Computational Model for Predicting Visual Fixations from Childhood to Adulthood

TL;DR

This paper introduces an age-adaptive saccadic model that predicts human eye movement patterns across different developmental stages, outperforming existing saliency models in generating age-specific scanpaths.

Contribution

The study develops a flexible saccadic model trained on diverse age groups, capturing age-specific eye movement signatures and improving gaze prediction accuracy.

Findings

Age-specific saccade amplitude and orientation distributions identified

Model generates realistic, age-dependent scanpaths

Outperforms state-of-the-art saliency models in accuracy

Abstract

How people look at visual information reveals fundamental information about themselves, their interests and their state of mind. While previous visual attention models output static 2-dimensional saliency maps, saccadic models aim to predict not only where observers look at but also how they move their eyes to explore the scene. Here we demonstrate that saccadic models are a flexible framework that can be tailored to emulate observer's viewing tendencies. More specifically, we use the eye data from 101 observers split in 5 age groups (adults, 8-10 y.o., 6-8 y.o., 4-6 y.o. and 2 y.o.) to train our saccadic model for different stages of the development of the human visual system. We show that the joint distribution of saccade amplitude and orientation is a visual signature specific to each age group, and can be used to generate age-dependent scanpaths. Our age-dependent saccadic model not only outputs human-like, age-specific visual scanpath, but also significantly outperforms other state-of-the-art saliency models. In this paper, we demonstrate that the computational modelling of visual attention, through the use of saccadic model, can be efficiently adapted to emulate the gaze behavior of a specific group of observers.