To choose between manifestly distinct options, it is suggested that the brain assigns values to goals using a common currency. Although previous studies have reported activity in ventromedial prefrontal cortex (vmPFC) correlating with the value of different goal stimuli, it remains unclear whether such goal-value representations are independent of the associated stimulus categorization, as required by a common currency. Using multivoxel pattern analyses on functional magnetic resonance imaging (fMRI) data, we found a region of medial prefrontal cortex to contain a distributed goal-value code that is independent of stimulus category. More ventrally in the vmPFC, we found spatially distinct areas of the medial orbitofrontal cortex to contain unique category-dependent distributed value codes for food and consumer items. These results implicate the medial prefrontal cortex in the implementation of a common currency and suggest a ventral versus dorsal topographical organization of value signals in the vmPFC.

Rationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.

Contemporary computational accounts of instrumental conditioning have emphasized a role for a model-based system in which values are computed with reference to a rich model of the structure of the world, and a model-free system in which values are updated without encoding such structure. Much less studied is the possibility of a similar distinction operating at the level of Pavlovian conditioning. In the present study, we scanned human participants while they participated in a Pavlovian conditioning task with a simple structure while measuring activity in the human amygdala using a high-resolution fMRI protocol. After fitting a model-based algorithm and a variety of model-free algorithms to the fMRI data, we found evidence for the superiority of a model-based algorithm in accounting for activity in the amygdala compared to the model-free counterparts. These findings support an important role for model-based algorithms in describing the processes underpinning Pavlovian conditioning, as well as providing evidence of a role for the human amygdala in model-based inference.