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Recent Developments in Neuroscience Research on
Human Motivation
Hormones and Affect in Adolescent Decision Making
Corinna Laube Wouter van den Bos
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To cite this document: Corinna Laube Wouter van den Bos . "Hormones and Affect
in Adolescent Decision Making" In Recent Developments in Neuroscience Research on
Human Motivation. Published online: 22 Nov 2016; 259-281.
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HORMONES AND AFFECT IN
ADOLESCENT DECISION MAKING
Corinna Laube and Wouter van den Bos
ABSTRACT
Teenagers are typically described as impulsive and risk taking. Yet
recent research shows that this observation does not hold in all contexts.
Rather, adolescents show higher impulsivity and risk taking than children
or adults in affective contexts. Motivational and affective processes are
therefore of particular interest when trying to understand typical adoles-
cent behavior. Additionally, pubertal hormones are hypothesized to play
a special role in adolescents’ motivated decision making. However,
evidence for the mechanisms underlying this relationship is sparse. In this
chapter, we aim to integrate findings from human and animal studies in
order to elucidate the specific impact of pubertal hormones on motiva-
tional processes in adolescence. Against this background, we critically
discuss and reinterpret recent findings in psychology and neuroscience,
speculate about underlying mechanisms, and suggest new approaches for
future studies of adolescent behavior.
Keywords: Adolescence; puberty; testosterone; risk taking;
impulsivity; affect
Recent Developments in Neuroscience Research on Human Motivation
Advances in Motivation and Achievement, Volume 19, 259281
Copyright r2017 by Emerald Group Publishing Limited
All rights of reproduction in any form reserved
ISSN: 0749-7423/doi:10.1108/S0749-742320160000019013
259
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ADOLESCENCE AS A PERIOD OF
MOTIVATED BEHAVIOR
“Adolescence is practically synonymous in our culture with risk taking, emotional
drama and all forms of outlandish behavior.” (Friedman, 2014)
Adolescence describes the developmental phase between childhood and
adulthood. It is characterized by a transition from dependence to indepen-
dence, as well as by sexual maturation. The path to independence is asso-
ciated with a process of major social re-orientation, in which the influence
of parents becomes less pronounced and that of peers increases (Nelson,
Leibenluft, McClure, & Pine, 2005;van den Bos, 2013). The start of adoles-
cence is often defined by the onset of puberty, whereas its end is culturally
defined: whenever an individual is considered to be an adult. Puberty cov-
ers a shorter time interval; it is the process of hormonal and physiological
changes by which individuals reach sexual maturity and is found in most
mammals (Spear, 2010).
One salient characteristic of adolescence is an increase in risky and
impulsive behavior (Braams, van Duijvenvoorde, Peper, & Crone, 2015;
O’Brien, Albert, Chein, & Steinberg, 2011;Steinberg et al., 2009). Social
problems in adolescence such as drunk driving, suicide, and teenage preg-
nancy are often attributed to this increased risk taking and impulsivity
(Dahl, 2004). Furthermore, risky behavior results in a significant increase
in adolescents’ visits to the emergency room (Steinberg, 2007) and to a
200% increase in the mortality rate among teenagers (Dahl, 2004).
Adolescence as a time of turbulence and excess is not a creation of mod-
ern society. Historical accounts of adolescent impetuosity go back as far as
ancient Greece (300600 BC), with typical teenage behaviors such as rash-
ness, sexual excess, frivolity, drunkenness, and lack of self-control being
portrayed in ancient Greek art (Harlow & Laurence, 2002). “Irrational”
adolescent behavior has traditionally been attributed to “raging hormones”
(Buchanan, Eccles, & Becker, 1992); more recently, neuroscientific methods
have shown that teenagers’ prefrontal cortex, an area involved in decision
making and rationality, is not yet fully developed (Yurgelun-Todd, 2007).
However, the latest scientific discoveries suggest that typical adolescent
behavior has multiple interactive causes and may often be adaptive rather
than irrational (Crone & Dahl, 2012;Dahl, 2004;Pfeifer & Allen, 2012).
Besides the development of cognitive control, motivational processes also
seem to play an important role in adolescent behavior (Crone & Dahl,
2012;Ernst, 2014;Somerville & Casey, 2010;Steinberg, 2008). It has been
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argued that these adolescent-specific changes in motivational processes
afford both risks and opportunities (Blakemore & Robbins, 2012;Crone &
Dahl, 2012).
The shift in attention to motivational processes has sparked renewed
interest in the role of pubertal hormones in adolescent behavior. It is
hypothesized that these hormones specifically impact motivational pro-
cesses during this developmental phase, which in turn leads to increased
risk taking and impulsive behavior, but also supports positive develop-
ment in adolescence (Crone & Dahl, 2012). However, little is known
about the mechanisms that mediate the relationship between hormones
and changes in motivational processes during adolescence or how these
changes in motivational processes may heighten adolescent risk taking
and impulsivity.
In this chapter, we aim to integrate findings elucidating the specific
impact of pubertal hormones on motivational processes in adolescence.
First, we review findings from self-report and behavioral studies of adoles-
cent decision making in order to better understand the changes that occur
in motivational processes during this developmental period and how those
changes relate to a specific subset of behaviors: risk taking and impulsiv-
ity. Next, we consider the role of hormones in pubertal development and
how they relate to adolescent risk taking and impulsivity. Together, these
findings suggest that pubertal hormones indeed have a sizeable influence
on risky and impulsive behavior in adolescence, and that this effect is
accompanied by context-dependent changes in motivational processing.
To better understand the possible mechanisms by which pubertal
hormones impact behavior, we turn to recent findings from neuroscience.
Here, we focus on adolescent-specific changes in motivational brain
systems and the potential impact of hormones on these systems. We con-
clude by deriving general educational implications and identifying direc-
tions for future research.
Note that we constrain adolescent behavior to risk taking and impulsiv-
ity given the fact that we currently find the most available evidence
in these two domains. Finally, although we recognize that increased ado-
lescent risk taking in the real world can be partly attributed to decreased
parental control (Defoe, Dubas, Figner, & van Aken, 2015;Romer,
2010) and to developmental changes in cognitive skills, such as reasoning
skills (Reyna & Brainerd, 2012), addressing these issues would go beyond
the scope of this chapter. On an endocrinological level, we focus on
testosterone, which is known to be associated with risky and impul-
sive behavior.
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THE ROLE OF MOTIVATIONAL PROCESSES IN
ADOLESCENT DECISION MAKING: FINDINGS FROM
SELF-REPORT AND BEHAVIORAL STUDIES
Self-report measures indicate that impulsivity, sensation seeking, and con-
sequently risk taking are elevated during adolescence. A common definition
of impulsivity is the tendency to act without planning or considering poten-
tial consequences (e.g., “I like to stop and think things over before I do
them” [reversed item]). Sensation seeking can be defined as the tendency to
seek out experiences and situations that are novel, exciting, or rewarding;
typical items are “I quite enjoy taking risks” or “Life with no danger in it
would be too dull for me” (Harden & Tucker-Drob, 2011). Findings from
behavioral and self-report studies indicate that impulsivity tends to show a
linear decline as a function of age (Quinn & Harden, 2013;Steinberg, 2008;
Steinberg et al., 2009;Vaidya, Latzman, Markon, & Watson, 2010;
van den Bos, Rodriguez, Schweitzer, & McClure, 2015), whereas sensation
seeking follows an inverted U-shape curve (Cauffman et al., 2010;Harden &
Tucker-Drob, 2011;Romer & Hennessy, 2007;Steinberg, 2008). Harden
and Tucker-Drob (2011) have replicated these developmental patterns of
age trends in a longitudinal study with a sample of 7,640 adolescents.
Impulsivity and sensation seeking can thus be regarded as distinct traits that
are influenced by qualitatively different developmental processes in adoles-
cence. Specifically, impulsivity is strongly associated with lack of cognitive
control, whereas sensation seeking is thought to stem from sensitivity to
motivational cues (Steinberg, 2007, 2008). Yet although the two constructs
are distinct, they are both associated with developmental changes in risky
behavior. For instance, a longitudinal study by Quinn and Harden (2013)
showed that age-related changes in both impulsivity and, to a lesser extent,
sensation seeking accounted for variability in substance use change.
Specifically, individuals who showed slower declines in impulsivity and
sensation seeking showed faster increases in substance use. This pattern of
results suggests that adolescent behavior is the outcome of an interaction
between distinct motivational forces and (limited) cognitive control, all of
which are still under development.
Laboratory experiments have provided further insights into (1) the
cognitive/affective processes involved in adolescent behavior and (2) the
conditions under which this behavior tends to occur. It is important to
note, however, that a recent meta-analysis of behavioral risk studies by
Defoe et al. (2015) revealed a discrepancy between patterns of risk taking
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in the real world and adolescent behavior as studied in the laboratory. In
contrast to many reports about real-world risk taking (Dahl, 2004;
Willoughby, Good, Adachi, Hamza, & Tavernier, 2013), laboratory stu-
dies have not generally found risk-taking behavior to peak in adoles-
cence: Although adolescents take more risks than adults, and early
adolescents take more risks than mid-late adolescents, lab studies typi-
cally show no difference between early adolescents and children in terms
of risk taking. One possible reason for this discrepancy is that the struc-
ture of the tasks used in laboratory studies does not match that of the
real-world environment.
For instance, a common way to study risky decision making is to present
simple gambles (wheels of fortune) that link outcomes of different magni-
tudes with different probabilities (see Ernst et al., 2004). In a study by Van
Leijenhorst et al. (2010), participants were presented with choices between
options with a high probability of a small reward (low-risk/low-reward)
and options with a low probability of a large reward (high-risk/high-
reward). Such tasks differ from the real world in several crucial ways. First,
in the real world, the true probabilities are often unknown (Hertwig &
Erev, 2009). Second, real-world adolescent risk taking is thought to occur
mainly in arousing contexts. For example, the risk of fatal injury for a
16- or 17-year-old driver increases with the number of passengers (Chen,
Baker, Braver, & Li, 2000). In the following, we review evidence from
laboratory studies suggesting that adolescents indeed show more risk-
taking behavior than either adults or children in experimental paradigms
that involve arousing conditions, to which they are more sensitive.
Figner, Mackinlay, Wilkening, and Weber (2009) designed a dynamic
risk-taking task, the Columbia Card Task, to study age differences in risk
taking and underlying information use. In this task, participants are shown
32 cards face down on a computer screen. Each card indicates a gain
(which is added to the trial payoff) or a loss. Players can turn over as many
cards as they like until they encounter a loss card, which terminates the
trial. The aim of the game is to score as many points as possible. Figner
et al. (2009) implemented two conditions: one “hot” and one “cold” condi-
tion. In the hot condition, participants received immediate feedback and
were allowed to make stepwise incremental decisions about turning over
another card. In the cold condition, they had to make a single decision on
the total number of cards to be turned over in each trial and received out-
come feedback only at the very end of the game. Figner et al. (2009) argued
that both receiving immediate feedback and deciding which card to turn
over next trigger affective processes. They found that adolescents took
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more risks than children and adults in the hot condition, whereas risk
taking was similar across all ages in the cold condition.
The Iowa gambling task is another dynamic risk-taking task that is
frequently used to assess risk taking in the laboratory. It approximates real-
life decision making under uncertainty (Bechara, Damasio, Damasio, &
Anderson, 1994)that is, the probabilities and outcomes are not described
upfront but have to be learned during the task. Specifically, participants are
presented with four decks of cards, each containing cards that reward or
punish the player by adding or subtracting points or amounts of money
from his or her account. Two of the decks lead to net increases over the
course of repeated play (the advantageous decks); the other two lead to net
losses (the disadvantageous decks). Cauffman et al. (2010) used a modified
version of the Iowa Gambling Task to measure affective risk taking in a
diverse sample of 901 individuals aged between 10 and 30 years.
Adolescents increasingly played from the advantageous decks; this learned
preference for the advantageous decks was interpreted as approach beha-
vior, and displayed an inverted U-shape relation to age, peaking in mid- to
late adolescence.
In the Balloon Analog Risk Task (BART; Lejuez et al., 2002), another
“hot” laboratory risk-taking task, participants have to inflate a computer-
ized balloon one pump at a time. They earn a monetary reward each time
they pump the balloon but lose the entire reward if the balloon bursts.
Consequently, larger rewards can be earned by taking more risks. Several
recent studies using the BART have found a peak in risk-taking behavior
in adolescents, relative to children and adults (Braams et al., 2015;van
Duijvenvoorde et al., 2015); others have found no such peak in adolescence
(see Defoe et al., 2015).
Finally, several studies have shown that the mere presence of peers
increases adolescents’ risk taking relative to adults’ (Gardner & Steinberg,
2005;O’Brien et al., 2011). For instance, Gardner and Steinberg (2005)
found that the presence of peers more than doubled the number of risks
teenagers took in a video driving game. However, it had no effect at all on
adults. Similarly, in a study with a delay discounting task, O’Brien et al.
(2011) found that adolescents preferred more immediate rewards to later
ones when in the presence of their peers. In this study, participants had to
choose between a smaller sooner reward (e.g., $200 today) and a larger
later reward (e.g., $1,000 in one year). Adolescents were more likely to
choose the smaller sooner reward in the presence of peers than when alone.
The presence of peers is hypothesized to be a highly arousing context for
adolescents and this increased arousal is thought to change their behavior
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in a similar fashion as the arousing context provided by “hot” gambling
games. It is also possible that adolescents in peer social contexts take risks
in order to send social signals (Milchunas, Sala, & Lauenroth, 1988).
In conclusion, there is substantial evidence that adolescents show
increased risk taking and impulsivity in laboratory studies under arousing
conditions. Given that the cognitive components of the tasks administered
are assumed to be stable across conditions, these findings suggest that there
is something specific about how adolescents process the affective motiva-
tional components of the tasks. However, behavioral studies can provide
only limited insights into the underlying mechanisms. In the next section,
we argue that hormones play an important role in modulating the affective
motivational processes involved in adolescent risk taking and impulsivity.
THE ROLE OF PUBERTAL HORMONES
The idea that the particularly pronounced changes in mood and behavior
observed in adolescence result from biological factors such as a rapid
change in hormone levels goes back to at least the early 1900s (Hall, 1904).
This change in pubertal hormones is not specific to humans but is a cross-
species phenomenon, and a variety of species show puberty-typical beha-
viors such as novelty seeking and increased peer interactions (Spear, 2000).
Yet it remains unclear how pubertal hormones relate to increased risk
taking and impulsivity in adolescence. Previous research has focused on
age, rather than pubertal maturation, as a predictor of developmental
changes in risk behavior. However, a handful of studies suggest a link
between pubertal hormones and developmental changes in arousal, motiva-
tion, and emotion (Blakemore, Burnett, & Dahl, 2010;Crone & Dahl,
2012;Forbes & Dahl, 2010;Peper & Dahl, 2013;Steinberg, 2005). Before
we review the relationship between hormones, specifically testosterone, and
adolescent behavior, we briefly outline the mechanisms behind the hormo-
nal changes seen in puberty.
Pubertal Development
Pubertal development is associated with a rapid rise in gonadal hormone
release initiating development of secondary sexual characteristics, such as
breast development in girls and pubertal hair growth, as well as other
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physiological changes, such as physical growth. The sex hormones regulating
bodily changes are testosterone, oestradiol, and dehydroepiandrosterone
(DHEA). The hormonal cascade initiating the release of these hormones
from the gonads begins in the brain, and a feedback mechanism known as
the hypothalamicpituitarygonadal (HPG) axis regulates endocrine
function. Pubertal onset is associated with the activation of neurons
located in the hypothalamus that secrete gonadotropin-releasing hor-
mone (GnRH). GnRH then travels to the pituitary gland, where it
regulates the synthesis and secretion of two pituitary gonadotropins:
luteinizing hormone (LH) and follicle-stimulating hormone (FSH) (see
Fig. 1). Released into the bloodstream, LH and FSH then jointly act to
stimulate the production of gonadal steroid hormones, completing the
process of egg and sperm development. The appearance of secondary sex-
ual characteristics in peripheral tissues (e.g., breast development in girls
and facial hair in boys) in turn leadstoelevatedlevelsofestrogenand
progesterone.
How does the brain know when to activate the HPG axis and trigger
puberty? Both internal and external cues provide information on the avail-
ability of necessary resources for successful reproduction. Internal cues
Fig. 1. Dopaminergic and Hormonal Pathways in the Brain.
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include biological prerequisites for pregnancy, such as metabolic levels of
insulin, glucose, and leptin that indicate somatic growth and metabolic fuel
availability; lower body fat has been associated with delayed pubertal
onset (Frisch, 1984). External cues include information on the availabil-
ity of a suitable mate and food. Furthermore, females typically enter and
move through adolescence more quickly than males (Savin-Williams &
Weisfeld, 1989), suggesting biological sex as an important factor in pub-
ertal timing. To conclude, the timing of pubertal onset should be viewed
as a neurological rather than a gonadal event (Sisk & Zehr, 2005). That
is, it is the nervous system that integrates different cues from the body
and the environment and, as such, determines pubertal onset (Sisk &
Zehr, 2005).
Pubertal Maturation Measures
Different methods are used to assess pubertal development. The indirect
method of Tanner staging (Tanner, 1962) categorizes individuals along
an ordinal puberty scale from 1 (no development) to 5 (adult develop-
ment). These standardized categories capture visible secondary sexual
characteristics such as breast or genital development and pubic hair
growth. Tanner stages can be determined by physical exam conducted by
a trained clinician or by self-report. Another common way of measuring
external pubertal status is the Pubertal Developmental Scale (PDS)
(Petersen, Crockett, Richards, & Boxer, 1988). Like the Tanner scale, the
PDS asks adolescents about hair growth, skin changes, and growth
spurts, with sex-specific items, such as menarche and breast development
in females and genital growth and facial hair in males. The resulting com-
posite puberty score reflects the effects of adrenal and gonadal hormones
as well as growth hormones.
Shirtcliff, Dahl, and Pollak (2009) examined the interrelations of multi-
ple indices of puberty and found that PDS scores are related to levels of
basal hormones, such as testosterone in boys. However, even the best mea-
sure of external pubertal status captures less than half of the variability in
basal hormones (Shirtcliff et al., 2009). Measuring hormone levels in saliva
or blood can thus give important insights into pubertal maturation that are
not available from overt physical measures alone. Given the variety of
changes accompanying puberty, the best pubertal status measurement will
depend on the issues being investigated.
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The Influence of Testosterone on Adolescent Behavior
Vermeersch, T’Sjoen, Kaufman, and Vincke (2008) found a positive rela-
tionship between testosterone and risk taking in a sample of 301 adoles-
cent boys, independent of age and pubertal development as measured by
the Tanner scale. Interestingly, this effect was partly mediated by peer
influence: boys with high levels of testosterone also had peers who were
more involved in risky behaviors, which in turn influenced their own risk
taking. This finding is in line with substantial evidence indicating that
individual levels of testosterone also predict differences in risky behavior
in adults (Booth, Johnson, & Granger, 1999;Campbell et al., 2010;
Goudriaan et al., 2010;Rosenblitt, Soler, Johnson, & Quadagno, 2001;
Stanton, Liening, & Schultheiss, 2011;Stenstrom, Saad, Nepomuceno, &
Mendenhall, 2011).
Furthermore, testosterone has been described as a “social hormone”
and is often related to behaviors such as status seeking and social domi-
nance (Eisenegger, Haushofer, & Fehr, 2011). Single time-point measure-
ments of testosterone correlate positively with high dominance in
both adolescents (Rowe, Maughan, Worthman, Costello, & Angold,
2004;Vermeersch, T’Sjoen, Kaufman, Vincke, & Van Houtte, 2010)and
adults (Carre
´, Putnam, & McCormick, 2009;Grant & France, 2001;
van den Bos, Golka, Effelsberg, & McClure, 2013). However, according
to Wingfield’s challenge hypothesis, testosterone predicts social behavior
only when status is threatened or challenged (Josephs, Newman,
Brown, & Beer, 2003;Wingfield, Ball, Dufty, Hegner & Ramenofsky,
2013). For instance, a review by Sapolsky (1991) showed that testosterone
predicts social rank in primates only when hierarchy in the group is
unstable. Consequently, the effects of testosterone on behavior are highly
context dependent. There is an interesting parallel here with behavioral
studies on risk taking and impulsive behavior, which have found adoles-
cents to show more risky behavior in arousing and social contexts. These
findings may in turn be related to the context-specific effects of testoster-
one. Whatever form the underlying relationships prove to take, it is clear
that the context needs to be taken into account when considering effects
of pubertal hormones on adolescent behavior.
In sum, risky and impulsive behavior in adolescence may be attributed
to (1) developmental change in motivational processing, with risky beha-
vior increasing specifically in arousing contexts; (2) rapid change in gona-
dal hormone levels at pubertal onset. Importantly, these findings suggest
a link between changes in motivational processing and changes in
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pubertal hormone levels. However, the mechanisms behind these effects
remain unclear. Neuroscience can be a helpful tool in identifying
these mechanisms.
THE NEUROBIOLOGY OF MOTIVATED
ADOLESCENT BEHAVIOR
Over the past decades, numerous studies have investigated adolescent brain
development (for a review, see Crone & Dahl, 2012), and several closely
related theoretical models have emerged. The most influential are the dual-
system models (Casey, Getz, & Galvan, 2008;Steinberg, 2008) based on
the two-system model of willpower by Metcalfe and Mischel (1999), which
postulates a cool, cognitive “know” system and a hot, emotional “go”
system. The cool system enables self-regulation and self-control by being
cognitive, emotionally neutral, flexible, slow, and strategic. The hot system
is the origin of emotionality, fears and passion, and is driven by impulsive
and reflexive forces. These two systems are in continuous interaction; their
balance varies as a function of stress, developmental level, and individual
self-control. Neural dual-system models propose that increased risk taking
in adolescence is the result of an interaction between distinct brain
networks: a motivational system associated within subcortical limbic struc-
tures, including the striatum, and a control system associated with the
prefrontal cortex.
The triadic model by Ernst et al. (2014) further distinguishes between
approach and avoidance behavior. In this model, the limbic system is
divided into two subsystems: the ventral striatum, which is particularly sen-
sitive to rewards and is thus associated with approach, and the amygdala,
which is particularly responsive to aversive or fearful stimuli and is thus
associated with avoidance.
Importantly, neurodevelopmental models suggest that changes in adoles-
cent behavior are specifically due to the differential developmental patterns
of these distinct systems. Moreover, all models emphasize the special role
of the motivational system in early adolescence. Specifically, it is hypothe-
sized that a rapid increase in dopaminergic activity within the motivational
system leads to increased reward seeking during early adolescence. At the
same time, the slower maturation of cognitive control systems results in
unregulated or risky behavior, “like driving a car with a sensitive gas pedal
and bad brakes” (Steinberg, 2014). Consequently, unregulated reward
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seeking may result in heightened vulnerability to risk taking during
adolescence.
Although all models predict that the motivational brain system develops
early in adolescence, evidence has been mixed. In line with the models’ pre-
dictions, some studies have found that the ventral striatum, an important
region in the brain’s reward circuitry, shows peak activity in adolescence
(Braams, Peters, Peper, Gu
¨roˇ
glu, & Crone, 2014;Galvan et al., 2006);
others have found no such evidence (e.g., Bjork, 2004;Bjork, Smith,
Chen, & Hommer, 2010;Richards, Plate, & Ernst, 2013). Richards et al.
(2013) systematically reviewed the fMRI reward paradigms used in studies
with adolescents versus adults and found task design to have a sizable
impact as one source of variability across findings. However, they high-
lighted that the mixed results cannot be attributed solely to the type of
task and that it remains unclear how other variables, such as pubertal
status and environmental context, influence the neural systems underlying
reward-related behavior.
One possible reason for the mixed findings is that most studies focused
on age rather than using direct measures of pubertal status. Yet chronolo-
gical age is not a reliable predictor of pubertal status. For instance, data
from a five-year longitudinal study in the 1970s showed that puberty may
begin from age 8.0 to 14.9 years in females and from age 9.7 to 14.1 years
in males and is complete by age 12.4 to 16.8 years in females and by age
13.7 to 17.9 years in males (Lee, 1980). Given the considerable degree of
individual difference in pubertal onset, it seems likely that many studies
with a focus on chronological age have failed to detect pubertal changes in
motivational behavior and related brain activity.
Indeed, a handful of studies suggest that pubertal status is a good pre-
dictor of reward-related activity. For example, a recent longitudinal study
by Braams et al. (2015), using a large sample (N= 299), found that the
average developmental trajectory of nucleus accumbens (NAcc) activation,
an important region in the brain’s reward circuitry (see Fig. 1), showed an
inverted U-shape pattern. Moreover, change in NAcc activation was posi-
tively correlated with change in pubertal testosterone. This finding is in line
with previous research reporting a relationship between pubertal testoster-
one and ventral striatum activity (Forbes et al., 2010;Op De Macks et al.,
2011). For instance, Forbes et al. (2010) found that testosterone was posi-
tively associated with striatal activity during reward anticipation in boys,
but negatively correlated with outcome-related caudate reactivity. Their
finding suggests that the effects of testosterone on reward processing may
be variable and depend on the decision phase.
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What is still missing is a clear understanding of how hormones may lead
to changes in striatal activation. Animal studies may be informative here,
as puberty is not specific to humans but is a cross-species phenomenon
(Spear, 2004). Indeed, single-unit recordings and lesion studies in animals
have provided numerous insights into the mechanisms that underlie brain
and behavioral changes during adolescence (Spear, 2004; for a review, see
Casey, Duhoux, & Cohen, 2010). For instance, it is well established that
dopamine plays an important role in goal-directed behavior, reward, and
motivation (see Schultz, 2015). There are two major pathways of dopamine
projection (see Fig. 1). First, the meso-corticolimbic dopamine system is
associated with reward, learning, and motivation. Central regions of this
system include the ventral tegmental area (VTA), the NAcc, and the pre-
frontal cortex. Second, the nigrostriatal dopamine system (Ilango et al.,
2014;Rossi, Sukharnikova, Hayrapetyan, Yang, & Yin, 2013), which
was originally associated with motor function (Wise, 2004), originates in
the substantia nigra, where dopamine neurons primarily project to the
dorsal striatum.
Animal studies have found evidence of adolescence-specific changes in
both dopamine pathways. Focusing on the nigrostriatal pathway in peria-
dolescent rodents, for instance, Stamford (1989) found reduced basal dopa-
mine levels in adolescence. Laviola, Macrı
`, Morley-Fletcher, and Adriani
(2003) interpreted these findings as a reason for lower basal levels of moti-
vation and general underarousal, possibly resulting in typical human ado-
lescent characteristics such as boredom and dissatisfaction. Interestingly,
general underarousal is thought to lead to sensation seeking (Herpertz &
Sass, 2000). Note, however, that adolescent rodents do not generally have
less dopamine per se than adults; in fact, they have a larger dopamine sto-
rage pool (Stamford, 1989). In rewarding contexts, such as novel environ-
ments that can be explored, dopamine release is higher in adolescent than
in adult rodents (Laviola, Pascucci, & Pieretti, 2001). Consequently, the
potential amount of dopamine release is higher in adolescence than in
adulthood, but this potential can be reached only when a stimulating con-
text is given.
Sex steroids are known to modulate dopamine signaling and may thus
play an important role in regulating adolescent developmental changes in
the dopamine system (Sinclair, Purves-Tyson, Allen, & Weickert, 2014).
For instance, several studies with adolescent rodents have indicated a regu-
lating effect of testosterone on dopamine neurotransmission. Purves-Tyson
et al. (2012) investigated whether gonadectomy (the surgical removal of
testes or ovaries) or experimental augmentation of testosterone influences
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dopamine levels. On the one hand, they found testosterone to be positively
associated with increases in enzymes that play an important role in dopa-
mine metabolism (e.g., catechol-O-methyltransferase [COMT], monoamine
oxidase A [MAOA], and monoamine oxidase B [MAOB]). Specifically,
these enzymes break down dopamine into its essential parts and are
involved in its inactivation. Testosterone may thus lead to inactivation of
dopamine. On the other hand, Purves-Tyson et al. (2012) also found testo-
sterone to be associated with increases in the enzyme tyrosine hydroxylase
(TH), which is an integral part of the dopamine synthesis process. Thus,
testosterone may increase local dopamine production in the substantia
nigra. Taken together, these results suggest that testosterone has multi-
faceted effects on dopamine functioning, with various behavioral conse-
quences. Specifically, the findings are in line with the idea that adolescents
generally feel underaroused due to lower dopamine levels (Laviola et al.,
2003). However, when they find themselves in novel, rewarding environ-
ments, more dopamine may be released from their larger storage pool,
making them more risk taking and impulsive.
In a second study, Purves-Tyson et al. (2014) looked at the proteins
responsible for dopamine packaging and reuptake (e.g., vesicular mono-
amine transporter [VMAT] and dopamine transporter [DAT]), as well as
dopamine receptors (DRD1D5) and investigated whether they were chan-
ged by testosterone in the nigrostriatal pathway of adolescent male rats.
Their results suggested that increased testosterone at adolescence may
change the dopamine responsivity of the nigrostriatal pathway by modulat-
ing the capacity of neurons to transport and respond to dopamine. For
instance, pubertal testosterone led to an increase in excitatory dopamine
receptors in the substantia nigra and the striatum, possibly reflecting
greater sensitivity to dopamine via testosterone exposure. Furthermore, the
general increase in dopamine receptors in the substantia nigra in response
to adolescent testosterone may mirror changes in the available dopamine.
Consequently, testosterone may lead to more midbrain dopamine signaling
during puberty, presumably modulating reward-related decision making,
such as risk taking and impulsive behavior in adolescence.
Interestingly, Purves-Tyson et al. (2014) also found differential effects of
testosterone on dopamine transportation and synthesis in the substantia
nigra versus the dorsal striatum: Whereas dopamine activity in the dorsal
striatum was increased after gonadectomy and weakened by testosterone
replacement, the opposite was true in the substantia nigra. Consequently,
these results suggest that the regulation of dopamine-related molecular
parameters by testosterone is greater in the substantia nigra than in the
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dorsal striatum. This interpretation is in line with the findings of a study
by Matthews, Bondi, Torres, and Moghaddam (2013) showing that early-
adolescent male rats have decreased dopamine release and synthesis in the
dorsal striatum.
In conclusion, studies in adolescent male rodents have shown that testo-
sterone has differential effects across dopaminergic pathways, increasing
dopamine neurotransmission in the substantia nigra and decreasing dopa-
mine levels in the dorsal striatum. In order to disentangle the site-specific
effects of testosterone, it is crucial to investigate the functional outcomes of
these molecular changes. Specifically, researchers need to understand the
context dependency of the effect of testosterone, which may be due to inter-
actions with other neuromodulators and/or hormones. Another question to
be addressed is how developmental changes on a molecular level are related
to brain activity as measured by the blood-oxygen-level dependent (BOLD)
signal. Studies with adolescent male rats give reason to believe that testos-
terone impacts the motivational system. However, this interaction is likely
to be complex and many unknowns remain, including how testosterone-
induced molecular changes in the VTA, the substantia nigra, and the dorsal
and ventral striatum influence behavior.
IMPLICATIONS
The risk taking behavior typical of adolescence results from an interaction
of biological and environmental influences. As noted throughout this
chapter, adolescents show unique developmental brain characteristics that
are associated with particular behavioral patterns. Clearly, the best way to
reduce maladaptive behavior in teenagers is not to try to change their
nature by teaching them to think differently. Telling teenagers about the
risks of smoking marijuana and drunk driving or instructing them to con-
sider the potential consequences of their actions is unlikely to change their
behavior (Albert & Steinberg, 2011). Instead, the focus should be on the
surrounding context and on ensuring that it matches teenagers’ biological
developmental state and capabilities.
For instance, pubertal hormones have shown to influence the circadian
rhythm, leading the typical 12-year-old to stay up later and sleep in later
on weekends. In combination with the use of devices like TV or computers,
this small biological change can result in sleep deprivation, which can in
turn lead to behavioral problems. Specifically, the blue light spectrum has
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strong effects on the human circadian system, leading to late and restless
sleep (see Peper & Dahl, 2013). Limiting teenagers’ access to devices
absorbing blue light at night may reduce the risk of pubertal effects on the
circadian system resulting in health problems such as sleep deprivation.
Another idea would be to delay the start of the school day to 10 am, thus
ensuring that teenagers get enough sleep.
More generally, policy and regulations need to be modified to limit teen-
agers’ opportunities to engage in risky behaviors. Steinberg (2015) offers
concrete policy recommendations that focus primarily on restricting the
time that adolescents are left unsupervised.
SUMMARY AND FUTURE DIRECTIONS
In this chapter, we have sought to integrate findings elucidating the specific
impact of pubertal hormones on motivational processes in adolescence.
A rapid change in gonadal hormone levels at pubertal onset has been
linked to motivational changes that may prompt increased risk taking and
impulsivity during adolescence. Mechanisms that mediate this relationship
are associated with a neuronal motivation system, in which dopamine is of
special interest. Neurobiological models of adolescent brain development
highlight the impact of pubertal hormones on reward-related regions,
resulting in strong reward-approach behavior, which is in turn hypothe-
sized to account for increased risk taking and impulsivity in adolescence.
Although the current literature does not offer unreserved support for the
hypothesis that reward-sensitive brain regions are more responsive during
adolescence, rodent studies have highlighted the impact of testosterone on
dopamine neurotransmission in the substantia nigra. However, there is a
lack of research that specifically (1) tests the relationship between pubertal
hormone levels and risky and impulsive behavior in adolescence; (2) investi-
gates the impact of pubertal hormone levels on brain regions associated
with motivational processing; (3) describes how the relationship between
pubertal hormones levels and brain regions changes over time; and
(4) explains how this change is linked to developmental changes in risk
taking and impulsivity.
To address these questions, we propose the following directions for
future research into pubertal effects on behavior and brain functioning:
First, studies should apply measures of pubertal age, ideally hormonal, as
well as assessing chronological age. Second, the integration of different
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levels of analysis is essential. Studies that combine measures of behavior,
its neural correlates, and how they interact with pubertal hormones such as
testosterone in specific contexts are needed. Third, and most importantly,
assessments at multiple time points are needed to capture intraindividual
change over time. Longitudinal studies of development are imperative and
can provide insights into interactions early versus late in puberty. Finally,
gender differences must be considered, and hormones other than testoster-
one that undergo rapid changes during adolescence should also be investi-
gated (e.g., estradiol and oxytocin).
In summary, investigating developmental changes on a hormonal level
and focusing on neurotransmitters such as dopamine in the human adoles-
cent brain is a promising avenue for gaining further insights into the speci-
fic neurobiological mechanisms underlying risky and impulsive behavior in
adolescence.
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