Illustration of estimated parameters and their relation to differences in value-based decision-making and learning (representative participants).

a. The reward sensitivity beta scales how action weights (i.e., a combination of estimated probability and potential reward value) are translated into choices. Higher reward sensitivities translate to more deterministic choices (i.e., exploitation), whereas lower reward sensitivities lead to more random choices (i.e., exploration). b. The learning rate alpha captures how quickly estimated win probabilities are updated if new information is available. High learning rates (upper panel) lead to fast updates and quick forgetting of long-term outcomes. The black line depicts the latent win probability, while the points depict the estimated win probability based on the reinforcement learning model. c. Weighting of the estimated win probability of each option compared to the offered rewards is scaled by λ. Low values (.5, lower panel) increase the importance of the learned win probabilities. Color in panel b and c indicates the observed choices in these exemplary runs.