I will also discuss a method combining generative adversarial networks (GANs) and Markov-chain Monte Carlo (MCMC) that can be readily employed to compute the IO for more complex estimation tasks. Finally, I will demonstrate the use of reinforcement learning to approximate the foveated IO, which generates optimal eye movement strategies for visual search tasks. Together, these machine learning methods represent a set of feasible computational tools for approximating the IO and hold great potential for evaluating emerging imaging technologies and addressing fundamental questions in vision science.
