Explain how the brain matures from infancy to early adulthood, evaluate the extent to which critical periods explain development, and explain how neuroplasticity lets experience shape the developing brain.
Each is in the Psychology glossary, with every other term in the subject.
The biologically driven sequence of changes in brain structure from before birth to early adulthood, including synaptogenesis, synaptic pruning and myelination, which proceeds roughly from the back of the brain to the front.
A limited window in development during which a specific experience is necessary for a function to develop normally; if the experience is missing, the function cannot develop fully later.
A model of adolescent risk-taking in which a reward system that becomes highly responsive at puberty matures earlier than a prefrontal control system that develops gradually into the twenties.
The process in which axons become covered in a fatty myelin sheath, which speeds up and synchronises signals between brain regions; it continues into the twenties, with frontal pathways among the last.
The developmental process in which infants lose the ability to tell apart sounds or faces they do not experience, while becoming better at those they do, mainly between 6 and 12 months.
A window in development when the brain is especially responsive to a type of experience, so learning is easiest then, though some learning remains possible afterwards.
The elimination of synapses that are used little, while frequently used connections are strengthened; it peaks in childhood and adolescence and makes neural networks more efficient.
Describe the main processes of brain maturation: synaptogenesis, synaptic pruning and myelination, and their timing.
Explain why the prefrontal cortex matures last and how the dual systems model uses this to explain adolescent risk-taking.
Explain the difference between critical and sensitive periods, with examples from vision, language and early care.
Discuss the extent to which critical periods explain development.
Explain the role of neuroplasticity in development, including experience-expectant and experience-dependent plasticity.
Describe the longitudinal MRI study by Giedd et al. (1999) and evaluate it with reference to measurement and causality.
Apply research on brain development to unfamiliar situations and evaluate it with reference to the concepts.
Brain development studies how the brain grows and reorganises from before birth to early adulthood, and how these changes relate to developing abilities such as seeing, speaking, reasoning and controlling impulses. It helps to explain why young children learn some skills so easily, why early neglect can have lasting effects, and why adolescents take more risks than children or adults.
Brain development is an area of study in the human development context. It draws on two approach terms from the biological approach, neuroplasticity and localisation of function, and links them to cognitive and sociocultural explanations of development: the brain matures on a biological timetable, but experiences provided by families, schools and cultures decide much of how it is wired. Research in this area raises questions of change (is development maturation or learning?), causality and
With a free account, Augment remembers where you stopped and what you have practiced.
The formation of new synapses between neurons, which is fastest before birth and in infancy, when the brain produces far more connections than it will keep.
A newborn baby’s brain weighs about a quarter of an adult brain, yet it already contains most of the neurons it will ever have. What changes over the following two decades is mainly the connections between neurons, the insulation of their fibres and the balance between different regions. These biologically driven changes are called brain maturation.
Maturation does not happen in a vacuum. Neuroplasticity, the brain’s ability to change its structure in response to experience, means that the connections that form and survive depend on what the child sees, hears and does. Plasticity is greatest in childhood, when the brain is building its basic circuits, and continues at a lower level throughout life. Brain development is therefore best understood as maturation and experience working together: biology provides a timetable and a large surplus of connections, and experience selects which of them are kept.
In the last months before birth and the first years after it, neurons form new synapses at an enormous rate. This synaptogenesis produces far more connections than the adult brain will keep. Huttenlocher and Dabholkar (1997) counted synapses in post-mortem brain tissue from people who had died at different ages. Synaptic density reached its maximum in the auditory cortex at about three months after birth, but in the prefrontal cortex not until after 15 months.
Different regions follow different timetables, and the regions that handle basic sensation mature before those that handle complex thought. The surplus of connections gives the young brain great flexibility: it can be wired for any language, any visual environment and any set of caregivers, depending on what the child actually meets.
After the period of overproduction, the brain begins to remove synapses. Connections that are used often are strengthened and kept, while those that are rarely used are eliminated. This synaptic pruning makes neural networks faster and more efficient, in the same way that removing little-used roads can make a city’s main routes clearer. Huttenlocher and Dabholkar (1997) found that net synapse elimination had ended by about age 12 in the auditory cortex, but continued into mid-adolescence in the prefrontal cortex.
Pruning is the main way experience shapes the developing brain. Because experience decides which connections are used, two children with the same genes but different environments will keep different sets of synapses. Pruning also explains why the grey matter of the cortex becomes thinner during adolescence: the loss of surplus synapses reduces grey matter volume, even as the child’s abilities improve.
While pruning removes connections, myelination improves those that remain. Myelin is a fatty layer wrapped around axons by support cells; it insulates them so that signals travel many times faster and arrive in better synchrony. Myelination begins before birth in the brainstem and sensory pathways and continues into the twenties, with the long pathways linking the prefrontal cortex to other regions among the last to be completed.
Myelination is visible on MRI as an increase in white matter, the tissue made mainly of myelinated fibres. Its slow progress helps to explain why abilities that depend on several regions working together quickly, such as planning, controlling impulses and processing complex sentences, keep improving through adolescence.
YouTubeThe brain does not mature all at once. Gogtay et al. (2004) scanned 13 healthy children every two years for eight to ten years, between the ages of 4 and 21, and combined the scans into time-lapse maps of grey matter. Grey matter loss, a sign of pruning, began in the areas at the back and top of the brain that handle basic sensation and movement, then spread forward. The higher-order association areas that integrate information, especially the prefrontal cortex, matured last.
The prefrontal cortex supports executive functions: planning ahead, holding information in mind, switching between tasks, weighing risks and controlling impulses. Because it matures late, these abilities continue to develop through adolescence and into the early twenties. This is a clear link between localisation of function and development: a function localised mainly in one region develops on that region’s timetable.
Adolescents take more risks than children or adults: they are more likely to drive dangerously, experiment with drugs and act impulsively, especially with friends. This is puzzling if development simply means steady improvement, because adolescents can reason about risk almost as well as adults. Casey, Getz and Galvan (2008) and Steinberg (2008) proposed that two brain systems mature on different timetables.
The first system is a socio-emotional or reward system, centred on the ventral striatum, which responds to rewards such as excitement, novelty and the approval of peers. It becomes much more responsive around puberty, partly because of hormonal changes. The second is a cognitive control system, centred on the prefrontal cortex, which allows a person to stop, think and resist temptation. It develops gradually, as pruning and myelination continue, and reaches maturity only in the early to mid-twenties. During the years between, sensitivity to reward is high while cognitive control is still developing.
Steinberg et al. (2018) tested the model outside the USA and Europe, with more than 5,000 people aged 10 to 30 in 11 countries in Africa, Asia, Europe and the Americas, including China, India, Kenya, Colombia and Jordan. Using questionnaires and behavioural tasks, they found that sensation seeking rose through adolescence, peaked at about 19 and then declined, while self-regulation rose steadily to a plateau between 23 and 26. The size of the effects varied between countries, but the pattern was similar everywhere, which suggests a shared biological basis.
The model also explains why the presence of friends matters so much. In experiments on risky driving games, adolescents take more risks when peers are watching than when alone, and the effect is much smaller in adults. On the dual systems view, peers make the reward system even more active, which widens the gap with a still-developing control system. Social context therefore changes how the biological imbalance shows itself in behaviour.
YouTubeSome kinds of development seem to depend on experience arriving at the right time. The idea comes from biology: in the 1930s Konrad Lorenz showed that newly hatched geese follow the first moving object they see, but only within a short window after hatching. If a behaviour or ability can develop only if the right experience comes within a fixed window, that window is a critical period.
Most research on human development has found that windows are softer than this. Learning is easiest and most complete during a sensitive period, but some learning remains possible afterwards, and the end of the window is gradual rather than sudden. Many researchers now reserve “critical period” for a few systems with a sharp cut-off and use “sensitive period” for most human abilities.
The clearest evidence comes from the visual system. Hubel and Wiesel (1970) sewed shut one eyelid of kittens for different periods at different ages and then recorded the activity of cells in the visual cortex. Closing one eye during the first few months of life caused a sharp, lasting drop in the number of cells that responded to that eye. The susceptible period began suddenly near the start of the fourth week, stayed high until between the sixth and eighth weeks, and had disappeared by about the end of the third month. Closing one eye of an adult cat for over a year had no detectable effect.
Even after the eye was reopened for up to five years, the cortex recovered only a little. Hubel and Wiesel received the Nobel Prize in 1981 for this work, which showed that experience-expectant input during a specific window is needed to wire a sensory system. Similar effects in children explain why conditions that blur or block one eye, such as a squint or a cataract, are treated as early as possible.
The linguist Eric Lenneberg proposed in 1967 that language can be acquired fully only between early infancy and puberty. Evidence for a sensitive period in language comes from several directions. The case study of Genie, a girl from California who was kept isolated with almost no language input until she was found at the age of 13 in 1970, is often cited: Curtiss (1977) reported that with intensive teaching she learned many words but never mastered grammar. As evidence, the case is weak, because Genie had suffered severe abuse and malnutrition and may have had a developmental disability, so it cannot show that the late start alone caused her difficulties.
Stronger evidence comes from second-language learners. Johnson and Newport (1989) tested 46 native speakers of Korean or Chinese who had moved to the USA between the ages of 3 and 39. On a test of English grammar, performance fell steadily with age of arrival up to puberty; those who arrived after puberty scored lower, with large differences between individuals that were unrelated to age of arrival. The decline could not be explained by how long participants had lived in the USA or by their motivation, which supports a maturational limit on learning grammar.
Sensitive periods begin very early. Infants can initially tell apart the speech sounds of all languages, but between 6 and 12 months they become better at the sounds of their own language and worse at others, a process called perceptual narrowing.
Kuhl et al. (2006) found that American and Japanese infants were equally good at telling apart English /r/ and /l/ at 6–8 months, but by 10–12 months the American infants had improved and the Japanese infants had declined. Kuhl, Tsao and Liu (2003) then gave nine-month-old American infants twelve sessions with native Mandarin speakers: the decline in Mandarin sound perception was reversed, but only with live speakers, not with the same sessions on video.
The psychiatrist John Bowlby argued that attachment to a caregiver must form within a critical period in the first years of life. Research on children raised in institutions tests this idea in the brain and in behaviour. In the Bucharest Early Intervention Project (BEIP), Nelson et al. (2007) followed 136 young children who had lived in Romanian institutions almost from birth, a form of severe deprivation linked to institutionalisation. Half were randomly allocated to high-quality foster families created by the researchers; half stayed in institutional care. A group of children who had never been in institutions provided a comparison.
Foster care improved cognitive scores, but did not close the gap with children raised in their own families. The gains at 42 and 54 months were largest for the children placed in foster care youngest, which points to a sensitive period. Chugani et al. (2001) used positron emission tomography (PET) with ten children adopted from Romanian orphanages and found reduced activity in the orbitofrontal cortex, the amygdala and the hippocampus, suggesting that severe early deprivation leaves traces in brain function as well as behaviour.
Domain | Type of window | Key evidence | What happens if experience is late |
|---|---|---|---|
Binocular vision | Critical (in kittens) | Hubel and Wiesel (1970) | Lasting loss of cortical response to the deprived eye |
Speech-sound perception | Sensitive | Kuhl et al. (2003, 2006) | Non-native contrasts harder, but can be relearned with live input |
Grammar | Sensitive | Johnson and Newport (1989); Genie | Learning possible, but usually less complete after puberty |
Cognitive and social development after deprivation | Sensitive | BEIP: Nelson et al. (2007) | Earlier placement brings more recovery; gaps remain |
Critical periods offer a simple and powerful explanation: development depends on the right experience at the right time, and missed windows leave lasting effects. The evidence supports this explanation strongly for some systems and only partly for others. A balanced judgement needs to weigh both.
The visual research of Hubel and Wiesel shows a true critical period in a sensory system, with a sharp onset, a sharp end and very little recovery afterwards. Language shows a clear age effect: Johnson and Newport found that grammar learning declined steadily with age of arrival up to puberty, and perceptual narrowing shows that the brain commits to the sounds of its own language within the first year. The BEIP found larger gains when foster placement came early. Across these studies, the timing of experience predicts outcomes, which a simple “more experience is better” account cannot explain.
Most of the human evidence fits sensitive periods better than critical ones. Kuhl, Tsao and Liu (2003) reversed the decline in non-native sound perception with just twelve sessions, which shows the window was not closed. Adults can learn second languages to high levels, even if they rarely sound like native speakers, and late learners in Johnson and Newport’s study varied widely. Foster care in the BEIP improved outcomes even for children placed after the age of two. The brain also keeps its plasticity into adulthood, as studies of adult learning such as research on London taxi drivers have shown.
Much of the strongest evidence comes from animals or from rare and extreme cases. Kittens with sewn eyelids are a useful animal model, but human visual development may be more flexible. Cases such as Genie involve abuse, malnutrition and possible disability as well as missed language input, so the cause of later difficulties cannot be isolated. Natural comparisons, such as immigrants who arrive at different ages, are confounded by differences in schooling, motivation and the amount and type of language they hear.
Critical periods explain when the brain is most open to experience, but not what the experience is or how much of it a child receives. Development also depends on continuous processes, such as gradual improvements in executive function, on the quality of relationships, and on the cultural tools a child learns to use. Critical periods are therefore best seen as one part of an explanation in which biological timing, the child’s own activity and the social environment interact.
Neuroplasticity is the mechanism that lets experience shape the developing brain. Two kinds of plasticity work during development. Experience-expectant plasticity depends on experiences that almost every child meets, such as patterned light, speech and responsive care; the brain overproduces connections in anticipation of this input, and the input selects which survive. Sensitive periods are mostly periods of experience-expectant plasticity. Experience-dependent plasticity depends on experiences specific to one child, such as learning a particular language, playing an instrument or growing up in a city, and continues throughout life.
Experience-expectant | Experience-dependent | |
|---|---|---|
Developmental role | Wires the basic systems all humans share | Builds the individual skills and knowledge of each person |
Timing | Mainly in sensitive periods early in life | Throughout life |
Developmental example | Binocular vision needs input from both eyes in early childhood | A child who learns two languages develops more grey matter in language-related areas |
Plasticity also explains why development can recover. Children are more able than adults to recover from brain injury, because their brains have more spare connections and greater capacity for reorganisation, and children moved out of deprived environments improve. The same plasticity makes the developing brain vulnerable: chronic stress, neglect and poverty during childhood are associated with differences in brain structure, especially in areas involved in memory and self-regulation. Because plasticity allows change in either direction, the quality of a child’s environment has a large effect on how the brain develops.
Brain development is often presented as purely biological, but a full explanation draws on all three approaches. The biological timetable of maturation interacts with cognitive processes, such as how much a child practises a skill, and with the sociocultural environment that provides the experiences.
Approach | Contribution to brain development | Example |
|---|---|---|
Biological | Maturation follows a genetic timetable; plasticity mechanisms such as pruning and myelination | Prefrontal cortex matures last; synaptic density peaks at different ages in different regions |
Cognitive | The child’s own activity and practice select which circuits are used | Reading practice strengthens connections between visual and language areas |
Sociocultural | Families and cultures provide the experiences that the brain expects or depends on | Live social interaction, not video, maintained infants’ Mandarin perception; foster care improved outcomes in the BEIP |
Brain development also offers a biological perspective on psychological models of development. The late maturation of the prefrontal cortex is consistent with the gradual development of abstract reasoning and self-control that stage theories such as Piaget’s describe, while the evidence that social interaction shapes the developing brain is consistent with sociocultural theories that emphasise learning with others. Brain research does not decide between these models, but it constrains them: a model of development must fit what is known about when different brain systems mature.
Most human evidence on brain development comes from magnetic resonance imaging (MRI), which measures the volume and density of grey and white matter. Pruning and myelination are inferred from these measures rather than observed: a fall in grey matter volume is consistent with the loss of synapses but could also reflect other changes, such as myelin growing into the edges of the cortex. Direct counts of synapses, as in Huttenlocher and Dabholkar (1997), are possible only in post-mortem tissue from a small number of people, often children who died of illness. Linking brain changes to abilities adds a further step, because the same behaviour can be measured in many ways.
Separating the effects of maturation from those of experience is the central problem of developmental research. As children get older, their brains mature and they also accumulate experience, attend school and take on new social roles, so age-related brain changes cannot be attributed to biology alone. Natural experiments and randomised studies help. In the BEIP, random allocation to foster care allowed the researchers to conclude that the family environment caused better outcomes, which correlational studies of adopted children could not show.
Brain development shows both kinds of change. Some processes, such as myelination, are gradual and continuous; others, such as the rapid perceptual narrowing between 6 and 12 months or the onset of puberty, produce relatively sudden shifts. The dual systems model describes adolescence as a period of imbalance rather than a stage with sharp boundaries, and individual differences in timing are large. Brain research therefore supports a view of development in which continuous biological change can still produce stage-like changes in behaviour.
The idea that the first three years determine a child’s future has been used to sell products and to frighten parents, even though the brain stays plastic long after early childhood. Adolescent brain research has been presented to courts in the USA, for example in expert briefs to the Supreme Court on the punishment of young offenders, which raises questions about how far group averages should influence decisions about individuals.
Research such as the BEIP raises ethical questions of its own, because some children were randomly allocated to remain in institutions; the researchers argued that foster care was not yet available in Romania and that the study helped build a national foster-care system.
Most longitudinal imaging studies have scanned healthy children from the USA and Europe, often from families who volunteer for research. The BEIP and Steinberg et al. (2018) are important exceptions. Because the developing brain is shaped by experience, findings from one cultural group may not describe the timing or pattern of development in others, and group averages can hide large individual differences.
Earlier imaging studies of children were cross-sectional: they compared different children at different ages and suggested that grey matter simply declined and white matter increased in a straight line from childhood to adulthood. Giedd et al. aimed to track how grey and white matter actually change within the same individuals during childhood and adolescence, and whether different lobes develop on different timetables.
The study was carried out at the National Institute of Mental Health in the USA. The sample was a large group of healthy children and adolescents aged about 4 to 21, recruited by the institute’s child psychiatry branch. Many participants were scanned two or more times, about two years apart. The researchers measured the volume of white matter and of cortical grey matter in the frontal, parietal, temporal and occipital lobes, and modelled how each changed with age using the repeated scans of the same individuals.
This mixed longitudinal design has a major advantage over a cross-sectional one. Because the same child is compared with themselves at different ages, differences between children, such as family background or head size, are controlled, and the shape of each developmental curve can be estimated rather than assumed.
White matter volume increased roughly linearly with age throughout the age range, consistent with continuing myelination. Cortical grey matter did not simply decline. It followed an inverted U-shape: it increased before adolescence and then decreased. The peak differed between lobes: frontal grey matter peaked at about age 11 in girls and 12 in boys, while temporal grey matter peaked later, at about 16.
Giedd et al. concluded that grey matter development is non-linear and region-specific, and that the brain continues to change substantially through adolescence. The pre-adolescent increase in grey matter is consistent with a second wave of synapse growth, and the later decrease with pruning; the steady rise in white matter is consistent with myelination. The later peak in the temporal lobe, and the later maturation of frontal regions reported in subsequent studies such as Gogtay et al. (2004), support the view that higher-order regions mature last.
MRI measures tissue volume, not synapses or myelin directly, so the explanation in terms of pruning and myelination is an inference. It fits post-mortem evidence such as Huttenlocher and Dabholkar (1997), which strengthens it, but other processes could contribute. Volume is also only one aspect of development: two brains with similar grey matter volumes can be organised very differently.
The study describes when brain changes happen but not why. The timing of the grey matter peak is consistent with puberty, but the study could not separate the effects of hormones, genes and experience, all of which change with age. The longitudinal design shows that the changes happen within individuals, which is stronger than a cross-sectional comparison, but it remains correlational.
Participants were healthy volunteers recruited in the USA, and the sample was not designed to represent other countries or social groups. Because brain development responds to experience, the timing of peaks may differ in children who grow up in very different conditions. The curves are group averages, and individual children varied widely, so the results cannot be used to judge whether a particular child’s brain is developing normally.
Sources: Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., Paus, T., Evans, A. C. and Rapoport, J. L. (1999). Brain development during childhood and adolescence: a longitudinal MRI study. _Nature Neuroscience, 2(10), 861–863._
Brain development can appear in Paper 1 Section B, where a short scenario from human development asks you to apply an approach term such as neuroplasticity or localisation, and in Section C, where an essay asks you to evaluate or discuss brain development through one of the six concepts. Research on brain development, such as a longitudinal imaging study, can also be the unseen study in Paper 2 Section B.
Brain maturation involves synaptogenesis (a surplus of synapses early in life), synaptic pruning (removal of unused connections) and myelination (insulation of axons), each on a different timetable for different regions.
Maturation proceeds from the back of the brain to the front; the prefrontal cortex, which supports executive functions, matures last, into the twenties.
The dual systems model explains adolescent risk-taking as an imbalance between an early-maturing reward system and a later-maturing control system; Steinberg et al. (2018) found the same pattern in 11 countries.
A critical period is a window in which experience is necessary; a sensitive period is a window of greatest ease. Vision in kittens shows a critical period; most human abilities, such as language, show sensitive periods.
Evidence on critical periods includes Hubel and Wiesel (1970), Johnson and Newport (1989), Kuhl et al. (2003, 2006), Genie and the BEIP; the human evidence is often correlational or confounded.
Neuroplasticity lets experience shape development: experience-expectant plasticity wires basic systems early; experience-dependent plasticity builds individual skills throughout life.
Giedd et al. (1999) found that white matter increases steadily while grey matter rises and then falls, peaking at about 11–12 in the frontal lobe and about 16 in the temporal lobe.
Evaluation issues: MRI measures volume, not synapses; maturation and experience are hard to separate; samples are mostly Western; findings are easily overstated in public.
I can describe synaptogenesis, synaptic pruning and myelination and when each happens.
I can explain why the prefrontal cortex matures last and how the dual systems model explains adolescent risk-taking.
I can explain the difference between critical and sensitive periods with an example of each.
I can discuss the extent to which critical periods explain development, using evidence on both sides.
I can explain the role of neuroplasticity in development, including experience-expectant and experience-dependent plasticity.
I can describe the study by Giedd et al. (1999) and evaluate it with reference to measurement and causality.
I can apply brain development research to an unfamiliar scenario in the human development context.