Computational Model for Predicting Visual Fixations from Childhood to Adulthood
This work addresses the need for age-specific visual attention models in fields like psychology and human-computer interaction, though it is incremental as it adapts existing saccadic frameworks to new data.
The paper tackled the problem of predicting visual fixations across different age groups by developing a saccadic model that uses eye data from 101 observers to generate age-specific scanpaths, and it significantly outperformed other state-of-the-art saliency models.
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.