A modular framework for object-based saccadic decisions in dynamic scenes

TL;DR

This paper introduces a modular, decision-based model for simulating human eye movements in dynamic scenes, integrating evidence accumulation and object-based choices to better understand active visual exploration.

Contribution

It extends the drift-diffusion model to handle multiple objects, linking decision making with scene perception in a novel, dynamic context.

Findings

Model accurately predicts eye movement patterns in dynamic scenes

Parameter analysis reveals key factors influencing saccadic decisions

Comparison with dataset supports model plausibility

Abstract

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.