A methodological perspective on learning in the developing brain

Reversal learning is influenced by cognitive flexibility and develops throughout early adolescence

Learning behavioural responses and adapting them based on feedback is crucial from a young age, continuing to develop into young adulthood. This study examines the development trajectory and contributing factors from childhood to adulthood using a reversal learning paradigm. We tested 202 participants aged 10 to 22 in an online study, where they learned and reversed stimulus-outcome associations in a new blocked design paradigm and were assessed for working memory capacity. Results showed that reversal learning performance improved with age, particularly for 10- to 14-year-olds. Flexible responses to negative feedback correlated with better reversal learning. Additionally, pubertal development and working memory were positively associated with reversal learning. These findings align with previous research, highlighting flexible feedback responses as a key factor in reversal learning. As the overall rate of flexible reactions did not change with age, it could support reversal learning independent of age, potentially changing its role during development.

Interpretation of individual differences in computational neuroscience using a latent input approach

Computational neuroscience offers a valuable opportunity to understand the neural mechanisms underlying behavior. However, interpreting individual differences in these mechanisms, such as developmental differences, is less straightforward. We illustrate this challenge through studies that examine individual differences in reinforcement learning. In these studies, a computational model generates an individual-specific prediction error regressor to model activity in a brain region of interest. Individual differences in the resulting regression weight are typically interpreted as individual differences in neural coding. We first demonstrate that the absence of individual differences in neural coding is not problematic, as such differences are already captured in the individual specific regressor. We then review that the presence of individual differences is typically interpreted as individual differences in the use of brain resources. However, through simulations, we illustrate that these differences could also stem from other factors such as the standardization of the prediction error, individual differences in brain networks outside the region of interest, individual differences in the duration of the prediction error response, individual differences in outcome valuation, and in overlooked individual differences in computational model parameters or the type of computational model. To clarify these interpretations, we provide several recommendations. In this manner we aim to advance the understanding and interpretation of individual differences in computational neuroscience.

Risks and rewards in adolescent decision-making

Adolescent decision-making has been characterized as risky, and a heightened reward sensitivity may be one of the aspects contributing to riskier choice-behavior. Previous studies have targeted reward-sensitivity in adolescence and the neurobiological mechanisms of reward processing in the adolescent brain. In recent examples, researchers aim to disentangle the contributions of risk- and reward-sensitivity to adolescent risk-taking. Here, we discuss recent findings of adolescent's risk preferences and the associated neural mechanisms. We highlight potential frameworks that target individual differences in risk preferences in an effort to understand adolescent risk-taking, and with an ultimate goal of leveraging undesirable levels of risk taking.