Present simple and continuous: emergence of self-regulation and contextual sophistication in adolescent decision-making.

Neuropsychologia 65 (2014) 302–312

Contents lists available at ScienceDirect

Neuropsychologia journal homepage: www.elsevier.com/locate/neuropsychologia

Present simple and continuous: Emergence of self-regulation and contextual sophistication in adolescent decision-making Anastasia Christakou n Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading, RG6 6AL, United Kingdom

art ic l e i nf o

a b s t r a c t

Available online 16 September 2014

Sophisticated, intentional decision-making is a hallmark of mature, self-aware behaviour. Although neural, psychological, interpersonal, and socioeconomic elements that contribute to such adaptive, foresighted behaviour mature and/or change throughout the life-span, here we concentrate on relevant maturational processes that take place during adolescence, a period of disproportionate developmental opportunity and risk. A brief, eclectic overview is presented of recent evidence, new challenges, and current thinking on the fundamental mechanisms that mature throughout adolescence to support adaptive, self-controlled decision-making. This is followed by a proposal for the putative contribution of frontostriatal mechanisms to the moment-to-moment assembly of evaluative heuristics that mediate increased decision-making sophistication, promoting the maturation of self-regulated behaviour through adolescence and young adulthood. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Decision-making Adolescence Self-regulation Corticostriatal circuits

1. Introduction During adolescence, the individual transitions towards independent, self-governed behaviour. While children may rely on the surrogate decision checks and controls provided by parents or carers, adolescents accumulate the arsenal of cognitive capacity, self-knowledge, and world experience to orchestrate increasingly independent and individual behaviour and thought. However, the remarkable potential of adolescent cognitive development is marked by disproportionate increases in risky decisions, betraying poor self-control and lack of foresight (Steinberg, 2008). Significant changes affecting cognitive function, emotional reactivity, abstract thought, self-referential processing, and social functioning (such as peer orientation and conflict with parents) occur during adolescence. These changes facilitate the emergence of sophisticated thinking and reasoning during this period, but they also increase young people's vulnerability to problematic behaviours (such as drug and alcohol abuse, smoking, unprotected sex, eating disorders), as well as emotional and mood disturbances (such as anxiety and depression) (Casey, Jones, & Hare, 2008; Casey & Caudle, 2013; Ernst, Pine, & Hardin, 2006; Paus, Keshavan, & Giedd, 2008). Although most adolescents will transition to adulthood quite successfully, the challenges they face present an opportunity for understanding the emergence of sophisticated

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decision-making, in the service of intentional, self-controlled, adaptive behaviour (Casey, Duhoux, & Malter Cohen, 2010; Crone & Ridderinkhof, 2011). 1.1. Adaptive behaviour The importance of adaptive behaviour for mental and behavioural well-being is intuitively appreciable. Paradoxically however, defining such behaviour is challenging. As with any broad psychological concept that stems more from personal insight than from the observation of natural history, what we mean by adaptive behaviour can vary depending on our disciplinary perspective, or the level of analysis that our methodologies afford. Instead of trying to force multiple insights from different disciplines into one prescriptive definition, a broad description of adaptive behaviour is offered: behaviour that utilises past experience to flexibly resolve conflicts in the pursuit of multiple present and future goals. This description encompasses an important assumption, namely that the individual can remember themselves in the past, and imagine themselves in the future. The “self” that the individual remembers and imagines can be of a largely “undifferentiated” nature, meaning that it does not have to be explicitly autobiographical (Kwan et al., 2012) (although this may help (Suddendorf & Corballis, 2007)). What is transferred across timescales is a representation of instrumental effectiveness (how successfully prior goals were met in the past, and through what means) and transferability (how similar current/future goals are).

A. Christakou / Neuropsychologia 65 (2014) 302–312

Again, goals here can be simply the end-points of instrumental behaviour, not necessarily long-term, verbalised concepts of achievement. Nevertheless, the explicit consideration of goals as drivers of behaviour evokes a level of instrumental intentionality, beyond behavioural impulsivity or reactivity to the affective value or emotional content of stimuli. It further incorporates learning from experience, and actively forming context-appropriate behavioural goals based on that experience, an idea to which we return later. Finally, the current definition of adaptive behaviour includes a conflict between multiple goals. For instance, what the individual wants (or can have) now and what they need to achieve in order to look after their future self are typically co-activated non-overlapping representations (alas, we cannot shield ourselves from all instances of cake while trying to maintain a healthy diet). The adaptive resolution of this conflict is not always unidirectional (e.g. even given an overarching goal of maintaining a healthy diet, what is the adaptive value of refusing a piece of birthday cake at a party? This question does not have a single answer, and can lead – and has led – to oddly heated debate). To resolve conflict between goals, we rely on valuation and cognitive control mechanisms, that allow us to deploy the optimal balance between harnessing readily appreciable rewards (e.g. enjoying a piece of cake, or going to the pub), and working towards more complex or future goals (e.g. maintaining a healthy diet, or revising for an exam). The idea of a balance between orienting towards immediate and more distal goals is important: we typically equate the ability to delay gratification with selfcontrol, but for behaviour to be truly adaptive, our commitment to future goals has to be flexible. There are occasions when choosing the immediately rewarding alternative is the best (or not the worst) course of action. One such example is choosing the reward of joining the unavoidable house party, instead of the punishing experience of staying in one's room, trying to revise for the distant exam. In this case, choosing the nobler, distal goal would be ineffective, and could have disproportionately anxiogenic consequences – the individual will be unable to concentrate and become unproductive, leading to escalating negative interpretations of the world (as imposing the distraction) and the self (as incapable to perform). This trivial, but vivid example demonstrates a possible path from maladaptively balancing conflicting goals, towards a psychological state discordant with the individual's core selfconcept (“I am a diligent student”). Such discrepancies have the potential to expose individuals at risk of mental disorder to the expression of pathological interpretation biases (Higgins, 1987). Recent neuroimaging evidence is describing a link between subjective evaluation processes (in the ventral prefrontal cortex (PFC) and ventral striatum (Bartra, McGuire, & Kable, 2013)) and the impact of negative affect on the dysregulation of selfcontrolled behaviour. Importantly, self-referential processes are implicated in this relationship, such as the modulation of selfesteem by negative affect (Wagner, Boswell, Kelley, & Heatherton, 2012). Self-referential processes markedly increase in complexity after puberty (Labouvie-Vief, Chiodo, Goguen, Diehl, & Orwoll, 1995), and adolescence is associated with the emergence of selfconcept and disproportionately enhanced self-awareness compared to childhood and adulthood (Sebastian, Burnett, & Blakemore, 2008; Weil et al., 2013). Thus the involvement of self-referential processes in the modulation of self-control further emphasises the importance of the teenage years for the development of adaptive behaviour. Foresighted, adaptive behaviour relies on a number of key interdependent abilities: inhibitory control mechanisms (e.g. interrupting maladaptive behavioural programmes and re-allocating attention to more appropriate stimuli); regulation or suppression of emotional reactivity and of sensitivity to inappropriate incentives

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(e.g. looking after one's future self by foregoing the satisfaction of current needs, whether real or perceived); context-sensitive decision-making (e.g. sometimes we need to take chances on quick, possibly emotive, decisions, but sometimes we need to stop, calculate, and reason). 1.2. How does the brain change during the teenage years to support these abilities? Diffusion tensor imaging (DTI) studies, that measure changes in the microstructure of white matter, show linear increases in white-matter density and myelination across the brain well into adulthood, including evidence for sex differences in regional changes, and coupling to cognitive function development (Giorgio et al., 2008; Simmonds, Hallquist, Asato, & Luna, 2013). In contrast to white matter, grey matter development seems to follow an inverted U-shaped pattern of regional developmental changes. Grey matter density shows progressive and regressive changes in different time-courses for different brain regions. The latest changes are observed in the PFC, parietal cortex and superior temporal cortex. Grey matter density in the PFC peaks around puberty and declines thereafter (Gogtay & Thompson, 2010; Sowell et al., 1999; Sowell, Thompson, & Toga, 2004). This connectivity refinement during adolescence is believed to promote experience-driven specialisation and efficient inter-regional information exchange. Connectivity with subcortical regions is also shaped during adolescence. For instance, age-dependent increases in corticostriatal fibre myelination have been correlated with more efficient functional recruitment of cortico-striatal circuitry and increased inhibitory control (Liston et al., 2006). Subcortical structures also show structural changes during adolescence. Grey matter in striatal subdivisions generally decreases with age, and grey matter in the amygdala and hippocampus shows an inverted U-shape pattern (Wierenga et al., 2014), with sex differences observed in structural developmental patterns in the basal ganglia, hippocampus and amygdala (Dennison et al., 2013; Giedd, Raznahan, Mills, & Lenroot, 2012). For instance, in the post-pubertal basal ganglia, the putamen and pallidum appears to be larger in males with some evidence that the caudate nucleus is proportionately larger in females (Giedd et al., 2012). During adolescence amygdala volume increases significantly only in males, while hippocampal volume increases significantly only in females (Giedd et al., 1996). The connectivity of the dopaminergic midbrain nuclei also shifts during development. Ventral tegmental area (VTA) connectivity increases in adulthood, while substantia nigra (SN) connectivity is reduced. The VTA innervates the PFC, limbic regions, and the ventral striatum, and shows age-related increases in connectivity with limbic regions and with the default mode network. The SN innervates more dorsal (associative and motor) striatal subdivisions, and shows reduced connectivity with motor and limbic regions. This maturational pattern is thought to be consistent with observed functional changes during adolescence (Tomasi & Volkow, 2014). The precise timeline of the various developmental effects, and the extent to which they overlap, is currently not well understood. Very few studies have the power, in terms of a broad age-range and sufficient sample size, to address potential local nonlinearities in these developmental trajectories. There is evidence from developmental psychology of distinct or asynchronous developmental trajectories in different domains of function. For example fundamental cognitive abilities are thought to be in place by the end of childhood (often well before puberty), but emotional reactivity appears to exhibit an inverted U shape trajectory peaking in mid-adolescence (Blakemore & Robbins, 2012).

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However, the structural and functional neural underpinnings of these trajectories are not systematically addressed in cognitive neuroscience research (Crone & Ridderinkhof, 2011; Luna, Padmanabhan, & O’Hearn, 2010). Adolescent data from cognitive neuroscience appears to show continuing modulation of the neural substrates of cognitive processes, but it is progressively thought that the interaction of these processes with other systems, such as affective reactivity and socioemotional experience, is what matures, not cognitive ability per se. This issue is further illustrated in the following section.

2. Components of effectively self-regulated decision-making In the previous section, structural evidence suggested a composite developmental pattern of progressive large scale gains in inter-regional communication, and substantial re-modelling of limbic and motivational networks, accompanied by a protracted, experience-led re-organisation of grey matter in frontal brain regions. At the same time, both anecdotal and empirical evidence describe teenagers as characterised by increased risk-taking and sensation-seeking, reduced self-control, and ineffective emotional regulation (Steinberg, 2008, 2010). Despite criticisms of the strength with which such descriptions are propagated in the literature and the popular press (Arnett, 1999; Casey & Caudle, 2013; Pfeifer & Allen, 2012), epidemiological evidence further suggests that the onset of psychiatric disturbances (mood disorders, impulse control disorders, schizophrenia, substance abuse) peaks during adolescence (Paus et al., 2008). Although little empirical work has, to date, specifically co-examined structural and functional maturation (Pfeifer & Allen, 2012; Poldrack, 2010) (but see (Crone & Ridderinkhof, 2011; Liston et al., 2006; Peper et al., 2012)), these findings have led to an influential dual-systems model of adolescent neurocognitive development: executive control mechanisms are only slowly developing through adolescence, lagging behind a full arsenal of emotional responsiveness and motivational preparedness (Somerville & Casey, 2011; Steinberg, 2010; Van Leijenhorst et al., 2010). Does this account explain the emergence of adaptive, self-controlled decision-making during adolescence, and how is new evidence revising the dual-systems model? 2.1. Inhibitory control The development of inhibitory control processes attract substantial attention from developmental cognitive neuroscientists because of their potential to explain common impulse control problems that emerge and peak during adolescence, such as substance use and other risk-taking behaviours. For example, reduced activation during early adolescence in a broad network of areas typically associated with inhibitory control in a go/no-go task (including inferior frontal gyrus, motor and cingulate cortices, and striatum), predicts subsequent transition to heavy alcohol and other substance use (Norman et al., 2011). Aspects of inhibitory control, such as response restraint, seem to plateau by early adolescence, around the age of 12 years (Levin et al., 1991), while others, such as response cancellation, are thought to continue on an optimisation trajectory through to at least mid-adolescence (Sinopoli, Schachar, & Dennis, 2011). Discrete trajectories in the development of different inhibitory modes suggest that they are mediated by at least partly separable neural mechanisms and are reviewed in detail elsewhere (e.g. (Luna et al., 2010)). Maturing structural and functional corticostriatal connectivity in particular has been associated with increasing efficiency in recruitment of corticostriatal circuitry during response inhibition (as measured in go/no-go and stop-signal tasks) (Liston et al., 2006;

Rubia, Smith, Taylor, & Brammer, 2007). In a more recent study using a stop-signal task, the development of two forms of inhibitory control were compared: reactive inhibition (stopping a prepared response), and proactive inhibition (anticipation of stopping). Results indicated that although performance and brain activation were comparable for adolescents and adults during reactive inhibition, age effects in performance and corticostriatal functional connectivity were manifest during proactive inhibition. This suggests that inhibitory function per se may be already tuned by early adolescence, but its consistent deployment in relation to the associative structure of the environment (i.e. knowing when to stop) may be immature (Vink et al., 2014), and there may be significant gender effects in these mechanisms (Rubia et al., 2013). Recent theoretical and empirical progress in the study of intentional inhibition and its development are shedding new light in its neural and psychomotor organisation, as discussed in more detail elsewhere in this issue. Further evidence for a hierarchical, or non-uniform, pattern of development in these processes comes from a longitudinal fMRI study that examined executive and motor control, as well as error processing networks between the ages of 9 and 26. Specifically, motor control regions showed no age-related differences in activation, and executive control regions decreased in activation into adolescence and then stabilised. Importantly, motor controlrelated activation variability between participants decreased with age, suggesting that motor control processes emerge early in development and are shaped within a narrow operational margin by adulthood. The only mechanism that was associated with performance was error-related processing, with related activation in the cingulate cortex showing increases through adolescence and into adulthood (Ordaz, Foran, Velanova, & Luna, 2013). These findings are in line with relevant thinking in developmental psychology, proposing that cognitive functions differentiate during development (Garrett, 1946), affording a progressively wider and more nuanced arsenal of cognitive abilities to the individual. For example, a large sample aged 4 to 14 years of age were administered tasks that required either the maintenance of a rule in working memory, or the inhibition of a pre-potent response tendency. Using a latent factor modelling procedure (Little, Slegers, & Card, 2006), the study demonstrated that the two processes were not separable in younger children (before the age of 9.5 years), and only emerged as independent processes after puberty (Shing, Lindenberger, Diamond, Li, & Davidson, 2010). It is important to emphasise that the development of cognitive control – as is the case with all cognitive development – is of course a life-span, rather than an adolescent, process. The antecedents of perturbations in measured inhibitory and other control processes during adolescence (Mueller et al., 2010), and even adulthood (Elton et al., 2014), can be traced to early-life events. Similarly, chronic systemic insults, such as smoking or substance abuse, during adolescence may alter the ongoing development of the neural underpinnings of inhibitory control (Galván, Poldrack, Baker, McGlennen, & London, 2011). Even after considering the life-span complexity of developmental processes, it is clear from the evidence that progressive improvement in inhibitory control during adolescence is far from the whole story. For example, it was recently demonstrated, using a negative occasion setting task (Ross & Holland, 1981), that adolescent rats are able to learn when responses should be inhibited, but unable to express that inhibition before they transition to adulthood (Meyer & Bucci, 2014).

2.1.1. Executive function is modulated by affective state and context It is becoming increasingly clear that the hallmark of executive function in adolescence is its idiosyncratic sensitivity to the incentive and emotional context within which it is recruited.


For example, in a fast, event-related anti-saccade task, reward contingencies were manipulated on a trial by trial basis, and neural activation was measured separately during reward anticipation and during response inhibition using fMRI (Geier, Terwilliger, Teslovich, Velanova, & Luna, 2009). The prospect of reward improved the latency of correct inhibitions across the entire age-range of the study (13 to 30 years of age), but adolescents also showed speeded incorrect inhibitory responses under the rewarded condition. Nevertheless, adolescents additionally showed increased inhibition accuracy, a benefit of reward which was not seen in the adult group (although potential ceiling effects cannot be discounted). Compared to adults, adolescents showed an attenuated BOLD response in the ventral striatum during the cue period of rewarded trials, but an increased response in the same region during the response preparation period, as well as increased responses in premotor and oculomotor regions. The study suggests that although the basic inhibitory machinery is in place by adolescence, its recruitment in the presence of incentives may be cruder than in adulthood because of immature striatal processing, conceivably leading to the typically disinhibited behavioural patterns of teenagers. In addition to the presence or opportunity for reward, the more general incidental emotional context of behaviour seems to affect inhibitory control in early adolescence. When go/no-go targets are presented superimposed on top of emotional scenes (with positive, negative, or neutral content; from the International Affective Picture System, IAPS) there are distinct age effects on performance of the impact of the emotional valence of the irrelevant, distractor image. Children, adolescents and adults were all shown to slow down their responses during negative trials, but young adolescents also showed reduced accuracy, compared even to younger children, suggesting this age group become disproportionately sensitive or reactive to negative emotional contexts (Cohen-Gilbert & Thomas, 2013). In a simple but pertinent experimental design, a go/no-go response was determined by threat or safety cues in the emotional expression of a face stimulus superimposed in the task. Inhibitory accuracy (correct no-go trials) increased from early adolescence well into adulthood during both threat and safe conditions. Performance analysis was combined with examining the contribution of latent processes proposed to shape the executed decision (go or no-go) using a drift diffusion model. The study suggests that both general response tendencies (such as increased caution and reduced “go” bias) and stimulus-depended processing mechanisms (such as improved mapping of threat features to the no-go response) contribute to performance improvements with age (Cohen-Gilbert et al., 2014). Importantly, the asymmetric developmental effects of stimulus valence (safety versus threat) hint at a mechanism through which social information may acquire its prominent role in adolescent decision-making. Understanding the effect of incentive and emotional context on cognitive control during development is of particular importance in the study of developmental affective disorders, given significant evidence that anxious and depressed young people show marked interpretation biases to negative or threatening stimuli and atypical neural and behavioural responses to rewards and punishments (Hardin et al., 2009). 2.2. Emotional reactivity and regulation Various formulations of the dual-systems model have been particularly useful in guiding the study of the development of emotional reactivity and regulation in adolescence. Sex-dimorphic grey matter reorganisation plays a key role in emotion regulation abilities, including cognitive reappraisal (Vijayakumar et al., 2014). For example, increased volume in anterior cingulate (Giuliani,

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Drabant, & Gross, 2011) and amygdala (Hermann, Bieber, Keck, Vaitl, & Stark, 2014) have been associated with more effective recruitment of emotion regulation and cognitive reappraisal in adults, while during adolescence, cortical thinning in left lateral prefrontal cortex is associated with maturing emotion regulation in females but not in males (Vijayakumar et al., 2014). These findings suggest that although the efficiency of cortico-subcortical interactions, especially between the frontal cortex and amygdala, may underpin the adult instantiation of emotion regulation, more nuanced prefrontal mechanisms determine its developmental trajectory, possibly through the verbalisable generation and maintenance of regulatory goals and strategies (Ochsner & Gross, 2005). These findings also illustrate that, given substantial differences in the prevalence and onset of emotion dysregulation between the sexes, studies of sex differences in adolescence are pivotally important in understanding the development of emotion regulation and its pathology. The use of cognitive reappraisal as a regulatory mechanism is immature in adolescents but can be promoted by explicit reappraisal instruction (McRae et al., 2012). There is evidence, however, that the neural mechanisms that allow adults to use cognitive processes when handling emotional stimuli may not be mature in adolescents, who show reduced ventromedial PFC activation when processing previously cued emotional expressions (Barbalat, Bazargani, & Blakemore, 2013). Similarly, age-dependent functional activation changes in PFC regions correlate with trait emotional processing abilities (Perlman, Hein, & Stepp, 2014). Strengthening cortico-subcortical connectivity is also pervasive in evidence from this domain, as was seen in the development of inhibitory control processes above. In particular, maturation of the functional connectivity of the amygdala and hippocampus with prefrontal areas has been shown to underpin maturation of emotion processing (Passarotti, Sweeney, & Pavuluri, 2009; Vink, Derks, Hoogendam, Hillegers, & Kahn, 2014). An influential developmental study of emotion regulation showed increased amygdala activation in adolescents compared to both children and adults during an emotional go/no-go task. Critically, adolescents showed habituation of their amygdala response with repeated stimulus presentations to adult levels. Individuals who showed reduced habituation of amygdala reactivity were characterised by high trait anxiety and reduced functional connectivity between the PFC and the amygdala (Hare et al., 2008). Maturing cortico-striatal activation and increased connectivity is also critical in emotional processing in adolescence, for example with reference to social and inter-personal processes. Longitudinal data show that activation and connectivity in the ventromedial PFC, amygdala, and ventral striatum change with age when people view emotional face displays (Pfeifer et al., 2011). In a crosssectional sample, measures of self-conscious emotion and associated autonomic arousal increased disproportionately during adolescence when participants believed that they were viewed by a peer. These effects were associated with increasing functional connectivity between the medial PFC and the striatum (Somerville et al., 2013). A note of caution comes from recent evidence that demonstrates substantial variability over time in the activation of these networks, most notably in the amygdala, calling for caution in the interpretation of activation differences, especially in interventional or patient studies (van den Bulk et al., 2013). 2.3. Reward processing 2.3.1. Reactivity to reward – motivational preparedness Developmental neuroimaging studies typically describe an imbalance between over-reactive ventral striatal reward processing and immature prefrontal regulatory mechanisms (Barkley-Levenson

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& Galván, 2014; Fareri, Martin, & Delgado, 2008; Galvan et al., 2006; Somerville, Jones, & Casey, 2010; Van Leijenhorst et al., 2010). Inevitably, with accumulating evidence, the picture is becoming more complicated. Not unlike the case of inhibitory control, the measurement of reward processing during development is proving to be particularly sensitive to context and task design (an excellent overview of experimental paradigms used in the study of the development of reward processing in adolescence is presented in a recent, comprehensive systematic review (Richards, Plate, & Ernst, 2013)). An intriguing example of context-dependent modulation of reward processing is the effect of agency. The hypothesis is that, given reasonably mature components of cognitive control in adolescence, in decision-making situations – where the aim of behaviour is clear and unambiguous – adolescents would overtly recruit top-down processing, which would eliminate any effects of disproportionate bottom-up reactivity to reward compared to adults. In a cross-sectional fMRI study of reward anticipation and delivery, adolescent and adult participants were required to make equivalent responses with or without decision-making for large or small rewards. As predicted, there were no age differences in neural activation when decision-making was required for the receipt of reward. Moreover, in the absence of decision-making, age effects in neural activation were incompatible with a straightforward dual-systems interpretation, with adolescent anticipatory activation differentiating reward magnitudes in the insula and cingulate, and adult consumatory activation differentiating reward magnitudes in the precuneus (Jarcho et al., 2012). More recently, the first longitudinal study of striatal activation during reward and loss anticipation (Lamm et al., 2014) used the monetary incentive delay (MID) task (Knutson, Adams, Fong, & Hommer, 2001) in combination with fMRI, unravelling further complexity. The study re-examined the striatal activation of midadolescents (at approximately 16 years of age) when they were young adults (roughly 4 years later). The results showed that striatal activation in higher incentive trials was greater in the later time-point, suggesting that baseline striatal sensitivity to incentives is lower in adolescence compared to adulthood. These studies show that considering reward reactivity per se holds limited explanatory power for understanding adolescent motivated behaviour and adaptive decision-making. Consequently, the application of more specialised reward processing and evaluation paradigms and the consideration of reward learning processes have recently come to the fore, making substantial contributions.

2.3.2. Temporal discounting of reward The subjective value of reward and punishment is not a stable quantity, even in the brain of the same individual. Rewards that are delayed in time are typically valued less compared to immediately available alternatives, even of a lesser absolute value. Conversely, punishments imagined in the future are less effective at modulating behaviour than more imminent, more modest threats (although far less experimental work has addressed the temporal discounting of punishment) (Loughran, Paternoster, & Weiss, 2012). The relative ability to delay gratification by waiting longer for larger rewards (or resisting the subjective decay of rewards that are delayed) is considered a metric of self-control (Rachlin & Green, 1972). A useful experimental distillation of the temporal discounting phenomenon is a simple task where participants are required to choose between a variable, small amount of money available now, or a fixed, larger amount of money available after a delay (typically between a few days and a few months). The task enables the estimation of the individual's “discounting curve”, which plots the drop in subjective value of the fixed, delayed

amount as a function of increasing delay (Christakou, Brammer, & Rubia, 2011). The steepness of this curve correlates with trait measures of impulsivity, and maps onto impulse control symptomatology, such as in ADHD, alcohol misuse, nicotine dependence, etc., and its interaction with psychopathology such as depression (Demurie, Roeyers, Baeyens, & Sonuga-Barke, 2012; Fields, Sabet, Peal, & Reynolds, 2011; Imhoff, Harris, Weiser, & Reynolds, 2014; Reynolds & Fields, 2012; Stanger et al., 2012; Stanger et al., 2013). The steepness of discounting rewards as a function of delay decreases with age from early adolescence to mid-adulthood, and this improvement in delay of gratification was associated with maturation of connectivity between the ventromedial PFC and the ventral striatum (Christakou et al., 2011). Dopaminergic function in these regions is important in discounting (at least of hypothetical rewards). Differential effects of genetic polymorphisms of components of the dopaminergic system were demonstrated (Paloyelis, Asherson, Mehta, Faraone, & Kuntsi, 2010), in line with evidence that discounting behaviour as measured in adolescence has a heritable component (Anokhin, Golosheykin, Grant, & Heath, 2011). Intriguing data from animal studies suggest that dissociable genetic components may contribute to adolescent-specific, compared to stable, life-time aspects of impulsive decision-making as measured by temporal discounting paradigms (Pinkston & Lamb, 2011).

2.3.3. Learning about reinforcement A recent fruitful venture in understanding the development of value-based decision-making has been the consideration of learning about the value of decisions by reinforcement. Dissatisfied with the general account that adolescents may be hypersensitive to reward compared to adults, Cohen et al. (2010) designed a study to examine which aspect of reward processing differentiates the age groups . In an fMRI-compatible probabilistic choice task, the expected value of decisions and reward feedback were separated using a simple mathematical model of choice behaviour. In this way, the authors were able to examine whether what differentiates adolescent from adult reward processing activation is the representation of value during decisions, or the updating of that value during feedback. Reward feedback is thought to be represented in the brain in the form of a prediction error, a signal of the discrepancy between the reward that was expected based on prior experience and the reward actually received. Prediction errors then can be positive, if the decision outcome is better than expected, or negative, if it is worse than expected (note that positive prediction errors are not the same as rewards, and negative prediction errors are the same as punishments). In the Cohen et al. study, the neural representation of the value of the chosen stimulus appeared to diminish with age in medial PFC. Conversely, the strength of the representation of prediction errors in the striatum peaked during adolescence, but was encoded in a more dorsal striatal region to the one that represented prediction errors in adult participants. In a similar study, van den Bos and colleagues tested children, adolescents and adults in a probabilistic task in the fMRI scanner. They also used a reinforcement learning model to examine nuances in the performance and neural activation of the different age groups. In contrast to the Cohen et al. study, their data did not show any age differences in the neural representation of prediction errors. However, they describe significant shifts in the strength of functional connectivity between the medial PFC and the ventral striatum, which were associated with developmental improvements in utilising negative feedback to guide future behaviour (Van den Bos, Cohen, Kahnt & Crone, 2012). We recently examined a similar question using an fMRI variant of the Iowa Gambling task (IGT) (Christakou, Brammer, Giampietro, &


Rubia, 2009). The IGT is a singularly influential decision-making paradigm (Bechara, Damasio, Damasio, & Anderson, 1994), designed to mimic real-life decision-making by mixing positive and negative outcomes of varying likelihood as consequences of choosing between decks of cards. Two of the four decks of the task are disadvantageous, leading to higher immediate wins but long-term losses, and two decks are advantageous, leading to lower immediate wins but long-term gains. We and others have previously shown that successful IGT performance relies on early negative feedback forming a heuristic representation of negative expectancy for the disadvantageous decks (Christakou et al., 2009). In our developmental study (Christakou et al., 2013) we used a reinforcement learning model to examine the development of IGT performance, which was shown to improve significantly during adolescence, stabilizing in adulthood. We provided further evidence that task performance relies more on negative feedback, here in the form of prediction errors. We further showed that the relative balance of positive and negative prediction errors changed with age: a shift from undifferentiated impact on behaviour to greater impact of negative prediction errors accompanied age-depended performance improvements. The correlation between the neural representation of prediction errors and improved performance strengthened with age in ventral and dorsal PFC, ventral striatum, and temporal and parietal cortices. Importantly, there was a qualitative difference between adolescents and adults in the association between successful performance, utilisation of negative prediction errors and their neural representation. Increased utilisation of negative prediction errors was associated with increased activation in ventromedial PFC in adults, but with decreased activation in ventrolateral PFC and striatum in adolescents (Christakou et al., 2013). These results are in line with animal studies that suggest that adolescent rats may be more resistant to degradation of reinforcement contingencies, responding more than adults during extinction of a reinforced behaviour (i.e. through negative prediction errors) (Andrzejewski et al., 2011), despite learning the initial instrumental response just as readily (through positive prediction errors) (Sturman, Mandell, & Moghaddam, 2010). The importance of understanding learning by reinforcement in adolescent decision-making lies in its potential to explain real-life behaviours observed in changing or ambiguous environments, such as the ones that young people are increasingly faced with. A recent study of peer approval implemented a reinforcement learning model to positive social feedback and negative social feedback (although this was operationalised as the absence of positive social feedback) (Jones et al., 2014). The study demonstrated that the vmPFC and ventral striatum represent social feedback prediction errors, just as is the case with conventional rewards. It also showed that adolescents were equally likely to incorporate social feedback into guiding their behaviour regardless of its magnitude, in contrast to children and adults who showed scaling of their learning rate with social feedback magnitude. This finding suggests that adolescents may become uniquely responsive to social cues of peer acceptance compared to children and adults, through reduced sensitivity to the scale of positive social feedback. Another example where reinforcement learning can be fruitfully applied is in understanding the nuances of long-term effects on decision-making of adolescent drug and alcohol abuse, given recent evidence from animal studies that increased risk-taking behaviour in adulthood was causally linked to alcohol-induced reinforcement learning imbalance in adolescent rats (Clark et al., 2012). Crucially, this data helps to explain the way in which chronic alcohol exposure during adolescence may lead to altered choice behaviour promoting risk-taking in adulthood (a key contributing factor to the persistence of substance abuse problems later in life), given that differences in general reward responsivity

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between individuals with or without adolescent alcohol exposure failed previously to account for persistent effects in adulthood (Nasrallah et al., 2011).

3. Maturation of context-sensitive goal realignment In Section 2, the distinct impact of experimental context on developmentally differentiated behaviour and brain activity was highlighted. An uncomfortable observation is that, at least through studies of self-controlled decision-making, adolescent behaviour is “pathologised by analogy” to impulsive behaviours (although clearly not intentionally (Strang, Chein, & Steinberg, 2013)), given evidence that reward-related behaviours and impulsivity exhibit different developmental trajectories (Steinberg, 2010). Nevertheless, such generalisations do not do justice to the intricate subtlety of neurodevelopmental processes that shape the transition to adulthood. Recent calls for moving the developmental cognitive neuroscience of decision-making beyond retro-fitting experimental evidence to dual-systems accounts (Casey & Caudle, 2013; Payne, 2011), have proposed that two avenues can be explored to further clarify the picture: the application of a stricter functionalneuroanatomical framework (Pfeifer & Allen, 2012; Poldrack, 2010), and a stronger link to the rich propositions and evidence base of developmental theory (Crone & Ridderinkhof, 2011). 3.1. Functional neuroanatomy considerations One problematic assumption in the neurobiological study of human decision-making that is perpetuated in the literature, is the presumed or implied dissociation of function between prefrontal cortical areas and striatal subdivisions, with the PFC purportedly exerting cognitive control, and the striatum responding to the value of stimuli in the environment. This dissociation fits comfortably in the dual-systems perspective of adolescent development, but it is a gross generalisation and has the potential to mislead the interpretation of experimental findings. Increasingly, experimental work takes a network view of corticostriatal development (Christakou et al., 2011; Liston et al., 2006; van den Bos et al., 2012), acknowledging the fact that the PFC and striatum work in intimate coordination, especially in novel or changing environments (Christakou, Robbins, & Everitt, 2004; Dalley, Everitt, & Robbins, 2011; Everitt & Robbins, 2005). In fact, there are suggestions that the PFC forms representations (including representations of subjective value) after the striatum has computed and passed on the relevant operational parameters (Laubach, 2005). A similar convention in developmental neuroscience is reference to the ventral striatum as a key reward processing area, and the automatic assumption that reward-processing differences between age groups will implicate the ventral striatum exclusively. However, the topology of processing hedonic and informational qualities of reward is not as straightforward, and may be differentiated principally based on neurochemistry, as opposed to topology. Importantly, this topology is certainly beyond the stretch of anatomically coarse typical fMRI analyses. By contrast, exciting evidence from animal studies demonstrates that adults and adolescents do differ in reward processing in the striatum, but in a way that was unanticipated by human neuroimaging studies. Specifically, by recording single unit activity and local field potentials in behaving rats, it was shown that ventral striatal responses to reward did not differ between adults and adolescents. Instead, it was dorsal striatal responses that distinguished between the age groups: adolescent rats showed reward-related activity in the dorsal striatum, when adults did not (Sturman & Moghaddam, 2012). These findings suggest that adolescentspecific behaviours are mediated by the informational, associative

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components of reward processing, not simply by reactivity to the hedonic or subjective value of rewards. Importantly, these differences were observed in the absence of any age effects on behaviour, further suggesting a qualitative shift in the network recruited to perform the same task. A functional shift in striatal functioning is in line with recent evidence for maturing striatal connectivity with the midbrain dopaminergic nuclei during adolescence (Tomasi & Volkow, 2014). Such results help move us beyond localisation models of neuroanatomical maturational processes, and highlight the importance of applying a systems approach to their study. 3.2. How can cognitive theory enrich the neuroscience of decision-making development? Cognitive psychology proposes that a fast, automatic, and unconscious information processing mode (system 1) competes with a slow, effortful, conscious mode (system 2) for dominance in adjudicating over competing courses of action or thought (Evans, 2003). This dual-process scheme has been generally applied to the study of reasoning, social cognition, and judgement and decisionmaking (Evans, 2008; Reyna, 2012). It obviously also draws a striking parallel with the dual-systems models of neurocognitive adolescent development discussed above, roughly mapping emotional and incentive reactivity to System 1, and cognitive/inhibitory control to System 2. Drawing parallels between the two models would anticipate the emergence of a decision-making functional acme in the adult form: adult neurocognitive architecture is complete, and (therefore) adults should be able to make normative and composed decisions based on reason, aligning their subjective evaluation of options with the optimal available alternative. However, many cognitive dual-process theories assume that both thinking modes are available and functional in adulthood, but change in their relative dominance or readiness of deployment depending on the context (Furlan, Agnoli, & Reyna, 2013; Reyna & Farley, 2006). The definition of decision optimality in the normative framework is insufficient to characterise the forces that shape the behaviour of real-world biological organisms. In the real world we need to deal with uncertainty and incomplete information in order to solve decision-making tasks of the simplest nature, let alone ones that rely on the formation of subjective preference (akin, if not identical, to the induction of a belief). An elegant phylogenetic solution to such profound difficulties faced by decision-making agents, has been the emergence of heuristics and biases (Tversky & Kahneman, 1974). Heuristics and biases allow individuals to package thought and action patterns into readily deployable schemata, without the need for timeconsuming verbatim computations of the value and utility of stimuli in situations that have been encountered in the past. Typically discussed as variably amusing or catastrophic failures of rationality in adult judgement and decision-making, in fact the formation of heuristics and biases is a particularly effective evolutionary solution to increasingly complicated informational environments and elaborate social structures (Cosmides & Tooby, 1996). Importantly, increases in normative reasoning during development are observed in parallel with increases in the relative influence of heuristics and biases on decision-making (Reyna & Farley, 2006; Reyna, 2012). Although most fundamental classes of heuristics and biases are in place by the end of childhood, some continue their developmental trajectory during adolescence, and have been suggested to account for adolescent-specific decisionmaking styles (Reyna, 2012). An attractive example comes from the application of “fuzzy-trace” theory (Reyna & Brainerd, 1995) to understanding risky decision-making during adolescence (Reyna &

Farley, 2006; Reyna, 2012; Reyna et al., 2011). According to this dual information-processing account, individuals can use verbatim or heuristic (“gist”-like) representations of the world to make decisions. Maturing decision-makers move from reliance on more precise, verbatim calculations towards “gist” (or “fuzzy”) thinking about risk and reward. This explains some risky behaviours in adolescents, because, according to fuzzy-trace theory, the benefits of risky behaviour are often high whereas the risks are often low, producing a “rational calculus of risk promotion” in decisionmakers who rely on verbatim analysis (e.g. when weighing up the potential harmful effect of smoking a single cigarette and its potential desirable effect on peer acceptance) (Reyna et al., 2011). By contrast, “gist” thinking allows the more mature decision-maker to activate an overriding gist representation of decision quality (e.g. “smoking is bad for you”). This line of thinking can be extended to other, less straight-forward types of choice behaviour; to return to the example of the student who is conflicted between joining a house party and preparing for a distant exam, activation of an “if you cannot beat them, join them” heuristic, would provide a low-cost shortcut to a non-trivial adaptive solution. The net effect is the realignment of the individual's behavioural goals, not based on the verbatim calculation of their utility, but nevertheless effortlessly incorporating variables such as their feasibility and appropriateness given the context. It is important to note that considering gist and verbatim thinking explains unique variance in adolescent risky decisionmaking, beyond reward sensitivity and inhibitory control (Reyna et al., 2011), illustrating the potential of such an approach to make a significant contribution to developmental neuroscience studies of decision-making. The proposal then is that, beyond the characteristics outlined in Section 2, adolescent decision makers pursue goals that are represented or constructed differently compared to adults, even on the basis of identical information. In contrived experimental situations, we expect all participants to be solving the same problem when they perform our laboratory tasks. This is not necessarily so. Participants may respond in a value preference task (e.g. a temporal discounting task), not by explicitly calculating the expected value of options, but by a priori thresholding their tolerance of the alternatives (e.g. “I will not wait for more than a month for anything less than d100”), which would form a heuristic basis for decisions that might appear reasoned and self-controlled, but emerge via an intuitive, non-deliberative process. Importantly, animals and humans have been proposed to use heuristic rules or biases (such as savouring or dread) to tackle inter-temporal choice problems (Bateson & Kacelnik, 1996; Loewenstein, 1987). 3.3. What brain substrates could realign behavioural goals? The goal-realignment concept suggests that part of the maturational process that we observe in adolescent decision-making is the emerging ability to package experience in heuristic constructs which are used as abstract goals and behavioural anchors (e.g. “smoking is bad”). This leads to the prediction that there are situations when adolescents may be able to extract the same associative micro-structure from their environment, but are unable, slow, or inefficient to use it to form non-explicit, latent, or subjective goals. At a neurobiological level, the challenge is to identify the fundamental building blocks of creating the representational packages needed for such heuristic goal-setting to emerge, and to understand how they interact with accumulating experience during development. Using a variant of the Iowa gambling task (IGT) we have observed a qualitative shift in the way participants of different ages improve their performance and neural representation of


prediction errors. Adults relied more strongly on negative prediction errors to solve the task, and those with high fidelity of prediction error representation in the ventromedial PFC were better performers. Conversely, adolescents did not differentiate between the utility of positive and negative prediction errors to solve the task, and those who showed high fidelity of prediction error representation in the ventrolateral PFC and the ventral striatum were poorer performers (Christakou et al., 2013). We and others have shown that successful performance in the IGT and related tasks relies on tracking negative outcomes (Christakou et al., 2009; van den Bos et al., 2012; Wheeler & Fellows, 2008; Wood, Busemeyer, Koling, Cox, & Davis, 2005). This is thought to be the behavioural manifestation of the emergence of “somatic markers” (Levin, Eisenberg, & Benton, 1991) or heuristic representations (Dunn, Dalgleish, & Lawrence, 2006) of the quality of available choices. The ventromedial PFC represents the relative value of stimuli or actions, taking into account the motivational state and current context of the individual. By contrast, the ventrolateral PFC has been suggested to specialise in credit assignment (Rushworth, Noonan, Boorman, Walton, & Behrens, 2011; Walton, Behrens, Buckley, Rudebeck, & Rushworth, 2010), a fundamental aspect of associative learning, which, lacks contextual sophistication, and may be more akin to the verbatim valuation concept of cognitive theory. As we hypothesised previously, developmental data in our IGT variant suggested that adolescents may as yet lack the benefit of the sophisticated contextualisation of reward information provided by the ventromedial PFC. Relying instead on ventrolateral PFC to assign outcome quality to events and on phylogenetically older limbic regions such as the subgenual ACC and VS to provide motivational information may prove counterproductive in situations where immediate rewards need to be discounted in favour of longerterm motivational goals. A maturational shift between prefrontal processes could be subserved by the shift in dopaminergic innervation of the cortex and striatum by the VTA and SN during development (Tomasi & Volkow, 2014) as described previously in Section 1.2. The decreasing influence of the SN may reduce the effectiveness of reward and other salient information to more directly recruit behavioural output via the associative and motor striatum and the motor cortices. Conversely, the increasing connectivity of the VTA could marshal the intervention of evaluative systems, packaging incentives into context-appropriate behavioural goals before they are allowed to modulate behaviour. It is likely (and testable) that the maturing interactions of this network are responsible for the transition to the proposed emergence of context-sensitive, qualitative, “gist”-like valuations of decision alternatives. For example, future work in this area could use implicit probabilistic categorisation tasks in combination with region-of-interest structural and functional neuroimaging in a longitudinal design, to map the functional transition between corticostriatal circuitry and the proposed emergent transition towards qualitative information processing. The above analysis was built primarily on evidence from trialand-error learning processes, implying that heuristic goal re-alignment is a fundamental process that operates at a low, non-verbal operational level. How would such a fundamental mechanism be projected to the high-level, real world decisionmaking problems of the examples used above, such as risky decision-making or the balance between current needs and future aspirations? At the apex of the developing neurocognitive arsenal of adolescents towards intentional, self-regulated behaviour are metacognitive abilities, such as counterfactual thinking (Baird & Fugelsang, 2004) and implementation intentions (Gollwitzer, 1993). A recent study into the neural mechanisms of pre-commitment as a

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mediator of enhanced self-control showed that when participants were allowed to pre-commit to a larger, delayed reward, their ability to resist the temptation of a smaller, immediate reward was enhanced (Crockett et al., 2013). The lateral frontopolar cortex (LFPC) was involved in pre-commiting to the larger-delayed option, in a manner that suggests it is involved in signalling the opportunity and deciding on the utility of forming such a forward intention. This is in line with current thinking on the role of the frontopolar cortex in adjudicating between courses of actions based on prospective working-memory estimates of the potential future value of each option (Boorman, Behrens, Woolrich, & Rushworth, 2009; Boorman, Behrens, & Rushworth, 2011; Charron & Koechlin, 2010; Daw, O’Doherty, Dayan, Seymour, & Dolan, 2006). Further, evidence for the structural maturation of grey matter in frontopolar cortex (i.e. a linear decrease in cortical thickness with age) during childhood and adolescence (O’Donnell, Noseworthy, Levine, & Dennis, 2005) suggests that it may buttress maturation of prospective valuation processes during this developmental time-frame. Importantly, the Crockett et al. study provides some evidence that the effectiveness of LFPC processes to overcome an individual’s impulsivity during pre-commitment is mediated by ventromedial PFC, in line with a candidate “goal re-alignment” mechanism that recruits the ventromedial PFC/ventral striatal subjective valuation system, which so profoundly matures in adolescent decision-making (Christakou et al., 2013, 2011).

4. Further considerations Despite their central role in adolescent development, social factors that shape decision-making tendencies in young people have not been addressed in this paper. However, the impact of social influences on the maturation of neural substrates of goalrealignment is likely to be profound (Furlan et al., 2013; Reyna & Farley, 2006), and their study warrants the increasing attention it is receiving from developmental cognitive neuroscience. Likewise, significant sex differences, which rarely are the focus of investigation because of the extra burden on resources, are key to understanding this developmental period. Most of the neurobiological drivers of adolescent development are triggered or tightly linked to puberty, and most psychopathological phenotypes are strongly sex-dependent, both in nature and in prevalence. Another important issue that has only been fleetingly referred to in this paper is that the developmental trajectories of subprocesses, both neural and psychological, that emerge during adolescence may be asynchronous, which complicates their interaction in producing higher-level phenotypes such as adaptive goal selection across this age range. This is an issue that deserves attention given its potential to advance our understanding of selfregulation and its dysfunction. Longitudinal studies have a significant contribution to make in this regard. Finally, the parallels between implementation intentions and the concept of goal-realignment highlight the importance of understanding the emergence of the neural mechanisms that support goal-realignment during adolescence. Implementation intentions have outperformed other forms of intervention in promoting health-related and reducing risky behaviours in both adolescents and adults (Conner & Higgins, 2010; Gollwitzer & Sheeran, 2006; Higgins & Conner, 2003). Understanding the fundamental neural mechanisms that allow individuals to “re-examine” their behavioural goals can lead to improved intervention and prevention strategies, both for pathological states as well as for the promotion of healthy behaviours across the life span.

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