# Optimal control of eye-movements during visual search

## TL;DR

This paper presents a computational model of human eye movements during visual search, using stochastic optimal control and reinforcement learning, validated by human eye-tracking experiments.

## Contribution

It introduces a novel model that incorporates biological constraints and accurately replicates human eye movement patterns during search tasks.

## Key findings

- Model reproduces statistical properties of human eye movements.
- Simulated trajectories match scaling behavior observed in humans.
- Validation through psychophysical eye-tracking experiments.

## Abstract

We study the problem of optimal oculomotor control during the execution of visual search tasks. We introduce a computational model of human eye movements, which takes into account various constraints of the human visual and oculomotor systems. In the model, the choice of the subsequent fixation location is posed as a problem of stochastic optimal control, which relies on reinforcement learning methods. We show that if biological constraints are taken into account, the trajectories simulated under learned policy share both basic statistical properties and scaling behaviour with human eye movements. We validated our model simulations with human psychophysical eye-tracking experiments