What Motivates Adolescents? Neural Responses to Rewards and Their

Neuroscience and Biobehavioral Reviews 70 (2016) 135–147

Contents lists available at ScienceDirect

Neuroscience and Biobehavioral Reviews

jou rnal homepage: www.elsevier.com/locate/neubiorev

Review article

What motivates adolescents? Neural responses to rewards and their

inﬂuence on adolescents’ risk taking, learning, and cognitive control

a,b a,b a,b,c

Anna C.K. van Duijvenvoorde , Sabine Peters , Barbara R. Braams ,

a,b,∗

Eveline A. Crone

Department of Psychology, Leiden University, The Netherlands

Leiden Institute for Brain and Cognition, Leiden University, The Netherlands

Department of Psychology, Harvard University, United States

r t i c l e i n f o a b s t r a c t

Article history: Adolescence is characterized by pronounced changes in motivated behavior, during which emphasis on

Received 2 March 2016

potential rewards may result in an increased tendency to approach things that are novel and bring poten-

Received in revised form 22 June 2016

tial for positive reinforcement. While this may result in risky and health-endangering behavior, it may also

Accepted 24 June 2016

lead to positive consequences, such as behavioral ﬂexibility and greater learning. In this review we will

Available online 25 June 2016

discuss both the maladaptive and adaptive properties of heightened reward-sensitivity in adolescents by

reviewing recent cognitive neuroscience ﬁndings in relation to behavioral outcomes. First, we identify

Keywords:

brain regions involved in processing rewards in adults and adolescents. Second, we discuss how func-

Decision making

Risk-taking tional changes in reward-related brain activity during adolescence are related to two behavioral domains:

Learning risk taking and cognitive control. Finally, we conclude that progress lies in new levels of explanation by

Developmental changes: adolescence further integration of neural results with behavioral theories and computational models. In addition,

Cognitive neuroscience we highlight that longitudinal measures, and a better conceptualization of adolescence and environ-

Motivation mental determinants, are of crucial importance for understanding positive and negative developmental Affect trajectories.

Social context

Contents

1. Adolescence as a period of change in motivated behavior ...... 136

2. The neuroscience of reward sensitivity ...... 137

3. Reward sensitivity in adolescence ...... 138

4. Adolescent reward-sensitivity and risk taking...... 138

4.1. Reward-sensitivity and risk taking ...... 138

4.2. The inﬂuence of social context on reward-sensitivity ...... 139

4.2.1. Peer presence ...... 139

4.2.2. Inﬂuence of others’ behavior on risky choice ...... 139

4.2.3. Vicarious rewards: winning for someone else...... 140

5. Inﬂuence of adolescent reward sensitivity on cognitive control and learning ...... 141

5.1. Extrinsic rewards and cognitive control functions ...... 141

5.2. Intrinsic rewards ...... 141

5.3. Social inﬂuence on cognitive performance ...... 141

6. Towards a model-based approach ...... 143

7. Strengths and weaknesses of neuroscience research ...... 144

∗

Corresponding author at: Faculty of Social Science, Department of Psychology, Wassenaarseweg 52, 2333AK Leiden, The Netherlands.

E-mail address: [email protected] (E.A. Crone).

http://dx.doi.org/10.1016/j.neubiorev.2016.06.037

136 A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147

8. Conclusion ...... 144

Acknowledgments ...... 145

References ...... 145

Thought by itself moves nothing (Giedd et al., 1999; Gogtay et al., 2004; Tamnes et al., 2010;

Raznahan et al., 2011; Mills and Tamnes, 2014; Wierenga et al.,

Adolescence is one of the periods in life well known for its

2014), which is thought to reﬂect synaptic pruning, occurring in

changes in motivated, goal-directed, behavior. These changes are

a time- and region-speciﬁc manner (Huttenlocher 1990; Petanjek

thought to result in a greater emphasis on potential rewards, such

et al., 2011). At the same time white matter volume increases and

as a heightened behavioral motivation to obtain rewards and a

undergoes changes in the organization of its connections (Lebel and

heightened arousal in response to rewards (Galván, 2010). As a

Beaulieu, 2011; Simmonds et al., 2014). Together, these changes

crucial aspect of almost all behavior and actions, motivated behav-

allow for greater specialization and strengthening of connectivity

ior has been investigated from a number of perspectives including

between brain regions. These changes in structural development

economics, sociology, psychology, and neuroscience. Recent dis-

are paralleled by changes in functional activity in the brain, such

coveries in neurocognitive research have led to new perspectives

as activation in response to motivationally salient events (for an

on the dynamic changes in motivated behavior during adoles-

overview of studies, see Crone and Dahl, 2012; Crone et al., 2016),

cence. In the current review we will discuss changes in adolescent’s

and changes in subcortical and cortical functional connectivity dur-

reward sensitivity by reviewing recent cognitive neuroscience ﬁnd-

ing rest (Gabard-Durnam et al., 2014; Fareri et al., 2015b; van

ings in relation to behavioral outcomes.

Duijvenvoorde et al., 2016).

The structural and functional age-related changes in the brain

1. Adolescence as a period of change in motivated behavior have been summarized in neurocognitive models, which suggest

that adolescent motivation is particularly tuned towards reward-

Adolescence is deﬁned as an important transitional period ing stimuli. According to these models, which are also referred

between childhood and adulthood in which individuals gain inde- to as dual processing or imbalance models, adolescent brain

pendence and develop mature social goals (Crone and Dahl, 2012). development can be described as an imbalance in neural matu-

As such, adolescence represents a developmental time window rational patterns between the cortical-control system (including

characterized by strong needs for exploration, forming new rela- brain regions such as the prefrontal cortex), important for the

tionships, increasing intimacy, and rapid adjustment to changing regulation of thoughts and emotion, and the limbic system (includ-

social environments. Although the age range of adolescence differs ing brain regions such as striatum and amygdala), important for

between countries and cultures, it is generally agreed upon that affective-motivational functioning (e.g., Ernst, 2014; Somerville

in Western societies adolescence approximately spans the period et al., 2010; Steinberg, 2008). During adolescence the reactivity of

between ages 10 and 22 years. Puberty is an important phase of the affective-motivational system may be particularly heightened

adolescence and characterized by a rapid rise in gonadal hormones and—depending on adolescents’ motivations—may override con-

(Blakemore et al., 2010). These hormones are released through trolled responses in emotionally-salient contexts (Somerville et al.,

the hypothalamus-pituitary-gonadal axis and have a large inﬂu- 2010; Crone and Dahl, 2012). Consistent with these neurocognitive

ence on bodily characteristics and brain development (Peper and models, behavioral literature on adolescent risk-taking has indi-

Dahl, 2013; Goddings et al., 2014). Pubertal development reaches cated that in emotionally arousing situations, adolescents are more

a plateau in mid-adolescence at approximately age 15–16 years prone to taking risks. For instance, a recent meta-analysis showed

(Braams et al., 2015). The end phase of adolescence, however, is that heightened risk taking in adolescents compared to adults,

culturally deﬁned and is reached when individuals have achieved although particularly in contexts when rewards are encountered

mature social and personal responsibilities (e.g., Cohen et al., 2016). immediately (Defoe et al., 2015; see also Figner et al., 2009; van Dui-

Adolescence has traditionally been interpreted as a period of jvenvoorde et al., 2010). A similar ﬁnding is observed when

changes in motivated behavior. Some of the earliest theories in adolescents are in the presence of peers (Gardner and Steinberg,

developmental psychology have described adolescence as a period 2005; Peake et al., 2013), or when approaching unknown situations

of storm and stress, including conﬂict with parents, mood disrup- (Blankenstein et al., 2016; Tymula et al., 2012).

tions and a variety of risk behaviors, which were thought to be New models in this tradition increasingly acknowledge the com-

universal and biological (Arnett, 1999; Hall, 1904). Epidemiological plexity of brain and behavioral changes across adolescence. In order

reports have indeed observed an increase in risk taking behavior in to understand changes in adolescent motivated behavior these

adolescence, such as a higher incidence of trafﬁc accidents, delin- models stress a) the importance of studying brain-connectivity

quency, and substance abuse (see Eaton et al., 2011; Willoughby within and between limbic, affective-motivational, and cognitive

et al., 2013). To reconcile these ﬁndings with the general cognitive control brain circuits (Casey, 2015; Casey et al., 2016), b) the inﬂu-

increase observed in adolescence (e.g., Crone 2009), research tuned ence of social context on observed neural sensitivities (Shulman

to the role of affective-motivational processes in understanding et al., 2016; Nelson et al., 2016), and c) the importance of pubertal

changes in adolescent’s behavior (e.g., Boyer, 2006). Developmental developmental changes and cortical-subcortical ﬂexible interac-

studies reported a surge in sensation seeking during adolescence tions (Crone and Dahl, 2012). More speciﬁcally, the ﬁrst model

(Zuckerman, 1994) that may lead to increases in peer inﬂuence (Casey, 2015) highlights the hierarchical development of brain cir-

and risky behavior (Steinberg et al., 2008; Romer, 2010). Recent cuitry, in which development and sensitivity of subcortical circuits

approaches have linked these changes in motivated behavior to (e.g. subcortical–subcortical connections) precede others (e.g., top-

the long-lasting neurodevelopmental changes across adolescence. down cortical control connections), in order to develop. The second

Since the discovery that brain development continues through- model highlights that an imbalance between cortical and subcor-

out adolescence, research on adolescent brain development has tical brain systems may not occur under all circumstances and

expanded enormously. It was discovered that adolescence is a depends on contextual factors, such as peer presence (Shulman

period of continuous changes in brain morphology. In grey mat- et al., 2016). Finally, it has been proposed that increases in puber-

ter there is an overall reduction in volume and cortical thickness tal hormones at the onset of puberty trigger the limbic system to

A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147 137

Fig. 1. Recent dual-systems model proposed by (A) Casey et al. (2016), (B) Shulman et al., 2016 (ﬁrst printed in Smith et al., 2013), and (C) Crone and Dahl (2012).

ﬂexibly recruit cortical control regions, depending on the motiva- both animal and human research, several major dopamine-rich

tional goal of the behavior that is displayed (Crone and Dahl, 2012). pathways have been identiﬁed, including the mesolimbic and

Illustrations of a number of these recent models can be found in mesocortical pathways. These pathways project from the midbrain

Fig. 1. ventral tegmental area to the nucleus accumbens (mesolimbic

In summary, neurocognitive models regard adolescence as a dopamine pathway) and from the ventral tegmental area to parts

key developmental period for changes in motivation and have of the prefrontal cortex (mesocortical dopamine pathway).

deﬁned adolescence to coincide with a particularly greater sensi- In humans, functional magnetic resonance imaging (fMRI) has

tivity towards potential and obtained rewards. On the other hand, been used extensively to examine functional activation in the brain

these models have been characterized as heuristic models that during the anticipation and the receipt of reward (see Richards

provide general and relatively unfalsiﬁable hypotheses on develop- et al. (2013) for an excellent review). Several meta-analyses,

mental patterns (Pfeifer and Allen, 2012; van den Bos and Eppinger, which include reward-processing across a wide-range of deci-

2016). Future speciﬁcations of the models should describe how to sion paradigms, have consistently reported activation in a neural

derive speciﬁc and testable hypotheses. The importance of these reward network including structures of the subcortical limbic sys-

models, however, has been evident and an exciting step forward tem (Bartra et al., 2013; Cauda et al., 2011; Diekhof et al., 2012;

is moving into specifying models to match behavioral and neural Liu et al., 2011; Sescousse et al., 2013; see also Fig. 2). Further-

changes across development. more, these meta-analyses demonstrated activation in the medial

A greater reward-related motivation in adolescence has been prefrontal cortex (PFC), the orbitofrontal cortex (OFC) and ventral

predominantly linked to behaviors such as aberrant decision- medial PFC, as well as in the insula, the anterior cingulate cortex

making and risk taking such as alcohol abuse, substance use (ACC), the posterior cingulate cortex (PCC), the inferior parietal lob-

problems, and excessive risk taking. However, changes in reward- ule and regions in the lateral PFC (e.g. Liu et al., 2011). In another

related motivation may additionally lead to exploring new meta-analytic study, Sescousse et al. (2013) argued that overlap-

environments, achieving high social ranks, or experimenting with ping neural networks are involved in coding both primary and

new social roles in society (Crone and Dahl, 2012). Thus, reward- secondary reward values. In adults, neural correlates of primary

sensitivity may be equally important for understanding positive (such as food, sex and shelter) and secondary rewards (such as

developmental trajectories in learning, sports, and social behav- money or power) were found in the ventral striatum, the thalamus,

ior (Dahl and Vanderschuren, 2011; Telzer, 2016). In this review, the OFC, the insula, and the amygdala (see Fig. 2). These ﬁndings

we will discuss the inﬂuence that rewards may have on adoles- indicate that rewards elicit a consistent pattern of functional activa-

cent’s adaptive and maladaptive behavior across different domains tion across dopaminergic innervated reward regions in the human

in a neurocognitive perspective. To this end, we ﬁrst describe brain brain.

regions involved in processing of rewards in adults and adolescents, To directly assess the relation between dopamine responses

with a speciﬁc focus on core reward regions, such as the stria- and blood-oxygenated level-dependent (BOLD) brain activation, a

tum and its connected circuitry. Second, we discuss the functional recent study used an innovative combination of optogenetic meth-

changes in reward circuitry related to (mal)adaptive risk taking ods (allowing to experimentally control activation of neurons) and

and decision making during adolescence. Third, we describe how functional MRI in rats (Ferenczi et al., 2016). Findings showed,

reward circuitry is related to adolescent (mal)adaptive cognitive for the ﬁrst time, that dopamine neuron stimulation was directly

control and learning. In both domains we will consider social con- related to striatal BOLD activity. Also, the inﬂuence of the cortical

text as an important moderator of adolescent’s behavior. Fourth, circuitry over subcortical activation was evidenced by the ﬁnding

we will discuss the potential for using a model-based approach, that increased medial PFC excitability reduced striatal-midbrain

which allows for the development of speciﬁc, testable hypotheses coupling, and in this way suppressed reward-seeking behavior.

of decision-making and learning in adolescence. Finally, we will These ﬁndings highlight the importance of the interaction between

discuss the challenges for studying adolescence as a key transition cortical and subcortical circuitry in driving motivated, reward-

period for motivated behavior, along with the broader implications driven, behavior and present an important model for human data

and conclusions arising from this review. (see also Casey, 2015 for a discussion of translational models in

adolescent’s motivated behavior).

When considering the development of these reward networks,

2. The neuroscience of reward sensitivity

animal research has shown marked changes in the expression and

pruning of dopamine receptors during the transition into and out

Reward processing in the brain has been examined from a vari-

of adolescence (Spear, 2000; Spear, 2011), which may underlie

ety of perspectives. Animal research has shown that dopamine

developmental changes in adolescent’s reward-related behavior

innervated regions such as the basal ganglia and its cortical targets

and neural activation (Luciana and Collins, 2013). Developmental

(i.e., cortico-striatal loops) are central to basic reward process-

changes in the dopamine system are, however, not only character-

ing and motivated behavior (e.g., Haber and Knutson, 2010). In

138 A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147

Fig. 2. Results of meta-analyses of reward-processing from (A) Liu et al. (2011), (B) Sescousse et al. (2013), and (C) Silverman et al. (2015).

ized by changes in the receptor density, but also by changes in the and their developmental changes, will be highlighted in some of the

different ﬁring modes (phasic/tonic) of dopamine neurons, and by reviewed studies.

changing interactions with other neurotransmitter systems (par-

ticularly glutamate and GABA) that may be relevant for motivated

4. Adolescent reward-sensitivity and risk taking

behavior (Ernst and Luciana, 2015). The complexity of this system

is evident. A remaining challenging step is therefore to apply these

Neurocognitive models of adolescent brain development have

insights of neurotransmitter remodeling in order to understand

suggested that adolescent risk taking and decision making is driven

changes in adolescent motivated behavior (e.g. Luciana et al., 2012;

by heightened activation of the neural reward system during ado-

Doremus-Fitzwater et al., 2011).

lescence, especially focusing on the ventral striatum. Risky behavior

may come with substantial individual and societal costs, yet in

some cases greater risk taking could be an adaptive feature, which

3. Reward sensitivity in adolescence

may be particularly prominent in adolescence (Crone and Dahl,

2012). For instance, when exploring new environments, taking

The speciﬁc neural architecture of reward processing changes

an unknown risk may entail several beneﬁts. Here, we describe

across adolescence has been extensively studied across a large

ﬁndings on adolescents’ neural reward-sensitivity in relation to

variety of paradigms. A recent meta-analysis pooled data from 26

maladaptive and adaptive behavioral risk-taking.

studies and more than 800 individuals to identify the network of

brain regions involved in adolescent reward processing (Silverman

et al., 2015). Interestingly, adolescents and adults recruited over- 4.1. Reward-sensitivity and risk taking

lapping brain regions, including the ventral striatum, the insula,

and the posterior cingulate cortex. However, adolescents generally One way to test neural reward-sensitivity and its behavioral

showed an increased likelihood of activation in these subcortical outcomes in adolescence is by using decision-making tasks where

and cortical reward-related regions. choices can result in rewards or losses. For example to test the

Activation of these reward regions across adolescence may developmental pattern of neural responses to rewards in an adoles-

depend on interactions with long-maturing brain regions such cent sample, we conducted a two wave longitudinal study including

as the lateral PFC and parietal cortex, which have been typically 254 adolescents between the ages of 8 and 27 years to examine

related to a series of cognitive control functions (e.g., response neural responses to winning and losing money in a gambling game

inhibition, relational reasoning, working memory, and learning) (Braams et al., 2015). Results showed strong activation in the ven-

(Crone 2009; Luna et al., 2010; Niendam et al., 2012; Diamond, tral striatum, and speciﬁcally the nucleus accumbens (NAcc), when

2013). Additionally, co-activations are regularly observed between winning compared to losing across all ages. Moreover, a quadratic

reward-related regions and brain regions important for perspective trend of reward-related neural activation in the NAcc, peaking

taking, mentalizing and social behaviors, including the medial PFC in late-adolescence, described the data best. These results extend

and the temporal-parietal junction (Blakemore et al., 2010; Mills ﬁndings from the meta-analysis comparing adolescents and adults

et al., 2014). Taken together, activity in core reward-regions is likely (Silverman et al., 2015) by showing stronger reward activity in the

dependent on a larger interconnected network of subcortical and ventral striatum in adolescents compared to adults and children

cortical regions in driving behavioral outcomes. These interactions, (see also Galván et al., 2006).

A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147 139

To further test the functional relevance of these age-related 4.2. The inﬂuence of social context on reward-sensitivity

ﬁndings, we also investigated the relationship between neural

responses to rewards and pubertal developmental, hormone levels, Adolescence is a time of social reorientation in which a shift

behavioral risk taking on the balloon analogue risk task (BART), and occurs from a focus on parents to a focus on peers (Rubin et al.,

self-reported reward-sensitivity. The latter was measured with the 2008) and social contexts may inﬂuence neural response to rewards

behavioral inhibition and behavioral activation scale (BIS/BAS), a in adolescence. In the next section, we discuss the inﬂuence of

well-validated questionnaire in which subscales measure different social context on adolescents’ reward-sensitivity across three social

aspects of reward-related behavior. Results showed positive associ- domains: peer presence, peer inﬂuence, and vicarious rewards (i.e.

ations between reward activation in NAcc and self-reported reward gaining for other).

drive. That is, those participants who indicated they were willing to

exert more effort for a reward also showed greater NAcc responses 4.2.1. Peer presence

to rewards (Braams et al., 2015; see also van Duijvenvoorde et al., In a computerized risky driving task that was played either

2014; Op de Macks et al., 2011). Another two-year longitudinal alone or in the presence of a peer (Chein et al., 2011), adoles-

study observed an increase and peak in reward sensitivity in late cents took more risky crossings and experienced a higher number

adolescence (as indicated by BAS reward-responsiveness). Grey of crashes (ages 14–18) when peers were present. Young adults did

matter volume of the NAcc explained part of the developmental not demonstrate this peer effect. The authors observed enhanced

changes in reward-responsiveness (Uroseviˇ c´ et al., 2012). activation in the ventral striatum and OFC when peers were

Several studies have tested whether neural activation in reward present, but only for the adolescent age group, and speciﬁcally

areas is related to real-life risk taking behavior. These studies for the anticipatory decision phase. A similar effect was found

showed that reward-related neural activation is indeed posi- when adolescents (14–19 years) and adults (25–35 years) played

tively related to a variety of risky real life behaviors including a gambling task where they had to guess whether a next play-

risky sexual behavior, illicit drug use and binge drinking (Bjork ing card would be higher or lower than the current card (Smith

and Pardini, 2015; Braams et al., 2016; Galván et al., 2007; et al., 2015). When playing the game alone, no differences were

see Fig. 3). Also structural developmental studies observed rela- observed between adolescents and adults, but when playing with

tions with real-life substance use, indicating that adolescents peers present, adolescents showed stronger activity in the ven-

with smaller NAcc volumes were more likely to initiate sub- tral striatum. Speculatively, one mechanism that may underlie

stance use in a 2-year follow-up period (Uroseviˇ c´ et al., 2015). the inﬂuence of peer presence is a heightened sensitivity to peer

Finally, functional coupling between subcortical limbic struc- evaluation (Somerville et al., 2013). A recent study showed that

tures and the OFC has been related to risky real-life behavior. while seemingly being viewed by a peer in a live video feed, ado-

That is, self-reported rule-breaking behavior has been related lescents’ self-report indicated rising embarrassment. Adolescents

to a functional coupling of the striatum and OFC (Qu et al., also showed greater physiological arousal than children and adults,

2015). Moreover, alcohol use in adolescent boys was associated which was mimicked by emergent recruitment of the medial PFC,

with lower connectivity between the amygdala and OFC (Peters most prominently increasing up to mid-adolescence. Adolescent-

et al., 2015). Together, this set of studies suggests that func- emergent engagement of the medial PFC has been suggested to

tional activity, connectivity, and structural development within reﬂect, or perhaps result in, a high degree of salience, emotional

a subcortical reward-network may be a marker for the propen- arousal, and self-relevance in social-evaluative situations. Interest-

sity to display behaviors possibly detrimental to individual ingly, in the driving task neural activations in the ventral striatum

health. were stronger for adolescents who reported less resistance to peer

On the other hand, adolescents’ increased response towards inﬂuence (Chein et al., 2011), further strengthening the hypothesis

rewards may also drive adaptive risk taking, a link that has yet been that adolescents are more sensitive to social evaluations.

tested in few studies. For instance, a recent study showed that for Apparently just the mere presence of peers has a pronounced

adolescent girls (ages 8–25), a higher secretion of testosterone was effect on adolescent behavior and neural activation, more so than in

related to increased risk taking, but this risk taking led to higher adults. This seems consistent with real-life scenarios in which risk-

ﬁnancial outcomes (Peper et al., 2013). Moreover, OFC morphol- taking behaviors in adolescents occur more readily when they are

ogy mediated the relation between testosterone and increased the among their peers (see Steinberg, 2008). It may be that the presence

level of this adaptive risk-taking (gaining money by taking risks). or potential evaluation of peers, triggers risky behavior by increas-

This relation was not observed in boys, who instead demonstrated ing salient contextual cues, such as the potential for rewards (or

increased maladaptive risk-taking with higher testosterone lev- losses). To investigate whether adolescent’s reward-sensitivity can

els. These ﬁndings suggest that there may be gender differences also be reduced by social context, a recent study tested whether

in how pubertal hormones affect brain development, and possibly, the presence of mothers modulated neural reward responses and

this may result in different outcomes of risk-taking in boys and observed risky choices in adolescents (Telzer et al., 2015). Adoles-

girls. Consistent with these ﬁndings, a recent study demonstrated cent risky choice was reduced in the presence of mothers compared

that adolescent girls (11–13-years) who showed more reward to when being alone, as well ventral striatum activation during

exploration (a risky, but possibly adaptive choice) in an explore- risky choice. In contrast, during safe decisions, there was increased

exploit paradigm had stronger resting-state connectivity between recruitment of the lateral PFC and greater functional connectiv-

the insula and lateral prefrontal cortex than girls that explored ity between lateral PFC and ventral striatum in the presence of

less (Kayser et al., 2016). Until now, only a few studies addressed mothers. These ﬁndings ﬁt well with suggestions that connectivity

sex differences and pubertal inﬂuence in (mal)adaptive risk taking. between the medial and lateral PFC and the striatum are crucial

However, there is accumulating evidence that pubertal hormones for integrating signals relevant to social contexts (Somerville et al.,

may differentially inﬂuence boy’s and girl’s brain and behavior (see 2013).

Gur and Gur for an excellent review, 2016). Including such effects

in future studies may be highly informative to understand indi- 4.2.2. Inﬂuence of others’ behavior on risky choice

vidual differences in adolescent’s adaptive and maladaptive risk An intriguing question is the extent to which peers can alter

taking. risk taking towards riskier versus safe directions (see also Alberts

et al., 2013). Behavioral studies showed that adolescents’ risk per-

ceptions and prosocial behavior can increase or decrease based

140 A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147

Fig. 3. Adolescent brain development and alcohol use. (A) shows that more activity in the ventral striatum when winning money was associated with higher amount

of consumed alcohol per night (Braams et al., 2016). (B) shows that less connectivity between the amygdala and OFC in adolescence was associated with more alcohol

consumption last month (Peters et al., 2015).

on peer-expressed norms (Knoll et al., 2015; van Hoorn et al., that arose from reciprocation and depended on the closeness of

2016; Blankenstein et al., 2016). A recent imaging study tested the relationship with the interaction partners (Fareri et al., 2015a).

whether neural responses may be predictive of the inﬂuence of Similar results were obtained in a study with adolescent partic-

peer-expressed norms of driving behavior (Cascio et al., 2014). First, ipants in which money could be won for best friends and disliked

adolescents’ (ages 16–17) neural responses were measured during others. Ventral striatum responses were higher when winning for

a response inhibition task. In a second behavioral session, adoles- a friend than losing for a friend, whereas this was not found when

cents were paired with a peer expressing either risky or safe driving winning money for a disliked other. Interestingly, this effect was

norms. Results showed that adolescents displayed riskier behav- modulated by the strength of the social bond. Girls who showed

ior in presence of the risky compared to the safe peer. Moreover, the highest responses to winning money for their best friend also

activation in the ventral striatum and prefrontal cortex during the reported higher friendship quality with this same friend (Braams

response inhibition task predicted the inﬂuence of the safe peer on et al., 2014). Striatum responses in this study may be related to

adolescents’ driving behavior. Thus, higher activation in these brain empathic feelings towards friends, which makes reward of others

regions was associated with engaging in fewer risks when paired feel as rewarding as rewards for self. Given the stronger focus on

with cautious peers (Cascio et al., 2014). These ﬁndings demon- peer networks in adolescence, this suggests that reward sensitivity

strate that stronger inhibition activity in a cognitive control task can may be an important factor in forming social bonds.

predict how much adolescents are inﬂuenced by the social norms An interesting new direction of research has linked the elevated

of a safety-promoting peer. neural response to rewards in adolescence to prosocial motivations.

In a longitudinal study in adolescents, Telzer et al. (2014) investi-

gated neural responses to monetary gains in a risk-taking task that

4.2.3. Vicarious rewards: winning for someone else

resulted in either gains for the self (hedonic rewards: self-pleasure)

The previous sections focused on the extent to which peers

and a donating task that resulted in gains for family members

enhance behaviors with potential negative outcomes, such as risk

(eudaimonic rewards: pleasure for others). They reported that

taking while driving. However, sharing and vicarious rewards may

those adolescents who showed stronger ventral striatum activity

also be highly rewarding for adolescents. This has been tested in

to hedonic rewards reported an increase in problem behavior one

social decision-making paradigms in which money can be gained

year later, but adolescents who showed stronger ventral striatum

for different beneﬁciaries. A study by Fareri et al. (2012) with adult

reactivity to eudaimonic rewards showed a decrease in problem

participants showed that ventral striatum responses are stronger

behavior over time.

when sharing money with a friend compared to a computer or a dis-

These ﬁndings suggest that changes in adolescents’ reward-

liked other. With a model-based approach it was found that striatal

related responses pose opportunities for positive prosocial

and medial PFC responses predicted a social value reward signal

A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147 141

development, such as increases in cooperation, sharing and helping, (one or ﬁve ‘points’), whereas incorrect trials resulted in no points.

that can be inﬂuenced by the social context that we ﬁnd our- Response times were longer in adolescents when large rewards

selves in. These ﬁndings also provide important starting points for were at stake, suggesting an upregulation of control in a high-stakes

research in adolescents with social developmental problems such setting. Additionally, frontal and parietal areas—brain regions typ-

as autism, social anxiety and antisocial behavior. Very little is yet ically associated with executing cognitive control—were activated

known about how exaggerated neural reward responses to risk and more in adolescents compared to adults for high rewards. Also,

reward-seeking behaviors affects adolescents with problem behav- these regions showed increased functional connectivity with the

iors and how the environment plays a role in guiding or shaping ventral striatum when large compared to small rewards were at

developmental pathways (Schriber and Guyer 2015). stake. An interesting next step would be to relate this frontal-

parietal activation to the speciﬁc inhibitory decision process of

waiting for a larger reward.

5. Inﬂuence of adolescent reward sensitivity on cognitive

Finally, a recent longitudinal study using a similar design

control and learning

(10–22-years) observed that the effect of incentives on perfor-

mance was susceptible to large individual differences: for some

Compared to the number of studies that have focused on the

individuals, incentives (both rewards and losses) improved perfor-

inﬂuence of reward-sensitivity on risk taking in adolescence, it

mance, whereas for others, it decreased performance, which may

seems that less is known about how enhanced reward-sensitivity

have leveled out the general effects of incentives on performance

affects cognitive functioning, which may show equally adaptive

(Paulsen et al., 2015). In the absence of incentives (neutral con-

and maladaptive features. The driving force behind general cog-

dition) the authors observed that ventral striatal activation was

nitive development is thought to be cognitive control, deﬁned as

associated with better response inhibition in younger participants,

the ability to control thoughts and actions. Cognitive control abil-

which was interpreted as an increased activation of reward-related

ities start to emerge in early childhood, gradually improve over

circuitry to intrinsic rewards, which in turn may enhance cognitive

childhood and through adolescence, and consist of multiple con-

control. Interestingly, this relation reversed (i.e., hindered response

structs such as working memory, inhibition, and ﬂexibility as well

inhibition) in adulthood.

as different higher level functions such as reasoning, performance-

monitoring, and planning (Diamond, 2013). Cognitive functioning

5.2. Intrinsic rewards

may be greatly inﬂuenced by reward-sensitivity, as it may inﬂu-

ence the level of motivation for applying cognitive control. For

Brain regions involved in processing of extrinsic rewards such

instance, cognitive control may be hindered in a social context, such

as money are also consistently activated during more intrinsic

as through decreased concentration when peers are present, but it

rewards (e.g., ‘being right’), and to responses to correct or incor-

is also possible that (social) rewards lead to increases in cognitive

rect performance feedback in cognitive rule-learning tasks (Aron

performance, due to increased motivation.

et al., 2004; Daniel and Pollmann, 2010). A study of Satterthwaite

et al. (2012) tested whether core reward-regions were activated

5.1. Extrinsic rewards and cognitive control functions for correct responses even in the absence of any feedback. In a

standard n-back working-memory task (N = 304; ages 8–22 years)

A set of studies has used a combined behavioral and imag- ventral striatum activity was greater after correct compared to

ing approach to test whether extrinsic rewards and higher stakes incorrect responses, scaled with task-difﬁculty, and was correlated

help or hinder adolescents’ control (e.g. by upregulating motivation with individual’s task performance. Interestingly, the magnitude

and control or choking under pressure respectively). That is, stud- of ventral striatum activation peaked in mid-adolescents, which

ies have examined the effect of adding incentives to performance ﬁts well with studies of neural activation to monetary rewards

and neural activity during response inhibition tasks such as the that used a similar age range (Braams et al., 2015) and may sug-

anti-saccade task (Luna et al., 2013). In this type of tasks, it is gen- gest that adolescence is a period of not only heightened external,

erally observed that children and adolescents commit more errors but also heightened intrinsic reward-sensitivity (i.e., being right,

(pointing to immature response inhibition) than adults. In a ﬁrst or the inner drive to perform well). Possibly, the effect of reward

study (Geier et al., 2010) adolescents (13–17 years), but not adults responsiveness may follow an inverted U pattern, such that some

(18–30 years) showed an increase in performance when monetary level of external or internal reward may boost motivation towards

incentives were offered. Relative to adults, adolescents additionally better cognitive performance (Satterthwaite et al., 2012), whereas

demonstrated reduced ventral striatum activity during cue assess- too much focus on reward may result in performance decrements

ment, but enhanced activity while preparing an action for rewarded (choking), thereby hindering performance.

compared to neutral trials. Similar ﬁndings were reported in a sec-

ond study (Padmanabhan et al., 2011): when monetary rewards 5.3. Social inﬂuence on cognitive performance

were offered for correct performance, adult (18–25 years) perfor-

mance remained high and improved in children (8–13 years) and Several studies have focused on the effect of social context

adolescents (13–17 years) compared to neutral trials. Moreover, it on cognitive performance in adolescence. A positive social con-

was observed that only adolescents showed increased responses text is rewarding for humans (Steinberg, 2008), but this may

in the parietal cortex and striatum when comparing reward trials be especially so in adolescence (Blakemore and Mills, 2014). An

with neutral trials. These ﬁndings suggest that the upregulation of interesting question to address is how social context inﬂuences

activation in these neural systems may be a mechanism for how cognitive performance and learning. One way to investigate the

extrinsic rewards may improve behavioral control particularly in inﬂuence of peer-presence on adolescents’ cognitive performance

adolescence. and learning is by studying the so-called audience effect, i.e. the

A cross-sectional study using a different type of cognitive task change in cognitive performance when an observer is present.

(Teslovich et al., 2014) also found evidence for a facilitating effect of In adults, most studies have shown an increase in performance

rewards speciﬁcally for adolescents’ cognitive control. In this task with an observer for simple tasks (a helping effect) but a decrease

adults (21–30 years) and adolescents (11–21 years) were instructed in performance for complex tasks (for a meta-analysis, see Bond

to detect the direction of movement of a cloud of moving dots. and Titus, 1983). A study in children (Kim et al., 2005) tested

Correct trials were rewarded with either a small or large reward the effects of an observer (a friend) during an inhibitory go-nogo

142 A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147

Fig. 4. An overview of the deﬁnition of childhood, adolescence and adulthood, in the studies that are included in the meta-analysis of Silverman et al. (2015; exceptions

are made for studies that are included in the meta-analysis, but do not report age ranges), and studies that are discussed in this review. Light blue represents children (as

defined in the studies). Blue represents adolescence (as defined in the studies), and dark blue represents adults (as defined in the studies). Adult age groups are represented

up to age 30 years for illustrative purposes. Studies that have tested age as a continuous measure are displayed in black. The graph shows that there are large differences

between studies in how adolescence is deﬁned in ages in years. Moreover, there is overlap between studies in the ages that refer to as children and adolescents. What

can be concluded from this ﬁgure is that here is a great need for more detailed measurement of adolescence in terms of age in years, but also in terms of pubertal development

A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147 143

task in children (7–11 years). This study reported stronger neural instance, a recent study tested to what extent advice from an

responses to negative feedback (as measured with EEG) while chil- adult expert inﬂuences risky decision making in early adoles-

dren were being observed compared to being alone. Additionally, cents (ages 12–14), late adolescents (ages 15–17), and adults (ages

a recent study including adolescents found a hindering effect of an 18–45) by use of a formal model-based approach (Engelmann

audience on adolescents’ relational reasoning performance (Wolf et al., 2012). Results showed that advice inﬂuenced particularly the

et al., 2015). Younger adolescents (10–14 years), older adolescents weighting of probabilities in adolescence. Also, risk-averse advice

(14–17 years) and adults (21–34 years) performed a relational rea- increased the correlation strength between activity in the lateral

soning task while alone, with an experimenter, or with a friend PFC and valuation of safe choices, but only in adolescents. Other

present. Performance of adults was not inﬂuenced by social condi- studies have decomposed adolescents’ choice behavior on objec-

tion, but both older and younger adolescents performed worse in tive expected values (Barkley-Levenson and Galván, 2015; van

the presence of a friend compared to an experimenter. Suggestively, Duijvenvoorde et al., 2015) and risk (van Duijvenvoorde et al.,

these first developmental findings may be consistent with findings 2015). The latter study found that activation in the insular and

in adults (Bond and Titus, 1983) that peer presence effects may help prefrontal cortex tracked trial-to-trial variations in risk, which

in relatively simple (inhibitory) tasks but hinder in complex tasks was exaggerated in adolescents compared to children and adults.

such as relational reasoning. These studies are examples of how such models may be used

An exciting new domain tests how performance in learning to decompose and test developmental changes in adolescents’

tasks may be inﬂuenced not only by the presence of others, but decision-making.

also by communication with others, such as communicated rules, Second, a widely used class of models focuses on learn-

advice, or other forms of explicit instructions. A recent study tested ing, and particularly how outcome evaluation inﬂuences our

this by using an abstract learning paradigm, in combination with a subsequent behavior and predictions (e.g., reinforcement-learning

false instruction of what would be the correct stimulus to choose. models). That is, when decision outcomes do not match expec-

Children and adolescents were less hampered by this instruction tations formed on the basis of previous experience (e.g. trials),

compared to adults, and were thus better able to learn from their they trigger a learning signal that is referred to as a prediction

own outcomes. Although not yet tested, the authors speculate that error. Rewards that are better than predicted generate positive

instructions may modulate reward-sensitive learning signals. For prediction errors and lead to response acquisition, whereas worse

instance, neural activation in striatum and the prefrontal cortex rewards generate negative prediction errors and lead to extinction

could be upregulated in adults by instruction, leading them to over- of behavior. When the prediction error becomes zero, no further

weigh this information, which would not yet be present in children learning occurs and the prediction remains stable. The extent to

and adolescents (Decker et al., 2015). which a prediction error alters subsequent subjective valuation

of choice options depends on one’s learning rate. High learning

rates give heavy weighting to recent outcome, whereas lower

6. Towards a model-based approach

learning rates lead to more integration over a longer feedback his-

tory.

The reviewed studies suggest an important link between neu-

Adolescents have shown heightened prediction error signals

ral reward-sensitivity and behavioral outcomes such as risk taking

compared to adults to both positive (Cohen et al., 2010) and

and cognitive control. These ﬁndings ﬁt well with neurocog-

negative outcomes (Hauser et al., 2015) in a reward network

nitive models, in which motivationally-salient events such as

including the striatum and insula. More recent studies suggest

rewards or social contexts may increase activation or connec-

that developmental differences in response to rewards may not

tivity of the affective-motivational system. However, typically

be due to changes in learning signals, but how these learn-

these models do not further specify the mechanisms underly-

ing signals drive subsequent behavior. That is, learning-rates

ing the observed change in neural responses, and predict how

were related to changes in functional connectivity between the

this would link to speciﬁc behavioral processes. Applying quan-

striatum and medial PFC (van den Bos et al., 2012). It has

titative, model-based analyses has proven to be highly useful

been suggested that these ﬁndings indicate a greater inﬂuence

in delineating more precise mechanisms underlying develop-

of positive and negative outcomes in adolescents’ motivated

mental shifts in learning and decision-making. Not only does

behavior that are dependent on the interaction between sub-

this help in theorizing and interpretation of the changes in

cortical and cortical neural systems (Hartley and Somerville,

reward processing across adolescence, it also moves away from

2015).

purely descriptive differences between age groups. For illustration,

Taken together, the use of such models in cognitive neuroscience

we describe two prominent model-based examples that spec-

moves imaging research away from testing what changes, towards

ify processes of value-based decision making and outcome-based

how these changes occur. Although it is important to critically

learning.

evaluate the use of computational models within and across dif-

First, expectation models are classes of models that focus on the

ferent age groups (e.g., Nassar and Frank, 2016), such approaches

process of decision-making, and what computations may under-

may have several advantages. First, as stated, they allow decom-

lie the evaluation of certain choice options (e.g., Pachur et al.,

posing behaviors into its underlying processes, thereby increasing

2013). A ﬁrst step in these models is the translation of objective

our understanding what drives adolescents behavior (e.g., van

choice attributes (probabilities, gain amounts, loss amounts) into

Duijvenvoorde et al., in press). Second, they may better explain

subjective representations. Then, after computing the subjective

adolescents’ behavior on task and context variations. Third, the pre-

value of available options, the option with the highest subjective

diction of individual differences will be increased if we can include

value is chosen. Applying such a model-based decomposition of

the underlying mechanisms of the observed processes (Pachur

decision parameters to adolescents’ motivated decision-making

et al., 2013). Based on the potential of such computational mod-

could help us to understand why we observe heightened risk

els we expect that these methods will be increasingly applied in

taking in some adolescents. Studies have started to use this

future developmental studies.

method in developmental studies, with promising results. For

1 2

and social maturity (see text for explanation). This study included an adult-age group of >45 years. These studies include a longitudinal extension of the original Braintime

sample, including ages up to 27 years. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

144 A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147

7. Strengths and weaknesses of neuroscience research 8. Conclusion

We started out by describing the long-standing interest in moti- In this review we aimed to integrate current ﬁndings on

vational changes in adolescence, and how neuroscience methods adolescents’ motivated behavior, and speciﬁcally adolescents’

provided us with new perspectives on long-debated questions. reward-sensitivity by using a brain-behavioral approach. The

What have we learned from neuroscience that may help to reﬁne reviewed studies generally support a heightened reward response

developmental theories on adolescent motivation? One of the great in adolescence, predominantly observed in key reward-regions

advantages of neuroscience studies is the level of detail of the mea- such as the ventral striatum and OFC, which most likely rely on an

surement tools. By examining structural and functional changes in interconnected network of subcortical and cortical reward-related

brain networks, it is possible to examine growth trajectories not regions, including the amygdala, insula and lateral PFC. Studying

only at the level of behavior, but also at the level of underlying subcortical and subcortical-cortical connectivity is an increasingly

neural architecture that supports behavior. As we highlighted, one used tool and has the potential to study the interactive develop-

exciting opportunity is to further integrate behavioral models and ment of these networks and the relation to motivated behavior.

neural measures. That is, models of decision-making and learning Findings indicate that adolescents’ reward-sensitivity may lead

can be combined with neuroscience measures to detect and inter- to increased sensation seeking, risk taking, and hindering effects

pret speciﬁc neural signals. As such, these levels of analyses provide on cognitive performance. In other contexts, however, heightened

new insights in high-stake questions, such as what drives sensation reward-sensitivity may boost cognitive control resources, and pro-

seeking and risk-taking in adolescence. mote instrumental or prosocial behavior (see also Telzer, 2016).

However, the progress in understanding adolescent changes in Thus, an important question for future research is which of these

motivation lies not only at the level of combining neuroscience and environmental contexts are more likely to trigger which behav-

behavioral methods. One of the ways neuroscience studies can be ioral outcome, and for which adolescents these inﬂuences are most

improved is by having a better conceptualization of what adoles- prominent. For instance, one could hypothesize that individual

cence is, and by making use of predictions based on developmental differences in sensitivity to environmental inﬂuences, such as a

theory. There is a surprising lack of consistency in the neuro- reactive temperament, may be particularly important at the onset

science literature in the selection of age ranges that are believed of adolescence during which affective-motivational changes cre-

to encompass adolescence (see Fig. 4), with many being unable to ate a window that ampliﬁes behavior that may lead to negative

test adolescent-speciﬁc developmental trajectories (e.g. including or positive developmental trajectories. Multiple encounters with

pre and post-adolescent ages). This may result in an inability to health-endangering behaviors, or negative peer-inﬂuences, may

test a transitional framework, focusing on changes in and out of propel some adolescents more easily towards risky behavior with

adolescence, and may result in inconsistent ﬁndings (e.g. Bjork and possible negative individual and societal outcomes.

Pardini, 2015). In addition, the focus has been solely on deﬁning A similar developmental trajectory might be proposed for pos-

(calendar) age with surprisingly little measurements of maturity. itive developmental outcomes. That is, if certain experiences, such

For example, Cohn and Westenberg (2004) suggested that age by as cooperation, sharing and helping, are experienced as relatively

itself is a poor proxy of development. These authors argue that more rewarding for some adolescents (adolescents with a reac-

deﬁning adolescence as stages of development and recognizing tive temperament), then multiple encounters of these experiences

individual differences in timing of transitions between these stages, may set the stage for a trajectory in which adolescents feel more

is a much more fruitful way of understanding adolescence. There committed to these prosocial goals also when developing into

is some progress in the neuroscience literature in terms of deﬁning adulthood. This hypothesis predicts that those adolescents who

biological maturity, with a focus on pubertal development in sev- show highest emotional reactivity to rewards in early adolescence

eral recent studies (Braams et al., 2015; Op de Macks et al., 2011; also show the largest beneﬁt of prosocial experiences (Crone and

Peper and Dahl, 2013; van Duijvenvoorde et al., 2014), which we Dahl, 2012).

also highlighted in the current review, but there is no emphasis yet The heightened reward-sensitivity in adolescence—and its

on deﬁning social maturity. Thus, this is a level of analysis where inﬂuence on motivated behavior—may be highly relevant for

neuroscience can beneﬁt from developmental approaches. research in clinical populations, such as children that have

A further step forward that is established in developmental behavioral problems, antisocial development or attention-deﬁcit

psychology, but not yet in neuroscience literature, is the value hyperactivity disorder (ADHD). A recent meta-analysis showed that

of longitudinal designs. These designs, with preferably more than reinforcement potentially normalizes inhibitory control in children

three time points when interested in U-shaped developmental pat- and adolescents with ADHD to baseline levels of healthy controls

terns (Crone and Elzinga, 2014), have the advantage of allowing for (Ma et al., 2016). Further research on the behavioral effects of

the ﬁt of growth models within individuals, and allow for a test- reward-sensitivity should therefore take into account individual

ing of changes over time (for recent examples, see Braams et al., differences and subgroups (e.g., Fair et al., 2012).

2015; Ordaz et al., 2013; Peters et al., 2016a). In addition, longitu- In this review, we demonstrated that advancements in cognitive

dinal designs allow for prediction models, which make it possible neuroscience methods allow for an additional level of explanation

to test whether one process can predict change in another process to understand motivational changes in adolescence. Nonetheless,

over time. For example, we recently showed that brain connectiv- further breakthroughs arise when research is more speciﬁc in terms

ity was predictive of changes in impulse control (Achterberg et al., of combining paradigms with behavioral models. Speciﬁcity is also

2016) and alcohol use (Peters et al., in press) two years later, while warranted in the deﬁnition of adolescence, including understand-

controlling for ﬁrst time point measurements. Another important ing transition periods in adolescence, such as the onset of puberty

beneﬁt of longitudinal methods is that it allows for a more detailed and the changes in social maturity. An integrative bio-psychological

test of when developmental goes astray. This is especially impor- perspective has great potential for bridging levels of explanation

tant in adolescence, given that most of the psychiatric disorders from basic neural measures to behavioral outcomes, to applications

related to mood and motivation (such as depression, social anxi- in the classroom and clinical interventions. The described trajecto-

ety and substance abuse) emerge for the ﬁrst time in adolescence ries propose adolescence as a crucial period for exploration and

(Giedd et al., 2008; Lee et al., 2014). social learning, and underline that this is a high-stake period of

interventions. Ultimately, these insights will lead to a better under-

A.C.K. van Duijvenvoorde et al. / Neuroscience and Biobehavioral Reviews 70 (2016) 135–147 145

standing towards the needs of youth and fostering opportunities for Crone, E.A., Dahl, R.E., 2012. Understanding adolescence as a period of

social-affective engagement and goal ﬂexibility. Nat. Rev. Neurosci. 13 (9), the new generation.

636–650, http://dx.doi.org/10.1038/nrn3313.

Crone, E.A., Elzinga, B., 2014. Changing brains: how longitudinal fMRI studies can

inform us about cognitive and social-affective growth trajectories. WIREs

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Crone, E.A., van Duijvenvoorde, A.C.K., Peper, J.S., 2016. Annual Research Review:

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Crone, E.A., 2009. Executive functions in adolescence: inferences from brain and

authors thank Sibel Altikulac¸ for her contributions to the ﬁgures.

behavior. Dev. Sci. 12 (6), 825–830.

The work presented here was based on an ERC Starting Grant (ERC

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