Reversal learning strategy in adolescence is associated with prefrontal cortex activation

Abstract

Adolescence is a critical maturation period for human cognitive control and executive function. In this study, a large sample of adolescents (n = 85) performed a reversal learning task during functional magnetic resonance imaging. We analyzed behavioral data using a reinforcement learning model to provide individually fitted parameters and imaging data with regard to reward prediction errors (PE). Following a model-based approach, we formed two groups depending on whether individuals tended to update expectations predominantly for the chosen stimulus or also for the unchosen one. These groups significantly differed in their problem behavior score obtained using the child behavior checklist (CBCL) and in a measure of their developmental stage. Imaging results showed that dorsolateral striatal areas covaried with PE. Participants who relied less on learning based on task structure showed less prefrontal activation compared with participants who relied more on task structure. An exploratory analysis revealed that PE-related activity was associated with pubertal development in prefrontal areas, insula and anterior cingulate. These findings support the hypothesis that the prefrontal cortex is implicated in mediating flexible goal-directed behavioral control.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa

“…The caudate and nucleus accumbens are known to respond to salient stimuli (28) and encode prediction error signals during stimulus reward learning, which may suggest altered dopamine functioning in anorexia nervosa (29). The insula contains the primary taste cortex and integrates body perception signals, but it also contributes to cognitive control and tracks error (30–32). Insula prediction error signaling has been associated with flexible behavior control, and heightened response could alter reversal learning in adolescent anorexia nervosa.…”

Distinction of self-produced touch and social touch at cortical and spinal cord levels

“…To account for movement associated variance, realignment parameters were included as regressors-of-no-interest. Because this paradigm might be prone to more movement artifacts, we also included the first temporal derivative of motion parameters in x , y , z directions plus additional regressor censoring scans with more than 1-mm scan-to-scan movement (85). In addition, we compared movement parameters between conditions and found no significant difference [ F (12, 146) = 0.756, P = 0.69].…”

Reinforcement Learning and Bayesian Inference Provide Complementary Models for the Unique Advantage of Adolescents in Stochastic Reversal

“…Such model-independent analyses have led to many interesting insights, but have been unable to test hypotheses about specific cognitive processes that are at work while subjects perform the task. In an effort to better understand these processes, more recent studies have started to employ computational modeling, most often in the RL framework (e.g., Boehme et al, 2017; Chase et al, 2010; Gläscher et al, 2009; Hauser et al, 2015; Javadi et al, 2014; Metha et al, 2020; Peterson et al, 2009).…”