Prioritization of Perceptual Decisions

Scheduling theory provides optimal policies for allocating limited resources to multiple tasks over time. However, little research has examined how cognitive mechanisms perform relative to these optimal policies. We introduce a novel experimental paradigm to study human scheduling decisions using a set of perceptual tasks (random dot kinematograms, RDKs) that vary in difficulty. Participants selected and completed RDKs one at a time, with the goal of completing as many as possible before a deadline. In Experiment 1, RDK difficulty was explicitly labeled. Participants showed near-optimal scheduling, selecting easier tasks first, especially under time pressure. In Experiment 2, difficulty had to be inferred from dynamic RDK displays. This resulted in less optimal scheduling, closer to random selection. In Experiment 3, for generality, we replicate Experiment 1 using a typing task, demonstrating that scheduling remains near optimal under a short deadline. We develop a cognitive model of scheduling based on the Plackett-Luce model of ranked preferences. The model captures key differences between experiments in terms of the strength parameters associated with each difficulty level. Our paradigm and modeling approach provide a foundation for studying human scheduling across a range of task types and goals. This work bridges theories of optimal scheduling with cognitive models of sequential decision making.