Increased monetary incentives enhance goal-directed reinforcement learning but fail to suppress automatic learning processes

Abstract

Adaptive reinforcement learning requires assigning outcomes to the features of actions that causally determine them. Yet humans also learn reward associations with action features that are explicitly known to be outcome-irrelevant, and these associations can bias subsequent choices. Whether such maladaptive learning is regulated by motivational incentives remains unclear. Here, we tested whether increasing monetary stakes differentially modulates the influence of outcome-relevant and outcome-irrelevant learned values on choice. Participants (N = 287) completed a multi-armed bandit task in low- and high-stakes conditions while explicitly instructed that card identity predicted reward whereas card location did not. Computational modelling revealed that higher stakes increased the influence of outcome-relevant values on choice, but did not change the influence of outcome-irrelevant values. Critically, outcome-irrelevant value influences persisted even though they produced substantially greater monetary losses under high stakes. These findings show that monetary incentives selectively enhance the use of outcome-relevant learned values without improving the selectivity of credit assignment. More broadly, they suggest that only some components of reinforcement learning are sensitive to motivational control.