n reinforcement learning (RL) experiments, participantslearn to associate stimuli with rewarding responses. RLmodels capture such learning by estimating stimulus-responsevalues. But what is a response? RL algorithms can model anyresponse type, whether it is a basic motor action (e.g. pressinga key), or a more abstract, non-motor choice (e.g. selectingpizza at the restaurant). Are these different responses learnedthe same way? In this study, we examine differences betweenlearning a rewarding association between (1) a stimulus and amotor action and (2) two stimuli. We show that learningdiffers between these two conditions, contrary to the commonimplicit assumption that response type does not matter.Specifically, participants were slower and less accurate inlearning to select a rewarding stimulus. Using computationalmodeling, we show that the values of motor actions interferedwith the values of stimulus responses, resulting in moreincorrect choices in the latter condition.

As we transition from child to adult, we navigate the worlddifferently. In this world, many of the relationships betweenevents are unclear or uncertain because they are probabilisticin nature. We wanted to know how learning about probabilis-tic relationships changes with development and to interrogatethe underlying processes. We investigated these questions in aprobabilistic reinforcement learning task (The Butterfly Task)with 302 participants aged 8-30. We found performance in thistask increased with age through early-twenties, then stabilized.Using hierarchical Bayesian methods to fit computational rein-forcement learning models, we showed that this performanceincrease was driven by 1) an increase in learning rate (i.e. de-crease in integration time horizon); 2) a decrease in exploratorychoices. By contrast, forgetting rates did not change with age.We discuss our findings in the context of other studies and hy-potheses about adolescent brain development.