Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Abstract

Optimal decision making mandates organisms learn the relevant features of choice options. Likewise, knowing how much effort we should expend can assume paramount importance. A mesolimbic network supports reward learning, but it is unclear whether other choice features, such as effort learning, rely on this same network. Using computational fMRI, we show parallel encoding of effort and reward prediction errors (PEs) within distinct brain regions, with effort PEs expressed in dorsomedial prefrontal cortex and reward PEs in ventral striatum. We show a common mesencephalic origin for these signals evident in overlapping, but spatially dissociable, dopaminergic midbrain regions expressing both types of PE. During action anticipation, reward and effort expectations were integrated in ventral striatum, consistent with a computation of an overall net benefit of a stimulus. Thus, we show that motivationally relevant stimulus features are learned in parallel dopaminergic pathways, with formation of an integrated utility signal at choice.