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

Developmental Science
Alexander LaTourrette, Sandra R Waxman

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 ...Continue Reading

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Citations

Aug 21, 2020·Proceedings of the National Academy of Sciences of the United States of America·Alexander S LaTourrette, Sandra R Waxman
Aug 8, 2020·Frontiers in Psychology·Nikola A Kompa, Jutta L Mueller
Aug 22, 2021·Infancy : the Official Journal of the International Society on Infant Studies·Ashley L RubaBetty M Repacholi
Sep 8, 2021·Cognitive Science·Matthijs WesteraSebastian Padó

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