Experiments on the efficiency of human search sometimes reveal large differences between individual participants. We argue that reward-driven task-specific learning may account for some of this variation. In a computational reinforcement learning model of this process, a wide variety of strategies emerge, despite all simulated participants having the same visual acuity. We conduct a visual search experiment, and replicate previous findings that participant preferences about where to search are highly varied, with a distribution comparable to the simulated results. Thus, task-specific learning is an under-explored mechanism by which large inter-participant differences can arise.
An Exploration-driven Reinforcement Learning Model for Video Streaming Scheduling in 5G-Powered Drone
