A Resource-Rational Principle for Modeling Visual Attention Control
This work provides a unified computational account for Human-Computer Interaction design, addressing the problem of interpretable and generalizable attention models, though it appears incremental by building on existing control and simulation methods.
The dissertation tackled modeling visual attention by developing a resource-rational, simulation-based framework using Partially Observable Markov Decision Processes, which reproduced empirical effects and explained trade-offs in tasks like reading and multitasking.
Understanding how people allocate visual attention is central to Human-Computer Interaction (HCI), yet existing computational models of attention are often either descriptive, task-specific, or difficult to interpret. My dissertation develops a resource-rational, simulation-based framework for modeling visual attention as a sequential decision-making process under perceptual, memory, and time constraints. I formalize visual tasks, such as reading and multitasking, as bounded-optimal control problems using Partially Observable Markov Decision Processes, enabling eye-movement behaviors such as fixation and attention switching to emerge from rational adaptation rather than being hand-coded or purely data-driven. These models are instantiated in simulation environments spanning traditional text reading and reading-while-walking with smart glasses, where they reproduce classic empirical effects, explain observed trade-offs between comprehension and safety, and generate novel predictions under time pressure and interface variation. Collectively, this work contributes a unified computational account of visual attention, offering new tools for theory-driven and resource-efficient HCI design.