Introduction

All major developmental psychology theories have tried to explain how people grow and change over the course of a lifetime. These theories have different implications for how their findings may be applied to our everyday lives. Psychoanalytic theories, mostly influenced by the work of Sigmund Freud, proposed that development occurs through a series of psychosexual stages, emphasizing the importance of the unconscious mind and childhood experiences. Erik Erikson formed an eight-stage theory of psychosocial development describing how one grows and changes through the entire lifespan by overcoming various conflicts at each stage while building upon previous experiences. Watson, Pavlov, Skinner and Bandura, main representatives of learning theories, focused on how the environment impacts behaviour through classical conditioning, operant conditioning and social learning. On the other hand, Piaget in his theory of cognitive development focused on the development of mental processes, skills, and abilities which are in accordance with ongoing biological changes.

Most of these theories have mainly focused on the stages of development early in life – from infancy to adolescence – leaving the impression that after adolescence no significant leaps in development occur. However, a large and relatively new body of evidence that has emerged recently revealed that becoming an adult is much more complicated and temporally extended than previously believed. It seems that the transition from adolescence to adulthood is not just marked by changes in social markers or legal boundaries, but also by dramatic changes in brain structure and function, and consequently, in cognitive skills. Might this be a life stage that deserves more attention from various research disciplines, rather than a new cultural trend that is occurring depending on social backgrounds and likely economic prospects? Social studies are mostly focused on explaining the reasons behind the lengthening period between the onset of puberty and the fulfilling of cultural expectations around adult roles, like financial independence and family formation.

Social markers of transition to adulthood

Young adulthood is broadly defined as the age between 18 and 40 years. In 1950s and 1960s social scientists from numerous fields showed that most demographic events that mark the entrance into adulthood occurred early during this life stage, within a relatively limited time-span and usually in a sequential order – people left home and entered the workforce, got married and became parents a few years later (Hendry and Kloep 2007). Most young men and women married in their late teens or very early twenties and had their first child about a year later. Until marriage, they lived with their parents and only 25% (almost all men) attained any higher education (Arnett 2000; Billari and Liefbroer 2010). These key transitions were commonly considered to be the criteria for reaching adulthood. However, during the last two decades, it has been increasingly argued that the process of transitioning to adulthood has become more complex, diversified, and prolonged than ever before (Billari and Liefbroer 2010; Jensen and Arnett 2012; Lesthaeghe 2010). Nowadays young people explore many more options while searching for a suitable career, partner, or housing possibilities. They stay in school longer, marry later, and have their first child later than in the past. For example, Schoon and Lyons-Amosba (2016) identified five distinct pathways to employment in UK: (1) early work orientation involving continuous employment from the age 16, (2) transition to employment around the age 19 after some education, (3) persistent unemployment at age 19 as a result of constant transitions between being employed or in school, (4) inactivity due to illness or taking care of family home, and (5) extended education followed by transition to work up to the age 23.

The pathways into adulthood have certainly become more flexible, variable, and less uniform. Some move more slowly through these processes and others make relatively fast transitions to independent adult life. In a study among Finnish university students during an 18-year follow up, researchers identified six pathways into adulthood related to family and work roles: (1) career and family pathway in which all key life transitions to adulthood occurred in a ‘‘normative’’ or expected order, (2) slow starters in which both career and family transitions were postponed, (3) singles with slow careerin which individuals had difficulties in forming and committing to a partnership and were slow in starting their work career, (4) fast starters characterized by fast transition in all of the key life domains, (5) career and unsteady partnerships where individuals moved early to employment and entered in a partnership during university studies but then experienced several partnerships and repeated break-ups, and (6) fast partnership and late parenthood pathway with early formed partnership that remained steady, but that transitioned to parenthood relatively late (Salmela-Aro et al. 2011). Billari and Liefbroer (2010) showed that throughout Europe many demographic events occur rather late in young adulthood, the time-span between the first and the last transition is relatively long, and many of these events (e.g., completing schooling, obtaining a full time job, establishing an independent household, forming a family) occur and often reoccur during this time.

It seems that today traditional social markers do not stand for attaining adulthood anymore. Rather, adult status relies more on personal assessment of maturity. Some researchers even believe that this period of transition to adulthood should be described as a distinct phase in the life course between adolescence and adulthood. Arnett (2000) proposed a new period of development from late adolescence through early twenties, focusing on ages 18 – 25. He points out that this period is neither adolescence nor young adulthood, and refers to it as the “emerging adulthood”. According to Arnett’s theory, “emerging adulthood” is: a) the age of identity explorations, including frequent changes in love partners, educational and occupational selection, b) the age of instability, because it adds an element of stress and anxiety, c) the self-focused age, because it is the least structured time of life and the least bound by obligations to others, d) the age of feeling in-between adolescence and adulthood, because many of young adults at this age do not feel like adolescents any more but do not yet feel as adults either, e) the age of possibilities as a time of high hopes and great expectations, with a window of opportunity for people to make dramatic changes in their lives (Arnett 2000; Schwartz et al. 2005; Arnett 2007; Tanner et al. 2009).

There is no doubt that managing everyday lives during young adulthood becomes more complex than during adolescence. It includes planning and carrying out many diverse activities, e. g., making short-term plans such as creating a daily to-do list or long-term goals such as saving enough money to enjoy a comfortable retirement. An individual at this age can be deemed criminally responsible, is able to own firearms or purchase alcohol, drive a car, vote, or join the armed forces. There is a great variability as to how different aspects of the law define age of majority in various countries.

Defining adulthood through legal boundaries

Determining exactly when the largest period of human development – the adult life – begins is unclear even when looking through legal definitions. The age of majority is the threshold of adulthood as recognized by the law. It is the age when an individual, through the eyes of the law, assumes control and responsibility over his person, actions and decisions. It does not depend on the mental or physical maturity of an individual, and it is not always set to 18, but varies between 15 and 21 (Curtis 2015). In most countries around the world, the youngest age at which one can be held responsible and subjected to punishment for certain offences which are considered less serious by criminal code specific to each country is between 10 (e.g., Australia) and 12 (e.g., Belgium, Canada). For more serious offences, the lowest age limit for criminal liability varies between ages 14 to 16 (Child Rights International Network 2017).

The legal age requirement for handgun possession is 18 in most of the USA, but states have set their own minimum age laws and in some, a 14-year old can legally own rifles or shotguns (Smartgunlaws.org 2014). In most European countries 18 is also the minimum age, except for hunting and target shooting, when less than 18 is acceptable under adult guidance, with no minimum age limit (Official Journal of the European Union 2008). In most countries around the world the military draft starts at 18, however with parental consent, the possibility of enlisting varies between 14 and 17 (Central Intelligence Agency 2016). The marriage age of consent is set to 18 in most countries, but with parental and/or the consent of a judge the lowest age limit varies from 13 to 18 (Robertson 2016). Driving age minimum varies from 14 to 18, while minimum legal purchase age for alcohol ranges from 16 to 20 in most countries around the world, but is set to 21 in the USA (The Federal Trade Commission 2013; Kadiri 2014).

Excessive alcohol consumption often escalates during early adult years. More than 30% of young adults between the ages of 18 and 24 report binge drinking (consuming several alcoholic drinks within two hours, generally five for men and four for women) at least once in two weeks in the USA (SAMHSA 2015), 24% of Australians in the same age group report binge drinking at least once a month (AIHW 2014), while in Europe 22% of those aged 15 – 24 years report binge drinking at least once a week (Hanewinkel et al. 2012). Actually, the highest rates of substance and alcohol use disorders are found between the ages 18 and 25 (Carter et al. 2010). Unlike most other periods of life, the leading causes of death in late adolescence and early young adulthood (between 15 and 24 years of age) are road traffic accidents, homicide and suicide (Sethi et al. 2007; WHO 2014). Even though adolescence is commonly referred to as “the rules are made to be broken” age, the prevalence of several types of risk-taking behaviours like risky driving at high speed or while intoxicated actually peaks during early 20s (Steinberg 2008).

Researchers agree that this inclination to risk-taking behaviour (e.g., drunk driving, substance abuse) is strongly related to slow maturation of cognitive control system which regulates these impulses (Luna et al. 2010). Becoming an “adult” involves enormous changes in roles and responsibilities, which require greater use of effortful control and sophisticated cognitive skills in order to modify emotions and behaviour, make appropriate decisions, consider consequences, prioritize, focus on important details and shift between tasks and activities. The optimal use of these abilities evolves gradually, and depends on brain-based control mechanisms. While brain-based control mechanisms are still developing, it is more likely to act impulsively when confronted with stressful or emotional decisions. Even though cognitive challenges in young adulthood are generally very high, looking in the literature from 10 – 15 years ago we can find virtually nothing about typical development of brain structures that underpin these complex processes after childhood.

What’s brain got to do with it?

During 1980s and 1990s researchers published extensive data on synapse formation in the cerebral cortex of the rhesus monkey (Goldman-Rakic 1987; Rakic et al. 1994; Bourgeois et al. 1994). Their data revealed several similarities between human and monkey brains: a phase of rapid synaptogenesis followed by a plateau phase of above adult levels of synaptic density in early infancy, and consequently elimination during childhood and adolescence. However, the postnatal refinement of cortical microstructure progresses along a more protracted timetable in humans relative to other primates. Whereas other primates are born with brains that are already about 70% of adult mass, in humans only about 25% of adult mass is achieved at birth while a large proportion of brain size growth takes place postnatally, allowing for social and environmental factors to impact the formation of neural network (Bianchi et al. 2013). Overall, in human cerebral cortex peak synapse density occurs in mid-childhood around the age 5, while pruning of synapses extends into the third decade of life, especially in prefrontal regions. Synaptic density peaks earlier in phylogenetically older brain areas (i.e., at 3 months in auditory cortex) compared to newer cortical areas (i.e., at 15 months in the middle frontal gyrus), and dendritic growth occurs in parallel with synaptogenesis (Huttenlocher 1979; Huttenlocher and Dabholkar 1997; Petanjek et al. 2011).

The advent of magnetic resonance imaging (MRI) opened a whole new era allowing researchers to non-invasively document large-scale processes of brain development, provide insights into the sequence of these developmental processes in longitudinal experiments, and document how they occur in living subjects. Many cross-sectional and several longitudinal studies using MRI have demonstrated that healthy human brain development occurs through childhood and adolescence. These life periods are marked by significant changes due to undergoing maturation of behavioural, emotional, hormonal, and cognitive processes. However, brain maturation does not end with adolescence, but rather continues into young adulthood.

The most consistent findings from research using structural MRI in typically developing human brain show that the cerebral volume does not change significantly after the age 9 – 10 (Brown and Jernigan 2012). However, grey matter volume follows an inverted U-shaped developmental trajectory, where total volume in the brain overall exhibits a pre-pubertal increase followed by post-pubertal loss (Gogtay and Thompson 2010). This is consistent with post mortem observations of increased synaptic pruning (elimination of unused neuronal connections) during adolescence and early young adulthood. Grey matter density on MRI is an indirect measure of a complex architecture of glia, vasculature, and neurons with dendritic and synaptic processes. Grey matter loss is considered an index of the time-course of maturation of a region (Sowell et al. 2003). Neuroatomical studies have shown that regions sub-serving primary functions, such as motor and sensory systems, mature earliest. Temporal and parietal association lobes responsible for basic language skills and spatial attention mature next. Finally, the prefrontal and lateral temporal lobes, which integrate primary sensorimotor processes and are implicated in complex cognitive behaviour such as memory, planning or foresight of consequences, seem to mature last (Blakemore 2012b).

Myelination, the thickening of the myelin sheath surrounding axons, is one of the most prolonged developmental processes in the human brain. Myelin acts as an insulator and massively increases the speed of transmission of electrical impulses from neuron to neuron, and consequently, from one brain area to another (Paus 2010). In contrast to grey matter, white matter volume increases more or less linearly throughout the first three decades of life, showing more rapid changes at early ages, and slowing or levelling off during young adulthood (Westlye et al. 2010; Tamnes et al. 2011). White matter density and myelination changes are not region-specific as grey matter, but rather wide-spread across the brain. Studies using diffusion tensor imaging (DTI, brain imaging technique that provides unique insights into brain network connectivity) have shown that maturation of commissural fibres that connect one cerebral hemisphere to the other and projection fibres that connect cerebrum with other parts of that brain and/or spinal cord occurs earliest, association fibres that connect regions of the cortex within the same hemisphere continue maturation at later ages, while frontal-temporal connections display the most prolonged development (Lebel and Beaulieu 2011). The question that arises is what are the cognitive and behavioural implications of this refined neuroanatomical reorganization that continues into young adulthood?

The interplay between brain maturation and cognitive development

It is well known that the ability to use cognitive control over behaviour and thoughts improves across childhood and adolescence. The concept of cognitive control (also called executive control or executive functions) refers to a set of interdependent cognitive abilities that are needed to monitor and change behaviour flexibly and in accord with the internal goals and situational demands (Luna et al. 2010). It is an umbrella term and its subcomponents are commonly evaluated with tasks that reflect constituent functions such as: holding in mind and carrying out goal-directed plans, inhibiting unwanted or inappropriate behaviour, shifting the mind set and adapting to diverse situations. Primate (Goldman-Rakic 1987; Bourgeois et al. 1994), human lesion (Alvarez and Emory 2006; Barbey et al. 2012), and neuroimaging (Luna et al. 2010; Crone and Ridderinkhof 2011) studies suggest that such skills rely heavily on the prefrontal lobe, but the whole brain integrity is necessary for efficient cognitive control functioning. It seems that brain regions that are responsible for these cognitive skills are already “on-line” early in development. Nonetheless, even though adolescents and early young adults can perform complex voluntary goal-directed behaviour, their decisions are often inconsistent and suboptimal (Luna et al. 2010). Studies report changes in location of activation and the amount of activation in the neural response underlying cognitive control functions from childhood to adulthood.

The key components of cognitive control that are thought to be crucial for regulation of behaviour are response inhibition and performance monitoring. The former enables individuals to actively suppress, interrupt or delay an action, while the latter refers to the ability to monitor performance and detect errors (Luna et al. 2010). Both of these cognitive control functions improve through childhood, adolescence and early young adulthood, and are supported by the primarily prefrontal cognitive control network (PFC), including anterior cingulate cortex (ACC). Functional MRI studies show that improvements in response inhibition and performance monitoring are accompanied by both increased brain activation in inferior frontal cortex in adults (ages 20 – 42 and 23 – 25) compared to adolescents (ages 10 – 17 and 18 – 19), increased recruitment of ACC and PFC in adults (age ranges 18 – 47) compared to adolescents (ages 10 – 17), and decreased activity in medial frontal cortex in adults (ages 25 – 30) compared to adolescents (ages 9 – 19) (Knežević et al. 2016; Rubia et al. 2006; Rubia et al. 2007). Young adults between the ages 19 and 25 show impulsive behavioural tendencies (premature responses, lower accuracy, shorter reaction times) compared to those aged 28 – 42 (Knežević and Marinković 2017), suggesting that response inhibition and performance monitoring do not fully develop before roughly the age of 25.

The ability to temporarily maintain information available for processing is known as working memory, another important subcomponent of cognitive control. Similar to response inhibition and performance monitoring, working memory improves through childhood, adolescence and young adulthood (Luna et al. 2010). It is supported by a widely-distributed brain network, including ventrolateral and dorsolateral PFC, as well as posterior parietal cortex. While adolescents show more diffused frontal network activation (including dorsolateral PFC, inferior frontal gyrus, middle temporal gyrus, ACC, posterior parietal, anterior insula), adults show most localized brain activity (dorsolateral PFC, ventromedial PFC and supramarginal gyrus) coupled with performance enhancement (Geier et al. 2009; Scherf et al. 2006). This age related change in working memory has been explained as increase in the ability to process information, meaning that working memory capacity functions more efficiently with advancing age (Crone and Ridderinkhof 2011).

Becoming an adult is not only about developing skills in order to change behaviour and thoughts in accordance with our own goals, but also to be able to understand and respond to changes in a social environment and adapt to novel situations. The ability to understand others mental states, emotions and actions is known as the theory of mind. The theory of mind becomes increasingly important during adolescence and early young adulthood when the focus is shifted from immediate family toward peers, friends, and romantic partners. While basic theory of mind skills like understanding that someone else can hold a belief that is different from our own are present in childhood, more complex skills like perspective taking or metacognition mainly develop across adolescence and early young adulthood (Blakemore 2012a; Crone and Dahl 2012). A large number of neuroimaging studies have identified several key regions responsible for metalizing: posterior superior temporal sulcus at the temporo-parietal junction, medial PFC and superior temporal lobes (Blakemore 2012a). This so called “social brain network” undergoes structural and functional changes during development. While adolescents show more activation in medial PFC than adults, adults show more activation in temporo-parietal junction than adolescents during various theory of mind tasks. The main change that is found in adolescent behaviour in parallel with structural and functional changes in the brain is the shift from self-oriented behaviour to pro-social behaviour oriented toward others. This kind of behaviour is particularly important for successful functioning in various social situations (Blakemore 2012a; Crone and Dahl 2012)

Interpreting findings on the transition to adulthood

Neuroimaging has shown that even though important aspects of brain circuitry are in place in adolescence and the performance is approximating that of adults, there are still inflexibilities in the brain networks that limit efficient and flexible use of cognitive control. The transition to adulthood seems to include relying more on a broader network of brain regions that share processing (e.g., prefrontal and posterior regions), freeing up executive control regions for more complex processing. This transition is supported by structural changes in the brain (e.g., synaptic pruning, myelination) that seem to leave remaining brain circuits better specialized and more efficient. The interplay of cognitive abilities and neuroatanomical restructuration can be explained in at least three ways: as maturational progress of additional brain areas, as a change in interaction between brain areas that were already active, or as a change in patterns of activation in different brain regions as a result of acquisition of new skills (Crone and Ridderinkhof 2011; Luna et al. 2010).

Continuous restructuration and changes in flexibility in recruitment of different brain areas in combination with changes in social-cognitive processing can create certain vulnerabilities to engage in harmful and reckless behavior. Social studies have shown that even though most young people make it through these years with more or less positive experiences, some experience tragic consequences. For instance, binge drinking which escalates during early 20s is associated with unintentional (e.g., car crashes, falls) and intentional injuries (e.g., sexual assault, domestic violence, firearm injuries), alcohol poisoning, sexually transmitted diseases, cardiovascular diseases (e.g., high blood pressure, stroke), neurophysiological and neurocognitive deficits (Courtney and Polich 2009; Taylor et al. 2010). At the same time, this is the age of onset of numerus mental disturbances (Miguel-Hidalgo 2013; Paus et al. 2008). The shift in relaying on different brain circuits at this age may be especially relevant for individuals with psychopathology (Luna et al. 2010), and thus establishing patterns of typical developmental changes in brain structure and cognitive functions in young adulthood is of high priority. Mental disorders at this age are common and often co-morbid, and may be particularly harmful for education and employment in this age group (Stein and Dumaret 2011).

The findings that refinements in brain structure, cognitive skills and related performance continue through adolescence and into early young adulthood challenge accepted views and current developmental models. Theoretical frameworks that we can borrow from psychology in order to interpret these findings are quite limited, since they mostly focus on early stages of human life. In an effort to merge findings from developmental psychology and neuroscience, Crone and Ridderinkhof (2011) pointed to Jean Paget’s theory, since his work has probably been the most influential on our thinking about brain and cognitive development. Despite his controversial theoretical postulations by which he underestimated children’s cognitive abilities, it is possible to associate Piaget’s ideas of stage development with sensitive periods of brain development. Piaget suggested that a child cannot reach a new stage before mastering the previous one. Research from neuroscience has shown that grey matter development in different brain regions is heterogeneous, and that grey matter changes in high-order brain region have to be completed before that region can contributre to specifized cognitive functions (Gogtay and Thompson 2010). In addition, the maturation of white matter connections between and within specific regions is important for age-related changes in neural structure and improved cognitive performance (Tamnes et al. 2011). Several studies have tried to examine how changes in brain structure correlate with cognitive functioning during development, and their findings show that improvement in intelectuall abilities from childhood to adolescence is related to maturation of both gray and white matter (Shaw et al. 2006; Sowell et al. 2004; Tamnes et al. 2011). It is unclear whether these changes occur suddenly or through gradual acquisition of experience and knowledge, however theories of brain maturation and cognitive development agree that changes in cognitive skills occur through an interplay between biological maturation and gaining experience (Crone and Ridderinkhof 2011).

Kloep and Hendry (2014) argue that developmental theories should put more emphasis on explaining the reasons behind human change (how and why), and move toward a more holistic approach. Some researchers propose that developmental studies should move away from stage theories that are bounded by age and move toward theories that are based on a wide range of trajectories or possible pathways (Kloep and Hendry 2014). While it is true that experience can cause developmental change, based on findings from neuroimaging and cognitive studies it seems that this change cannot happen if biological foundations that underlie certain abilities are not in place. Experience impacts development in many different ways and can often change its course, but it does not cause developmental change per se. Rather, it is the interplay of biological maturation and experiences which arise within dynamic environmental demands that result in developmental advancement. However, research linking changes in biological foundations (brain structure) to changes in actual human behaviour (cognitive functions) in various environmental surroundings is still very limited.

Moving forward: integrating knowledge from different research areas

Young adulthood is a life period of profound importance, a time of stunning achievements and internal and external changes that launch young people into adulthood. Becoming an “adult” involves enormous transformations in roles and responsibilities, which require adjustments in personal goals and motivations, such as developing priorities related to family, friends, romantic partners, community, education, career and religion. This is the age when young people make occupational and interpersonal choices and decisions which will affect the course of the rest of their life. One of the reasons that developmental theories are not able to encompass the full extent of the development is that despite the mutual interest – the well-being of young people – each research area has been studied in isolation. Even though classical traditional markers and events such as getting a stable job or becoming a parent seem to be much less important nowadays on the personal level, they still have a large impact on each individual and are important hallmarks of the adulthood in the society. Social studies show that the period of transitioning to adulthood is the least structured time of life, marked by instability and stress (Arnett 2004; Tanner et al. 2009). At the same time, neuroscience shows that adolescence and early young adulthood is a particularly sensitive developmental period which can influence young adult’s life course trajectory. One of the challenges for future studies is to better understand how individual differences in experiences with transitional events that mark the entrance into adulthood, including achievements or failures, interact with ongoing brain and cognitive refinement.

Research into the implications of postponing traditional adult roles and continued adolescent and post-adolescent development is relevant for understanding the unique strengths and vulnerabilities of young adults. Although cognitive control functions may not reach their developmental peak until approximately 25 years of age, most people are capable of performing many adult functions adequately at an earlier age – apparently between 16 and 21. Which skills undergo perturbation at this age and how does the quality of the environment interact with brain changes in the development of cognitive skills? On the other hand, what are the overall implication and social costs associated with the lengthy and uncertain processes by which young people in post-industrial countries move into adulthood? For example, large number of young adults not participating in the labour market and being economically active in their first 20-25 years of life could have a great cost on society (Hendry and Kloep 2007; Billari and Liefbroer 2010).

One of the problems in finding answers in currently available evidence lies in the selection of age groups, since almost all available developmental studies so far have collapsed across 18 to 30 year olds. This may represent a good frame of reference for the examination of broad changes that occur between different life-stages, however age-specific patterns of change still remain largely unknown (Crone and Ridderinkhof 2011). Future studies should carefully select age groups on the basis of available findings and theoretical frameworks in order to acquire the complete developmental picture.

Legal age boundaries are obviously not the same around the world and age of majority does not depend on the mental or physical maturity of an individual. There is no global consensus regarding the youngest age at which a person is considered to knowingly commit a criminal act and thus can be tried and convicted of a criminal offence. While discussion on culpability, brain and cognitive development is still ongoing, evidence taken from developmental neuroscience studies have already affected some legal proceedings and most scientists from multiple fields support the need for special consideration while prosecuting and sentencing adolescents under the age 18 (Steinberg 2013). However, if we rely on evidence from neuroanatomical and functional development which show slow maturation of cognitive control functions, like impulse control, and underlying brain structures responsible for adult behaviour during early 20, does the same apply to early young adults? Considerable evidence indicates that brain and behavioural plasticity does not end in adolescence. Much more research that directly links age differences in brain structure and function to behaviours in certain legally relevant situations is needed.

Conclusion

Important changes take place after the adolescent years, such as achieving legal adulthood, completing education, finding a full-time job and reaching financial independence. While the beginning of adolescence is marked by dramatic biological events, the end of adolescence and the entrance into adulthood is marked by social and cultural expectations. Cognitive challenges during this time are generally high and consist of different circumstances that require particular cognitive skills that are still changing in accordance with structural refinement of brain structures known to support them. Despite the mutual interest in better defining the transition period between adolescence and adulthood, research areas are still largely separated leaving gaps in our knowledge and understanding of strengths and vulnerabilities of young adults. It seems that the time has come when we simply cannot afford to ignore converging interdisciplinary findings from multiple research domains anymore.

References

Alvarez, J. A., & Emory, E. (2006). Executive function and the frontal lobes: A meta-analytic review. Neuropsychology Review, 16(1), 17-42, doi:10.1007/s11065-006-9002-x.

Arnett, J. J. (2000). Emerging adulthood – A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469-480, doi: 10.1037//0003-066x.55.5.469.

Arnett, J. J. (2004). Emerging Adulthood: The Winding Road from Late Teens through the Twenties. Oxford: University Press.

Arnett, J. J. (2007). Emerging Adulthood, a 21st Century Theory: A Rejoinder to Hendry and Kloep. Child Development Perspectives, 1(2), 80-82, doi: 10.1111/j.1750-8606.2007.00018.x.

Australian Institute of Health and Welfare, AIHW. (2014). Australia’s health 2014. http://www.aihw.gov.au/WorkArea/DownloadAsset.aspx?id=60129548150. Accessed 1 May 2017.

Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135, 1154-1164, doi: 10.1093/Brain/Aws021.

Bianchi, S., Stimpson, C. D., Duka, T., Larsen, M. D., Janssen, W. G., Collins, Z., et al. (2013). Synaptogenesis and development of pyramidal neuron dendritic morphology in the chimpanzee neocortex resembles humans. Proceedings of National Academi of Science, 110 Suppl 2, 10395-10401, doi: 10.1073/pnas.1301224110.

Billari, F. C., & Liefbroer, A. C. (2010). Towards a new pattern of transition to adulthood? Advances in Life Course Research, 15(2-3), 59-75, doi: 10.1016/j.alcr.2010.10.003.

Blakemore, S. J. (2012a). Development of the social brain in adolescence. Journal of the Royal Society of Medicine, 105(3), 111-116, doi: 10.1258/jrsm.2011.110221.

Blakemore, S. J. (2012b). Imaging brain development: the adolescent brain. Neuroimage, 61(2), 397-406, doi: 10.1016/j.neuroimage.2011.11.080.

Bourgeois, J. P., Goldman-Rakic, P. S., & Rakic, P. (1994). Synaptogenesis in the prefrontal cortex of rhesus monkeys. Cerebral Cortex, 4(1), 78-96.

Brown, T. T., & Jernigan, T. L. (2012). Brain Development During the Preschool Years. Neuropsychology Review, 22(4), 313-333, doi: 10.1007/s11065-012-9214-1.

Carter, A. C., Brandon, K. O., & Goldman, M. S. (2010). The College and Noncollege Experience: A Review of the Factors That Influence Drinking Behavior in Young Adulthood. Journal of Studies on Alcohol and Drugs, 71(5), 742-750.

Central Intelligence Agency (2016). Military service age and obligation. https://www.cia.gov/library/publications/the-world-factbook/fields/2024.html#139. Accessed 13 September 2016.

Child Rights International Network (2017). Minimum Ages of Criminal Responsibility. https://www.crin.org/en/home/ages. Accessed 19 May 2017).

Courtney, K. E., & Polich, J. (2009). Binge drinking in young adults: Data, definitions, and determinants. Psychological Bulletin, 135(1), 142-156, doi: 10.1037/a0014414.

Crone, E. A., & Dahl, R. E. (2012). Understanding adolescence as a period of social-affective engagement and goal flexibility. Nature Reviews Neuroscience, 13(9), 636-650, doi: 10.1038/nrn3313.

Crone, E. A., & Ridderinkhof, K. R. (2011). The developing brain: from theory to neuroimaging and back. Developmental Cognitive Neuroscience, 1(2), 101-109, doi: 10.1016/j.dcn.2010.12.001.

Curtis, A. C. (2015). Defining Adolescence. Journal of Adolescent and Family Health, 7(2), 40.

Geier, C. F., Garver, K., Terwilliger, R., & Luna, B. (2009). Development of working memory maintenance. Journal of Neurophysiology, 101(1), 84-99, doi: 10.1152/jn.90562.2008.

Gogtay, N., & Thompson, P. M. (2010). Mapping gray matter development: Implications for typical development and vulnerability to psychopathology. Brain and Cognition, 72(1), 6-15, doi: 10.1016/j.bandc.2009.08.009.

Goldman-Rakic, P. S. (1987). Development of cortical circuitry and cognitive function. Child Development, 58(3), 601-622.

Hanewinkel, R., Sargent, J. D., Poelen, E. A., Scholte, R., Florek, E., Sweeting, H., et al. (2012). Alcohol consumption in movies and adolescent binge drinking in 6 European countries. Pediatrics, 129(4), 709-720, doi: 10.1542/peds.2011-2809.

Hendry, L. B., & Kloep, M. (2007). Conceptualizing Emerging Adulthood: Inspecting the Emperor’s New Clothes? Child Development Perspectives, 1(2), 74-79, doi: 10.1111/j.1750-8606.2007.00017.x

Huttenlocher, P. R. (1979). Synaptic Density in Human Frontal-Cortex – Developmental-Changes and Effects of Aging. Brain Research, 163(2), 195-205.

Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. Journal of Comparative Neurology, 387(2), 167-178. Doi 10.1002/(Sici)1096-9861(19971020)387:2<167::Aid-Cne1>3.0.Co;2-Z.

Jensen, L. A., & Arnett, J. J. (2012). Going Global: New Pathways for Adolescents and Emerging Adults in a Changing World. Journal of Social Issues, 68(3), 473-492, Doi: 10.1111/j.1540-4560.2012.01759.x.

Kadiri, H. (2014). Alcohol purchase age limits in Europe. European Alcohol Policy Aliance.

Kloep, M., & Hendry, L. B. (2014). Some ideas on the emerging future of developmental research. Journal of Adolescence, 37(8), 1541-1545, doi:10.1016/j.adolescence.2014.09.002.

Knežević, M., & Marinković, K. (2017). Neurodynamic correlates of response inhibition from emerging to mid adulthood. Cognitive Development, 43, 106-118, doi:10.1016/j.cogdev.2017.03.002.

Knežević, M., Veroude, K., Jolles, J., & Krabbendam, L. (2016). Neural Correlates of Performance Monitoring During the Transition to Young Adulthood. Mind Brain and Education, 10(2), 10, doi: 10.1111/mbe.12104.

Lebel, C., & Beaulieu, C. (2011). Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood. Journal of Neuroscience, 31(30), 10937-10947, doi: 10.1523/Jneurosci.5302-10.2011.

Lesthaeghe, R. (2010). The unfolding story of the second demographic transition. Population and Development Review, 36(2), 211-251, doi: 10.1111/j.1728-4457.2010.00328.x

Luna, B., Padmanabhan, A., & O’Hearn, K. (2010). What has fMRI told us about the Development of Cognitive Control through Adolescence? Brain and Cognition, 72(1), 101-113, doi: 10.1016/j.bandc.2009.08.005.

Miguel-Hidalgo, J. J. (2013). Brain structural and functional changes in adolescents with psychiatric disorders. International Journal of Adolescent Medicine and Health, 25(3), 245-256, doi: 10.1515/ijamh-2013-0058.

World Health Organization, WHO. (2014). World Life Expectancy. http://www.worldlifeexpectancy.com/2016.

Paus, T. (2010). Growth of white matter in the adolescent brain: Myelin or axon? Brain and Cognition, 72(1), 26-35, doi:10.1016/j.bandc.2009.06.002.

Paus, T., Keshavan, M., & Giedd, J. N. (2008). OPINION Why do many psychiatric disorders emerge during adolescence? Nature Reviews Neuroscience, 9(12), 947-957, doi:10.1038/nrn2513.

Petanjek, Z., Judas, M., Simic, G., Rasin, M. R., Uylings, H. B., Rakic, P., et al. (2011). Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proceedings of the National Academy of Science, 108(32), 13281-13286, doi:10.1073/pnas.1105108108.

Rakic, P., Bourgeois, J. P., & Goldmanrakic, P. S. (1994). Synaptic Development of the Cerebral-Cortex – Implications for Learning, Memory, and Mental-Illness. Self-Organizing Brain: From Growth Cones to Functional Networks, 102, 227-243.

Robertson, S. (2016). Age of Consent Laws. https://chnm.gmu.edu/cyh/case-studies/230. Accessed 13 September 2016.

Rubia, K., Smith, A. B., Taylor, E., & Brammer, M. (2007). Linear age-correlated functional development of right inferior fronto-striato-cerebellar networks during response inhibition and anterior cingulate during error-related processes. Human Brain Mapping, 28(11), 1163-1177, doi: 10.1002/hbm.20347.

Rubia, K., Smith, A. B., Woolley, J., Nosarti, C., Heyman, I., Taylor, E., et al. (2006). Progressive increase of frontostriatal brain activation from childhood to adulthood during event-related tasks of cognitive control. Human Brain Mapping, 27(12), 973-993, doi: 10.1002/Hbm.20237.

Salmela-Aro, K., Kiuru, N., Nurmi, J. E., & Eerola, M. (2011). Mapping pathways to adulthood among Finnish university students: Sequences, patterns, variations in family- and work-related roles. Advances in Life Course Research, 16(1), 25-41, doi: 10.1016/j.alcr.2011.01.003.

Substance Abuse and Mental Health Services Administration, SAMHSA. (2015). National Survey on Drug Use and Health (NSDUH): Alcohol Use, Binge Alcohol Use, and Heavy Alcohol Use in Past Month among Persons Aged 12 or Older, by Demographic Characteristics: Percentages, 2014 and 2015. https://www.samhsa.gov/data/sites/default/files/NSDUH-DetTabs-2015/NSDUH-DetTabs-2015/NSDUH-DetTabs-2015.htm#tab2-46b. Accessed 1 May 2017).

Scherf, K. S., Sweeney, J. A., & Luna, B. (2006). Brain basis of developmental change in visuospatial working memory. Journal of Cognitive Neuroscience, 18(7), 1045-1058, Doi: 10.1162/jocn.2006.18.7.1045.

Schoon, I., & Lyons-Amosba, M. (2016). Diverse pathways in becoming an adult: The role of structure, agency and context. Research in Social Stratification and Mobility, 46, 11-20, doi: 10.1016/j.rssm.2016.02.008.

Schwartz, S. J., Cote, J. E., & Arnett, J. J. (2005). Identity and agency in emerging adulthood: Two development routes in the individualization process. Youth & Society, 37(2), 201-229, doi: 10.1177/0044118×05275965.

Sethi, D., Racioppi, F., & Bertollini, R. (2007). Preventing the leading cause of death in young people in Europe. J Epidemiol Community Health, 61(10), 842-843, doi:10.1136/jech.2007.063081.

Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., et al. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440(7084), 676-679. Doi 10.1038/Nature04513.

Smartgun.org Law Center to Prevent Gun Violence (2014). Minimum age. http://smartgunlaws.org/gun-laws/policy-areas/consumer-child-safety/minimum-age/ (accessed September 13 2016).

Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003). Mapping cortical change across the human life span. Nature Neuroscience, 6(3), 309-315, doi: 10.1038/nn1008nn1008 [pii].

Sowell, E. R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E., & Toga, A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. Journal of Neuroscience, 24(38), 8223-8231, doi:10.1523/JNEUROSCI.1798-04.2004.

Stein, M., & Dumaret, A. C. (2011). The mental health of young people aging out of care and entering adulthood: Exploring the evidence from England and France. Children and Youth Services Review, 33(12), 2504-2511, doi: 10.1016/j.childyouth.2011.08.029.

Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28(1), 78-106, doi: 10.1016/j.dr.2007.08.002.

Steinberg, L. (2013). The influence of neuroscience on US Supreme Court decisions about adolescents’ criminal culpability. Nature Reviews Neuroscience, 14(7), 513-518, doi:10.1038/nrn3509.

Tamnes, C. K., Fjell, A. M., Ostby, Y., Westlye, L. T., Due-Tonnessen, P., Bjornerud, A., Wallhovd, K. B. (2011). The brain dynamics of intellectual development: Waxing and waning white and gray matter. Neuropsychologia, 49(13), 3605-3611, doi: 10.1016/j.neuropsychologia.2011.09.012.

Tanner, J. L., Arnett, J. J., & Leis, J. A. (2009). Emerging adulthood: Learning and development during the first stage of adulthood. In M. C. Smith, & N. DeFrates-Densch (Eds.), Handbook of research on adult development and learning. Mahwah, New Jersey: Lawrence Erlbaum.

Taylor, B., Irving, H. M., Kanteres, F., Room, R., Borges, G., Cherpitel, C., et al. (2010). The more you drink, the harder you fall: A systematic review and meta-analysis of how acute alcohol consumption and injury or collision risk increase together. Drug and Alcohol Dependence, 110(1-2), 108-116, doi: 10.1016/j.drugalcdep.2010.02.011.

The Federal Trade Commission (2013). Alcohol Laws by State. https://www.consumer.ftc.gov/articles/0388-alcohol-laws-state. Accessed 13 September 2016.

Official Journal of the European Union (2008). Directives. http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32008L0051&from=FR. Accessed 13 September 2016).

Westlye, L. T., Walhovd, K. B., Dale, A. M., Bjornerud, A., Due-Tonnessen, P., Engvig, A., et al. (2010). Life-Span Changes of the Human Brain White Matter: Diffusion Tensor Imaging (DTI) and Volumetry. Cerebral Cortex, 20(9), 2055-2068, doi: 10.1093/cercor/bhp280.