  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.
