Findings from the current study

Findings from the current study should be interpreted in light of three core limitations. First, we did not detect significant threat bias using reaction time data from the dot-probe task. This limits our ability to interpret the present data as a function of individual patterns of attention bias. Perhaps having only 10 face models shown repeatedly throughout the study impedes significant findings and more trial-unique stimuli are needed. Several studies have found that histone methyltransferase the dot-probe paradigm failed to elicit a behavioral threat bias in behaviorally inhibited samples (Broeren and Muris, 2010; Cole et al., 2016; Morales et al., in press; Pérez-Edgar et al., 2011; White et al., 2016, in press). Further, traditional reaction time data measured by the dot-probe task may have poor internal reliability, poor test-retest reliability, and may not be associated with anxiety (Kappenman et al., 2014). However, using neural correlates, we did note individual differences in ERP modulations of attention processing. Other studies (e.g., Bar-Haim et al., 2005; Kappenman et al., 2015; Mueller et al., 2009; Sun et al., 2012) have also found that ERP measures can capture the chronometry of attention processes, despite the absence of behavioral effects. Similar patterns have been noted for fMRI studies of attention bias (Britton et al., 2012; Fu et al., in press; Hardee et al., 2013; Price et al., 2014). While behavioral response time reflects a global index of task performance, encompassing influences from cognitive, affective, and motor processes, modulation of specific ERP components may reflect more refined and specific stages of processing. Second, due to the nature of our cross-sectional design, we cannot make causal inferences. This snapshot in time cannot explain how these temperament-linked relations unfold over the course of development. The trajectory and development of BI into divergent outcomes – adaptive versus maladaptive – should be examined longitudinally to more fully understand the mechanisms of risk. For example, White et al. (2016, in press) showed that although children do not exhibit stable patterns of attention biases across development, BI predicted anxiety in children with concurrent attention bias to threat or attention bias away from happy faces.
Acknowledgements
Introduction The ability to learn from performance feedback is crucial to flexibly adapt to a changing environment. Behavioral performance during feedback learning shows a protracted development which continues into adolescence (Huizinga et al., 2006). Several studies have investigated the neural underpinnings of feedback processing. Studies in adults have shown that learning from feedback is associated with activity in a frontoparietal network, including dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), anterior cingulate cortex (ACC) and superior parietal cortex (SPC) (Carter and van Veen, 2007; Mars et al., 2005; Zanolie et al., 2008). Intriguingly, developmental neuroimaging studies have reported age-related activity changes in this network during feedback processing, suggesting an important link between feedback learning and neural maturation of the frontoparietal network (Crone et al., 2008; Peters et al., 2014a; Van Duijvenvoorde et al., 2008; Velanova et al., 2008). Despite these findings, little is known about developmental trajectories in the frontoparietal network and there is surprising little consistency in the direction of change, with some studies reporting increased neural activation with age and others decreased neural activation with age (Crone and Dahl, 2012). An important question in cognitive development concerns the shape of developmental trajectories. One possible hypothesis would be that activity in the frontoparietal network during feedback learning follows a linear trajectory, based on dual-systems models predicting steadily increasing frontoparietal recruitment from childhood to adulthood combined with an adolescent peak in socio-emotional sensitivity in subcortical systems (Ernst et al., 2006; Somerville and Casey, 2010; Steinberg, 2008). On the other hand, prior cross-sectional studies provided preliminary evidence for non-linear developmental patterns of frontoparietal activity during feedback learning (Peters et al., 2014a; Van den Bos et al., 2009; Van Duijvenvoorde et al., 2008). These findings indicated that young adolescents are capable of recruiting frontoparietal regions but in different situations than adults, arguing against a simple frontoparietal immaturity model with linear development in cognitive control regions.