  During probabilistic learning organisms often apply a sub-optimal
"probability-matching" strategy, where selection rates match reward
probabilities, rather than engaging in the optimal "maximization" strategy,
where the option with the highest reward probability is always selected.
Despite decades of research, the mechanisms contributing to
probability-matching are still under debate, and particularly noteworthy is
that no differences between probability-matching and maximization strategies
have been reported at the level of the brain. Here, we provide theoretical
proof for a computational model that explains the complete range of behaviors
between pure maximization and pure probability-matching. Fitting this model to
behavior of 60 participants performing a probabilistic reinforcement learning
task during fMRI scanning confirmed the model-derived prediction that
probability-matching relates to an increased integration of negative outcomes
during learning, as indicated by a stronger coupling between midbrain BOLD
signal and negative prediction errors. Because the degree of
probability-matching was consistent within an individual across nine different
conditions, our results further suggest that the tendency to express a
particular learning strategy is a trait-like feature of an individual.
