ArticleDevelopmental science2019
A little labeling goes a long way: Semi-supervised learning in infancy.
Article in Developmental science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Object recognition ability predicts category learning with medical images.Cognitive research: principles and implications · 2023Trial
- Social sampling: Children track social choices to reason about status hierarchies.Journal of experimental psychology. General · 2021Trial
- Structural Alignment and Linguistic Contrast Help Children Learn a Key Principle of Spatial Construction.Cognitive science · 2025Article
- The Delusional Hedge Algorithm as a Model of Human Learning From Diverse Opinions.Topics in cognitive science · 2025Article
- Word learning as category formation.PloS one · 2025Article
- Longer looks for language: Novel labels lengthen fixation duration for 2-year-old children.Journal of experimental child psychology · 2023Article
- Sparse labels, no problems: Infant categorization under challenging conditions.Child development · 2022Article
- From social contingency to verbal reference: A constructivist hypothesis.Psychological review · 2022Review
- Linguistic and developmental influences on superordinate facial configuration categorization in infancy.Infancy : the official journal of the International Society on Infant Studies · 2021Article
- Naming guides how 12-month-old infants encode and remember objects.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Superordinate categorization of negative facial expressions in infancy: The influence of labels.Developmental psychology · 2020Article
- How Abstract (Non-embodied) Linguistic Representations Augment Cognitive Control.Frontiers in psychology · 2020Article
- Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms.Journal of visualized experiments : JoVE · 2019Article
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.