Objects in the environment differ in their low-level perceptual properties (e.g., how easily a fruit can be recognized) as well as in their subjective value (how tasty it is). We studied the influence of visual salience on value-based decisions using a two alternative forced choice task, in which human subjects rapidly chose items from a visual display. All targets were equally easy to detect. Nevertheless, both value and salience strongly affected choices made and reaction times. We analyzed the neuronal mechanisms underlying these behavioral effects using stochastic accumulator models, allowing us to characterize not only the averages of reaction times but their full distributions. Independent models without interaction between the possible choices failed to reproduce the observed choice behavior, while models with mutual inhibition between alternative choices produced much better results. Mutual inhibition thus is an important feature of the decision mechanism. Value influenced the amount of accumulation in all models. In contrast, increased salience could either lead to an earlier start (onset model) or to a higher rate (speed model) of accumulation. Both models explained the data from the choice trials equally well. However, salience also affected reaction times in no-choice trials in which only one item was present, as well as error trials. Only the onset model could explain the observed reaction time distributions of error trials and no-choice trials. In contrast, the speed model could not, irrespective of whether the rate increase resulted from more frequent accumulated quanta or from larger quanta. Visual salience thus likely provides an advantage in the onset, not in the processing speed, of value-based decision making.

Learning to predict rewards based on environmental cues is essential for survival. It is believed that animals learn to predict rewards by updating predictions whenever the outcome deviates from expectations, and that such reward prediction errors (RPEs) are signaled by the mesolimbic dopamine system-a key controller of learning. However, instead of learning prospective predictions from RPEs, animals can infer predictions by learning the retrospective cause of rewards. Hence, whether mesolimbic dopamine instead conveys a causal associative signal that sometimes resembles RPE remains unknown. We developed an algorithm for retrospective causal learning and found that mesolimbic dopamine release conveys causal associations but not RPE, thereby challenging the dominant theory of reward learning. Our results reshape the conceptual and biological framework for associative learning.