition of children are promoted, while they are protected from illness and injury.
• Teachers possess the educational qualifications, knowledge, and commitment to promote children’s learning.
• Collaborative relationships with families are established and maintained.
• Relationships with agencies and institutions in the children’s communities are established to support the program’s goals.
• The indoor and outdoor physical environments are safe and well-maintained.
• Leadership and management personnel are well qualified, effective, and maintain licensure status with the applicable state agency.

Parents should review preschool programs using the NAEYC criteria as a guide and template for asking questions that will assist them in choosing the best program for their child.

Figure 8.15 – Children making crafts at preschool. 25

Selecting the right preschool is also difficult because there are so many types of preschools available. Zachry (2013) identified Montessori, Waldorf, Reggio Emilia, High Scope, Creative Curriculum and Bank Street as types of early childhood education programs that focus on children learning through discovery. Teachers act as facilitators of children’s learning and development and create activities based on the child’s developmental level. Here is a table summarizes characteristics of each type of program.

Table 8.3 – Types of Early Childhood Education Programs 26

For children who live in poverty, Head Start has been providing preschool education since 1965 when it was begun by President Lyndon Johnson as part of his war on poverty. It currently serves nearly one million children and annually costs approximately 7.5 billion dollars (United States Department of Health and Human Services, 2015). However, concerns about the effectiveness of Head Start have been ongoing since the program began. Armor (2015) reviewed existing research on Head Start and found there were no lasting gains, and the average child in Head Start had not learned more than children who did not receive preschool education.

Figure 8.16 – A photograph from when Head Start began. 27

A recent report dated July 2015 evaluating the effectiveness of Head Start comes from the What Works Clearinghouse. The What Works Clearinghouse identifies research that provides reliable evidence of the effectiveness of programs and practices in education, and is managed by the Institute of Education Services for the United States Department of Education. After reviewing 90 studies on the effectiveness of Head Start, only one study was deemed scientifically acceptable and this study showed disappointing results (Barshay, 2015). This study showed that 3- and 4-year-old children in Head Start received “potentially positive effects” on general reading achievement, but no noticeable effects on math achievement and social-emotional development.

Nonexperimental designs are a significant problem in determining the effectiveness of Head Start programs because a control group is needed to show group differences that would demonstrate educational benefits. Because of ethical reasons, low income children are usually provided with some type of pre-school programming in an alternative setting. Additionally, Head Start programs are different depending on the location, and these differences include the length of the day or qualification of the teachers. Lastly, testing young children is difficult and strongly dependent on their language skills and comfort level with an evaluator (Barshay, 2015). 28

Applications to Early Education

Understanding how children think and learn has proven useful for improving education. Activities like playing games that involve working with numbers and spatial relationships can give young children a developmental advantage over peers who have less exposure to the same concepts.

Mathematics

Even before they enter kindergarten, the mathematical knowledge of children from low-income backgrounds lags far behind that of children from more affluent backgrounds. Ramani and Siegler (2008) hypothesized that this difference is due to the children in middle- and upper-income families engaging more frequently in numerical activities, for example playing numerical board games such as Chutes and Ladders. Chutes and Ladders is a game with a number in each square; children start at the number one and spin a spinner or throw a dice to determine how far to move their token. Playing this game seemed likely to teach children about numbers, because in it, larger numbers are associated with greater values on a variety of dimensions. In particular, the higher the number that a child’s token reaches, the greater the distance the token will have traveled from the starting point, the greater the number of physical movements the child will have made in moving the token from one square to another, the greater the number of number-words the child will have said and heard, and the more time will have passed since the beginning of the game. These spatial, kinesthetic, verbal, and time-based cues provide a broad-based, multisensory foundation for knowledge of numerical magnitudes (the sizes of numbers), a type of knowledge that is closely related to mathematics achievement test scores (Booth & Siegler, 2006).

Playing this numerical board game for roughly 1 hour, distributed over a 2-week period, improved low-income children’s knowledge of numerical magnitudes, ability to read printed numbers, and skill at learning novel arithmetic problems. The gains lasted for months after the game-playing experience (Ramani & Siegler, 2008; Siegler & Ramani, 2009). An advantage of this type of educational intervention is that it has minimal if any cost—a parent could just draw a game on a piece of paper.

Cognitive developmental research has shown that phonemic awareness—that is, awareness of the component sounds within words—is a crucial skill in learning to read. To measure awareness of the component sounds within words, researchers ask children to decide whether two words rhyme, to decide whether the words start with the same sound, to identify the component sounds within words, and to indicate what would be left if a given sound were removed from a word. Kindergartners’ performance on these tasks is the strongest predictor of reading achievement in third and fourth grade, even stronger than IQ or social class background (Nation, 2008). Moreover, teaching these skills to randomly chosen 4- and 5-year-olds results in their being better readers years later (National Reading Panel, 2000).

Continuing Brain Maturation

Understanding of cognitive development is advancing on many different fronts. One exciting area is linking changes in brain activity to changes in children’s thinking (Nelson et al., 2006). Although many people believe that brain maturation is something that occurs before birth, the brain actually continues to change in large ways for many years thereafter. For example, a part of the brain called the prefrontal cortex, which is located at the front of the brain and is particularly involved with planning and flexible problem solving, continues to develop throughout adolescence (Blakemore & Choudhury, 2006). Such new research domains, as well as enduring issues such as nature and nurture, continuity and discontinuity, and how to apply cognitive development research to education, insure that cognitive development will continue to be an exciting area of research in the coming years. 29

Cognitive Differences

Sometimes children’s brains work differently. One form of this neurodiversity is Autism spectrum disorder.

Autism: Defining Spectrum Disorder

Autism spectrum disorder (ASD) describes a range of conditions classified as neuro-developmental disorders in the fifth revision of the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The DSM-5, published in 2013, redefined the autism spectrum to encompass the previous (DSM-IV-TR) diagnoses of autism, Asperger syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), and childhood disintegrative disorder. These disorders are characterized by social deficits and communication difficulties, repetitive behaviors and interests, sensory issues, and in some cases, cognitive delays.

Asperger syndrome was distinguished from autism in the earlier DSM-IV by the lack of delay or deviance in early language development. Additionally, individuals diagnosed with Asperger syndrome did not have significant cognitive delays. PDD-NOS was considered “subthreshold autism” and “atypical autism” because it was often characterized by milder symptoms of autism or symptoms in only one domain (such as social difficulties). In the DSM-5, both of these diagnoses have been subsumed into autism spectrum disorder.

Autism spectrum disorders are considered to be on a spectrum because each individual with ASD expresses the disorder uniquely and has varying degrees of functionality. Many have above-average intellectual abilities and excel in visual skills, music, math, and the arts, while others have significant disabilities and are unable to live independently. About 25 percent of individuals with ASD are nonverbal; however, they may learn to communicate using other means.

Social Communication Symptoms

Social impairments in children with autism can be characterized by a distinctive lack of intuition about others. Unusual social development becomes apparent early in childhood. Infants with ASD show less attention to social stimuli, smile and look at others less often, and respond less to their own name. Toddlers with ASD differ more strikingly from social norms; for example, they may show less eye contact and turn-taking and may not have the ability to use simple movements to express themselves. Individuals with severe forms of ASD do not develop enough natural speech to meet their daily communication needs.

Restricted and Repetitive Behaviors

Children with ASD may exhibit repetitive or restricted behavior, including:

• Stereotypy—repetitive movement, such as hand flapping, head rolling, or body rocking.
• Compulsive behavior—exhibiting intention to follow rules, such as arranging objects in stacks or lines.
• Sameness—resistance to change; for example, insisting that the furniture not be moved or sticking to an unvarying pattern of daily activities.
• Restricted behavior—limits in focus, interest, or activity, such as preoccupation with a single television program, toy, or game.
• Self-injury—movements that injure or can injure the person, such as eye poking, skin picking, hand biting, and head banging.

Figure 8.17 – A boy stacking cans. 30

While specific causes of ASD have yet to be found, many risk factors have been identified in the research literature that may contribute to its development. These risk factors include genetics, prenatal and perinatal factors, neuroanatomical abnormalities, and environmental factors. It is possible to identify general risk factors, but much more difficult to pinpoint specific factors.

ASD affects information processing in the brain by altering how nerve cells and their synapses connect and organize; thus, it is categorized as a neuro-developmental disorder. The results of family and twin studies suggest that genetic factors play a role in the etiology of ASD and other pervasive developmental disorders. Studies have consistently found that the prevalence of ASD in siblings of children with ASD is approximately 15 to 30 times greater than the rate in the general population. In addition, research suggests that there is a much higher concordance rate among monozygotic (identical) twins compared to dizygotic (fraternal) twins. It appears that there is no single gene that can account for ASD; instead, there seem to be multiple genes involved, each of which is a risk factor for part of the autism syndrome through various groups. It is unclear whether ASD is explained more by rare mutations or by combinations of common genetic variants.

The Diversity of the Autism Spectrum

The rainbow-colored infinity symbol represents the diversity of the autism spectrum as well as the greater neurodiversity movement. The neurodiversity movement suggests that diverse neurological conditions appear as a result of normal variations in the human genome. It challenges the idea that such neurological differences are inherently pathological, instead asserting that differences should be recognized and respected as a social category on a par with gender, ethnicity, sexual orientation, or disability status.

Figure 8.18 – A symbol of the autism spectrum. 31

Prenatal and Perinatal Factors

A number of prenatal and perinatal complications have been reported as possible risk factors for ASD. These risk factors include maternal gestational diabetes, maternal and paternal age over 30, bleeding after first trimester, use of prescription medication (such as valproate) during pregnancy, and meconium (the earliest stool of an infant) in the amniotic fluid. While research is not conclusive on the relation of these factors to ASD, each of these factors has been identified more frequently in children with ASD than in developing youth without ASD.

Environmental Factors

Evidence for environmental causes is anecdotal and has not been confirmed by reliable studies. In the last few decades, controversy surrounded the idea that vaccinations may be the cause for many cases of autism; however, these theories lack scientific evidence and are biologically implausible. Even still, parental concern about a potential vaccine link with autism has led to lower rates of childhood immunizations, outbreaks of previously controlled childhood diseases in some countries, and the preventable deaths of several children.

There is no known cure for ASD, and treatment tends to focus on management of symptoms. The main goals when treating children with ASD are to lessen associated deficits and family distress and to increase quality of life and functional independence. 32 Treatment for ASD should begin as soon as possible after diagnosis. Early treatment for ASD is important as proper care can reduce individuals’ difficulties while helping them learn new skills and make the most of their strengths.

The wide range of issues facing people with ASD means that there is no single best treatment for ASD. 33 So treatment is typically tailored to the individual person’s needs. Intensive, sustained special-education programs and behavior therapy yearly in life can help children acquire self-care, social, and job skills. The most widely used therapy is applied behavior analysis (ABA); other available approaches include developmental models, structured teaching, speech and language therapy, social skills therapy, and occupational therapy. 34

Figure 8.19 – A boy with ASD receiving therapy. 35

There has been increasing attention to the development of evidenced-based interventions for young children with ASD. Although evidence-based interventions for children with ASD vary in their methods, many adopt a psychoeducational approach to enhancing cognitive, communication, and social skills while minimizing behaviors that are thought to be problematic. 36

In this chapter we covered,

• Piaget’s preoperational stage.
• Vygotsky’s sociocultural theory.
• Information processing.
• How young children understand the world.
• Typical progression of cognitive and language development (milestones).
• Early childhood education.
• Autism spectrum disorder.

In the next chapter, we will finish covering early childhood education by looking at how children understand themselves and interact with the world.

Child Growth and Development Copyright © by Jean Zaar is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Cognitive Development Essay

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The basic premises of cognitive development theory

Discussion of piaget theory and vygotsky theory on intelligence development, stages of development in both theories, classroom application of both theorists’ views.

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 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).

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.

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).

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.

• Comparison of Piaget’s and Vygotsky’s Theories
• Lev Vygotsky Views on Constructivism
• Developmental Psychology: Cognitive Theories
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• Cognitive Psychology on Driving and Phone Usage
• Intelligence Definition and Measurement
• Response Paper: "Do I Have a Good Dream?"
• Humanist Psychology, Cognitive Psychology and Positive Psychology
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Cognitive Development in 3-5 Year Olds

The preschool period is a time of rapid growth along a number of developmental measures, especially children's thinking abilities, or cognition..

The preschool period is a time of rapid growth along a number of developmental measures, not the least of which is children’s thinking abilities, or cognition. Across this time period, children learn to use symbolic thought, the hallmarks of which are language and symbol use, along with more advanced pretend play. Children this age show centration of thought, meaning their focus is limited to one aspect of a situation or object. Memory abilities come online and children show their own ways of categorizing, reasoning, and problem solving.

Memory Memory is the ability to acquire, store, and recall information or experiences across time. It is not until age 3 that children can reliably do this, although they remain better at recognition than recall, and they do not show the ability to spontaneously use mnemonic strategies to assist remembering for a number of years. Preschoolers use language to encode and compare information for later retrieval; thus, talking about events increases children’s memory of them. Want to work on phonics and memory at the same time? Check out this fun  Clifford game .

Memories are more easily recalled when the child is a participant as opposed to an observer, or when something makes a significant impression. Children’s ability to create mental images of people or events also facilitates memory. Help your child learn to create and maintain images with these fun  puzzles .

Children tend to use routines to define understanding of events, and to recall sequence, but preschoolers’ sense of time is very general (e.g., they may use the word “yesterday” to mean a month ago). Want to develop your child’s sequencing skills? Try this  interactive game . As a result of their relatively weak memory skills, they can repeatedly hear the same story over and over, and delight in each retelling as if it were the first time.

Vygotsky Russian researcher Lev Vygotsky believed cognition advanced through social interactions and problem solving. Vygotsky’s work demonstrates that with the support of a More Knowledgeable Other (MKO) (adult or more skilled peer), children’s ability shows marked increase, as long as the interactions were not too advanced for the child’s present level of skill. He believed the right level of challenge would be in the child’s “Zone of Proximal Development (ZPD),” which would be optimized by scaffolding (support and guidance that the MKO would provide without taking over).

Vygotsky also noticed that, as children were moving towards independence with challenging tasks, they would talk to themselves. Termed private speech, this self-talk is highly prevalent in children ages 3-7. Thereafter, it mutates into inner speech or internal thought, although it is likely to resurface at challenging or confusing tasks. According to Vygotsky, children’s use of language in this way is the foundation of their executive function skills, including attention, memorization, planning, impulse control, etc.

Preschool Thinking Preschoolers are firmly in the stage Piaget called the preoperational (pre-logical) period (from 2-7). While current researchers question if preschoolers are as illogical as Piaget posited, anyone who has spent time with them knows they think differently than adults! Notably, they are not able to reverse actions (e.g., understand that if 3+3=6, then 6-3=3, or worrying that if they break a bone, it cannot be fixed). In addition, they are unable to conserve (to recognize that objects that change in form do not change in amount). In his famous penny conservation experiment, Piaget demonstrated that until about the age of 6, children would say that the spread out row of pennies had more than the row with the (equal number) of more squished together pennies, even if they themselves counted each row. Piaget explains this contradiction by stating that children’s logic in this time period is ruled by perceptions as opposed to reasoning.

The idea of perceptually-based centration expands beyond conservation to the preschoolers’ larger world view. In general, children this age are egocentric; they cannot spontaneously and independently vary from their own perspective. For example, children may say that grass grows so that they do not get hurt when they fall or because they like chocolate, everyone must. As an extension, they believe that everyone shares the same viewpoint as them, so of course they should get the cookies if they think that, everybody does. As a component of egocentric thought, preschoolers show animism, the belief that nature and objects are alive with human-like characteristics (e.g., when your child says that the ground made them fall). The ability to decenter is one of the hallmarks of the completion of the preoperational stage.

Children’s illogical thinking extends across various domains. For example, in their classification abilities, they cannot yet understand that one object can be classified multiple ways. For example, children may say there are more girls than children in a co-ed class, or that they don’t want fruit for snack, they want a pear. In the same way, they will often over-generalize their category labels. For example, a child may call all animals with four legs “dogs,” or all people with gray hair “grandma.”

In addition, preschoolers often rely on transductive reasoning, whereby they believe the similarities between two objects or the sequence of events provides evidence of cause and effect. For example, if a child sees their teacher at school in the morning and again when they leave, they may believe their teacher must live there. Similarly, if their friend is Italian and eats pasta, they may believe that eating pasta will make someone Italian. In these examples, we see the way preschoolers’ thoughts are dominated by their perceptions. As an extension, preschoolers demonstrate magical thinking, whereby they believe that if they wish for something, they have the power to make it happen, including accidentally wishing harm on a sibling, or being the cause of their parent’s divorce. Try  Flabby Physics  for some fun ways to develop your child’s sense of cause and effect.

Symbol Use The time from 3-5 is the heart of symbol development in young children. Use of symbols entails the ability to use one thing to represent another, for example to have the letters ‘dog’ represent an actual dog, have a drawing/map stand for a location, or to have a checker represent a cookie in a game. Preschoolers learn to mentally use and represent tangible objects through images, words, and drawings. Encourage your child’s drawing skills with these free fun apps: GlowFree or DoodleBuddy. While children cannot yet manipulate these symbols, or represent abstract ideas, the ability to use symbols rather than engage in simple motor play is a defining characteristic of the preschool period.

In fact, imaginative play is related to cognitive growth and achievement. For example, preschoolers who engage in more complex pretend play demonstrate advanced general intellectual development and are seen as more socially competent by their teachers. Children who create imaginary friends, who previously would have been red-flagged as at risk for maladjustment, demonstrate more advanced mental representations and more sociability with their peers than those who do not.

While there is no denying the unique perspective that preschoolers view the world with, there are contexts and domains within which these very young children do in fact think logically. The key to this “hidden ability” is the amount of knowledge or experience the child has in the particular domain or area of study. Importantly, the way this knowledge is acquired—through investment, engagement, exploration, and discovery—is the means by which preschoolers advance in their thinking and reasoning skills. 

• Vygotsky’s Theory 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.

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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.

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Sociocultural Theory 

The work of Lev Vygotsky (1934, 1978) has become the foundation of much research and theory in cognitive development over the past several decades, particularly what has become known as sociocultural theory.

Vygotsky’s theory comprises concepts such as culture-specific tools, private speech, and the zone of proximal development.

Vygotsky believed cognitive development is influenced by cultural and social factors. He emphasized the role of social interaction in the development of mental abilities e.g., speech and reasoning in children.

Vygotsky strongly believed that community plays a central role in the process of “making meaning.”

Cognitive development is a socially mediated process in which children acquire cultural values, beliefs, and problem-solving strategies through collaborative dialogues with more knowledgeable members of society.

The more knowledgeable other (MKO) is someone who has a higher level of ability or greater understanding than the learner regarding a particular task, process, or concept.

The MKO can be a teacher, parent, coach, or even a peer who provides guidance and modeling to enable the child to learn skills within their zone of proximal development (the gap between what a child can do independently and what they can achieve with guidance).

The interactions with more knowledgeable others significantly increase not only the quantity of information and the number of skills a child develops, but also affects the development of higher-order mental functions such as formal reasoning. Vygotsky argued that higher mental abilities could only develop through interaction with more advanced others.

According to Vygotsky, adults in society foster children’s cognitive development by engaging them in challenging and meaningful activities. Adults convey to children how their culture interprets and responds to the world.

They show the meaning they attach to objects, events, and experiences. They provide the child with what to think (the knowledge) and how to think (the processes, the tools to think with).

Vygotsky’s theory encourages collaborative and cooperative learning between children and teachers or peers. Scaffolding and reciprocal teaching are effective educational strategies based on Vygotsky’s ideas.

Scaffolding involves the teacher providing support structures to help students master skills just beyond their current level. In reciprocal teaching, teachers and students take turns leading discussions using strategies like summarizing and clarifying. Both scaffolding and reciprocal teaching emphasize the shared construction of knowledge, in line with Vygotsky’s views.

Vygotsky highlighted the importance of language in cognitive development. Inner speech is used for mental reasoning, and external speech is used to converse with others.

These operations occur separately. Indeed, before age two, a child employs words socially; they possess no internal language.

Once thought and language merge, however, the social language is internalized and assists the child with their reasoning. Thus, the social environment is ingrained within the child’s learning.

Effects of Culture

Vygotsky emphasized the role of the social environment in the child’s cognitive development.

Vygotsky claimed that infants are born with the basic abilities for intellectual development called “elementary mental functions” (Piaget focuses on motor reflexes and sensory abilities). These develop throughout the first two years of life due to direct environmental contact.

Elementary mental functions include –

o Attention o Sensation o Perception o Memory

Eventually, through interaction within the sociocultural environment, these are developed into more sophisticated and effective mental processes, which Vygotsky refers to as “higher mental functions.”

Tools of intellectual adaptation

Each culture provides its children with tools of intellectual adaptation that allow them to use basic mental functions more effectively/adaptively.

Tools of intellectual adaptation is Vygotsky’s term for methods of thinking and problem-solving strategies that children internalize through social interactions with the more knowledgeable members of society.

For example, memory in young children is limited by biological factors. However, culture determines the type of memory strategy we develop.

For example, in Western culture, children learn note-taking to aid memory, but in pre-literate societies, other strategies must be developed, such as tying knots in a string to remember, carrying pebbles, or repeating the names of ancestors until large numbers can be repeated.

Vygotsky, therefore, sees cognitive functions, even those carried out alone, as affected by the beliefs, values, and tools of intellectual adaptation of the culture in which a person develops and, therefore, socio-culturally determined.

Therefore, intellectual adaptation tools vary from culture to culture – as in the memory example.

Social Influences on Cognitive Development

Like Piaget, Vygotsky believes that young children are curious and actively involved in their own learning and discovering and developing new understandings/schema .

However, Vygotsky emphasized social contributions to the development process, whereas Piaget emphasized self-initiated discovery.

According to Vygotsky (1978), much important learning by the child occurs through social interaction with a skillful tutor. The tutor may model behaviors and/or provide verbal instructions for the child.

Vygotsky refers to this as cooperative or collaborative dialogue. The child seeks to understand the actions or instructions provided by the tutor (often the parent or teacher) and then internalizes the information, using it to guide or regulate their performance.

Shaffer (1996) gives the example of a young girl given her first jigsaw. Alone, she performs poorly in attempting to solve the puzzle. The father then sits with her and describes or demonstrates some basic strategies, such as finding all the corner/edge pieces, and provides a couple of pieces for the child to put together herself, and offers encouragement when she does so.

As the child becomes more competent, the father allows the child to work more independently. According to Vygotsky, this social interaction involving cooperative or collaborative dialogue promotes cognitive development.

To understand Vygotsky’s theories on cognitive development, one must understand two of the main principles of Vygotsky’s work: the More Knowledgeable Other (MKO) and the Zone of Proximal Development (ZPD).

More Knowledgeable Other

The more knowledgeable other (MKO) is somewhat self-explanatory; it refers to someone who has a better understanding or a higher ability level than the learner, concerning a particular task, process, or concept.

Although the implication is that the MKO is a teacher or an older adult, this is not necessarily the case. Often, a child’s peers or an adult’s children may be the individuals with more knowledge or experience.

For example, who is more likely to know more about the newest teenage music groups, how to win at the most recent PlayStation game, or how to correctly perform the newest dance craze – a child or their parents?

In fact, the MKO need not be a person at all. To support employees in their learning process, some companies are now using electronic performance support systems.

Electronic tutors have also been used in educational settings to facilitate and guide students through learning. The key to MKOs is that they must have (or be programmed with) more knowledge about the topic being learned than the learner does.

Zone of Proximal Development

The concept of the more knowledgeable other relates to the second important principle of Vygotsky’s work, the zone of proximal development .

This important concept relates to the difference between what a child can achieve independently and what a child can achieve with guidance and encouragement from a skilled partner.

Vygotsky consequently focuses much more closely on social interaction as an aid to learning, arguing that, left alone, children will develop – but not to their full potential.

He refers to the gap between actual and potential learning as the zone of proximal development (ZPD) – and argues that it is only through collaboration with adults and other learners that this gap can be bridged.

The zone of proximal development is the gap between the level of actual development, what the child can do on his own, and the level of potential development, what a child can do with the assistance of more advanced and competent individuals.

Social interaction, therefore, supports the child’s cognitive development in the ZPD, leading to a higher level of reasoning. It is generally believed that social dialogues have two important features.

The first is intersubjectivity, where two individuals who might have different understandings of a task, arrive at a shared understanding by adjusting to the perspective of the other.

The second feature is referred to as scaffolding. Adults may begin with direct instruction, but as children’s mastery of a task increases, so the adult tends to withdraw their own contributions in recognition of the child’s increasing success.

For example, the child could not solve the jigsaw puzzle (in the example above) by itself and would have taken a long time to do so (if at all), but was able to solve it following interaction with the father, and has developed competence at this skill that will be applied to future jigsaws.

ZPD is the zone where instruction is the most beneficial, as it is when the task is just beyond the individual’s capabilities. To learn, we must be presented with tasks just out of our ability range. Challenging tasks promote maximum cognitive growth.

As a result of shared dialogues with more knowledgeable others, who provide hints, instructions, and encouragement, the child can internalize the ‘how to do it’ part of the task as part of their inner or private speech. The child can then use this on later occasions when they tackle a similar task on their own.

Vygotsky (1978) sees the Zone of Proximal Development as the area where the most sensitive instruction or guidance should be given – allowing the child to develop skills they will then use on their own – developing higher mental functions.

Vygotsky also views peer interaction as an effective way of developing skills and strategies.  He suggests that teachers use cooperative learning exercises where less competent children develop with help from more skillful peers – within the zone of proximal development.

Evidence for Vygotsky and the ZPD

Freund (1990) conducted a study in which children had to decide which items of furniture should be placed in particular areas of a doll’s house.

Some children were allowed to play with their mother in a similar situation before they attempted it alone (zone of proximal development) while others were allowed to work on this by themselves (Piaget’s discovery learning).

Freund found that those who had previously worked with their mother (ZPD) showed the greatest improvement compared with their first attempt at the task.

The conclusion is that guided learning within the ZPD led to greater understanding/performance than working alone (discovery learning).

Vygotsky and Language

Vygotsky believed that language develops from social interactions for communication purposes. Vygotsky viewed language as man’s greatest tool for communicating with the outside world.

According to Vygotsky (1962), language plays two critical roles in cognitive development:

• It is the main means by which adults transmit information to children.
• Language itself becomes a very powerful tool for intellectual adaptation.

Vygotsky (1987) differentiates between three forms of language:

• Social speech, which is external communication used to talk to others (typical from the age of two);
• Private speech (typical from the age of three) which is directed to the self and serves an intellectual function;
• Private speech goes underground , diminishing in audibility as it takes on a self-regulating function and is transformed into silent inner speech (typical from the age of seven).

For Vygotsky, thought and language are initially separate systems from the beginning of life, merging at around three years of age.

At this point, speech and thought become interdependent: thought becomes verbal, and speech becomes representational.

As children develop mental representation, particularly the skill of language, they start to communicate with themselves in much the same way as they would communicate with others.

When this happens, children’s monologues are internalized to become inner speech. The internalization of language is important as it drives cognitive development.

“Inner speech is not the interiour aspect of external speech – it is a function in itself. It still remains speech, i.e., thought connected with words. But while in external speech thought is embodied in words, in inner speech words dies as they bring forth thought. Inner speech is to a large extent thinking in pure meanings.” (Vygotsky, 1962: p. 149)

Private Speech

Vygotsky (1987) was the first psychologist to document the importance of private speech.

He considered private speech as the transition point between social and inner speech, the moment in development where language and thought unite to constitute verbal thinking.

Thus, in Vygotsky’s view, private speech was the earliest manifestation of inner speech. Indeed, private speech is more similar (in form and function) to inner speech than social speech.

Private speech is “typically defined, in contrast to social speech, as speech addressed to the self (not to others) for the purpose of self-regulation (rather than communication).” (Diaz, 1992, p.62)

Private speech is overt, audible, and observable, often seen in children who talk to themselves while problem-solving.

Conversely, inner speech is covert or hidden because it happens internally. It is the silent, internal dialogue that adults often engage in while thinking or problem-solving.

In contrast to Piaget’s (1959) notion of private speech representing a developmental dead-end, Vygotsky (1934, 1987) viewed private speech as:

“A revolution in development which is triggered when preverbal thought and preintellectual language come together to create fundamentally new forms of mental functioning.” (Fernyhough & Fradley, 2005: p. 1)

In addition to disagreeing on the functional significance of private speech, Vygotsky and Piaget also offered opposing views on the developmental course of private speech and the environmental circumstances in which it occurs most often (Berk & Garvin, 1984).

Through private speech, children collaborate with themselves, in the same way a more knowledgeable other (e.g., adults) collaborate with them to achieve a given function.

Vygotsky sees “private speech” as a means for children to plan activities and strategies, aiding their development. Private speech is the use of language for self-regulation of behavior.

Therefore, language accelerates thinking/understanding ( Jerome Bruner also views language in this way). Vygotsky believed that children who engage in large amounts of private speech are more socially competent than children who do not use it extensively.

Vygotsky (1987) notes that private speech does not merely accompany a child’s activity but acts as a tool the developing child uses to facilitate cognitive processes, such as overcoming task obstacles, and enhancing imagination, thinking, and conscious awareness.

Children use private speech most often during intermediate difficulty tasks because they attempt to self-regulate by verbally planning and organizing their thoughts (Winsler et al., 2007).

The frequency and content of private speech correlate with behavior or performance. For example, private speech appears functionally related to cognitive performance: It appears at times of difficulty with a task.

For example, tasks related to executive function (Fernyhough & Fradley, 2005), problem-solving tasks (Behrend et al., 1992), and schoolwork in both language (Berk & Landau, 1993), and mathematics (Ostad & Sorensen, 2007).

Berk (1986) provided empirical support for the notion of private speech. She found that most private speech exhibited by children serves to describe or guide the child’s actions.

Berk also discovered that children engaged in private speech more often when working alone on challenging tasks and when their teacher was not immediately available to help them.

Furthermore, Berk also found that private speech develops similarly in all children regardless of cultural background.

There is also evidence (Behrend et al., 1992) that those children who displayed the characteristic whispering and lip movements associated with private speech when faced with a difficult task were generally more attentive and successful than their ‘quieter’ classmates.

Vygotsky (1987) proposed that private speech is a product of an individual’s social environment. This hypothesis is supported by the fact that there exist high positive correlations between rates of social interaction and private speech in children.

Children raised in cognitively and linguistically stimulating environments (situations more frequently observed in higher socioeconomic status families) start using and internalizing private speech faster than children from less privileged backgrounds.

Indeed, children raised in environments characterized by low verbal and social exchanges exhibit delays in private speech development.

Children’s use of private speech diminishes as they grow older and follows a curvilinear trend. This is due to changes in ontogenetic development whereby children can internalize language (through inner speech) to self-regulate their behavior (Vygotsky, 1987).

For example, research has shown that children’s private speech usually peaks at 3–4 years of age, decreases at 6–7, and gradually fades out to be mostly internalized by age 10 (Diaz, 1992).

Vygotsky proposed that private speech diminishes and disappears with age not because it becomes socialized, as Piaget suggested, but because it goes underground to constitute inner speech or verbal thought” (Frauenglass & Diaz, 1985).

Educational Implications

Vygotsky’s approach to child development is a form of social constructivism , based on the idea that cognitive functions are the products of social interactions.

Social constructivism posits that knowledge is constructed and learning occurs through social interactions within a cultural and historical context.

Vygotsky emphasized the collaborative nature of learning by constructing knowledge through social negotiation. He rejected the assumption made by Piaget that it was possible to separate learning from its social context.

Vygotsky believed everything is learned on two levels. First, through interaction with others, then integrated into the individual’s mental structure.

Every function in the child’s cultural development appears twice: first, on the social level, and later, on the individual level; first, between people (interpsychological) and then inside the child (intrapsychological). This applies equally to voluntary attention, to logical memory, and to the formation of concepts. All the higher functions originate as actual relationships between individuals. (Vygotsky, 1978, p.57)

Teaching styles grounded in constructivism represent a deliberate shift from traditional, didactic, memory-oriented transmission models (Cannella & Reiff, 1994) to a more student-centered approach.

Traditionally, schools have failed to foster environments where students actively participate in their own and their peers’ education. Vygotsky’s theory, however, calls for both the teacher and students to assume non-traditional roles as they engage in collaborative learning.

Rather than having a teacher impose their understanding onto students for future recitation, the teacher should co-create meaning with students in a manner that allows learners to take ownership (Hausfather, 1996).

For instance, a student and teacher might start a task with varying levels of expertise and understanding. As they adapt to each other’s perspective, the teacher must articulate their insights in a way that the student can comprehend, leading the student to a fuller understanding of the task or concept.

The student can then internalize the task’s operational aspect (“how to do it”) into their inner speech or private dialogue. Vygotsky referred to this reciprocal understanding and adjustment process as intersubjectivity.”

Because Vygotsky asserts that cognitive change occurs within the zone of proximal development, instruction would be designed to reach a developmental level just above the student’s current developmental level.

Vygotsky proclaims, “learning which is oriented toward developmental levels that have already been reached is ineffective from the viewpoint of the child’s overall development. It does not aim for a new stage of the developmental process but rather lags behind this process” (Vygotsky, 1978).

Appropriation is necessary for cognitive development within the zone of proximal development. Individuals participating in peer collaboration or guided teacher instruction must share the same focus to access the zone of proximal development.

“Joint attention and shared problem solving is needed to create a process of cognitive, social, and emotional interchange” (Hausfather,1996).

Furthermore, it is essential that the partners be on different developmental levels and the higher-level partner be aware of the lower’s level. If this does not occur or one partner dominates, the interaction is less successful (Driscoll, 1994; Hausfather, 1996).

Vygotsky’s theories also feed into the current interest in collaborative learning, suggesting that group members should have different levels of ability so more advanced peers can help less advanced members operate within their ZPD.

Scaffolding and reciprocal teaching are effective strategies to access the zone of proximal development.

Reciprocal Teaching

A contemporary educational application of Vygotsky’s theory is “reciprocal teaching,” used to improve students” ability to learn from text.

In this method, teachers and students collaborate in learning and practicing four key skills: summarizing, questioning, clarifying, and predicting. The teacher’s role in the process is reduced over time.

Reciprocal teaching allows for the creation of a dialogue between students and teachers. This two-way communication becomes an instructional strategy by encouraging students to go beyond answering questions and engage in the discourse (Driscoll, 1994; Hausfather, 1996).

A study conducted by Brown and Palincsar (1989) demonstrated the Vygotskian approach with reciprocal teaching methods in their successful program to teach reading strategies.

The teacher and students alternated turns leading small group discussions on a reading. After modeling four reading strategies, students began to assume the teaching role.

The results showed significant gains over other instructional strategies (Driscoll, 1994; Hausfather,1996).

Cognitively Guided Instruction is another strategy to implement Vygotsky’s theory. This strategy involves the teacher and students exploring math problems and then sharing their problem-solving strategies in an open dialogue (Hausfather,1996).

Based on Vygotsky’s theory, the physical classroom would provide clustered desks or tables and workspace for peer instruction, collaboration, and small-group instruction. Learning becomes a reciprocal experience for the students and teacher.

Like the environment, the instructional design of the material to be learned would be structured to promote and encourage student interaction and collaboration. Thus the classroom becomes a community of learning.

Scaffolding

Also, Vygotsky’s theory of cognitive development on learners is relevant to instructional concepts such as “scaffolding” and “apprenticeship,” in which a teacher or more advanced peer helps to structure or arrange a task so that a novice can work on it successfully.

A teacher’s role is to identify each individual’s current level of development and provide them with opportunities to cross their ZPD.

A crucial element in this process is the use of what later became known as scaffolding; the way in which the teacher provides students with frameworks and experiences which encourage them to extend their existing schemata and incorporate new skills, competencies, and understandings.

Scaffolding describes the conditions that support the child’s learning, to move from what they already know to new knowledge and abilities.

Scaffolding requires the teacher to allow students to extend their current skills and knowledge.

During scaffolding, the support offered by an adult (or more knowledgeable other) gradually decreases as the child becomes more skilled in the task.

As the adult withdraws their help, the child assumes more of the strategic planning and eventually gains competence to master similar problems without a teacher’s aid or a more knowledgeable peer.

It is important to note that this is more than simply instruction; learning experiences must be presented in such a way as to actively challenge existing mental structures and provide frameworks for learning.

Five ways in which an adult can “scaffold” a child’s learning:

• Engaging the child’s interest
• Maintaining the child’s interest in the task e.g., avoiding distraction and providing clear instructions on how to start the task.
• Keeping the child’s frustration under control e.g., by supportive interactions, adapting instructions according to where the child is struggling.
• Emphasizing the important features of the task
• Demonstrating the task: showing the child how to do the task in simple, clear steps.

As the child progresses through the ZPD, the necessary scaffolding level declines from 5 to 1.

The teacher must engage students’ interests, simplify tasks to be manageable, and motivate students to pursue the instructional goal.

In addition, the teacher must look for discrepancies between students” efforts and the solution, control for frustration and risk, and model an idealized version of the act (Hausfather, 1996).

Challenges to Traditional Teaching Methods

Vygotsky’s social development theory challenges traditional teaching methods. Historically, schools have been organized around recitation teaching.

The teacher disseminates knowledge to be memorized by the students, who in turn recite the information to the teacher (Hausfather,1996).

However, the studies described above offer empirical evidence that learning based on the social development theory facilitates cognitive development over other instructional strategies.

The structure of our schools does not reflect the rapid changes our society is experiencing. The introduction and integration of computer technology in society has tremendously increased the opportunities for social interaction.

Therefore, the social context for learning is transforming as well. Whereas collaboration and peer instruction were once only possible in shared physical space, learning relationships can now be formed from distances through cyberspace.

Computer technology is a cultural tool that students can use to meditate and internalize their learning. Recent research suggests changing the learning contexts with technology is a powerful learning activity (Crawford, 1996).

If schools continue to resist structural change, students will be ill-prepared for the world they will live.

Critical Evaluation

Vygotsky’s work has not received the same level of intense scrutiny that Piaget’s has, partly due to the time-consuming process of translating Vygotsky’s work from Russian.

Also, Vygotsky’s sociocultural perspective does not provide as many specific hypotheses to test as Piaget’s theory, making refutation difficult, if not impossible.

Perhaps the main criticism of Vygotsky’s work concerns the assumption that it is relevant to all cultures. Rogoff (1990) dismisses the idea that Vygotsky’s ideas are culturally universal and instead states that scaffolding- heavily dependent on verbal instruction – may not be equally useful in all cultures for all types of learning.

Indeed, in some instances, observation and practice may be more effective ways of learning certain skills.

There is much emphasis on social interaction and culture, but many other aspects of development are neglected, such as the importance of emotional factors, e.g., the joys of success and the disappointments and frustration of failure act as motivation for learning.

Vygotsky overemphasized socio-cultural factors at the expense of biological influences on cognitive development. This theory cannot explain why 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.

Vygotky’s theory has been applied successfully to education. Scaffolding has been shown to be an effective way of teaching (Freund, 1990), and based on this theory, teachers are trained to guide children from what they can do to the next step in their learning through careful scaffolding.

Collaborative work is also used in the classroom, mixing children of different levels of ability to make use of reciprocal / peer teaching.

Vygotsky vs. Piaget

Unlike Piaget’s notion that children’s cognitive development must necessarily precede their learning, Vygotsky argued, “learning is a necessary and universal aspect of the process of developing culturally organized, specifically human psychological function” (1978, p. 90).  In other words, social learning precedes (i.e., come before) development.

Vygotsky’s theory differs from that of Piaget in several important ways:

Vygotsky places more emphasis on culture affecting cognitive development.

Unlike Piaget, who emphasized universal cognitive change (i.e., all children would go through the same sequence of cognitive development regardless of their cultural experiences), Vygotsky leads us to expect variable development depending on cultural diversity. 

This contradicts Piaget’s view of universal stages of development (Vygotsky does not refer to stages like Piaget does).

Hence, Vygotsky assumes cognitive development varies across cultures, whereas Piaget states cognitive development is mostly universal across cultures.

Vygotsky places considerably more emphasis on social factors contributing to cognitive development.

• Vygotsky states the importance of cultural and social context for learning. Cognitive development stems from social interactions from guided learning within the zone of proximal development as children and their partners co-construct knowledge. In contrast, Piaget maintains that cognitive development stems largely from independent explorations in which children construct knowledge.
• For Vygotsky, the environment in which children grow up will influence how they think and what they think about. The importance of scaffolding and language may differ for all cultures. Rogoff (1990) emphasizes the importance of observation and practice in pre-industrial societies (e.g., learning to use a canoe among Micronesian Islanders).

Vygotsky places more (and different) emphasis on the role of language in cognitive development.

According to Piaget , language depends on thought for its development (i.e., thought comes before language). For Vygotsky, thought and language are initially separate systems from the beginning of life, merging at around three years of age, producing verbal thought (inner speech).

In Piaget’s theory, egocentric (or private) speech gradually disappears as children develop truly social speech, in which they monitor and adapt what they say to others.

Vygotsky disagreed with this view, arguing that as language helps children to think about and control their behavior, it is an important foundation for complex cognitive skills.

As children age, this self-directed speech becomes silent (or private) speech, referring to the inner dialogues we have with ourselves as we plan and carry out activities.

For Vygotsky, cognitive development results from an internalization of language.

According to Vygotsky, adults are an important source of cognitive development.

Adults transmit their culture’s tools of intellectual adaptation that children internalize.

In contrast, Piaget emphasizes the importance of peers, as peer interaction promotes social perspective-taking.

Behrend, D.A., Rosengren, K.S., & Perlmutter, M. (1992). The relation between private speech and parental interactive style. In R.M. Diaz & L.E. Berk (Eds.), Private speech: From social interaction to self-regulation (pp. 85–100) . Hillsdale, NJ: Erlbaum.

Berk, L. E. (1986). Relationship of elementary school children’s private speech to behavioral accompaniment to task, attention, and task performance. Developmental Psychology, 22(5) , 671.

Berk, L. & Garvin, R. (1984). Development of private speech among low-income Appalachian children. Developmental Psychology, 20(2) , 271-286.

Berk, L. E., & Landau, S. (1993). Private speech of learning-disabled and normally achieving children in classroom academic and laboratory contexts. Child Development, 64 , 556–571.

Cannella, G. S., & Reiff, J. C. (1994). Individual constructivist teacher education: Teachers as empowered learners . Teacher education quarterly , 27-38.

Crawford, K. (1996) Vygotskian approaches to human development in the information era. Educational Studies in Mathematics, (31) ,43-62.

Diaz, R. M., & Berk, L. E. (1992). Private speech: From social interaction to self-regulation. Lawrence Erlbaum.

Driscoll, M. P. (1994). Psychology of Learning for Instruction . Needham, Ma: Allyn && Bacon.

Frauenglass, M. & Diaz, R. (1985). Self-regulatory functions of children’s private speech: A critical analysis of recent challenges to Vygotsky’s theory. Developmental Psychology, 21(2) , 357-364.

Fernyhough, C., & Fradley, E. (2005). Private speech on an executive task: Relations with task difficulty and task performance . Cognitive Development, 20 , 103–120.

Freund, L. S. (1990). Maternal regulation of children’s problem-solving behavior and its impact on children’s performance . Child Development, 61 , 113-126.

Hausfather, S. J. (1996). Vygotsky and Schooling: Creating a Social Contest for learning. Action in Teacher Education, (18) ,1-10.

Ostad, S. A., & Sorensen, P. M. (2007). Private speech and strategy-use patterns: Bidirectional comparisons of children with and without mathematical difficulties in a developmental perspective. Journal of Learning Disabilities, 40 , 2–14.

Piaget, J. (1959). The language and thought of the child (Vol. 5) . Psychology Press.

Rogoff, B. (1990).  Apprenticeship in thinking: Cognitive development in social context . Oxford university press.

Saettler, P. (1990). The Evolution of American Educational Technology . Egnlewood, Co: Libraries Unlimited.

Schaffer, R. (1996) . Social development. Oxford: Blackwell.

Vygotsky, L. S. (1962). Thought and language. Cambridge MA: MIT Press.

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

Vygotsky, L. S. (1987). Thinking and speech. In R.W. Rieber & A.S. Carton (Eds.), The collected works of L.S. Vygotsky, Volume 1: Problems of general psychology (pp. 39–285) . New York: Plenum Press. (Original work published 1934.)

Winsler, A., Abar, B., Feder, M. A., Schunn, C. D., & Rubio, D. A. (2007). Private speech and executive functioning among high-functioning children with autistic spectrum disorders. Journal of Autism and Developmental Disorders, 37 , 1617-1635.

Wertsch, J. V., Sohmer, R. (1995). Vygotsky on learning and development. Human Development, (38), 332-37.

Further Reading

What is vygotsky’s theory.

Vygotsky believed that cognitive development was founded on social interaction. According to Vygotsky, much of what children acquire in their understanding of the world is the product of collaboration.

How is Vygotsky’s theory applied in teaching and learning?

Vygotsky’s theory has profound implications for classroom learning. Teachers guide, support, and encourage children, yet also help them to develop problem-solving strategies that can be generalized to other situations.

Children learn best not when they are isolated, but when they interact with others, particularly more knowledgeable others who can provide the guidance and encouragement to master new skills.

What was Vygotsky’s best know concept?

Lev Vygotsky was a seminal Russian psychologist best known for his sociocultural theory. He constructed the idea of a zone of proximal development ,  which are those tasks which are too difficult for a child to solve alone but s/he can accomplish with the help of adults or more skilled peers.

Vygotsky has developed a sociocultural approach to cognitive development. He developed his theories at around the same time as  Jean Piaget  was starting to develop his ideas (1920’s and 30″s), but he died at the age of 38, and so his theories are incomplete – although some of his writings are still being translated from Russian.

Like Piaget, Vygotsky could be described as a  constructivist , in that he was interested in knowledge acquisition as a cumulative event – with new experiences and understandings incorporated into existing cognitive frameworks.

However, while Piaget’s theory is structural (arguing that physiological stages govern development), Vygotsky denies the existence of any guiding framework independent of culture and context.

No single principle (such as Piaget’s equilibration) can account for development. Individual development cannot be understood without reference to the social and cultural context within which it is embedded. Higher mental processes in the individual have their origin in social processes.

What is Vygotsky’s Social Development Theory?

Vygotsky’s Social Development Theory is often referred to as the Sociocultural Theory.

Vygotsky’s Social Development Theory posits that social interaction is fundamental to cognitive development. Vygotsky emphasized the influence of cultural and social contexts on learning, claiming that knowledge is constructed through social collaboration.

His most known concept, the Zone of Proximal Development, refers to the difference between what a learner can do independently and what they can achieve with guidance.

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About preschooler play and cognitive development

Play is important for your child’s cognitive development – that is, your child’s ability to think, understand, communicate, remember, imagine and work out what might happen next.

Preschoolers want to learn how things work, and they learn best through play. Preschoolers at play are solving problems, creating, experimenting, thinking and learning all the time.

Spending time playing with your child is especially good for your child’s cognitive development. That’s because playing together builds your relationship and sends a simple but powerful message – you are important to me. This message is key to helping your child learn about who they are and where they fit in the world. It also gives your child confidence to keep exploring and learning about the world.

A warm and loving relationship with your child lays the foundation for all areas of your child’s learning and development.

What to expect: preschooler cognitive development and play

With time, experience and practice, preschoolers will probably:

• start to organise games and make friends
• start to understand concepts like ‘bigger’ and ‘taller’
• ask a lot of questions, especially ‘why’
• start to develop a sense of humour and delight in jokes and riddles
• develop some concept of time
• start negotiating with you if there’s something they want
• start predicting what will happen next – for example, in a story
• still not understand what’s real and what’s pretend.

At 4 years , preschoolers still have fairly short concentration spans, so you can expect that your child might get restless or bored if an activity goes on for too long.

Your 4-year-old child is also likely to start asking tricky questions about subjects like sexuality , death and distressing news stories . For example, your child might ask, ‘Where do babies come from?’

By 5 years , your child will probably sit through a full game or finish a whole puzzle – and that brings the new challenge of playing fair and learning to lose gracefully!

Starting preschool gives your child a lot to think about. There are new rules and routines that are different from those at home. This can be tiring and confusing at first. Your child might need time and plenty of love and support to adjust.

Many preschools have programs to help children prepare for the transition into preschool. You can also talk with your child’s teacher if you have concerns or want ideas to help your child handle the change.

Play ideas for encouraging preschooler cognitive development

Here are play ideas to support your child’s cognitive development:

• Play board games like ‘Snakes and ladders’ with your child, or card games like ‘Go fish’ or ‘Snap’.
• Read books and tell jokes and riddles.
• Encourage stacking and building games or play with cardboard boxes .
• Do jigsaw puzzles and memory games .
• Play games that combine moving and singing – for example, ‘If you’re happy and you know it’ .
• When you’re driving or on public transport, try ‘spotto’ games – for example, ‘Who can see something green?
• Encourage your child to help you with cooking – preschoolers can learn a lot from measuring, counting and naming healthy ingredients for family meals.
• Play outside. For example, you could make mud pies or go on a  nature walk together.

It’s a good idea to let your child take the lead with play , because preschoolers learn best when they’re interested in an activity. This way, you can use your child’s interests to help your child learn something new. Your child will generally let you know if they need help, so try not to jump in with solutions too early.

And during any kind of play, you can ask your child to describe what’s happening. This is a great way to show interest and encourage your child to practise language skills. For example, if you and your child are pretending to be vets, you could say, ‘What’s wrong with this animal? How are we going to make it feel better?’

If your child seems to be having difficulty learning at preschool or is still very upset about going to preschool after several weeks, it’s a good idea to talk about your concerns with your GP , your child and family health nurse or your child’s teacher.

Screen time, digital technology use and preschooler cognitive development

It’s good to know that using screen time and digital technology can support your child’s learning .

For example, your child could develop problem-solving skills by working out what online characters should wear in rainy weather. Or your child might build letter and number knowledge and vocabulary by watching a good-quality TV show like Sesame Street .

Here are things you can do to help your child learn through digital play :

• Choose good-quality apps, games and other media.
• Use screens and digital technology with your child.
• Help your child manage screen time and digital technology use.

And remember – healthy screen time and digital technology use is all about balance . It’s good for your child’s development to do plenty of different activities, including pretend and creative play, physical play, social play and reading, as well as digital play.

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Cognitive Development: Preschool

Children usually follow predictable patterns in how they grow and learn. This lesson will help you understand typical cognitive development, or how children develop thinking skills during the preschool years. You will learn about developmental milestones and what to do if you are concerned about a child’s development.

• Identify typical cognitive developmental milestones in preschool.
• Discuss what to do if you are concerned about a child’s development.
• Demonstrate developmentally appropriate expectations.

During preschool, amazing changes happen with children's thinking skills. Their memories are becoming stronger, which means they often remember surprising details. They can share their ideas in new and interesting ways. Their imaginations are becoming a primary vehicle for play and learning. They begin to compare, contrast, organize, analyze, and come up with more and more complex ways to solve problems, which helps their math skills and scientific reasoning become more sophisticated. This lesson will highlight cognitive developmental milestones for preschoolers.

Watching preschool children’s thinking skills develop as they encounter new people, places, and ideas is exciting. The chart below highlights cognitive development during the preschool years. Keep in mind that individual differences exist when it comes to the specific age at which children meet these milestones; each child is unique. As you may have already learned in other courses, milestones provide a guide for when to expect certain skills or behaviors to emerge. Think of milestones as guidelines to help you understand and identify typical patterns of growth and development, or to help you know when and what to look for as preschool children mature. You can use this information, what you learn from families, and your own knowledge to create interactions, experiences, and environments that support preschoolers learning and development.

Cognitive Developmental Milestones

• Can work toys with buttons, levers, and moving parts
• Plays make-believe with dolls, animals, and people
• Does puzzles with 3 or 4 pieces
• Copies a circle with pencil or crayon
• Turns book pages one at a time
• Builds towers of more than 6 blocks
• Screws and unscrews jar lids or turns door handle
• Avoids touching hot objects, like a stove, when warned

Age 4 

• Names some numbers
• Understands the idea of counting
• Remembers parts of a story
• Understands the idea of “same” and “different”
• Draws a person with 3 or more body parts
• Names a few colors
• Understands some direction words like “under” “on top”, “middle”
• Plays dress up and pretends to be someone or something else
• Tells you what comes next in a story
• Counts to 10
• Names some numbers between 1 and 5 when you point to them
• Can draw a person with at least 6 body parts
• Can write some letters of their name
• Names some letters when you point to them
• Pays attention for 5-10 minutes during activities
• Uses words about time like yesterday, tomorrow, morning, or night

Source: Centers for Disease Control and Prevention (2021). Developmental Milestones. . https://www.cdc.gov/ncbddd/actearly/pdf/FULL-LIST-CDC_LTSAE-Checklists2021_Eng_FNL2_508.pdf

You have the ability to help children learn and grow to their potential. Along with a family’s pediatrician, preschool teachers must be knowledgeable about children’s developmental milestones. Developmental milestones help adults to understand and recognize typical ages and stages of development for children. Milestones are not rigid rules for when or how a child should develop, instead, they provide a guide for when to expect certain cognitive, motor, language, and social emotional skills and behaviors to emerge. You can use your knowledge of these milestones to meet the individual needs of the children in your classroom.

Cognitive development is a unique process and is specific to each child. A family may wonder about their child's cognitive development and feel uncertain about what they are observing, as well as what to expect. Take the opportunity to first learn from a family and then consider offering additional developmental information, including possible warning signs. The Kids Included Together website, https://www.kit.org/ , can be a valuable resource for you, as can the Developmental Milestones and Act Early information located on the Centers for Disease Control and Prevention website, http://www.cdc.gov/ncbddd/actearly/milestones/index.html . The chart below also highlights possible warning signs for preschool children:

Possible Warning Signs for Preschool Children

3 years.

• Cannot work simple toys (such as peg boards, simple puzzles, turning a handle)
• Does not play pretend or make-believe
• Does not understand simple instructions

4 Years

• Has trouble scribbling
• Shows no interest in interactive games or make-believe
• Does not follow follow three-part commands
• Does not understand the concepts of "same" and "different"

5 Years

• Does not respond to people, or responds only superficially
• Can not tell what is real and what is make-believe
• Does not play a variety of games and activities
• Cannot give first and last name
• Does not draw pictures

If you are concerned about a child's development, talk with your trainer, coach, or administrator so that you can brainstorm and work together to talk with parents about your observations. These conversations may be difficult, but it can make the difference in meeting a child's needs. With the guidance of your coach, and program administrator, you can share information with families about typical child development and let them know you are available to talk. If your program provides developmental screening tools, these can help you start a conversation about your concerns.

Ultimately, if families are concerned about their child's development, they should talk to the child's pediatrician about their concerns. The pediatrician can perform developmental screenings and possibly refer the child for additional evaluations and specialized services. Families should also contact their local school district (for children over age 3). The school district can arrange a free evaluation of the child's development. This can help the child get the services and help they need.

The video below, developed by the U.S. Centers for Disease Control and Prevention, offers tips for identifying and acting on suspected developmental delays.

http://www.cdc.gov/NCBDDD/actearly/multimedia/video.html

Just as children's bodies grow throughout the preschool years, their brains are growing too. You will see major changes in a child's thinking skills between 3 and 5 years of age. Watch this video to learn about milestones during the preschool years.

Cognitive Development in Preschool

Understanding these milestones will help you know what kinds of learning experiences to plan in your classroom. Based on your knowledge of development, you can plan activities that are challenging but achievable for individual children. Remember, milestones are markers that let us know a child is growing in a healthy way. These markers are not thresholds or "tests" that a child must pass. As a preschool teacher, do the following to support children's development and learning:

• Set learning goals for individual children.
• Read all you can about the stages of development, especially for the ages of the children you serve.
• Post developmental milestone charts for reference.
• Recognize that children need different types of support from you as they move through the developmental stages.
• Provide a range of interesting materials that spark preschoolers' interests and allow for hands-on exploration.
• Provide a range of developmentally appropriate and culturally diverse books.
• Find teachable moments to encourage learning and development.
• Observe children and document their progress on a regular basis to determine where they are developmentally so that you can support and challenge their emerging skills.
• Remember that children are unique and progress at different rates and that one area of development may take longer than other areas.

Consult with your administrator, trainer, or coach if you feel that there might be a concern with how a child is developing.

Observing children can help you see where they are developmentally, which is important as you plan learning experiences for them. Complete the Stages of Development Observation Activity . Share your observations with your trainer, coach, or administrator.

Stages of Development Observation Activity

It is important to understand and remember developmental milestones. You can download the Milestones Poster and use it as a reference in your work. You may also choose to share this resource with families. 

Cognitive Development Milestones: Preschool

Demonstrate.

Centers for Disease Control and Prevention. (2013). Developmental Milestones. https://www.cdc.gov/ncbddd/actearly/milestones/

Early Childhood Learning & Knowledge Center. https://eclkc.ohs.acf.hhs.gov/  

Eileen Allen, K., & Edwards Cowdery, G. (2014). The Exceptional Child: Inclusion in early childhood education (8th ed.). Wadsworth Publishing.

National Association for the Education of Young Children. (n.d.). https://www.naeyc.org/

Zero to Three. (2021). https://www.zerotothree.org/

Key Concepts

Brain Architecture

Early experiences affect the development of brain architecture , which provides the foundation for all future learning, behavior, and health. Just as a weak foundation compromises the quality and strength of a house, adverse experiences early in life can impair brain architecture, with negative effects lasting into adulthood.

Brains are built over time, from the bottom up. The basic architecture of the brain is constructed through an ongoing process that begins before birth and continues into adulthood. Simpler neural connections and skills form first, followed by more complex circuits and skills. In the first few years of life, more than 1 million new neural connections form every second . * After this period of rapid proliferation, connections are reduced through a process called pruning, which allows brain circuits to become more efficient.

Brain architecture is comprised of billions of connections between individual neurons across different areas of the brain. These connections enable lightning-fast communication among neurons that specialize in different kinds of brain functions. The early years are the most active period for establishing neural connections, but new connections can form throughout life and unused connections continue to be pruned. Because this dynamic process never stops, it is impossible to determine what percentage of brain development occurs by a certain age. More importantly, the connections that form early provide either a strong or weak foundation for the connections that form later.

The interactions of genes and experience shape the developing brain. Although genes provide the blueprint for the formation of brain circuits, these circuits are reinforced by repeated use. A major ingredient in this developmental process is the serve and return interaction between children and their parents and other caregivers in the family or community. In the absence of responsive caregiving—or if responses are unreliable or inappropriate—the brain’s architecture does not form as expected, which can lead to disparities in learning and behavior. Ultimately, genes and experiences work together to construct brain architecture.

Cognitive, emotional, and social capacities are inextricably intertwined throughout the life course. The brain is a highly integrated organ and its multiple functions operate in coordination with one another. Emotional well-being and social competence provide a strong foundation for emerging cognitive abilities, and together they are the bricks and mortar of brain architecture. The emotional and physical health , social skills, and cognitive-linguistic capacities that emerge in the early years are all important for success in school, the workplace, and in the larger community.

Toxic stress weakens the architecture of the developing brain, which can lead to lifelong problems in learning, behavior, and physical and mental health. Experiencing stress is an important part of healthy development. Activation of the stress response produces a wide range of physiological reactions that prepare the body to deal with threat. However, when these responses remain activated at high levels for significant periods of time, without supportive relationships to help calm them, toxic stress results. This can impair the development of neural connections, especially in the areas of the brain dedicated to higher-order skills.

*The number “more than 1 million new neural connections per second” updates an earlier estimate of 700-1,000 new connections (which still appears in some of the Center’s printed publications, but as of April 2017 has been updated online and in all PDFs). All of these numbers are estimates, calculated in a variety of different ways, but we are making this change in our materials after a careful review of additional data that were called to our attention. The Center is deeply committed to a rigorous process of continuous refinement of what we know and an ongoing pledge to update that knowledge as additional data become available.

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• DOI: 10.1177/20427530241261294
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Leveraging digital interactive didactic games to enhance cognitive development in preschool education

• Aigerim Baikulova , Shyryn Akimbekova , +2 authors Bakytgul Moldagali
• Published in E-Learning and Digital Media 10 June 2024
• Education, Computer Science

10 References

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• Published: 05 June 2024

Structured early childhood education exposure and childhood cognition – Evidence from an Indian birth cohort

• Beena Koshy 1 ,
• Manikandan Srinivasan 2 ,
• Rangan Srinivasaraghavan 1 ,
• Reeba Roshan 1 ,
• Venkata Raghava Mohan 3 ,
• Karthikeyan Ramanujam 2 ,
• Sushil John 4 &
• Gagandeep Kang 2  

Scientific Reports volume  14 , Article number:  12951 ( 2024 ) Cite this article

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• Health policy
• Paediatric research
• Paediatrics

Experiences in early childhood form the bedrock of future human potential. In impoverished settings, structured early childhood education (ECE) in preschool years can augment overall childhood and later human abilities. The current study evaluates preschool learning exposure and childhood cognition, using longitudinal follow-up of a community-based birth cohort in Vellore, south India. The birth cohort study site in Vellore recruited 251 newborns between 2010 and 2012 from dense urban settlements and further followed up into childhood. Preschool enrolment details were obtained from parents. Childhood cognition was assessed by Weschler’s preschool primary scale of intelligence (WPPSI) and Malin’s intelligence scale for Indian Children (MISIC) at 5 and 9 years of age respectively. Bivariate and multivariate regression analyses were performed with adjustments for socio-economic status (SES), maternal education, stunting status and home environment. Out of 251 new-borns recruited into the MAL-ED birth cohort, 212 (84.46%) and 205 (81.7%) children were available for the 5 year and 9 year follow-up respectively. At 5 years, structured ECE of 18 to 24 months duration was significantly associated with higher cognition scores, with the highest increase in processing speed [β: 19.55 (11.26–27.77)], followed by full-scale intelligence [β: 6.75 (2.96–10.550)], even after adjustments for SES, maternal cognition, home factors and early childhood stunting status. Similarly adjusted analysis at 9 years showed that children who attended 1.5–2 years of structured ECE persisted to have higher cognition, especially in the performance domain [β: 8.82 (2.60–15.03)], followed by the full-scale intelligence [β: 7.24 (2.52–11.90)]. Follow-up of an Indian birth cohort showed that structured ECE exposure was associated with better school entry cognition as well as mid-childhood cognition. Strengthening ECE through a multi-pronged approach could facilitate to maximize cognitive potential of human capital.

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Introduction.

Cognition or intelligence can be defined as a ‘combination of multiple abilities in a child’ as proposed by Howard Gardner 1 . The first 1000 days of life starting from the antenatal period in the mother’s womb, to the perinatal period and the first 2 years of life, is crucial for child’s cognitive development, as maximum brain development and maturation happen during this period 2 . Factors such as early childhood malnutrition, infections, perinatal asphyxia, iron deficiency, lead toxicity and poverty are detrimental for cognitive development in children 3 , 4 , 5 . Children from poorer households are more likely to experience unsupportive parenting, lack of caregiver education, more stressful events, lack of stimulating environment including play materials and poor education, which in turn result in poor cognitive development and scholastic performance. This compromise in education and subsequent earning capacity in these children further produces an inter-generational transmission of poverty 2 . On the other hand, positive and stimulating environment prevailing at home and appropriate learning opportunities during preschool years can augment a child’s developmental and cognitive abilities 6 .

Global evidence suggests that early childhood education (ECE) offered at a better quality can positively impact a child’s intellectual, social and emotional development, and continued learning in future 7 , 8 . In a Brazilian cohort, children who received structured ECE, compared to those who did not, had higher cognition scores by 8 units at 5 years of age, after adjusting for home environment and socioeconomic status 6 . Similarly, ECE programmes in low-and-middle income country (LMIC) settings such as in Bangladesh have promoted academic achievements in mathematics, writing and reading domains for children in their primary grades 9 . In a preschool environment, in addition to direct learning opportunities in a structured manner, children can also have peer social interactions which can aid their indirect learning experiences. Thus, a centre-based education for pre-schoolers can help to attain better motor coordination, arithmetic skills, memory and concentration in children and may override many adverse childhood experiences 6 , 10 , 11 .

In India, the National Early Childhood Care and Education (ECCE) policy recommends that children between 3 and 6 years receive early education and care from Anganwadi centres, which are community-based 12 , 13 , 14 . Multi-age grouping is followed in which students of different ages and identified age levels are grouped in a single classroom to provide effective instruction. The medium of interaction in the ECCE centre should be the home language or mother tongue. In the early years, the focus would be on listening and speaking activities, facilitated through free play with peers. In the region where the study was done, the ECCE Programme in Tamil language was conducted 5 days a week and for a minimum of 4 h duration,

The ECE programs typically aim to enhance intellectual and social abilities of children which can form a fulcrum for their subsequent learning development. They had considerable positive short-term effects and somewhat smaller long-term effects on cognitive development more so for children from socio-economically disadvantaged families. Studies have shown that sustained high-quality early care and education can mitigate the consequences of poverty into adulthood 15 , 16 . Nevertheless, some studies and an international review highlight sub-optimal evidence of long-term effects of ECE 17 . Possible reasons for even a school-level ‘fade-out’ of ECE could be the quality of ECE, the absence of sustaining environments later and differing skillsets for learning in different age groups 18 . Another commonly cited explanation of the ‘fade-out’ effect of ECE programs on early academic skills is that it changes with time, and the gains often disappear after children transition to elementary school 15 , 18 . Thus, it is important to understand if structured ECE interventions have a long-term effect on cognition in the later childhood period, as this could help policymakers assess cost–benefit aspects better. In this context, the current study evaluates the association between structured ECE and cognition at 5 and 9 years of age in a birth cohort in Vellore, India. For this study, structured ECE was defined as specific structured language and learning inputs from early childhood curriculum and was provided in this area by both private and government aided schools in preschool settings. It is hypothesised that structured ECE will have a positive association with both 5- and 9 year cognition.

This paper presents the analysis of a birth cohort follow-up, as part of a multi-country cohort study done in eight LMICs evaluating the role of enteric infections in early life on growth and development in children (‘The Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) Network Cohort) 19 . The study site in Vellore, India recruited 251 newborns between 2010 and 2012 from dense urban slum settlements following parental consent 20 . Children were intensively followed up through home visits till two years of age to capture information on sociodemographic details, morbidity status, anthropometry, and dietary intake, with subsequent follow-up visits made at 3, 4, 5, 7 and 9 years of age. This study was approved by the Institutional Review Board, Christian Medical College, Vellore for initial recruitment as well as follow-up visits (IRB 6769 for the original cohort and follow-up, IRB 11821 for the 9 year follow-up) and was conducted following the guidelines laid by World Medical Association Declaration of Helsinki. Before each recruitment, we obtained parental informed consent in written format and for the 9-year follow-up, additional child assent.

Exposure variables

To evaluate child’s stunting status, trained Field research assistants (FRA) carried out standardized anthropometry measures using an infantometer for recording the length up to 2 years, and a stadiometer thereafter, till 9 years of age to the nearest cm. Z scores for length/height measurements were calculated based on Multicentre Growth Reference Study standards and children were classified as stunted at each time point if Z scores were below − 2 SD. Maternal intelligence quotient (IQ) raw scores were assessed using Raven’s Progressive Matrices by a single, trained psychologist. The Home Observation for the Measurement of the Environment (HOME) scale was administered by a trained social worker at 2 years to measure overall support received by the child at home from caregivers. At 5 years, caregivers were contacted for information about the number of months of structured ECE received by their children. The socioeconomic status (SES) assessment was based on the Water and Sanitation, Assets, Maternal education, and Income (WAMI) scores.

Outcome measures

A single, trained psychologist conducted cognition evaluation for children in the community study clinic using Weschler’s Preschool Primary Scale of Intelligence (WPPSI) and Malin’s intelligence scale for Indian children (MISIC) at 5 and 9 years respectively 3 , 21 . The WPPSI scale was translated, and pilot-tested in local settings to assess cognition under verbal, performance, and processing speed domains. The MISIC scale which was adapted for use in the Indian setting from the original Wechsler scale, evaluated cognition under verbal and performance domains. Raw scores obtained by children under each domain were converted into IQ scores for analysis.

Statistical analysis

Data was collected in paper forms and entered in the double-entry database system maintained by the central Data coordination centre of the MAL-ED study for the initial part of the birth cohort study. Descriptive statistics were used to summarize sex, SES and early childhood stunting status in percentages. Children living in households with WAMI scores ≥ 33rd percentile were classified as those belonging to relatively high SES. Stunting status of children at 2 and 5 years was used to group children into those who were never stunted, stunted at 2 years but recovered by 5 years, and, persistently stunted at 2 and 5 years. Based on tertile scores of structured ECE attendance expressed in months, children were classified into three groups. Children in group 1 had no exposure to structured ECE, whereas those in groups 2 and 3 attended 1–17 and 18–24 months, respectively in a structured preschool. HOME scores were summarized as median and interquartile (IQR) scores. Child’s IQ scores under each domain were presented as median (IQR) and box plots were used to visualize relationship between IQ scores, and groups based on preschool and structured preschool attendance. Correlation co-efficients were computed to check for correlation between domain scores of cognition measures at 5 and 9 years. To measure the association between structured ECE and IQ scores, bivariate and multivariate regression analyses were performed. The normality of dependent variables was assessed using the Shapiro–Wilk test. Linear regression was performed only for performance IQ (PIQ) at 5 years, while quantile regression was used to fit other domain scores such as verbal IQ (VIQ), processing speed IQ (PSIQ) and full-scale IQ (FSIQ) since normality assumptions for linear regression analysis were not met. Similarly, the predictor model for VIQ at 9 years was based on linear regression, while PIQ and total IQ scores at 9 years were modelled using quantile regression. Multivariable models included independent variables that were statistically significant in bivariate analysis at p < 0.05, and collinearity between the variables was analysed using correlation statistics. R 2 values and the Hosmer–Lemeshow goodness-of-fit test were considered for assessing model fitness in the case of linear regression models. Beta coefficients along with 95% confidence intervals (CI) were reported and a p-value less than 0.05 was considered as statistical significance. Statistical analysis was performed using STATA version 14 (Stata Statistical Software: Release 14, StataCorp LP, College Station, TX).

Ethics approval

This study was approved by the Institutional Review Board, Christian Medical College, Vellore for initial recruitment as well as follow-up visits (IRB 6769 for the original cohort and follow-up; IRB 11821 for the 9 year follow-up). Participants were enrolled after due written informed consent before inclusion in the study. This research was conducted ethically following the World Medical Association Declaration of Helsinki.

A cohort of 251 newborns were recruited as part of the Vellore cohort of MAL-ED by screening 301 pregnant women from the study area between 2010 and 2012. At recruitment, a female preponderance (55%) was observed in the cohort. About 212 (84.46%) and 205 (81.67%) children were available for follow-up at 5 and 9 years 21 and the most common reason for non-participation was migration of the family outside study area. The 5-year recruitment was conducted between 2015 February and 2017 February. The 9 year recruitment was planned between 2019 February and 2021 February. There was a disruption in recruitment for 6–7 months in 2020 due to the Covid-19 pandemic. We completed cognitive and other clinic-based assessments for all children by April 2021.

There was no significant difference in cohort characteristics such as sex and socioeconomic status between the follow-up time points (Table 1 ). Of 212 children followed up at 5 years, 41 (19.34%) were stunted at two years but recovered by 5 years, whereas 58 (27.36%) children were found to be stunted both at two and five years. Proportion of children who attended structured ECE was 54.25% with median months of attendance 8 months.

Median (IQR) WPPSI IQ scores measured at 5 years under verbal, performance, processing speed and full-scale domain were 81 (77–85), 84 (79–90), 103 (88–113) and 84 (79–89) respectively (Fig.  1 ). Bivariate analysis of cognition scores across tertile categories of structured ECE showed that children belonging to the highest tertile (18–24 months of attendance) had significantly higher IQ scores by 3, 6.15, 16 and 7 units in VIQ, PIQ, PSIQ and FSIQ domains respectively at 5 years, compared to those in the lowest tertile (nil attendance to structured preschool) (Table 2 ). Further, multivariate analysis accounting for maternal cognition, HOME scores, SES and early life stunting status showed that structured ECE between 18 and 24 months (highest tertile) was significantly associated with higher cognition scores in children at 5 years, with highest increase in PSIQ domain [β: 19.55 (11.26–27.77)], followed by FSIQ [β: 6.75 (2.96–10.55)], PIQ [β: 5.54 (2.11–8.98)] and VIQ domains [β: 3.32 (0.74–5.91)], compared to those who did not attend structured ECE (lowest tertile). It is notable that children in the second tertile of structured ECE with a median duration of 1–17 months also had higher PIQ and PSIQ in the bivariate analysis compared to those in the lowest tertile (nil attendance), however, this association was not found to be statistically significant in the multivariate analysis (Table 3 ). A sensitivity analysis was conducted categorizing the duration of structured ECE under two different scenarios to support the main analysis based on tertile categorization of ECE exposure. In the first scenario, structured ECE duration was categorized year-wise as per the existing education curriculum in India into ‘no attendance’, ‘1–12 months’ and ‘13–24 months’. Children exposed to 13–24 months of ECE had a significant increase in FSIQ by 4.8 compared to those who did not attend structured ECE [β: 4.82 (2.1–7.5)] in multivariate analysis. In the second scenario, structured ECE duration was divided as quartiles into ‘no attendance’, ‘1–8 months’, ‘8–18 months’ and ‘ > 18 months’ of exposure and included in multivariate analysis. Compared to children who did not have structured ECE, those exposed to ‘8–18 months’ and ‘ > 18 months’ had a significant increase in FSIQ scores by 3.4 [β: 3.4 (0.5–6.3)] and 6.4 [β: 6.4 (3.2–9.6)], respectively (data not shown).

Comparison of cognition scores across tertile groups based on ECE attendance in the MAL-ED cohort.

Median (IQR) cognition scores measured at 9 years under verbal, performance, and total IQ scores were 94.4 (87.2–100.6), 91.6 (82.9–101.2) and 93.4 (85.5–100.2) respectively. Correlation coefficients between verbal IQ, performance IQ and total scores IQ at 5 and 9 years were 0.6, 0.6 and 0.7 respectively (data not shown). Exposure to structured ECE showed a positive association with VIQ, PIQ and total IQ scores at 9 years. In bivariate analysis, children in the highest tertile category (18–24 months) of structured preschool attendance showed about 7–10 points increase in IQ scores across various domains of cognition, with effect size being highest in the PIQ domain compared to those who did not attend structured preschool (Table 4 ). This association remained significant in the multivariate model, with children in the highest tertile category scoring higher IQ scores than those in the lowest tertile, after adjusting for maternal IQ, SES and early-life stunting. Structured preschool exposure for about 18–24 months had the greatest impact on PIQ domain with a higher effect size [β: 8.82 (2.60–15.03)], followed by total IQ [β: 7.24 (2.52–11.90)] and verbal IQ domains [β: 5.25 (1.88–8.62)], compared to those without structured preschool attendance (Table 5 ). Similar to 5 years analysis, a sensitivity analysis was conducted categorizing the duration of structured ECE under two different scenarios. In the first scenario, where structured ECE duration was categorized year-wise into ‘no attendance’, ‘1–12 months’ and ‘13–24 months’, children exposed to 13–24 months of ECE had an increase in FSIQ by 4.5 compared to those who did not attend structured ECE [β: 4.5 (0.3–8.6)] in multivariate analysis. In the second scenario, where structured ECE duration was divided into quartiles, it was observed that children with structured ECE of ‘ > 18–24 months’ had a significant increase in FSIQ scores by 7.7 compared to those who did not have any exposure to ECE [β: 7.8 (3.0–12.4)] (data not shown).

This prospective follow-up study done among children living in an urban Indian slum setting evaluated the association between structured ECE and cognition scores at 5 and 9 years of life. Attending structured ECE was associated with higher cognition scores by 3–19 points at 5 years, with the highest increase seen in the processing speed domain of cognition, despite corrections with maternal cognition, SES, home factors, and early childhood stunting, compared to those who did not attend. This association between structured ECE and cognition remained significant even in later childhood, where an increase in IQ scores by 5–9 points at 9 years was observed after correcting for SES, maternal cognition, home factors, and childhood stunting status.

Global evidence states that early childhood adversities, particularly, poor home environment negatively impact cognitive abilities during childhood and continue to impact even in adult life 22 . In the current birth cohort studied in Vellore, a nurturing home environment in early childhood increased developmental cognition scores, while responsive caregiving resulted in augmented language development as evidenced by a previous publication from the same cohort 23 . It should also be highlighted that developmental trend analysis of this LMIC birth cohort showed a decline in overall developmental scores including that of cognition and language between 6 and 36 months of age, owing to SES, home environments, childhood stunting and low blood iron status 23 . A similar association was reported from a Brazilian cohort where children from healthier home environments had better cognition scores by 5 points in the fifth year of follow-up 6 .

The negative impacts that early childhood adversities have on cognition can be mitigated by appropriate psychosocial stimulation as reported in the same Brazilian cohort, where children attending nursery classes had higher cognition scores by 8 points at follow-up 6 . The present study concurs with this finding where structured ECE was associated with higher full-scale IQ scores by 7 units at 5 years. Another important finding of this study is the sustenance of this positive impact on cognition even at 9 years of age. This finding is in line with a Uruguay study which showed that preschool education had a positive impact on academic achievements both at the time of entering primary school and six years later 24 . Similar persisting effects of good quality ECE were reported from Abecedarian low-income cohort and the ‘1991 NICHD study of early childcare and youth development’, where ECE was associated with significant life success benefits as evidenced by additional years of education, better employment and vocational opportunities, and better wages 15 , 16 .

Preschool years represent a crucial period for the development of cognitive, linguistic, social, and psychomotor competencies in children. Early childhood care and education help to overcome environmental adversities due to impoverishment since the plasticity of the developing brain allows the reorganisation of brain circuits in response to psychosocial stimulation 8 , 25 . Another plausibility for the impact of centre-based early childhood interventions on academic achievements in later life would be based on the ‘cognitive advantage hypothesis’ where children from impoverished settings were able to capitalize the cognitive gains acquired due to interventions into academic achievements later in their life 15 . Aptly, the 75th World Health Assembly included opportunities for early learning in the Nurturing Care Framework to optimise early child development 26 . The current paper adds further evidence to this in terms of the need for a structured learning opportunity in early childhood.

In line with WHO and UNESCO’s recommendation of strengthening ECE as a cost-effective strategy for a country to make sustainable progress, the preschool education programme under the Integrated Child Development Services (ICDS) scheme has percolated in length and breadth of the country reaching out to tens of millions of children in India 8 , 27 , 28 , 29 , 30 .A co-prioritisation of preschool education, along with nutritional rehabilitation, has been highlighted by recent government initiatives and is in the right direction. A study from Maharashtra during 2004–05, introduced an ECE package in Anganwadis which included refresher training sessions to Anganwadi worker and financial support to purchase play materials. This intervention was found helpful in increasing IQ scores by 10 points in children compared to controls and is in concurrence with our findings 31 . Recent evidence from Tamil Nadu demonstrated the effectiveness of introducing an extra worker exclusively concentrating on ECE within the existing Anganwadi system, which resulted in exclusive ECE opportunities and an increase in cognition scores in mathematics, language and executive domains was noted in these children at 18 months, post-intervention 12 . Different methods to provide structured educational inputs in early childhood along with nutritional measures within the ICDS system can be explored by further studies in other Indian states to aid policy decisions for the country.

Genetic influences of cognitive abilities cannot be overlooked. In our current analysis as well as published literature from the same cohort, maternal cognitive capacity was shown to have a significant association not only with child development, and cognitive abilities but also with early childhood home environment 3 , 21 , 23 , 25 . Our analyses in the present study were corrected for maternal cognition recognising this genetic leverage.

There are many limitations to the current analysis. The cohort had a comparatively small sample size. We should also keep in mind that the Wechsler scale used at 5 years of age was not completely adapted for Indian settings. Both cognitive assessment measures used in the current analysis evaluate logical intelligence, but not other components of intelligence including but not limited to emotional intelligence, visuospatial intelligence, musical intelligence, etc. Quality of preschool and primary school education variables were not available for consideration in this study as well. Strengths of the study include minimal loss to follow-up, availability of good quality of early childhood data and India-specific cognitive analysis at 9 years of age.

Conclusions

This cohort study done in an impoverished urban Indian setting has brought out the positive association of attending structured ECE on school entry cognition at 5 years and mid-childhood cognition at 9 years of life. Strengthening ECE through a multi-pronged approach of structured preschool curriculum, provision of adequate play materials, providing trained manpower and enabling centres with information and communication tools could facilitate to maximize the cognitive potential of human capital in India.

Data availability

MAL-ED dataset till 5 years of age is uploaded on www.clinepidb.org . Further dataset can be shared by the Corresponding Author, on reasonable request.

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Acknowledgements

Authors thank children, their families and staff of the MAL-ED Network project.

The Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequence for Child Health and Development Project (MAL-ED) was a collaborative project supported by the Bill and Melinda Gates Foundation (BMGF), the Foundation for the NIH and the National Institutes of Health/Fogarty International Center (Grant number – OPP 47075). The 9 year follow-up of this Mal-ed India cohort was supported by an Intermediate clinical and public health (CPH) research fellowship awarded by the DBT-Wellcome Trust India Alliance to BK. (Fellowship grant number IA/CPHI/19/1/504611). Sponsors/Funders had no role in the study design, data collection and analysis, decision to publish or preparation of manuscript.

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Beena Koshy, Manikandan Srinivasan, Rangan Srinivasaraghavan, Venkata Raghava Mohan and Gagandeep Kang contributed to the study conception and design. Material preparation, data collection and analysis were performed by Beena Koshy, Manikandan Srinivasan, Reeba Roshan, Venkata Raghava Mohan, Sushil John and Karthikeyan Ramanujam. All authors read and approved the final manuscript.

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Koshy, B., Srinivasan, M., Srinivasaraghavan, R. et al. Structured early childhood education exposure and childhood cognition – Evidence from an Indian birth cohort. Sci Rep 14 , 12951 (2024). https://doi.org/10.1038/s41598-024-63861-8

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BOLD indicates blood oxygen level dependent. b1 indicates the direct effect of videogaming on BOLD signal. b2 and b3 represent for each covariate the direct effects on videogaming and BOLD signal, respectively. The dashed blue arrow and the black arrow represent the indirect and total effects of each covariate on BOLD signal, respectively. Circled arrows represent the variance of each variable in the model.

A-D, Adjusted means and standard errors from linear mixed models accounting for sociodemographic factors are visualized. F, A t score of 59 or less indicates nonclinical symptoms, a t score between 60 and 64 indicates that the child is at risk for problem behaviors, and a t score of 65 or greater indicates clinical symptoms. The t score of 60 is visually represented with a dashed blue line on the graph. Whiskers represent SEs. ADHD indicates attention-deficit/hyperactivity disorder; OCD, obsessive-compulsive disorder; RT, reaction time; and SSRT, stop signal reaction time.

a Significant differences with false discovery rate–corrected P  < .05.

b D' was calculated as the z -transformed hit rate minus the z -transformed false alarm rate.

SST indicates stop signal task.

eMethods. Stop Signal Task (SST), n-back Task, and fMRI Acquisition and Preprocessing

eMethods and eResults. List Sorting and Rey Auditory Verbal Learning Test

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• Clarifications for Variables and Findings and Corrections to Figures JAMA Network Open Correction August 8, 2023
• Video Games—Cognitive Help or Hindrance? JAMA Network Open Invited Commentary October 24, 2022 Kirk M. Welker, MD

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Chaarani B , Ortigara J , Yuan D , Loso H , Potter A , Garavan HP. Association of Video Gaming With Cognitive Performance Among Children. JAMA Netw Open. 2022;5(10):e2235721. doi:10.1001/jamanetworkopen.2022.35721

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Association of Video Gaming With Cognitive Performance Among Children

• 1 Department of Psychiatry, University of Vermont, Burlington
• Invited Commentary Video Games—Cognitive Help or Hindrance? Kirk M. Welker, MD JAMA Network Open
• Correction Clarifications for Variables and Findings and Corrections to Figures JAMA Network Open

Question   What is the association between video gaming and cognitive performance in children?

Findings   As part of the national Adolescent Brain Cognitive Development study and after controlling for confounding factors, results of this cross-sectional study of 2217 children showed very small levels of enhanced cognitive performance measured on inhibitory control and working tasks in children who played video games vs those who did not, although the video gamers had significantly higher attention problems, depression, and attention-deficit/hyperactivity disorder scores compared with the those who did not play video games. Functional MRI obtained clear blood oxygen level–dependent signal differences were associated with video gaming in task-related brain regions during inhibition control and working memory.

Meaning   These findings suggest that video gaming may be associated with very small cognitive performance enhancement involving response inhibition and working memory, and with alterations in underlying cortical pathways, but concerns about the association with mental health may warrant further study.

Importance   Although most research has linked video gaming to subsequent increases in aggressive behavior in children after accounting for prior aggression, findings have been divided with respect to video gaming’s association with cognitive skills.

Objective   To examine the association between video gaming and cognitive performance in children using data from the Adolescent Brain Cognitive Development (ABCD) study.

Design, Setting, and Participants   In this cross-sectional study, cognitive performance and blood oxygen level–dependent (BOLD) signal were compared in video gamers (VGs) and non–video gamers (NVGs) during response inhibition and working memory using task-based functional magnetic resonance imaging (fMRI) in a large data set of 9- and 10-year-old children from the ABCD study. A sample from the baseline assessment of the ABCD 2.0.1 release in 2019 was largely recruited across 21 sites in the US through public, private, and charter elementary schools using a population neuroscience approach aiming to mirror demographic variation in the US population. Children with valid neuroimaging and behavioral data were included, with adjustments performed for demographic, behavioral, and psychiatric confounding factors. Some exclusions included common MRI contraindications, history of major neurologic disorders, and history of traumatic brain injury. Collected data were analyzed between October 2019 and October 2020, with additional analyses in 2023.

Exposures   Participants completed a self-reported screen time survey, including an item asking children to report the time specifically spent on video gaming. All fMRI tasks were performed by all participants.

Main Outcomes and Measures   Cognitive performance, assessed with stop signal tasks (SST) and n-back tasks; and BOLD signal on fMRI during the tasks. Mental health symptoms were evaluated using the Child Behavior Checklist and included raw scores of behavioral (anxiety, depression, somatic, social, attention, rule breaking, and aggression concerns) and psychiatric categories ( Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition , diagnoses of depression, anxiety, somaticism, attention-deficit/hyperactivity disorder, oppositional-defiant disorder, and conduct disorder).

Results   A total of 2217 children (mean [SD] age, 119 [7.6] months; 9.91 [0.62] years; 1399 [63.1%] female) participated in this study. The final sample used in the stop signal task analyses consisted of 1128 NVGs (0 gaming hours per week) and 679 VGs who played at least 21 hours per week. The final sample used in the n-back analyses consisted of 1278 NVGs who had never played video games (0 hours per week of gaming) and 800 VGs who played at least 21 hours per week. The NVG vs VG groups did not differ on age but did differ on sex, race and ethnicity, combined parental income, body mass index, and IQ. There were no differences in body mass index and IQ after adjusting for sociodemographic variables. The Child Behavior Checklist behavioral and mental health scores were higher in VGs, with attention problems, depression, and attention-deficit/hyperactivity disorder scores significantly higher in the VGs compared with the NVGs. The VGs performed better on both fMRI tasks compared with the NVGs; the differences were statistically significant but very small. VGs had significantly faster stop signal reaction times compared with NVGs (adjusted means [SE]; 287.3 [9.8] vs 300.1 [9.6], standardized mean difference [SMD] 0.04 milliseconds; P  = .018) and correct go reaction times (adjusted means [SE], 514 [2.9] vs 552 [2.2] milliseconds; SMD 0.05; P  = .002). Following a similar pattern, 0-back D' measures of the n-back task were significantly higher in VGs compared with NVGs (adjusted means [SE], 2.33 [0.03] vs 2.18 [0.03]; SMD, 0.15; P  < .001). Similarly, adjusted means (SE) 2-back D′ scores were significantly higher in VGs relative to NVGs (1.87 [0.03] vs 1.72 [0.02]; SMD 0.15; P  < .002), and reaction times for correct responses during the 2-back conditions were faster in VGs relative to NVGs (adjusted means [SE]; 1025 [4.8] vs 1069 [3.7] milliseconds; P  < .002). Nonparametric analyses of fMRI data demonstrated a greater BOLD signal in VGs in the precuneus during inhibitory control. During working memory, a smaller BOLD signal was observed in VGs in parts of the occipital cortex and calcarine sulcus and a larger BOLD signal in the cingulate, middle, and frontal gyri and the precuneus.

Conclusions and Relevance   In this study, compared with NVGs, VGs were found to exhibit faster reaction times in measures of cognitive performance involving response inhibition and working memory and differences in fMRI BOLD signals in key regions of the cortex responsible for visual, attention, and memory processing. The very small differences in reaction times lack clinical relevance but were consistent with a potential association between videogaming and cognitive performance that involve response inhibition and working memory and the underlying cortical pathways. Concerns about the association with mental health symptoms may warrant further study.

Ask any parent how they feel about their child’s videogaming and you will almost certainly hear concerns about hours spent in a virtual world and the possibility of adverse effects on cognition, mental health, and behavior. A contributing factor to these concerns is the growth of video gaming within the last 20 years. In tandem, the demographic makeup of gamers has also been rapidly changing. In children aged 2 to 17 years, a large 2022 survey in the US showed that 71% play video games, an increase of 4 percentage points since 2018. 1 Given the substantial brain development that occurs during childhood and adolescence, these trends have led researchers to investigate associations between gaming and cognition and mental health. Most psychological and behavioral studies 2 suggest detrimental associations of video gaming, linking it to subsequent increases in depression, violence, and aggressive behavior in children after accounting for prior aggression. However, researchers have been divided with respect to whether playing video games is associated with cognitive skills and brain function. In contrast to the negative associations with mental health, video gaming has been proposed to enhance cognitive flexibility by providing skills that can be transferred to various cognitive tasks relevant for everyday life. One formulation for this broad transfer is that video gaming shares a number of perceptual and attentional demands (such as multiple object tracking, rapid attentional switches, and peripheral vision) with common cognitive tasks and can enhance reaction time (RT), creativity, problem solving, and logic. 3 , 4

In a previous review investigating video gaming and cognitive tasks, 3 gaming was found to be associated with attentional benefits, including improvements in bottom-up and top-down attention, optimization of attentional resources, integration between attentional and sensorimotor areas, and improvements in selective and peripheral visual attention. Video gamers (VGs) may also benefit from an enhanced visuospatial working memory capacity according to Boot et al, 5 who found that VGs outperformed non-VGs (NVGs) on various visuospatial working memory tasks, such as multiple object tracking, mental rotation, and change detection. Working memory improvements were similarly found after video game training in experimental vs control group research designs. 5 - 7 This finding is consistent with other studies suggesting that even short video game training paradigms can enhance cognitive control–related functions for long durations, such as reading abilities in dyslexic children 8 and, more particularly, working memory. 3

Task-based functional magnetic resonance imaging (fMRI) studies 4 , 9 - 11 have compared brain activity between VGs and NVGs. When presented with a complex visuomotor task, Granek et al 4 found that VGs exhibited more blood oxygen level–dependent (BOLD) activity in the prefrontal cortex but less overall brain activity compared with NVGs. In 1 study using an fMRI attentional letter detection task, Richlan et al 9 found no significant behavioral performance differences between 14 VGs and 14 NVGs, but VGs showed more brain activation in multiple frontoparietal regions and different activation patterns, suggesting that VGs may recruit different regions of the brain to perform attentional tasks. In the same study, 9 no differences between the 2 groups were observed during a working memory visuospatial task in overall performance (in accuracy or RT) or in brain activation. In a more recent study, Trisolini and colleagues 10 investigated sustained performance between VGs and NVGs in 2 attentional tasks. The results indicated that although VGs displayed significantly stronger performance at the beginning of the task, a substantial decrease in performance was observed over time. By the end of the task, NVGs performed more accurately and quicker. Moreover, in a study 11 investigating the short-term impact of different activities performed during a break before an n-back working memory test in an fMRI scan, 27 young adults who played video games during the break displayed poorer working memory task performance and less BOLD activity in the supplementary motor area compared with those who had listened to music. However, VGs showed neither performance nor BOLD differences compared with those who spent the break resting. The authors reasoned that the video-gaming demands may have fatigued specific cognitive resources that rely on the supplementary motor area and reduced the ability of VGs to focus attention on the subsequent working memory task. 11 This finding is in contrast with another study 3 that suggested that even short video game training paradigms can enhance cognitive control–related functions, particularly working memory, with the enhancement linked to activity changes in prefrontal areas, such as the dorsolateral prefrontal cortex and the orbitofrontal cortex.

In brief, although several studies have investigated the association between video gaming and cognitive behavior, the neurobiological mechanisms underlying the associations are not well understood because only a handful of neuroimaging studies have addressed this topic. In addition, findings from fMRI studies on video gaming in children and adolescents have not been replicated, which could be in part attributable to the relatively small sample sizes included in the analyses (N<80). In this study, we assess video-gaming associations with cognitive performance and brain activation during response inhibition and working memory using task-based fMRI in a large data set of 9- and 10-year-old children from the Adolescent Brain Cognitive Development (ABCD) study, 12 the largest long-term study of brain development and child health in 21 research sites across the US. We hypothesized, based on the literature, that VGs would perform better on the tasks and have altered cortical activation patterns compared with NVGs in key areas of the brain involved in inhibitory control and working memory.

This cross-sectional study used data from the baseline assessment of the ABCD study 2.0.1 release in 2019, which recruited a large sample of 9- to 10-year-old children from whom neuroimaging and behavioral data were acquired and quality controlled according to standard operating procedures for the ABCD study consortium. 5 All measurements were collected at enrollment in the ABCD study. The fMRI paradigms were preprocessed with standard automated pipelines using Analysis of Functional NeuroImages and included the stop signal task (SST) and the n-back task. Children were asked to report how many hours per week they play video games on a computer, console, smart phone, or other devices. Consent (parents) and assent (children) were obtained from all participants. The ABCD study was approved by the appropriate institutional review boards: most ABCD research sites rely on a central Institutional Review Board at the University of California, San Diego for the ethical review and approval of the research protocol, with a few sites obtaining local IRB approval.

The ABCD sample was largely recruited through public, private, and charter elementary schools. The ABCD study adopted a population neuroscience approach to recruitment 13 , 14 by using epidemiologically informed procedures to ensure demographic variation in its sample that would mirror the variation in the US population of 9- and 10-year-olds. 15 A probability sampling of schools was conducted within the defined catchment areas of the study’s nationally distributed set of 21 recruitment sites in the US. All children in each sampled school were invited to participate after classroom-based presentations, distribution of study materials, and telephone screening for eligibility. Exclusions included common MRI contraindications (such as stainless steel braces, cardiac pacemakers and defibrillators, internal pacing wires, cochlear and metallic implants, and Swan-Ganz catheters), inability to understand or speak English fluently, uncorrected vision, hearing or sensorimotor impairments, history of major neurologic disorders, gestational age less than 28 weeks, birth weight less than 1200 g, birth complications that resulted in hospitalization for more than 1 month, current diagnosis of schizophrenia, moderate or severe autism spectrum disorder, history of traumatic brain injury, or unwillingness to complete assessments. The ABCD study sample also includes 2105 monozygotic and dizygotic twins. The ABCD study’s anonymized data, including all assessment domains, are released annually to the research community. Information on how to access ABCD study data through the National Institute of Mental Health Data Archive is available on the ABCD study data-sharing webpage. 16

Participants were administered a screen time survey that asked how much time they spend engaged in different types of screen time on a typical weekday and a typical weekend day. The different screen time categories were as follows: “Watch TV shows or movies?”; “Watch videos (such as YouTube)?”; “Play video games on a computer, console, phone, or other device (Xbox, Play Station, iPad)?”; “Text on a cell phone, tablet, or computer (eg, GChat, Whatsapp, etc.)?”; “Visit social networking sites like Facebook, Twitter, Instagram, etc?”; and “Video chat (Skype, Facetime, etc)?” For each of these activities, the participants responded with how much time they spent per day doing them. They could answer none, less than 30 minutes, 30 minutes, 1 hour, 2 hours, 3 hours, or 4 hours. Answers were mostly none for the texting, social networking, and video chatting categories, as expected for this age range. For each participant, a total weekly video-gaming score was derived as the sum of (video-gaming hours per weekday × 5) + (video-gaming hours per weekend day × 2). A total weekly watching videos score was also derived for each participant. Using the video-gaming score, we defined a group of NVGs who never played video games (0 gaming hours per week) and a group of VGs who played a minimum of 3 hours per day (21 hours per week) or more. This threshold was selected because it exceeds the American Academy of Pediatrics screen time guidelines, 17 which recommends that video-gaming time be limited to 1 to 2 hours per day for older children.

The child’s age, sex, and race and ethnicity were reported by the parent at the baseline assessment. Race and ethnicity categories included Asian, Black, Hispanic, White, and other (which included American Indian, Alaska Native, Native Hawaiians, Pacific Islander, and multiple racial and ethnic categories). A trained researcher measured children’s height (to the nearest inch) and weight (to the nearest 0.1 lb). Height and weight were assessed 2 times, and means were recorded. Height and weight were converted to body mass index (BMI) scores (according to the Centers for Disease Control and Prevention BMI cutoffs 18 ). IQ scores were derived from the National Institutes of Health Toolbox cognition battery 19 as the mean of crystalized intelligence and fluid intelligence composite, age-corrected scores. The Pubertal Development Scale (PDS) 20 was used to assess the child’s pubertal stage. The PDS is a noninvasive measure that assesses current pubertal status in females and males, in which higher scores indicate further progression in puberty. Mental health symptoms were evaluated using the Child Behavior Checklist (CBCL) 21 , and included raw scores of behavioral (anxiety, depression, somatic, social, attention, rule-breaking, and aggression concerns) and psychiatric categories ( Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, diagnoses of depression, anxiety, somaticism, attention-deficit/hyperactivity disorder [ADHD], oppositional-defiant disorder, and conduct disorder).

The ABCD imaging protocol was designed to extend the benefits of high temporal and spatial resolution of imaging protocols of the Human Connectome Project 22 with the multiple scanner systems of participating sites. 23 High spatial and temporal resolution simultaneous multislice and multiband echo-planar imaging task-based fMRIs, with fast integrated distortion correction, were acquired to examine functional activity. For the 3-T scanners (Siemens and GE), the scanning parameters were as follows: matrix, 90 × 90; 60 slices; field of vision, 216 × 216; echo time/repetition time, 800/30 milliseconds; flip angle, 52°; and resolution, 2.4 × 2.4 × 2.4 mm. The fMRI acquisitions (2.4-mm isotropic with repetition time of 800 milliseconds) used multiband echo-planar imaging with slice acceleration factor 6. The order of fMRI tasks was randomized across participants. The fMRI preprocessing pipeline included a within-volume head motion estimation and correction and a correction for image distortions. Estimates of task-related activation strength (measured with BOLD activity levels of 10242 vertices/hemisphere) were computed at the individual participant level using a general linear model implemented in Analysis of Functional NeuroImages 3dDeconvolve, with additional nuisance regressors and motion estimates. Hemodynamic response functions were modeled in Analysis of Functional NeuroImages with 2 parameters using a γ-variate basis function plus its temporal derivative.

The SST and n-back task were selected from the ABCD imaging battery to probe inhibitory control and working memory, respectively. Participants practiced the 2 tasks before scanning to ensure they understood the instructions and were familiar with the response collection device. These 2 tasks yield robust neural activation patterns as demonstrated previously. 24 Quality control criteria included excluding participants based on poor image quality, motion, or task performance. The full details of the tasks and fMRI acquisition, preprocessing, and quality control are described in the eMethods in Supplement 1 and by Hagler et al. 22

The adaptive algorithm used in the SST allowed for calculation of the stop signal RT (SSRT; the time required to inhibit the motor response 24 ), which was used as the performance variable in analyses that assessed individual differences in response inhibition ability. The SSRT was computed by subtracting the median stop signal delay of all successful stop trials from the n th percentile go RT, where n represents the percentage of successful inhibitions (for details on the theoretical underpinnings for this estimation, see the study by Logan and Cowan 25 ). To evaluate behavioral task performance in the n-back task, D’ (calculated as the z -transformed hit rate minus the z -transformed false alarm rate) was computed for both the 2-back and 0-back conditions by calculating each participant’s hit rate (the proportion of targets for which the participant correctly indicated a match) and the false alarm rate (the proportion of nontargets for which the participant incorrectly indicated a match or did not respond). The hit and false alarm rates were then z transformed. Cognitive performance was also assessed with tasks not relying on visual-motor coordination (list sorting working memory task and Rey Auditory Verbal Learning Test), as described in the eMethods in Supplement 1 .

Participants were included if they had (1) 2 fMRI runs per task, (2) cortical vertex and subcortical voxel data available at the time of analysis, (3) hemispheric mean BOLD signal within 2 SDs of the sample mean for each task, (4) at least 200 df during the 2 scan runs, (5) mean framewise displacement less than 0.9 mm for both runs, (6) met task-specific performance criteria (described in the eMethods in Supplement 1 ), and (7) had complete information on the screen time survey and for all other variables (CBCL, age, sex, scanner serial number, puberty, race and ethnicity, and combined parental income).

Collected data were analyzed between October 2019 and October 2020, with additional analysis in 2023. Unadjusted demographic characteristics (age, sex, race and ethnicity, household income), BMI and IQ, and scanner manufacturer were compared between VGs and NVGs using 2-tailed t tests and χ 2 analyses. To compare the 2 groups on IQ, BMI, and mental health as outcome measures, we use linear mixed models, controlling for sociodemographic factors (age, sex, puberty, race and ethnicity, and household income), and including site as a random effect. Linear mixed models were also used to compare VG and NVG on the 4 task-performance measures: SSRT, correct go RT in the SST, and 0-back and 2-back D′ in the n-back. These models included age, sex, race and ethnicity, IQ, puberty, and combined parental income as adjustment variables, and site as a random effect. Based on the fits of these models, group-specific estimated marginal means (referred to as adjusted means), standard errors and standardized mean differences (SMDs) were calculated for each performance measure. Analyses were carried out in SPSS (version 28.0).

Cortical task-fMRI BOLD signal contrasts (10242 vertices/hemisphere) were compared between VGs and NVGs using vertexwise permutation analyses via the fit of a Permutation Analysis of Linear Models (PALM) general linear model. 26 Task-fMRI contrasts included correct stop vs correct go and incorrect stop vs correct go conditions of the SST, as well as 0-back vs fixation and 2-back vs fixation conditions of the n-back test. Throughout age (months), sex, scanner serial number, race and ethnicity, IQ, puberty, and combined parental income were included as adjustment variables. Furthermore, nonindependence of siblings was acknowledged using sibling status as a nested covariate in the model using PALM’s exchangeability blocks, 27 which restrict the shuffling to only occur among the observations that share the same family index (ie, number of siblings). Note, sibling status was only included in the neuroimaging analyses because the permutation design with exchangeability blocks allows for optimal modeling of nested covariates, such as sibling status and site.

Additional task measurements not relying on visuomotor coordination included a list sorting working memory task and the Rey Auditory Verbal Learning Test and are described in the eMethods in Supplement 1 .

All statistical tests were 2-sided. False discovery rate (FDR) was assessed with the Benjamini and Hochberg procedure, and corrected P values and statistical maps were considered significant at P  < .05.

To investigate the potential mediating role that time spent watching videos, behavioral problems, or psychiatric disorders have in the association between video gaming with BOLD signal activation during SST and n-back tasks, we used structural equation modeling to model the association between video gaming (independent variable) and activation in the SST and n-back task (dependent variable), with video watching, behavioral problems, and psychiatric disorders scores included as covariates ( Figure 1 ). β Coefficients from the fMRI general linear model (model described in the eMethods in Supplement 1 ) were extracted using MATLAB (MathWorks) for each task and contrast from vertexes showing significant differences between NVGs and VGs in the vertexwise analyses. Mean β coefficients were computed for each contrast and included as the BOLD signal variable in the model. Total behavioral problems and psychiatric disorder scores were calculated from the CBCL 21 as the sum of the scores of all of the problem and psychiatric items, respectively. The direct effect of video gaming on BOLD signal (parameter b1) served to check whether any initial association remained significant after controlling for the covariates included in the model. This determination was accomplished by letting each covariate predict both video gaming and BOLD signal such that each covariate could have direct effects (represented as b2 and b3) as well as an indirect effect on BOLD signal via video gaming (b1 × b2) ( Figure 1 ). In this regard, video gaming could be interpreted as a mediator of the covariates’ effects. The total effect of covariates on the BOLD signal equals b1 × b2 + b3, whereas the covariate-corrected effect of video gaming on the BOLD signal equals b1. The root mean square error of approximation, comparative fit and Tucker-Lewis indices, defined as measures of the goodness-of-fit of statistical models, were also calculated for each model. The model was specified in R software, version 4.0.4 (R Foundation for Statistical Computing) using the structural equation modeling package lavaan, 28 version 0.6-7.

A total of 2217 children (mean [SD] age, 119 [7.6] months; 9.91 [0.62] years; 1399 [63.1%] females) participated in this study ( Table 1 ). The final sample used in the SST analyses consisted of 1128 NVGs who had never played video games (0 gaming hours per week) and 679 VGs who played 21 hours per week or more. The final sample used in the n-back analyses consisted of 1278 NVGs who had never played video games (0 hours per week of gaming) and 800 gamers who played 21 hours per week or more.

The NVG vs VG between-group comparisons showed that groups did not differ on age, but did differ on sex, race and ethnicity, combined parental income, and raw BMI and IQ measures ( Table 1 ). Comparison of NVGs and VGs using linear mixed models showed the adjusted means of BMI and IQ did not differ between the 2 groups ( Table 2 ). Although mental health and behavioral scores from the CBCL were consistently higher in VGs, these differences reached statistical significance for attention problems, depression, and ADHD scores (FDR P  < .05) ( Figure 2 ). The t scores from the CBCL were less than 56 in both groups and thus, none of the measures in either group was high enough to reach clinical significance ( Figure 2 ).

Performance on the SST was in the anticipated range (mean [SE] SSRT, 293.7 [9.7] milliseconds; mean [SE] go RT, 538 [1.82] milliseconds), with a mean (SE) rate of correct inhibitions of 51.5% (0.001%). The distributions for D′ were as expected, with children performing better on the 0-back task (mean [SE] D′ = 2.25 [0.03] milliseconds) than the 2-back task (mean [SE] D′ = 1.8 [0.03] milliseconds; P  < .001). Linear mixed models compared task performance measures between NVGs and VGs with age, sex, puberty, race and ethnicity, household income, and scanner site included as covariates. Analyses showed that videogaming was associated with small improvements in performance in the SST and n-back tasks ( Figure 2 ). In the SST, compared with NVGs, VGs had statistically significantly faster reaction times. The adjusted means (SE) times for SSRT were 287.3 (9.8) vs 300.1 (9.6) milliseconds (SMD 0.04 milliseconds; P  = .02), and the adjusted means (SE) times for correct go RT were 514 (2.9) vs 552 (2.2) milliseconds (SMD, 0.5 milliseconds; P  = .002). Following a similar pattern, the 0-back D' score was significantly higher in VGs relative to NVGs (adjusted means [SE], 2.33 [0.03] vs 2.18 [0.03]; P  < .001) ( Table 2 ). Similarly, 2-back D′ was significantly higher in VGs relative to NVGs (adjusted means [SE], 1.87 [0.03] vs 1.72 [0.02]; P  < .002). Reaction time for correct responses during the 2-back condition were significantly faster in VGs relative to NVGs (adjusted means [SE], 1025 [4.8] vs 1069 [3.7]; P  < .002) ( Table 2 and Figure 2 ). Compared with NVGs, VGs, scored lower on the list sorting working memory task, and there were no differences between groups on the Rey Auditory Verbal Learning Test (see eMethods and eResults in Supplement 1 ).

Families with 2 siblings consisted of less than 5% and families with 3 siblings of less than 0.1% in both fMRI samples. In the correct stop vs correct go condition of the SST, vertexwise analyses showed significantly greater BOLD signal in VGs compared with NVGs in the bilateral precuneus ( Figure 3 ). No significant differences were observed in the incorrect stop vs correct go condition of the SST.

In the 2-back vs fixation condition of the n-back task, a significantly greater BOLD signal was observed in VGs compared with NVGs in bilateral parts of the dorsal posterior cingulate gyrus, subparietal cortex, middle and superior frontal gyri, and precuneus ( Figure 3 ). Meanwhile, a smaller BOLD signal was observed in VGs in the 2-back vs fixation condition in bilateral parts of the occipital cortex and the calcarine sulcus ( Figure 3 ). The direction, anatomical label, cluster size, and peak vertex number for each cortical region showed significant changes between VGs and NVGs ( Table 3 ). Cortical clusters showing these differences in the n-back sample also survive a Bonferroni familywise error correction at P  < .05. Similar patterns of BOLD differences between VGs and NVGs were observed in male and female groups examined separately. No significant differences were observed in the 0-back vs fixation condition of the n-back task.

The two structural equation models (for the SST and n-back task) showed good fits with root mean square error of approximation less than 0.04, a comparative fit index greater than 0.9, and Tucker-Lewis Index greater than 0.9. Video watching was positively associated with video gaming for both models (estimates, 0.12 for SST and 0.14 for n-back tasks; P  ≤ .001). However, video watching and total behavioral and psychiatric problems did not have significant direct (b3), indirect (b1 × b2), or total ([b2 × b1] + b3) effects on the BOLD signal in either model. Of importance, the direct effect of video gaming on the BOLD signal remained significant in both models.

Data were missing or partially missing on the screen time questionnaire for 11 NVG participants (0.5% of the sample). We reran our analyses on both SST and n-back task-fMRI data, as well as behavioral and mental health measures with and without those participants, and there were no differences in the adjusted means or statistical significance of our findings.

To date and to our knowledge, this is the largest study to assess the association among video gaming, cognitive performance, and brain function. The behavioral performance findings showed that VGs performed better on both the SST and n-back task compared with NVGs; however, the differences were very small and measured in fractions of milliseconds. The fMRI findings demonstrated that VGs show a greater BOLD signal in bilateral parts of the precuneus, using an SST probing inhibitory control. Moreover, results showed a smaller BOLD signal in VGs in parts of the occipital cortex and calcarine sulcus and more activation in cingulate, subparietal, middle, and frontal gyri, and the precuneus during the n-back working memory task. In line with psychological and behavioral studies 2 that suggest detrimental associations of video gaming with mental health in children, we observed significantly higher attention problems, depression, and ADHD scores in VGs compared with NVGs. The marginally higher scores in VGs in the other CBCL categories leave open the possibility that VGs may be on a trajectory to show more mental health symptoms with time and more exposure to video gaming.

The behavioral performance findings in the SST sample are in line with the behavioral findings of the studies by Chisholm et al 29 and Bavelier et al, 30 showing that VGs are less susceptible to attentional distraction and outperform NVGs on both selection-based and response-based processes, suggesting that enhanced attentional performance in VGs may be underpinned by a greater capacity to suppress or disregard irrelevant stimuli. However, these results contradict those obtained in previous studies 31 , 32 that used go/no-go tasks and those showing higher impulsivity levels to be associated with video gaming. These studies 31 , 32 adopted a different design and outcome measures, included young adult age ranges, and had small sample sizes (n < 56). The behavioral performance findings in the n-back task are also in accordance with previous studies showing enhanced visuospatial working memory performance in VGs compared with NVGs 5 , 33 and in experimental vs control groups after video game training sessions. 5 - 7 , 34 In both tasks, the significantly faster millisecond RTs in VGs compared with NVGs while simultaneously performing more accurately may reflect improved cognitive skills acquired through video gaming and not caused by impulsive responding. According to a previous EEG study, 35 earlier latencies in the visual pathways are another feature found in VGs, which may contribute to faster RTs in visual tasks after years of practice. The faster millisecond performance times on both the SST and n-back task is supported by previous studies showing that VGs outperform NVGs on a range of cognitive tasks 36 (a flanker task, an enumeration task, and 2 attentional blink tasks) and on crystallized and fluid intelligence measures assessed via the Youth National Institutes of Health Toolbox. 37 In addition, supporting our findings, research on video game training in groups of NVGs using action video games (mainly enhancing one’s attentional control) demonstrated that video game training consistently led to transferrable improvements in cognitive performance. 38

The imaging findings showing a greater BOLD signal associated with video gaming during the SST in the precuneus—a brain region involved in a variety of complex functions including attention, cue reactivity, memory, and integration of information—are consistent with previous fMRI studies 3 in children and young adolescents using response inhibition tasks showing more activation in VGs in parietal areas of the cortex, including the precuneus. More broadly, the findings agree with the evidence that VGs display enhanced overall neural recruitment in a range of attentional control areas during response inhibition tasks. 3 Of interest, in a previous study 39 investigating changes in resting state functional connectivity after video game practice in young participants using a test-retest design, the key finding was increased correlated activity during rest in the precuneus, suggesting that this area exhibits a practice effect associated with the cognitively demanding video games. 39 Advantages for VGs in various attention-demanding tasks have also been reported by Cardoso-Leite et al. 40 Moreover, in line with our findings, an electroencephalography study 41 showed that heavy-use VGs had larger event-related potential amplitudes relative to NVGs in response to numerical targets under high load conditions, suggesting that heavy-use VGs may show greater sensitivity than NVGs to task-relevant stimuli under increased load, which in turn may underpin greater BOLD changes and improved behavioral performance compared with mild-use VGs and NVGs.

Our finding of less activation in VGs in occipital areas while performing better on the n-back task is consistent with a previous fMRI study 33 that used a visuomotor task and showed less activation in occipitoparietal regions in VGs and improved visuomotor task performance; these findings suggest a reduction in visuomotor cognitive performance measures as a consequence of the video gaming practice. In addition, in line with our results, Granek et al, 4 using an increasingly complex visuomotor fMRI task, observed greater prefrontal activation in 13 VGs who played a mean (SD) of 12.8 (8.6) hours per week during the preceding 3 years compared with 13 NVGs, which the authors related to the increased online control and spatial attention required by VGs for processing complex, visually guided reaching. Similarly, Gorbet and Sergio 42 found that VGs showed less motor-related activity in the cuneus, middle occipital gyrus, and cerebellum, which they explained as an indicator that VGs have greater neural efficiency when conducting visually guided responses. In addition, previous fMRI research has found significantly greater activation related to video gaming in regions associated with working memory, including the subparietal sulcus and the precuneus. 43 , 44 In a more recent study, 45 changes in BOLD signal in the subparietal lobe, precentral gyrus, and precuneus from before to after training using a video game with a working memory component predicted changes in performance in an untrained working memory task, suggesting a practice-induced plasticity in these regions.

Although video watching is highly confounded with video gaming in our fMRI samples, our models indicate that the response inhibition and working memory effects remained significant when controlling for video watching (in addition to behavioral and psychiatric problems), suggesting that the observed BOLD alterations in the SST and n-back task are more specific to video gaming than video watching. This finding is important because it suggests that children must actively engage with a video’s content, as opposed to passively watching a video, to exhibit altered brain activation in key areas of the brain involved in cognition.

This study has some limitations, and the findings should be interpreted with caution. The 2 groups were different in terms of sex, race and ethnicity, parental income, and mental health and behavioral scores. While the results show statistically different SSRTs (287.3 [9.8] vs 300.1 [9.6] milliseconds), these are very small differences without clear implications. In addition, video games regroup a variety of gaming categories that include action-adventure, shooters, puzzle solving, real-time strategy, simulation, and sports. These specific genres of video games may have different effects for neurocognitive development 46 because they do not all equally involve interactive (ie, multisensory and motor systems) and executive function processes. In addition, single vs multiplayer games may also have differential impacts on the brain and cognition. 46 Not including the video-gaming genre in our analyses is a limitation of the current study because the screen time survey in the ABCD database does not include additional information on the genre of video games played. Future large studies investigating the association between video gaming and cognition would benefit from including game genre as a moderating variable in analyses. Another limitation of the current study is the use of only cross-sectional study designs, which cannot provide enough evidence to resolve causality or the directionality of the associations among video gaming and other variables. For example, we cannot resolve whether mental health issues or brain function changes precede and drive video gaming or whether video gaming results in mental health symptoms or altered neuroplasticity. Future works benefiting from the longitudinal design of the ABCD study will enable researchers to move beyond association toward causation using causal approaches, such as discordant twin analyses, bayesian causal networks, and machine learning.

Overall, even with consideration of the correlational nature of these cross-sectional data, the current findings are consistent with video gaming being associated with faster performance on cognitive tests that involve response inhibition and working memory and altered BOLD signal on these tasks, although the differences in task performances were very small and measured in fractions of milliseconds. The results raise the possibility that video gaming may provide a cognitive training experience with measurable neurocognitive effects. However, the CBCL behavioral and mental health scores were higher in children who played video games for 3 or more hours a day, with attention problems, depression, and ADHD scores significantly higher in the VGs compared with the NVGs. Future ABCD data releases will allow researchers to test for longitudinal effects in which video gaming might improve response inhibition, working memory, and other cognitive functions, as previously suggested in a longitudinal intervention study 34 in which episodic and short-term memory gains were maintained during a 3-month follow-up period, as well as the association of mental health symptoms with exposure to video gaming. The longitudinal design of the ABCD study will enable within-participant testing for the correlates of accumulated video-gaming practice over the years. By using methods such as cross-lagged correlations or causal inference, researchers can assess whether video gaming is associated with subsequent mental health symptoms, behavioral issues, or neurocognitive development in adolescents.

Accepted for Publication: August 20, 2022.

Published: October 24, 2022. doi:10.1001/jamanetworkopen.2022.35721

Retraction and Replacement: This article was retracted on April 10, 2023, to fix errors in the analysis in the Key Points, Abstract, main text, Table 1 , and Figure 2 (see Supplement 2 for the retracted article with errors highlighted and Supplement 3 for the replacement article with corrections highlighted).

Correction: This article was corrected on August 8, 2023, to add clarifications to the text and Supplement 1 , report the demographic variables in Table 1 and the adjusted outcomes in Table 2 , and correct Figures 1 and 2 .

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Chaarani B et al. JAMA Network Open .

Corresponding Author: Bader Chaarani, PhD, Department of Psychiatry, University of Vermont, 1 S Prospect St, Burlington, VT 05405 ( [email protected] ).

Author Contributions: Drs Chaarani and Garavan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Chaarani, Garavan.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Chaarani, Ortigara, Garavan.

Critical revision of the manuscript for important intellectual content: Chaarani, Yuan, Loso, Potter, Garavan.

Statistical analysis: Chaarani, Ortigara, Yuan, Loso, Garavan.

Obtained funding: Chaarani, Potter, Garavan.

Administrative, technical, or material support: Chaarani, Ortigara, Potter.

Supervision: Chaarani, Garavan.

Conflict of Interest Disclosures: Dr Potter reported receiving grants from the National Institutes of Health during the conduct of the study. No other disclosures were reported.

Additional Contributions: We thank Shana Adise, PhD, and Nicholas D. Allgaier, PhD (Department of Psychiatry, University of Vermont, Burlington), for conducting independent statistical analyses for the corrected article.

Additional Information: Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study ( https://abcdstudy.org ) held in the National Institute of Mental Health Data Archive. Computations were performed on the Vermont Advanced Computing Core supported in part by award OAC-1827314 from the National Science Foundation.

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How Stem Cell Therapy Supports Autism Cognitive Development?

The Silicon Review 19 June, 2024

Abnormal cognitive development can make it difficult for children with autism to socialize, communicate and learn, drastically impacting their quality of life. Fortunately, there is an autism treatment that can support cognitive functioning and restore quality of life.

In this article, we'll dive into stem cell therapy, exploring the different types of stem cells and how they can treat autism. We’ll also touch on safety, post treatment care, efficacy and treatment cost.

What are Stem Cells?

Stem cells are cells with self-renewal properties that can differentiate into many different types of cells, including muscle cells, bone cells or brain cells.

Types of Stem Cells Used in Therapy

Embryonic stem cells are stem cells obtained through the destruction of a human embryo, which poses serious ethical concerns. Embryonic stem cell therapy also comes with risk such as immune rejection and tumor growth.

Adult mesenchymal stem cells (MSCs) are a popular type of cell used in stem cell treatment for autism . MSCs can differentiate into a variety of cells, including brain cells, blood cells and muscle cells, allowing them to treat a range of conditions. MSCs can be ethically obtained from a donor or the recipient’s own cells.

Mechanisms of Stem Cell Therapy in Autism

To treat autism, MSCs differentiate into neurons and repair and regenerate damaged brain tissue, leading to long-term cognitive improvement. MSCs also have immunomodulatory properties, allowing them to treat immune system dysregulation and inflammation common in autism.

Clinical Applications and Benefits

Children with autism suffer from an impairment in cognitive functions, sociability and speech, which stem cells can improve through neurogenesis.

Autism has also been linked to gastrointestinal issues and the immune system .

Stem cell therapy for autism can treat the neuroinflammation, gastrointestinal inflammation and immune responses that can cause autism symptoms.

Autistic children treated with stem cell therapy often see benefits, including:

• improved sociability
• improve speech
• improved cognition
• reduced behavioral issues

Photo by Paige Cody on Unsplash

Specific Therapeutic Approaches

Use of donor vs. own cells.

Donor cells may be a better choice when the recipient’s own cells aren’t healthy enough due to age or health. Donor cells often come from the placenta or umbilical cord of a newborn, which means these cells are younger, healthier and more capable of repairing damaged cells.

A recipient’s own stem cells must be sent to a lab before treatment, which increases. On the other hand, donor cells can be used immediately.

Extracting stem cells from the patient can also be an invasive process and the quantity of stem cells can be limited. However, using a recipient's own cells comes with a lower risk of immune rejection.

Role of Exosomes in Enhancing Therapy

MSC-derived exosomes can cross the blood-brain barrier and stimulate neural differentiation and growth, which is beneficial in treating neurological diseases like autism.

Additional Treatments: Macrophages and T-reg Lymphocytes

T-reg lymphocytes play an important role in modulating the immune system, and their imbalance can lead to the progression of diseases like autism. Treatment with T-reg lymphocytes can support immune function and improve autism symptoms.

Macrophages produce different cytokines and play a key role in preventing inflammatory diseases, like autism, from progressing.

Safety and Efficacy

Mitigation of risks: infection, immune rejection, tumor growth.

To mitigate the risks of this therapy, choose a clinic that provides pretreatment evaluation to identify risks and post-treatment care to monitor for unexpected events, such as infections. Choose stem cell therapy that uses MSCs since MSCs don’t put you at risk of tumor formation or immune rejection.

Rigorous Testing for Purity, Viability, and Sterility

Choose a treatment center that prioritizes quality and safety. Clinics like Swiss Medica culture stem cells in a controlled environment and use viability assessments, trypan blue exclusion and flow cytometry to determine viability.

Ethical and Non-invasive Harvesting Methods

MSCs can be ethically extracted from the placenta or umbilical cord of a newborn. MSCs can also be harvested from the fat tissue, bone marrow or gum tissue of the recipient in a minimally invasive way.

Cost and Accessibility

Stem cell therapy is still a relatively new treatment and can be expensive, making it inaccessible to some. However, traveling to a country with a lower cost of living can help you find quality treatment at an affordable price.

In Conclusion

Stem cell therapy for autism is a promising treatment, but stem cell therapy comes with the stigma of ethical and safety issues. However, these concerns are associated with embryonic stem cells. MSCs are ethically sourced and safe to use.

Children with autism who are treated with MSCs can see significant improvement in social skills, cognition and speech. To mitigate any side effects, choose a clinic that offers comprehensive pretreatment evaluation and prioritizes safety and quality through rigorous testing.

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With AI at their fingertips, are students still learning?

The ubiquity of artificial intelligence may be affecting students’ cognitive development. Gareth Morris and Bamidele Akinwolemiwa consider how to address this

Gareth Morris

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The arrival of generative AI (GenAI) has completely shifted the discussion about humanity and technology, with the stories of tomorrow emerging today. But what does this mean for higher education? Some suggest that AI may become embedded in all operational areas of institutions. It’s certainly not impossible. Others, like Sal Kahn , suggest that the playing field can be not only levelled but potentially improved through Socratic AI uptake. On a broader social and wider institutional level the benefits are attractive, but the challenges are also significant.

Pedagogical innovations 

Students are now turning to GenAI tools to gain insight into questions they previously would have asked in class and to enhance, clarify or sometimes extend their notes. One challenge presented by this is that some students over-rely on these tools, which may affect their learning process. Even training on the ethical and appropriate use of an AI tool may not address their use of it in itself. 

• AI did not disturb assessment – it just made our mistakes visible
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More concerning is the influence that these tools will have on the student’s cognitive engagement with the subject matter. In some cases, students will no longer make an effort to learn certain skills and instead leave the thinking and writing process up to technology. We therefore need to implement creative and bespoke pedagogical approaches that anticipate students’ use of AI. It’s imperative that this informs teaching and assessment approaches, development of teaching and learning materials and the modification of learning outcomes for specific subject areas. 

Essentially, the skills expected of students in a world where these tools will become ubiquitous has to be critically evaluated both in the classroom and at an institutional level. We must develop methods that prevent students from delegating their cognitive engagement and the development of their critical thinking skills to technology.

The fallible coursework essay

Let’s take a practical analytical assessment norm as a starting point. The traditional coursework essay — a stalwart of university courses around the globe due to the capacity it has to assess intended course learning outcomes — is potentially fatally compromised. 

Traditionally, students write an essay by planning, drafting and refining their work. To do so, they might access and reflect on the suggestions of online writing tools such as Microsoft Word’s inbuilt review features, external websites such as Grammarly, peer feedback and formative input from course tutors. Academics have raised concerns over academic integrity and honesty over the past couple of decades, but plagiarism prevention service Turnitin helped to reassure many.

However, the new prospect of a more powerful, free-prompt-inspired super author, available to students at the click of a button, is looming large. What can course designers, programme managers and higher education leaders do?

The assessment solutions

Definitively identifying AI-written work, with no markers apparent and no previous draft data to refer back to, is extremely difficult. 

AI identifiers such as  Quillbot are useful, but not infallible. At times, they pick up on machine-translated work, but they also can miss it. False positives are also possible, with human-written texts being misidentified as AI-generated. 

One solution is a balance-of-probabilities approach tied into institutional academic integrity, honesty and authorship policies. Giving students appropriate writing skills training, clearly outlining expectations and the outcomes if they’re not met, will work in many cases. But let’s be realistic – not all. 

Another alternative is to change the assessment parameters. Portfolio-style works take on greater weighting. The emphasis is placed on the process and not simply the end product. If the process is too protracted and fallible because of time, extended exam style or practical experiment conditions could be applied to assess key skills. 

Taken a step further, institutional resources, such as writing centres and centrally managed IT applications, can also be used that level the playing field even more. In addition, adaptions can be made in assessment cases where extra time or support is required.

Of course, educators need to ensure that AI is not holding the pen, especially for module-learning outcomes designed to inform future employers that students have mastered these skills and competencies. Yet students are facing a future in which technologies only being dreamed of today are the backbone of the workforce. We need to design courses specifically for this purpose and measure how well students can work in tandem with these resources. But for now, and for the majority of courses, promoting honesty and integrity is the way forward for a better relationship with AI.

Gareth Morris works at the Centre of English Language Education and Bamidele Akinwolemiwa is a researcher and graduate teaching assistant, both at the University of Nottingham Ningbo.

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