Real-world visual statistics and infants’ first-learned object names

Elizabeth M. Clerkin, Elizabeth Hart, James M. Rehg, Chen Yu, Linda B. Smith

Research output: Contribution to journalArticlepeer-review

Abstract

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 monthold infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present—a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning.

Original languageEnglish (US)
Article number20160055
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Volume372
Issue number1711
DOIs
StatePublished - Jan 5 2017
Externally publishedYes

Keywords

  • Egocentric vision
  • Infants
  • Statistical learning
  • Visual statistics
  • Word learning

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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