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Reward-specific learning parameters change across normative adolescent development and are blunted in youth with high
Holly Sullivan-Toole1,2, Jeremy M Haynes1,3, Helen Schmidt1
1Psychology and Neuroscience Department, Temple University, Philadelphia, PA, USA.
Background:
Reward learning is thought to undergo refinement in adolescence, but little is known about how computational components of reinforcement learning develop. Given that adolescence is a sensitive period for reward system plasticity with associated vulnerability for depression, it is important to understand developmental trajectories of different reinforcement learning parameters in normative development and in youth at risk for depression.
Methods:
Youth aged 9-17 years completed the Play-or-Pass Iowa Gambling Task (PoP-IGT) across five timepoints. We calculated task metrics using a traditional scoring approach - yielding summary scores for good deck play, bad deck play, and net play - and a computational modeling approach - yielding parameters for reward learning rate, punishment learning rate, go bias, and sensitivity to win/loss frequency ignoring outcome magnitude. We examined normative developmental trajectories for each traditional and computational performance metric using multilevel models. Further, we examined whether maternal history of depression was associated with individual differences in these trajectories.
Results:
As hypothesized, youth showed a significant age-related increase in net play (p = 0.003), a measure of overall good performance. Exploratory analyses found that youth showed significant developmental change in reward-specific learning parameters including age-related increases in win/loss frequency sensitivity (FDR = 0.016) and age-related decreases in reward learning rate (FDR < 0.001). In line with hypotheses, youth at high risk for depression showed lower reward learning rates in early adolescence (p = 0.041).
Conclusions:
The observed developmental changes in traditional and computational metrics are largely consistent with the optimization of learning from rewards across adolescence. Further, the observed developmental changes in specifically reward-related computational parameters are consistent with heightened adolescent reward system plasticity. Additionally, there was support for our hypothesis that maternal history of depression may exert a unique effect on learning from rewards specifically, but further research across additional reward learning tasks is needed.
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