# Humans underweight delayed rewards in dispersed feedback > Source: > Published: 2026-06-29 19:01:15+00:00 Editorial analysis: For reinforcement-learning practitioners and human-in-the-loop system designers, systematic biases in how people weight temporally dispersed feedback can alter the reliability of human-generated reward signals and evaluation data. Per a preprint and repository listing for a paper titled "Delayed reward information is underweighted in reinforcement learning with dispersed feedback," authors Miruna Cotet, David Poensgen, and Ian Krajbich report that participants underweight **delayed reward information** relative to immediate feedback in tasks with dispersed feedback, based on **behavioral and eye-tracking** experiments (per a Chapman.edu preprint and a ResearchGate posting). The study used choices that produced both immediate and delayed outcome signals; the authors report an empirical gap in how much later feedback influences learning compared with earlier feedback (per the preprint). Editorial analysis: This pattern matters when designing feedback collection, reward shaping, or evaluation protocols that mix immediate and delayed signals.