Modeling Rational Adaptation of Visual Search to Hierarchical Structures

TL;DR

This paper presents a computational model that explains how humans adapt their visual search strategies in hierarchical environments, using reinforcement learning to optimize attention deployment based on environmental structure.

Contribution

It introduces a novel cognitive model that learns search strategies through reinforcement learning, without predefined heuristics, aligning with human performance in structured visual tasks.

Findings

Model's search strategies emerge from environmental adaptation.

Predictions match human search times across set sizes.

Structured environments facilitate faster visual search.

Abstract

Efficient attention deployment in visual search is limited by human visual memory, yet this limitation can be offset by exploiting the environment's structure. This paper introduces a computational cognitive model that simulates how the human visual system uses visual hierarchies to prevent refixations in sequential attention deployment. The model adopts computational rationality, positing behaviors as adaptations to cognitive constraints and environmental structures. In contrast to earlier models that predict search performance for hierarchical information, our model does not include predefined assumptions about particular search strategies. Instead, our model's search strategy emerges as a result of adapting to the environment through reinforcement learning algorithms. In an experiment with human participants we test the model's prediction that structured environments reduce visual search times compared to random tasks. Our model's predictions correspond well with human search performance across various set sizes for both structured and unstructured visual layouts. Our work improves understanding of the adaptive nature of visual search in hierarchically structured environments and informs the design of optimized search spaces.