Sequential learning from infant-perspective video reveals early simple views are critical for visual model development

Self-supervised models trained on infant egocentric video learned best when the curriculum started with data from the youngest infants (10-month-olds), whose visual world is slower and simpler. The youngest data yielded the strongest learning signal and top downstream task perfo…

• Study design: Video recordings from head-mounted cameras were used to train self-supervised learning models, separated by infant age group (10, 12, 14, 16, 18 months). (Exact n not reported in source)
• Key secondary finding: The benefit of the youngest group's data was specifically attributed to the "slowness and simplicity" of their early visual experience.
• Limitation: This is a computational simulation study and does not include validation on a clinical or human imaging task.

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