Figure 1. Overview of the experimental paradigm.

(A) Participant sample. Left: Number of participants in each age group, broken up by sex (self-reported). Age groups were determined by within-sex age quartiles for participants between 8–17 years (see Eckstein et al., 2022 for details) and 5 year bins for adults. Right: Number of participants whose data were excluded because they failed to reach performance criteria in at least one task. (B) Task A procedure of (‘Butterfly task’). Participants saw one of four butterflies on each trial and selected one of two flowers in response, via button press on a game controller. Each butterfly had a stable preference for one flower throughout the task, but rewards were delivered stochastically (70% for correct responses, 30% for incorrect). For details, see section 'Task design' and the original publication (Xia et al., 2021). (C) Task B Procedure (‘Stochastic Reversal’). Participants saw two boxes on each trial and selected one with the goal of finding gold coins. At each point in time, one box was correct and had a high (75%) probability of delivering a coin, whereas the other was incorrect (0%). At unpredictable intervals, the correct box switched sides. For details, see section 'Task design' and Eckstein et al., 2022. (D) Task C procedure (‘Reinforcement learning-working memory’). Participants saw one stimulus on each trial and selected one of three buttons ($A_1-A_3$) in response. All correct and no incorrect responses were rewarded. The task contained blocks of 2–5 stimuli, determining its ‘set size’. The task was designed to disentangle set size-sensitive working memory processes from set size-insensitive RL processes. For details, see section 'Task design' and Master et al., 2020. (E) Pairwise similarities in terms of experimental design between tasks A (Xia et al., 2021), B (Eckstein et al., 2022), and C (Master et al., 2020). Similarities are shown on the arrows connecting two tasks; the lack of a feature implies a difference. E.g., a ‘Stable set size’ on tasks A and B implies an unstable set size in task C. Overall, task A shared more similarities with tasks B and C than these shared with each other. (F) Summary of the computational models for each task (for details, see section 'Computational models' and original publications). Each row shows one model, columns show model parameters. ‘Y’ (yes) indicates that a parameter is present in a given model, ‘—’ indicates that a parameter is not present. ‘$\frac{1}{\beta }$ and $ϵ$’ refer to exploration / noise parameters; ${\alpha }_{+}$ (${\alpha }_{-}$) to learning rate for positive (negative) outcomes; ‘Persist. P’ to persistence; ‘WM pars’. to working memory parameters.