Causal Evidence for Learning-Dependent Frontal Lobe Contributions to Cognitive Control

The lateral prefrontal cortex (LPFC) plays a central role in the prioritization of sensory input based on task relevance. Such top-down control of perception is of fundamental importance in goal-directed behavior, but can also be costly when deployed excessively, necessitating a mechanism that regulates control engagement to align it with changing environmental demands. We have recently introduced the "flexible control model" (FCM), which explains this regulation as resulting from a self-adjusting reinforcement-learning mechanism that infers latent statistical structure in dynamic task environments to predict forthcoming states. From this perspective, LPFC-based control is engaged as a function of anticipated cognitive demand, a notion for which we previously obtained correlative neuroimaging evidence. Here, we put this hypothesis to a rigorous, causal test by combining the FCM with a transcranial magnetic stimulation (TMS) intervention that transiently perturbed the LPFC. Human participants (male and female) completed a nonstationary version of the Stroop task with dynamically changing probabilities of conflict between task-relevant and task-irrelevant stimulus features. TMS was given on each trial before stimulus onset either over the LPFC or over a control site. In the control condition, we observed adaptive performance fluctuations consistent with demand predictions that were inferred from recent and remote trial history and effectively captured by our model. Critically, TMS over the LPFC eliminated these fluctuations while leaving basic cognitive and motor functions intact. These results provide causal evidence for a learning-based account of cognitive control and delineate the nature of the signals that regulate top-down biases over stimulus processing. A core function of the human prefrontal cortex is to control the signal flow in sensory brain regions to prioritize processing of task-relevant information. Abundant work suggests that such control is flexibly recruited to accommodate dynamically changing environmental demands, yet the nature of the signals that serve to engage control remains unknown. Here, we combined computational modeling with noninvasive brain stimulation to show that changes in control engagement are captured by a self-adjusting reinforcement-learning mechanism that tracks changing environmental statistics to predict forthcoming processing demands and that transient perturbation of the prefrontal cortex abolishes these adjustmen