LAUSR

Theoretical models of dopamine function stemming from reinforcement learning theory have emphasized the importance of prediction errors, which signal changes in the expectation of impending rewards. Much less is known about the effects of mean reward rates, which may be of motivational significance due to their role in computing the optimal effort put into exploiting reward opportunities. Here, we used a reinforcement learning model to design three functional neuroimaging studies and disentangle the effects of changes in reward expectations and mean reward rates, showing recruitment of specific regions in the brainstem regardless of prediction errors. While changes in reward expectations activated ventral striatal areas as in previous studies, mean reward rates preferentially modulated the substantia nigra/ventral tegmental area, deep layers of the superior colliculi, and a posterior pontomesencephalic region. These brainstem structures may work together to set motivation and attentional efforts levels according to perceived reward opportunities.