Errors in action timing and inhibition facilitate learning by tuning distinct mechanisms in the underlying decision process
AbstractGoal-directed behavior requires integrating action selection processes with learning systems that adapt control using environmental feedback. These functions intersect in the basal ganglia (BG), which has at least two targets of plasticity: a dopaminergic modulation of striatal pathways and cortical modulation of the subthalamic nucleus (STN). Dual learning mechanisms suggests that feedback signals have a multifaceted impact on BG-dependent 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) 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.Author SummaryMany complex behavioral goals rely on one’s 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 two fundamental components of behavior become tuned to the control demands of the environment by combining principles of reinforcement learning with accumulator models of decision making. The synthesis of these two theoretical frameworks is motivated by previous work showing that reinforcement learning and control rely on overlapping circuitry in the basal ganglia. Leveraging knowledge about the interaction of learning and control signals in this network, we formulated a computational model in which performance feedback is used to modulate key mechanisms of the decision process to facilitate goal acquisition. Model-based analysis of behavioral data collected on an adaptive stop-signal task revealed two critical learning mechanisms: one that adjusts the accumulation rate of the “Go” signal to errors in action timing and another that exercises caution by raising the height of the execution boundary after a failed Stop trial. We show how these independent learning mechanisms interact over the course of learning, shedding light on the behavioral effects plasticity in different pathways of the basal ganglia.
