Modeling Rational Adaptation of Visual Search to Hierarchical Structures

Abstract Efficient attention deployment in visual search is limited by human visual memory, but this constraint can be mitigated by exploiting environmental structures. This paper presents a computational cognitive model that simulates how the human visual system uses visual hierarchies to prevent refixations during sequential attention deployment. The model embraces computational rationality, proposing that behaviors arise as adaptations to cognitive constraints and environmental structures. Unlike previous models that predict search performance based on hierarchical information, our model does not rely on predefined search strategies. Instead, its search strategy emerges through reinforcement learning as an adaptation to the cognitive and task environment. We test the model’s prediction that structured environments reduce visual search times compared to random layouts in an experiment with human participants. The model’s predictions align well with human search performance across various set sizes in both structured and unstructured visual layouts. The model also replicates eye movement patterns that exhibit such adaptation. Our work contributes to understanding the adaptive nature of visual search in hierarchical environments and inform the design of optimized search spaces.