A little labeling goes a long way: Semi-supervised learning in infancy

Alexander LaTourrette*, Sandra R. Waxman

*Corresponding author for this work

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

There is considerable evidence that labeling supports infants' object categorization. Yet in daily life, most of the category exemplars that infants encounter will remain unlabeled. Inspired by recent evidence from machine learning, we propose that infants successfully exploit this sparsely labeled input through “semi-supervised learning.” Providing only a few labeled exemplars leads infants to initiate the process of categorization, after which they can integrate all subsequent exemplars, labeled or unlabeled, into their evolving category representations. Using a classic novelty preference task, we introduced 2-year-old infants (n = 96) to a novel object category, varying whether and when its exemplars were labeled. Infants were equally successful whether all exemplars were labeled (fully supervised condition) or only the first two exemplars were labeled (semi-supervised condition), but they failed when no exemplars were labeled (unsupervised condition). Furthermore, the timing of the labeling mattered: when the labeled exemplars were provided at the end, rather than the beginning, of familiarization (reversed semi-supervised condition), infants failed to learn the category. This provides the first evidence of semi-supervised learning in infancy, revealing that infants excel at learning from exactly the kind of input that they typically receive in acquiring real-world categories and their names.

Original languageEnglish (US)
Article numbere12736
JournalDevelopmental Science
Volume22
Issue number1
DOIs
StatePublished - Jan 2019

Keywords

  • category learning
  • conceptual development
  • language acquisition
  • language and thought
  • semi-supervised learning

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Cognitive Neuroscience

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