Intrinsic rewards guide visual resource allocation via reinforcement learning

Abstract Humans and other animals prioritize visual processing of stimuli that signal rewards. While previous research has focused on tangible incentives (for example, money or food), the effects of intrinsic incentives—such as perceived competence—are less well understood. Here, across a series of visual estimation experiments, we manipulated observers’ subjective sense of confidence in their judgements using either deceptive trial-by-trial feedback or real discrepancies in stimulus reliability. We found that observers prioritized encoding of stimuli associated with lower uncertainty or error, benefiting performance for stimuli already estimated accurately, while further impairing performance for those estimated poorly. These reward-driven biases, while potentially adaptive, impaired overall accuracy in the present tasks by causing resource allocation to deviate from the error-minimizing strategy. To account for these findings, we supplemented a normalization model of neural resource allocation with a simple reinforcement learning rule. Intrinsic and external rewards cumulatively shaped the values assigned to different stimuli by the model, and the resulting discrepancies biased resource allocation and thereby estimation error, quantitatively matching the data. These findings reveal how intrinsic reward signals can shape resource allocation in ways that are both adaptive and counterproductive, offering a computational basis for the motivational biases underlying cognitive performance.