Frontal network dynamics reflect neurocomputational mechanisms for reducing maladaptive biases in motivated action

Motivation exerts control over behavior by eliciting Pavlovian responses, which can either match or conflict with instrumental action. We can overcome maladaptive motivational influences putatively through frontal cognitive control. However, the neurocomputational mechanisms subserving this control are unclear; does control entail up-regulating instrumental systems, down-regulating Pavlovian systems, or both? We combined electroencephalography (EEG) recordings with a motivational Go/NoGo learning task (N = 34), in which multiple Go options enabled us to disentangle selective action learning from nonselective Pavlovian responses. Midfrontal theta-band (4 Hz–8 Hz) activity covaried with the level of Pavlovian conflict and was associated with reduced Pavlovian biases rather than reduced instrumental learning biases. Motor and lateral prefrontal regions synchronized to the midfrontal cortex, and these network dynamics predicted the reduction of Pavlovian biases over and above local, midfrontal theta activity. This work links midfrontal processing to detecting Pavlovian conflict and highlights the importance of network processing in reducing the impact of maladaptive, Pavlovian biases. Author summary: The anticipation of reward and punishment are key drivers of behavior: we tend to take action for rewards while holding back in the face of punishment. This motivational bias might have an overall evolutionary advantage but can also work against us in specific situations. Here, we first asked whether this motivational bias relies on innate, automatic action tendencies or whether this bias might actually itself be learned. Secondly, we studied which brain processes reduce the impact of these motivational drives when they become dysfunctional. By comparing the actions of human participants to the predictions of several mathematical models, we showed that the motivational bias in action relies partly on automatic tendencies and partly on asymmetric learning from experience. We then observed that activity over the midfrontal cortex specifically increased as a function of how dysfunctional the automatic tendencies were. Additionally, this midfrontal cortex activity was functionally connected to the motor and lateral frontal cortices, which play a role in activating and inhibiting behavior. By incorporating this connectivity into the mathematical models, we showed that stronger midfrontal connectivity predicted reduced impact of dysfunctional automatic tendencies on behavior. We propose that the midfrontal cortex detects dysfunctional action tendencies and implements cognitive control by signaling across the network