Effortful Bayesian updating: A pupil-dilation study

Abstract

When confronted with new information, rational decision makers should update their beliefs through Bayes’ rule. In economics, however, new information often includes win-loss feedback (profits vs. losses, success vs. failure, upticks vs. downticks). Previous research using a well-established belief-updating paradigm shows that, in this case, reinforcement learning (focusing on past performance) creates high error rates, and increasing monetary incentives fails to elicit higher performance. But do incentives fail to increase effort, or rather does effort fail to increase performance? We use pupil dilation to show that higher incentives do result in increased cognitive effort, but the latter fails to translate into increased performance in this paradigm. The failure amounts to a “reinforcement paradox:” increasing incentives makes win-loss cues more salient, and hence effort is often misallocated in the form of an increased reliance on reinforcement processes. Our study also serves as an example of how pupil-dilation measurements can inform economics.