The Developing Infant Creates a Curriculum for Statistical Learning

Summary

• What is the nature of the environment that supports learning (in infants)?
• Data collected from head-cameras and eye-trackers worn by infants in everyday environments help address this question
• Analyses of these egocentric views has shown systematic changes throughout development, effectively creating a curriculum for learning
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Background

• In the first two years of life, infants make significant progress in many domains, e.g. language, vision, and social behaviors
• Statistical learning methods rely on both the learning machinery and the data on which it operates
  • There’s an assumption that the learning environment is rich but noisy, and therefore the focus has been on learning machinery that can sort through this messy data
• The infant’s view of the world changes as its sensorimotor abilities develop
  • Start out with limited acuity and locomotion
  • Learning to crawl enables moving towards distant objects
  • Manually playing with objects creates a dataset of multiple views of a single object

Methods

• Analyses of head-camera images revealed distinct changes in visual experience
  • Under three months of age, consists primarily of close, frontal views of faces which dimishes as they reach their first birthday
  • After one year, hands become more prominent, with hands of others generally acting on objects
• Individuals with congenital cataracts removed as early as four months show permanent deficits in face processing
  • Might be due to missing out on the dataset of close frontal views of faces infants acquire in their first three months
• Everday learning environments are characterized by a few types of objects appearing very frequently
  • These skewed distributions may help facilitate learning: consistency, bootstrapping, or desirable difficulty

Results

• Infants create a curriculum for learning
  • After their first year, egocentric views of toddlers differ from 8-10 months as well as adults
  • Toddler visual experiences are shaped by what they can manually do
  • The changing abilities of infants open and close environments for learning
• Many approaches to statistical learning assume the computational problem, dataset, and learning machinery remain constant, which clearly does not reflect the reality of an infant

Conclusion

• Data for learning and the learning machinery cannnot be studied separately
  • The changing structure of the egocentric view is key to the robustness of infant learning