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

Controlling behavior to flexibly achieve desired goals depends on the ability to monitor one’s own performance. It is unknown how performance monitoring can be both flexible, to support different tasks, and specialized, to perform each task well. We recorded single neurons in the human medial frontal cortex while subjects performed two tasks that involve three types of cognitive conflict. Neurons encoding conflict probability, conflict, and error in one or both tasks were intermixed, forming a representational geometry that simultaneously allowed task specialization and generalization. Neurons encoding conflict retrospectively served to update internal estimates of conflict probability. Population representations of conflict were compositional. These findings reveal how representations of evaluative signals can be both abstract and task-specific and suggest a neuronal mechanism for estimating control demand. Description The neural basis of cognitive control What are the neural mechanisms that enable humans to flexibly control and monitor their own actions in various non-routine and novel tasks? Fu et al. recorded the activity of more than 1000 neurons in the medial frontal cortex of human epilepsy patients while they performed complex cognitive tasks. They found that domain-general and domain-specific performance monitoring neurons were intermixed within this brain region. The population activity gave rise to a geometry that allowed domain-general signals to be read out with more than 90% accuracy on single trials while at the same time retaining the ability to separate different conflict conditions. These results show how the human medial frontal cortex resolves the fundamental trade-off between task generalization and specialization, which is critical for cognitive flexibility. —PRS Representations of evaluative signals in human frontal cortex are both abstract and task specific. INTRODUCTION Monitoring our own behavior without explicit feedback is a prominent human ability, enabling us to evaluate whether we made an error, are experiencing conflict, or are in an easy or difficult task environment. In real-life situations, we often need to rapidly learn to perform novel tasks with minimal instruction and time for practice, which requires domain-general performance monitoring that functions “out of the box. ” At the same time, we need to resolve unforeseen errors, difficulty, and other performance disturbances with task-specific measures ad hoc. Doing so requires domain-specific performance monitoring processes so as to improve upon and specialize task performance. Are there neural representations that support both domain-specific and domain-general performance monitoring, and if so, how are these requirements satisfied simultaneously? RATIONALE In this work, we focused on the medial frontal cortex (MFC), which is known to serve a central role in performance monitoring. We recorded from single neurons in the MFC while neurosurgical patients performed two tasks in which errors were caused by different kinds of conflict. We identified neurons whose responses encoded performance monitoring variables in one or both tasks and characterized the resulting high-dimensional neural representations supported by neuronal populations. Although specialization and generalization place different constraints on the structure of neural representations, our work suggests that the geometry of population activity can be configured to accommodate both seemingly conflicting demands. RESULTS We recorded from 1431 single neurons in the MFC [dorsal anterior cingulate cortex (dACC) and pre–supplementary motor area (pre-SMA)] across 34 patients. Subjects were asked to press, as quickly as possible, one of three buttons to indicate the ink color of the word “red,” “green,” or “blue” (task 1, Stroop task) or to report the unique number among a string of numbers (task 2, multisource interference task). We modeled serial performance of both tasks using a hierarchical Bayesian framework. Our models assumed that subjects maintained an internal estimate of the probability of encountering a certain type of conflict (conflict probability, or CP) and iteratively updated this estimate using Bayes’ law. Reaction times were generated by a drift-diffusion model, with CP biasing the drift rates. These behavioral analyses showed that the reaction times and likelihood of making an error varied systematically with trial history. At the single-neuron level, cells encoded different types of conflict, CP, and/or errors in one or both tasks. Individual neurons thus demonstrated heterogeneous task specificity when encoding these performance-monitoring variables, precluding a simplistic interpretation of domain generality at the single-neuron level. At the population level, however, these cells formed a high-dimensional representation with a geometry that allowed task-invariant decoding of all three variables on single trials, while at the same time allowing decoding of task-specific performance monitoring variables. Representations of conflict probability were consistent with that expected of the dynamics of a line attractor, with levels of CP stably maintained throughout the trial. The neural states indicating the presence of multiple types of conflict were equal to the vector sum of the states indicating each individual type of conflict, thereby revealing a compositional representation. Lastly, retrospective representations of conflict served to update internal estimates of conflict probability. CONCLUSION We leveraged the opportunity to record from the same populations of single neurons in the human MFC during two tasks to identify the structure of neural representations supporting performance monitoring. Population activity could be factorized into task identity and task-invariant dimensions that were orthogonal to each other. This geometry of the population activity could allow downstream brain regions to read out both domain-general and domain-specific signals from the same group of neurons and to initiate corresponding physiological and/or behavioral adaptations. These findings reveal how representations of evaluative signals can be both abstract and task-specific and suggest a neuronal mechanism for estimating control demand. Representations of evaluative signals in the human frontal cortex are both abstract and task-specific. (A) Recording locations. (B) Analysis epochs. (C) Response of example neuron in both tasks, demonstrating domain generality for errors (red) and no differentiation of different types of correct conflict trials (all other colors). (D) Compositionality of population-level conflict representations. (E) Conflict probability displaces dynamics in neural state space.