Executive functions (EFs) include a number of higher-level cognitive control abilities, such as cognitive flexibility, inhibition, and working memory, which are instrumental in supporting action control and the flexible adaptation changing environments. These control functions are supported by the prefrontal cortex and therefore develop rapidly across childhood and mature well into late adolescence. Given that executive control is a strong predictor for various life outcomes, such as academic achievement, socioeconomic status, and physical health, numerous training interventions have been designed to improve executive functioning across the lifespan, many of them targeting children and adolescents. Despite the increasing popularity of these trainings, their results are neither robust nor consistent, and the transferability of training-induced performance improvements to untrained tasks seems to be limited. In this review, we provide a selective overview of the developmental literature on process-based cognitive interventions by discussing (1) the concept and the development of EFs and their neural underpinnings, (2) the effects of different types of executive control training in normally developing children and adolescents, (3) individual differences in training-related performance gains as well as (4) the potential of cognitive training interventions for the application in clinical and educational contexts. Based on recent findings, we consider how transfer of process-based executive control trainings may be supported and how interventions may be tailored to the needs of specific age groups or populations.

No abstract

Cognitive control requires choosing contextual information to update into working memory (input gating), maintaining it there (maintenance) stable against distraction, and then choosing which subset of maintained information to use in guiding action (output gating). Recent work has raised the possibility that the development of rule-guided behavior, in the transition from childhood to adolescence, is linked specifically to changes in the gating components of working memory (Amso, Haas, McShane, & Badre, 2014). Given the importance of effective rule-guided behavior for decision making in this developmental transition, we used hierarchical rule tasks to probe the precise developmental dynamics of working memory gating. This mechanistic precision informs ongoing efforts to train cognitive control and working memory operations across typical and atypical development. The results of Experiment 1 verified that the development of rule-guided behavior is uniquely linked to increasing hierarchical complexity but not to increasing maintenance demands across 1st, 2nd, and 3rd order rule tasks. Experiment 2 then investigated whether this developmental trajectory in rule-guided behavior is best explained by change in input gating or output gating. Further, as input versus output gating also tend to correlate with a more proactive versus reactive control strategy in these tasks, we assessed developmental change in the degree to which these two processes were deployed efficiently given the task. Experiment 2 shows that the developmental change observed in Experiment 1 and in Amso et al. (2014) is likely a result of increased efficacy of output gating processes, as well as greater strategic efficiency in that adolescents opt for this costly process less often than children.

Accumulating evidence suggests that individual differences in punishment and reward sensitivity are associated with functional alterations in neural systems underlying error and feedback processing. In particular, individuals highly sensitive to punishment have been found to be characterized by larger mediofrontal error signals as reflected in the error negativity/error-related negativity (Ne/ERN) and the feedback-related negativity (FRN). By contrast, reward sensitivity has been shown to relate to the error positivity (Pe). Given that Ne/ERN, FRN, and Pe have been functionally linked to flexible behavioral adaptation, the aim of the present research was to examine how these electrophysiological reflections of error and feedback processing vary as a function of punishment and reward sensitivity during reinforcement learning. We applied a probabilistic learning task that involved three different conditions of feedback validity (100%, 80%, and 50%). In contrast to prior studies using response competition tasks, we did not find reliable correlations between punishment sensitivity and the Ne/ERN. Instead, higher punishment sensitivity predicted larger FRN amplitudes, irrespective of feedback validity. Moreover, higher reward sensitivity was associated with a larger Pe. However, only reward sensitivity was related to better overall learning performance and higher post-error accuracy, whereas highly punishment sensitive participants showed impaired learning performance, suggesting that larger negative feedback-related error signals were not beneficial for learning or even reflected maladaptive information processing in these individuals. Thus, although our findings indicate that individual differences in reward and punishment sensitivity are related to electrophysiological correlates of error and feedback processing, we found less evidence for influences of these personality characteristics on the relation between performance monitoring and feedback-based learning.

This study examined how self-relevant failure influences error monitoring-as reflected in the errorrelated negativity (Ne/ERN) -and behavioral adaptation during subsequent feedback-based learning. We applied two phases (pre-and posttest) of a probabilistic learning task. Between pre-and posttest, participants were assigned to one of two groups receiving either failure feedback or no feedback during a visual search task described as diagnostic of intellectual abilities. To disentangle the effects of failure and motivational disengagement due to prolonged task performance, we linked the posttest to intelligence (Experiment 1) or described it in neutral terms (Experiment 2). Failure induction was associated with an increase in Ne/ ERN amplitude at posttest in both experiments, although there were no differences in overall performance. In contrast, the Ne/ERN decreased from pre-to posttest in the no-failure-feedback group, particularly in Experiment 2. Furthermore, failure feedback affected error-related behavioral adjustments, suggesting a shift toward a reactive, error-driven mode of behavior control. These findings emphasize the importance of affective-motivational state in error processing and subsequent behavioral adaptation.

The prospect of improving or maintaining cognitive functioning has provoked a steadily increasing number of cognitive training interventions over the last years, especially for clinical and elderly populations. However, there are discrepancies between the findings of the studies. One of the reasons behind these heterogeneous findings is that there are vast inter-individual differences in how people benefit from the training and in the extent that training-related gains are transferred to other untrained tasks and domains. In this paper, we address the value of incorporating neural measures to cognitive training studies in order to fully understand the mechanisms leading to inter-individual differences in training gains and their generalizability to other tasks. Our perspective is that it is necessary to collect multimodal neural measures in the pre- and post-training phase, which can enable us to understand the factors contributing to successful training outcomes. More importantly, this understanding can enable us to predict who will benefit from different types of interventions, thereby allowing the development of individually tailored intervention programs.

No abstract

Executive functions are higher level cognitive control functions supporting the flexible adaptation to changing environments. They include abilities such as updating, shifting, and inhibition, all of which are subject to significant age‐related changes across the adult lifespan. These age differences are associated with age‐related changes in the neural substrate supporting executive processes. Recent research on cognitive aging has shown that the brain is plastic up to very old age and that executive control can be improved by intensive cognitive and physical training in adulthood. This entry outlines the concept of executive functions, their neural correlates, and their plasticity across the adult lifespan.