A Mathematical Model of Exploration and Exploitation in Natural Scene Viewing

Abstract

AbstractUnderstanding the decision process underlying gaze control is an important question in cognitive neuroscience with applications in diverse fields ranging from psychology to computer vision. The decision for choosing an upcoming saccade target can be framed as a dilemma: Should the observer further exploit the information near the current gaze position or continue with exploration of other patches within the given scene? While several models attempt to describe the dynamics of saccade target selection, none of them explicitly addresses the underlying Exploration–Exploitation dilemma. Here we propose and investigate a mathematical model motivated by the Exploration–Exploitation dilemma in scene viewing. The model is derived from a minimal set of assumptions that generates realistic eye movement behavior. We implemented a Bayesian approach for model parameter inference based on the model’s likelihood function. In order to simplify the inference, we applied data augmentation methods that allowed the use of conjugate priors and the construction of an efficient Gibbs sampler. This approach turned out to be numerically efficient and permitted fitting interindividual differences in saccade statistics. Thus, the main contribution of our modeling approach is two–fold; first, we propose a new model for saccade generation in scene viewing. Second, we demonstrate the use of novel methods from Bayesian inference in the field of scan path modeling.Author summaryThe Exploration–Exploitation dilemma is general concept that has been investigated in human information processing. We investigate whether the Exploration–Exploitation trade–off is a viable approach to model sequences of fixations generated by a human observer in a free viewing task with natural scenes. Variants of the basic model are used to predict to the experimental data based on Bayesian inference. Results indicate a high predictive power for both aggregated data and individual differences across observers. The combination of a novel model with state-of-the-art Bayesian methods lends support to the Exploration–Exploitation framework in the field of eye-movement research.