The geometry of domain-general performance monitoring representations in the human medial frontal cortex

Flexibly adapting behavior to achieve a desired goal depends on the ability to monitor one9s own performance. A key open question is how performance monitoring can be both highly 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. Neural population representations of conflict, error and control demand generalized across tasks and time while at the same time also encoding task specialization. This arose from a combination of single neurons whose responses were task-invariant and non-linearly mixed. Neurons encoding conflict ex-post served to iteratively update internal 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.