# A Computational Model of Early Word Learning from the Infant's Point of View

arXiv: https://arxiv.org/abs/2006.02802 · HTML: https://mlsanity.com/paper/2006.02802/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents](https://mlsanity.com/paper/2201.04990/) (2022)
- [Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play](https://mlsanity.com/paper/2312.04118/) (2023)
- [Predicting Word Learning in Children from the Performance of Computer Vision Systems](https://mlsanity.com/paper/2207.09847/) (2022)
- [Discovering Hidden Visual Concepts Beyond Linguistic Input in Infant Learning](https://mlsanity.com/paper/2501.05205/) (2025)
- [Reverse-engineer the Distributional Structure of Infant Egocentric Views for Training Generalizable Image Classifiers](https://mlsanity.com/paper/2106.06694/) (2021)
- [Brain-Inspired Perspective on Configurations: Unsupervised Similarity and Early Cognition](https://mlsanity.com/paper/2510.19229/) (2025)

## Abstract (arXiv)

Human infants have the remarkable ability to learn the associations between object names and visual objects from inherently ambiguous experiences. Researchers in cognitive science and developmental psychology have built formal models that implement in-principle learning algorithms, and then used pre-selected and pre-cleaned datasets to test the abilities of the models to find statistical regularities in the input data. In contrast to previous modeling approaches, the present study used egocentric video and gaze data collected from infant learners during natural toy play with their parents. This allowed us to capture the learning environment from the perspective of the learner's own point of view. We then used a Convolutional Neural Network (CNN) model to process sensory data from the infant's point of view and learn name-object associations from scratch. As the first model that takes raw egocentric video to simulate infant word learning, the present study provides a proof of principle that the problem of early word learning can be solved, using actual visual data perceived by infant learners. Moreover, we conducted simulation experiments to systematically determine how visual, perceptual, and attentional properties of infants' sensory experiences may affect word learning.