Toddlers' Active Gaze Behavior Supports Self-Supervised Object Learning

TL;DR

This study shows that toddlers' active gaze behavior, including eye and head movements, plays a crucial role in supporting their self-supervised learning of view-invariant object recognition, as demonstrated through eye tracking and computational modeling.

Contribution

It introduces a novel combination of head-mounted eye tracking and unsupervised learning to investigate how toddlers' gaze strategies facilitate object recognition development.

Findings

Gaze behavior supports invariant object representation learning

Limited high-acuity visual field size is crucial for learning

Toddlers' active viewing strategies aid self-supervised learning

Abstract

Toddlers learn to recognize objects from different viewpoints with almost no supervision. During this learning, they execute frequent eye and head movements that shape their visual experience. It is presently unclear if and how these behaviors contribute to toddlers' emerging object recognition abilities. To answer this question, we here combine head-mounted eye tracking during dyadic play with unsupervised machine learning. We approximate toddlers' central visual field experience by cropping image regions from a head-mounted camera centered on the current gaze location estimated via eye tracking. This visual stream feeds an unsupervised computational model of toddlers' learning, which constructs visual representations that slowly change over time. Our experiments demonstrate that toddlers' gaze strategy supports the learning of invariant object representations. Our analysis also shows that the limited size of the central visual field where acuity is high is crucial for this. Overall, our work reveals how toddlers' gaze behavior may support their development of view-invariant object recognition.