A modular framework for object-based saccadic decisions in dynamic scenes
This work addresses the challenge of active scene exploration for vision and cognitive science researchers, but it appears incremental as it adapts an existing model to a new context.
The authors tackled the problem of simulating human eye-movement behavior in dynamic scenes by modeling it as a sequential decision-making process using an extended drift-diffusion model for multiple object-based options, and validated the approach with an ablation study and comparison on the VidCom dataset.
Visually exploring the world around us is not a passive process. Instead, we actively explore the world and acquire visual information over time. Here, we present a new model for simulating human eye-movement behavior in dynamic real-world scenes. We model this active scene exploration as a sequential decision making process. We adapt the popular drift-diffusion model (DDM) for perceptual decision making and extend it towards multiple options, defined by objects present in the scene. For each possible choice, the model integrates evidence over time and a decision (saccadic eye movement) is triggered as soon as evidence crosses a decision threshold. Drawing this explicit connection between decision making and object-based scene perception is highly relevant in the context of active viewing, where decisions are made continuously while interacting with an external environment. We validate our model with a carefully designed ablation study and explore influences of our model parameters. A comparison on the VidCom dataset supports the plausibility of the proposed approach.