The Neural Coding of Feedback Learning across Child and Adolescent Development

Abstract

The ability to learn from environmental cues is an important contributor to successful performance in a variety of settings, including school. Despite the progress in unraveling the neural correlates of cognitive control in childhood and adolescence, relatively little is known about how these brain regions contribute to learning. In this study, 268 participants aged 8-25 years performed a rule-learning task with performance feedback in a 3T MRI scanner. We examined the development of the frontoparietal network during feedback learning by exploring contributions of age and pubertal development. The pFC showed more activation following negative compared with positive feedback with increasing age. In contrast, our data suggested that the parietal cortex demonstrated a shift from sensitivity to positive feedback in young children to negative feedback in adolescents and adults. These findings were interpreted in terms of separable contributions of the frontoparietal network in childhood to more integrated functions in adulthood. Puberty (testosterone, estradiol, and self-report) did not explain additional variance in neural activation patterns above age, suggesting that development of the frontoparietal network occurs relatively independently from hormonal development. This study presents novel insights into the development of learning, moving beyond a simple frontoparietal immaturity hypothesis.

Frontostriatal White Matter Integrity Predicts Development of Delay of Gratification: A Longitudinal Study

“…Consistent with the existing literature, we hypothesized that the ability to delay gratification improves with increasing age (Green et al, 1994;Olson et al, 2007; and that the integrity of frontostriatal WM matures with increasing age (Olson et al, 2009;Bava et al, 2010;Schmithorst and Yuan, 2010;Simmonds et al, 2014;Peper et al, 2015). The longitudinal design allowed us to test in more detail the shape of change .…”

“…Between childhood and adulthood, vigorous advancements in the ability to sustain goal-directed cognition in the face of immediate rewards are observed (Eigsti et al, 2006;Olson et al, 2007;. This ability to delay gratification can be captured in delay-discounting tasks, estimating an individual's preference for a smaller immediate reward over larger, delayed rewards.…”

“…Several studies showed that the striatum is more activated by decisions involving immediately available rewards, whereas prefrontal and parietal cortices are activated when individuals control the temptation to choose immediate rewards (McClure et al, 2004;Somerville et al, 2011;Peters and Buchel, 2011). These results led to the question of whether maturation of prefrontalstriatal white matter (WM) connections concurs with and predicts future-oriented choices across development.…”

“…The measurements most commonly derived from DTI are fractional anisotropy (FA), measuring the directional variation of diffusion, and mean diffusivity (MD), measuring the amount of diffusion (Basser and Pierpaoli, 1996). Several DTI studies revealed higher WM integrity across adolescence (Olson et al, 2009;Bava et al, 2010;Simmonds et al, 2014;Peper et al, 2015), although the shape of the trajectory is not yet well understood, some reporting linear and others nonlinear changes (for an overview, see Schmithorst and Yuan, 2010). Moreover, recent studies in adults (Peper et al, 2013) and adolescents (Van den Bos et al, 2015) reported an association between higher frontostriatal WM integrity and increased preference for delayed rewards.…”

Predicting reading and mathematics from neural activity for feedback learning.

“…In order to examine neural effects of feedback learning and its relation to reading and mathematics performance, region-of-interest (ROI) analyses were performed with the Marsbar toolbox in SPM8 (Brett, Anton, Valabregue, & Poline, 2002) based on earlier studies demonstrating that these areas show developmental changes for feedback learning (Crone, Zanolie, Van Leijenhorst, Westenberg, & Rombouts, 2008;Peters, Braams, et al, 2014;van Duijvenvoorde, Zanolie, Rombouts, Raijmakers, & Crone, 2008) and were also used in a prior study with the same experimental task (Peters, Braams, et al, 2014). The DLPFC ROIs, even after masking, were still very large (right: 28488 mm; left: 28240 mm), therefore, we created 6 mm radius spheres based on four local maxima within the DLPFC regions (two per hemisphere).…”