Reinforcement learning parameters and model-independent performance indices improve over runs.

a. Learning rate for wins, αwin, does not change over runs (b = -0.006, p = .19). b. Learning rate for losses, αloss, decreases over runs (b = -0.06, p .001). c. Reward sensitivity β increases over runs (b = 0.72, p .001). d. Weighting of win probabilities compared to reward magnitudes, λ, increases over runs (b = 0.03, p .001). e. Response times decrease over runs (b = -0.23, p .001) f. The average reward increases over runs (b = 1.09, p .001). The dots show mean values with 95% bootstrapped confidence intervals.