The Geometry of Domain-General Performance Monitoring in the Human Medial Frontal Cortex

Abstract

Flexibly controlling behavior to achieve a desired goal depends on the ability to monitor one’s own performance. A key open question is how performance monitoring can be both flexible to support multiple tasks and specialized to support specific tasks. We characterized performance monitoring representations by recording single neurons in the human medial frontal cortex (MFC). Subjects performed two tasks that involve three types of cognitive conflict. Neurons encoding error, conflict and predicted control demand in one or both tasks coexisted in the same population. Collectively, they gave rise to a representational geometry that simultaneously allowed task specialization and generalization. Representations of conflicts were compositional. Neurons encoding conflict retrospectively served to update estimates of control demand as predicted by a Bayesian model. These findings reveal how the MFC representation of evaluative signals are both abstract and specific, suggesting a mechanism for computing and maintaining control demand estimates across trials and tasks.