Abstract
Goal-directed behavior requires integrating action selection processes with learning systems that adapt control using environmental feedback. These functions are known to intersect at a common neural substrate with multiple known targets of plasticity (the cortico-basal ganglia-thalamic network), suggesting that feedback signals have a multifaceted impact on future decisions. Using a hybrid of accumulation-to-bound decision models and reinforcement learning, we modeled the performance of humans in a stop signal task where participants (N 75: 37 males, 38 females) learned the prior distribution of the timing of a stop signal through trial-and-error feedback. Changes in the drift rate of the action execution process were driven by errors in action timing, whereas adaptation in the boundary height served to increase caution following failed stops. These findings highlight two interactive learning mechanisms for adapting the control of goal-directed actions based on dissociable dimensions of feedback error.SIGNIFICANCE STATEMENT Many complex behavioral goals rely on the ability to regulate the timing of action execution while also maintaining enough control to cancel actions in response to "Stop" cues in the environment. Here we examined how these fundamental components of behavior become tuned to the control demands of the environment by combining principles of reinforcement learning with accumulation-to-bound models. Model fits to behavioral data in an adaptive stop signal task revealed two adaptive mechanisms: (1) timing error-related changes in the rate of the execution signal; and (2) an increase in the execution boundary after failed stops. These findings demonstrate unique effects of timing and control errors on the underlying mechanisms of control, the rate and threshold of accumulating action signals.