Due to the limitations of scanning young children, the majority of our knowledge about the neural bases for the development of EF comes from studies examining electrical event-related potentials (ERPs) recorded during EF tasks. One component of interest, the P300, is a stimulus-locked component thought to be generated from frontal and temporal-parietal regions, and to be involved in updating working memory and inhibition (Polich, 2007). The P300 is seen approximately 300 ms post-stimulus in adults, but is delayed to 800–1200 ms post-stimulus in children (Tucker, 1993), suggesting increasing efficiency of EF networks with age.

A classic P300 paradigm is an oddball task, in which participants respond to a rare target among many distracters, but it is well established that the P300 is elicited by EF tasks as well. For example, in a flanker task, the P300 has higher amplitude after incongruent vs. congruent trials, suggesting this component might reflect inhibition of extraneous stimulus processing (Tucker, 1993). The P300 has also been found in the context of a hot EF task in 8-year-old children (Carlson et al., 2009). On a child version of the Iowa Gambling Task, the P300 had a higher amplitude after punishment than after reward trials, and the amplitude difference between loss and reward trials predicted children's performance on the task: Those who showed a more pronounced P300 response to losses vs. rewards learned to avoid disadvantageous and high-frequency punishment choices to a greater extent over the course of the task. In this case, the P300 served as a neural signature of focusing attention on a stimulus that provides important information about whether something should be approached or avoided. In the context of this task, greater sensitivity to punishment led to more avoidance of bad plays, and thus, better performance.

Another component of interest is the stimulus-preceding negativity, or SPN. This component occurs after a response has been made and just before feedback occurs. The SPN has recently been measured in children and seems to occur in the context of reward-based tasks. For example, Stavropoulos and Carver (2013) reported an SPN in 6- to 8-year-old children during a reward-based guessing game. They found larger SPN amplitudes for rewards that were accompanied by a smiling face than those accompanied by a scrambled face, suggesting that social stimuli were perceived as more salient. Although the Stavropoulos and Carver (2013) study involved no punishment, when negative feedback does occur, the SPN tends to be larger prior to receiving negative than positive feedback. This pattern has been found in school-age children for both a probabilistic learning task (Groen et al., 2007) and a gambling task (Carlson et al., 2009). Because research indicates that people are generally more sensitive to punishment than reward (Vaish et al., 2008), these results suggest that the SPN may reflect the emotional salience of an anticipated stimulus. Indeed, the SPN appears to be generated by the insular cortex and may reflect dopaminergic activity there (Bocker et al., 1994). As with the P300, the SPN may also be a neural signature of learning from feedback (Carlson et al., 2009). Given the probable link between SPN and risk and reward-processing, this component could be a particularly informative neural signature to examine prior to adolescence. Children who are more sensitive to anticipated punishment than anticipated reward as reflected by the SPN might show better adaptive outcomes in adolescence.

Adolescence is a time of significant cortical reorganization, potentially even a sensitive period during which current developmental trajectories can be reinforced or re-directed. Many of the neurobehavioral changes that take place during adolescence may be influenced by changes in hormone levels associated with puberty (Steinberg, 2005). At the same time, there is a dramatic change in the context in which teenagers function. Moving from elementary school to middle and high school involves adapting to new peer groups, increased academic expectations, and increased exposure to high-risk activities. Given the high level of flux in the brain, body, and environment, it is unsurprising that behavior problems often emerge for the first time in adolescence.

Adolescents are more likely than older and younger individuals to engage in risky behavior such as the use of illegal drugs (Substance Abuse and Mental Health Services Administration, 2007) and engagement in unsafe sex (Finer and Henshaw, 2006). However, adolescents do not appear to evaluate the risks or consequences of behavior differently than adults in hypothetical situations (e.g., Beyth-Marom et al., 1993), suggesting that the PFC functions adequately in “cool” contexts that are not emotionally salient. Rather, behavioral differences in adolescence are more marked by the “heat of the moment” when a risky decision is made.

Developmental models characterize human adolescence as a period of increased risk-taking due to immature EF/PFC development, which is not yet up to the challenge of coping with more active reward-processing circuitry (i.e., ventral striatum) (Galvan et al., 2006; Ernst et al., 2009; Steinberg, 2010). It is noteworthy that substantial reorganization of the neural systems underlying EF takes place during adolescence. In the PFC, gray matter reaches peak thickness in early adolescence, and is pruned during the next few years (Paus, 2005). In addition, connectivity between limbic and pre-frontal brain regions increases substantially during adolescence (Eluvathingal et al., 2007). The implications of these changes are that by the end of adolescence, the PFC operates more efficiently and there is greater coordination between pre-frontal and limbic systems.

Behavioral and neuroimaging research suggests that increased risk-taking by adolescents may stem from a greater sensitivity to potential rewards than in other age groups. For example, in affective decision-making tasks such as the Iowa Gambling Task, adolescents are more approach-oriented than are pre-adolescents or adults. One study found that although both adults and adolescents played increasingly more from advantageous decks over the course of the task, only adults decreased their plays from disadvantageous decks (Cauffman et al., 2010). Furthermore, in an fMRI study, Galvan et al. (2006) found that adolescents activated the reward-sensitive nucleus accumbens more than children or young adults during a reward-processing task, and activated the OFC, which is thought to play a regulatory role in risk and reward processing, less than adults. Thus, appetitive reward-sensitive systems may mature earlier in adolescence than regulatory systems, possibly contributing to the observed increase in risky behavior (Ernst et al., 2009).

Despite the reported increase in risky behavior among adolescents, substantial individual differences exist. Better EF skills could be a protective factor that reduces risk-taking behavior in adolescents. Research shows that childhood EF, particularly hot EF, predicts a variety of outcomes in adolescence. Seminal work by Mischel et al. (1989; reviewed in Zayas et al., 2014) re-evaluated high-school students who had completed the delay of gratification task during preschool, and found that individuals who refrained from eating a desirable treat and waited 15 min for a larger reward scored significantly higher on their SATs than those who did not wait, independent of IQ assessed at age 4. In addition, parents rated the adolescents who had delayed as preschoolers higher in social cognitive skills and emotional coping. In further follow-up studies, delay of gratification at age 4 predicted more efficient EF (Eigsti et al., 2006) and less interference on a social-reward version of a go-nogo task at the behavior and neural (fMRI) levels (Casey et al., 2011). Other research has found longitudinal relations between preschool delay of gratification performance and physical health. Children who had settled for a lesser reward at age 4 were 30% more likely to be overweight at age 11 (Seeyave et al., 2009). Thus, the ability to delay gratification in childhood appears to reflect individual differences that influence the development of many aspects of adaptive behavior.

In contrast to the delay of gratification task, there is less longitudinal evidence linking performance on hot affective decision making tasks with later EF and life outcomes. Nonetheless, extant work suggests that affective decision-making tasks could prove useful in evaluating propensities toward risk-taking, particularly in adolescence. Adolescents both engage in more sensation seeking behavior than younger children and adults and make more decisions based on reward rather than punishment feedback (Cauffman et al., 2010; Albert and Steinberg, 2011). These patterns might derive from the same mechanisms, such as dopaminergic activity in the brain (e.g., Ernst et al., 2009). Such mechanisms presumably operate earlier in development as well, in which case it may be possible to assess the risk for future sensation seeking behavior by examining behavioral and neural sensitivity to reward and punishment at earlier ages.

Unlike delay tasks, gambling tasks tap into risk as well as reward processing and may involve larger cognitive demands (Hongwanishkul et al., 2005). Research suggests that the ability to optimize one's gambling strategy develops sometime after the emergence of initial cool EF skills (Hooper et al., 2004; Prencipe et al., 2011). Based on simplified versions of the Iowa Gambling Task, young children seem unable to optimize long-term outcomes, responding only to immediate losses or gains. Not until sometime between middle childhood and early adolescence do children begin to integrate the frequency of gains and losses with long-term consequences (Huizenga et al., 2007; Carlson et al., 2009). Research also suggests that from childhood to adolescence, individuals become more physiologically sensitive to the anticipation of gains and losses, showing larger skin conductance responses before choosing frequent loss doors at age 16–18 than at age 10–14 (Crone and van der Molen, 2007). Although children may perform as well as adults at these ages, they still show some differential brain activity; for example, 9–12 year-old children have been shown to activate the anterior cingulate cortex (ACC), which is involved in error monitoring, more than adults on high-risk trials. This finding suggests that the task may be more effortful for them (Van Leijenhorst et al., 2006).

In addition to performance indices, individual differences in neural responses during affective decision-making tasks have also been found to predict later behavioral outcomes. In an ERP study of adolescent monozygotic twins, the P300 effect (amplitude of the P300 in loss vs. gain trials) predicted later alcohol abuse: In each twin pair, one individual began to abuse alcohol in adulthood, and these individuals tended to have had lower amplitude P300 responses to loss trials in early adolescence (Carlson et al., 1999). This study suggests that a blunted P300 effect could be an endophenotype for later high-risk behavior. In the current study, one goal was to extend this finding to examine the extent to which neural responses during affective decision making predict less extreme forms of sensation seeking in a low-risk sample. A related goal, given the literature linking preschool EF to life success, was to assess the extent to which both hot and cool EF predict other adaptive outcomes, such as academic performance and social skills.

The overarching goal of our research was to characterize individual differences in hot and cool EF that might lead individuals to divergent pathways in adolescence. We re-contacted a cohort of typically-developing children who had been assessed at age 8 on both cool EF measures and a relatively hot EF measure (gambling task) when they were 12 years old and entering adolescence. This longitudinal study had two specific aims: (i) to assess the stability of individual differences in cool EF from middle childhood to early adolescence, an age period that has not yet been the focus of longitudinal research on EF, and (ii) to examine the degree to which cool EF, as well as affective decision-making and its neural correlates at age 8 years (middle childhood), predicted adaptive behavior (academic performance, social skills, and sensation seeking) at age 12 years. We chose Flanker and DCCS tasks to examine cool EF, and a child-friendly gambling task to examine hot EF/affective decision-making, as these are the most paradigmatic and well-supported tasks in the literature to measure these constructs. We hypothesized there would be long-term stability of individual differences in EF. With respect to adaptive behavior, we hypothesized that better performance on an affective decision-making task and/or neural correlates of sensitivity to reward and punishment would predict higher academic achievement and social adjustment and lower sensation-seeking in pre-adolescence. This is the first study, to our knowledge, to examine longitudinal correlates of both cool EF and a hot affective decision-making task in this age group.

We examined descriptive statistics for the Flanker and DCCS at age 8 and 12 (Table 1). At age 8, reaction times for incongruent/non-dominant trials were negatively correlated with accuracy, indicating that children slowed down to perform well on the tasks at this age. Therefore, we used percent accuracy on these more difficult trials as predictors of future EF and adaptive behavior. For age 12, accuracy scores reached ceiling, so we used a composite of accuracy and RT using the NIH toolbox algorithm.

We examined whether verbal ability (assessed at age 8 using the PPVT-4) was correlated with EF performance and questionnaire scores. Verbal ability was not significantly correlated with any variables of interest (r's = −0.002 to 0.25). However, it was marginally correlated with academic performance (r = 0.25, p < 0.09), so we controlled for verbal ability when examining correlations with academic performance.

In addition, we examined gender differences for each variable. Girls obtained significantly higher scores on the DCCS [F_{(1, 65)} = 3.06, p < 0.01] and self-reported better social skills [F_{(1, 75)} = 6.5, p < 0.02] at age 12. No other significant gender differences were found.

To examine the stability of EF, we included only the 67 children who completed at least one EF task at both time points. At age 8, Flanker and DCCS performance were measured using accuracy on incongruent and non-dominant (color) trials, respectively. Accuracies for incongruent/non-dominant trials on the two tasks were not significantly correlated at this age [r₍₆₆₎ = 0.20, p = 0.1], although this is likely due to a ceiling effect on Flanker task accuracy at age 8. Reaction times were significantly correlated across the two tasks [r₍₆₆₎ = 0.29, p < 0.02]. RTs were positively correlated with accuracy [Flanker: r₍₆₆₎ = 0.27, p < 0.03; DCCS: r₍₆₆₎ = 0.41, p = 0.001], indicating that children at this age slowed down to achieve better performance, whereas at later ages, faster RTs indicate greater efficiency.

At age 12, accuracy on these tasks reached ceiling levels, so performance was measured using an algorithm from the NIH toolbox (Zelazo et al., 2013) that combined accuracy and reaction time (in which participants receive a higher score for responding quickly after full accuracy is reached). This algorithm computes a score from 0 to 10 (sample range = 5–8.67). At age 12, performance on the DCCS and Flanker were uncorrelated, r₍₆₇₎ = 0.015. As shown in Table 2, age 8 Flanker accuracy predicts age 12 Flanker, but not DCCS performance, while age 8 DCCS accuracy predicts age 12 DCCS, but not Flanker performance. RTs at age 8 were not significantly correlated with performance at age 12 (ps > 0.08). These results indicate longitudinal stability within but not across each EF task.

We examined links between our EF measures and self- and teacher-reported social skills, thrill/adventure seeking (self-report only), and academic competence (teacher report only) at age 12. DCCS performance was positively correlated with self-reported social skills, r₍₆₄₎ = 0.29, p = 0.02, but negatively correlated with academic competence r₍₄₂₎ = −0.32, p = 0.04 (Table 2). Because there was an effect of gender on DCCS performance at age 12, we performed a partial correlation controlling for gender, which did not affect the magnitude of these correlations. Flanker performance was not significantly correlated with any outcome variables.

We tested the degree to which accuracy on EF tasks at age 8 predicts adaptive behavior at age 12. Flanker incongruent trial accuracy significantly predicted teacher-rated academic competence, but no other outcome variables. This correlation remained significant when controlling for verbal ability and age 12 Flanker performance (see Table 2). DCCS non-dominant trial accuracy did not predict any outcome variables.

Next, data were analyzed to assess the degree to which performance on and neural correlates of a risky decision-making task at age 8 (Hungry Donkey) predicted individual differences in variables of interest at age 12. To measure performance, we examined the total number of trials in which a net loss was incurred. Two neural correlates were of interest, stemming from our previous findings (Carlson et al., 2009): (i) the magnitude of the post-stimulus P300 and (ii) the pre-stimulus/anticipatory SPN components in response to reward and loss trials.

The P300 was of significantly larger magnitude in response to loss vs. reward trials, [F_{(1, 78)} = 31.2, p < 0.001] and the SPN was significantly larger (more negative-going) after high-frequency loss door selections than low-frequency loss door selections [F_{(1, 78)} = 6.51, p < 0.02]. However, our primary question was whether individual differences in the magnitude of P300 to loss trials and SPN to high-frequency loss door selections predicted adaptive outcomes at age 12. Individual differences in P300 effect (magnitude to loss-minus-reward trials) was negatively correlated with self-reported thrill/adventure seeking. In other words, the larger the neural response to punishment (vs. reward) outcomes at age 8, the less likely participants were to report an interest in thrill/adventure 4 years later in pre-adolescence. The P300 effect was not significantly correlated with other outcome variables.

In addition, individual differences in SPN magnitude to low-minus-high-frequency loss doors, in which more positive values reflect pre-outcome anticipation of loss, significantly predicted academic competence at age 12, even when controlling for verbal ability. This finding suggests that a neural correlate of risk-aversion at age 8 was related to higher academic competence at age 12, but not to other outcome variables. The P300 and SPN components did not predict EF performance at age 12, and the loss count on the HDT task did not predict any outcomes at age 12 (see Table 2 for summary).

To our knowledge, this is the first study to demonstrate longitudinal stability of performance on specific EF tasks in this age range from middle childhood to adolescence. Our findings add to a growing body of evidence suggesting that individual differences in EF remain stable beyond the preschool period (Eigsti et al., 2006; Casey et al., 2011). In our sample, the inhibition (ANT/Flanker) and shifting/updating (DCCS) aspects of EF showed stability across the age range tested: Individual differences in ANT performance at age 8 predicted Flanker performance at age 12, and DCCS performance at age 8 predicted DCCS performance at age 12. However, performance on these two tasks were uncorrelated with each other in this age range. Given that different dimensions of cool EF tend to be strongly correlated in the preschool years (e.g., Wiebe et al., 2011), our findings suggest that dimensions of cool EF may become more differentiated over time, which is compatible with prior cross-sectional work reporting separability of working memory updating and inhibitory control beginning around age 9–10 (Shing et al., 2010). Taken together, these results support an idea of increasing specialization of circuits within the PFC for specific cognitive functions across development (e.g., Zelazo and Carlson, 2012).

A noteworthy aspect of our longitudinal design is that we documented that inhibitory control (ANT) at age 8 predicted teacher-rated academic competence at age 12. This finding fits with other work linking EF and later academic achievement in a variety of age groups (Blair and Razza, 2007; Best et al., 2011). Links between early EF and later academic performance make sense, given that the abilities to inhibit prepotent responses and to ignore distractions are necessary to develop the self-control necessary to be attentive in class and to study or do homework instead of engaging in other activities. The fact that this finding was independent of verbal ability (PPVT) lends further support to the emerging belief that EF matters for later academic achievement over and above, and perhaps more than IQ (for review, see Duckworth and Carlson, 2013).

Neural responses during the child-friendly gambling task (a hot EF task) at age 8 predicted a variety of adaptive behaviors at age 12. Greater sensitivity to loss trials and high-frequency loss doors, as indexed by the magnitude of P300 and SPN difference scores, predicted lower propensity for thrill/adventure seeking and better academic outcomes, respectively. Interestingly, avoidance of losses during the task did not correlate with later outcomes, suggesting that our ERP measures were more sensitive than behavior. Although children showed sensitivity to rewards and punishments at a neural level, they may not have been fully able to translate that sensitivity into improved performance during the course of the task. However, our results suggest that greater sensitivity to punishment vs. reward may play an important role in the development of children's trajectories toward more cautious/conscientious vs. higher risk-taking behavioral patterns.

Children who had shown attenuated P300 amplitudes after loss trials relative to reward trials reported more desire to engage in risky behaviors in early adolescence. This finding fits well with results linking reduced P300 amplitude to risky behaviors such as alcohol use in adolescence (Carlson et al., 1999; McGue et al., 2001). Although ours was a low-risk sample that reported low levels of externalizing behaviors in general, these findings add to evidence that the P300 could be a psychophysiological marker linked to propensity for risk-taking and/or sensation seeking. These findings also suggest that children who devoted more attentional resources to loss trials (reflected in higher P300 amplitudes) may tend toward more risk-averse behavioral trajectories.

While the post-feedback P300 at 8 years old was associated with adolescent thrill/adventure seeking, it is interesting that pre-feedback SPN at 8 years old predicted academic outcomes in adolescence. Specifically, we found that greater magnitude of SPN responses to high-frequency loss doors predicted greater academic success. The SPN is believed to index a “somatic marker” (Damasio, 1996) involving cortical and subcortical activity related to the expectation for relevant positive or negative feedback (Brunia et al., 2011). For example, it is enhanced under conditions in which outcomes are linked to actions vs. occurring at random (Masaki et al., 2010), suggesting that a sense of control during a task is necessary to elicit the SPN. In our study, SPN responses tended to be larger just before high-probability loss outcomes than low-probability loss outcomes. On the surface, our findings might seem to contradict those of Stavropoulos and Carver (2013), who found that anticipation of more socially rewarding vs. less rewarding feedback elicited a larger SPN in children. However, both findings make sense if SPN is a marker of the salience or motivational relevance of stimuli. While the former study did not involve punishment, ours did, so children were likely motivated primarily to avoid losses. In the current study, children who showed a larger SPN in the moment just after making a risky selection and just before a loss outcome was revealed might be more sensitive to the fact that they made a non-optimal response and therefore expect to receive negative feedback. In other words, they had a “feeling” or intuition detectable at a neural level that they were about to suffer a loss on the next trial. The present study suggests that perhaps they felt they had agency to avoid future losses and were better able to learn from mistakes in general, which may in turn facilitate learning from pedagogical instruction and success in school.

This interpretation about the SPN is speculative, in part because the interpretation and study of SPN is just beginning to be applied in children (e.g., Stavropoulos and Carver, 2013). However, a cross-sectional study recently showed that activity in insular cortex, believed to be a primary generator of the SPN, increased between age 5 and adulthood during a gambling task, corresponding to an age-related increase in risk aversion (Paulsen et al., 2011). In addition, emerging evidence links the SPN to the dopaminergic learning systems believed to underlie the error-related negativity component (ERN) in adults (Moris et al., 2013). The ERN varies in magnitude according to the difference between expected and received feedback and has been better characterized developmentally than the SPN. Examining both of these components in development and linking them to laboratory and real-world behavior would yield rich information about how neural sensitivity to reward and punishment relates to learning and adaptation.

This research has limitations and suggests future directions. Although we found that cool EF and ERP components during a hot EF task predicted some individual differences 4 years later, we found no evidence to support other predicted longitudinal relations. Behavioral performance on the HDT did not predict later outcomes, suggesting that on this task, ERP components were a more sensitive measure of individual differences in sensitivity to reward and punishment. However, these neural measures did not predict later social skills, which were related (for self-report) only to concurrent DCCS performance. Surprisingly, at the same time, age 12 DCCS performance was negatively correlated with teacher-rated academic performance. Self-rated social skills and teacher-rated academic competence were not significantly correlated, which is not surprising at this age in a low-risk sample. Possibly, children in our sample who are more successful academically were more conscientious in general, and this negatively affected their score on the DCCS, where slowing down in order to be accurate would result in a cost. We also found no evidence that cool EF at either time point predicted thrill/adventure seeking, but this null finding fits with developmental literature showing evidence of a dissociation between impulsivity and sensation seeking (Steinberg et al., 2008).

Another limitations is that our sample was relatively homogeneous in terms of ethnic and socioeconomic background. Children were generally low-risk and not (yet) endorsing many of the substance use and social risk-taking behaviors on the SSSC. We are continuing to follow this cohort through adolescence when some of these items will become more sensitive. Nonetheless, there were sufficient individual differences in academic competence and thrill/adventure seeking (e.g., enjoyment of riding one's bike fast down a steep hill) to detect longitudinal predictions from 4 years earlier. As well, we did not use the same versions of the EF tasks at age 8 and 12 in this longitudinal sample because the abbreviated NIH Toolbox versions were not available at Time 1 and developed in the interim. Nevertheless, the within-task stability of the Flanker and DCCS was significant. Finally, given our relatively small sample size, correction for multiple comparisons would have reduced some findings to non-significance, but we note that we were selective in our comparisons and had a priori hypotheses regarding each of them.

Despite these limitations, this is the first longitudinal study, to our knowledge, to examine the development of both hot and cool EF and their relations to adaptive behavior between middle childhood and pre-adolescence. We found that individual differences in performance on EF tasks were stable across this age range and that certain aspects of cool and hot EF predicted individual differences in thrill/adventure seeking and academic outcomes at age 12.

These novel findings generate many potential directions for future research, especially regarding adolescent brain development and the prediction of individual differences in adaptive behavior. Hot and cool EF appear to interact in complex ways that change across development and these aspects of EF may relate to behavior differently in childhood, adolescence, and adulthood. For example, hot EF may become increasingly important relative to cool EF in adolescence, when individuals begin to take greater control of their environment and make more decisions for themselves. In addition, adolescents show more sensitivity to social facilitation from peers than children or adults in the context of a risky decision-making task (Gardner and Steinberg, 2005). Therefore, we might expect peers to play a larger role in either facilitating or hindering adaptive behavior in adolescence than in other age groups. With this in mind, both social understanding and EF may be key to the successful navigation through adolescence. Future research could also more deeply explore the relations between EF and risk aversion, as opposed to risk-taking. Such work could have implications for anxiety disorders, which may be linked to maladaptive levels of risk aversion (Robin and Martin, 2010) and are especially prevalent in the teenage years. Exploration of the role of hot EF in development, particularly at a neural level, is a new area that holds great promise for deepening our understanding of human brain-behavior relations, and we expect that future studies will yield information with high applicability for developmental theory, educational practice, and clinical science.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.