Eye of the beholder: Pupillary response reflects how subjective prior beliefs shape reinforcement learning with fake news

Abstract

Information selection plays a crucial role in how individuals navigate online content. While confirmation bias has been implicated in this phenomenon, its interaction with reinforcement learning dynamics and internal confidence signals remains poorly understood. Here, we examined how veracity judgments and confidence shape choices when probabilistic rewards are tied to different epistemic attributes of news headlines. Participants completed a three-phase paradigm that combined news classification, a probabilistic learning task with varying reward contingencies, and a final reevaluation phase. Using real and false headlines judged for veracity and confidence, we created personalized sets of stimulus categories that were later used in a two-armed bandit task. In different blocks of trials, reinforcement was probabilistically associated with either the perceived truthfulness or confidence of each item. Across all experimental phases, pupil dilation provided neurophysiological signatures of belief-related processing. At a behavioral level, participants showed higher accuracies and learning rates when rewards were contingent on their previous judgments of veracity, whereas performance was markedly reduced when reinforcement favored confidence, especially low-confidence options. Pupillometric data revealed predecisional modulations tied to subjective confidence, while computational modeling showed that participants relied on feature-based generalization when veracity predicted reward and shifted toward valence-sensitive updating when contingencies no longer matched their prior epistemic structure. Together, these results reveal how veracity and confidence jointly guide reinforcement-driven choices and modulate the flexibility of belief-related decisions. By integrating cognitive, computational, and physiological data, our study provides a mechanistic understanding of how prior beliefs shape learning in complex and misinformation-rich contexts.