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Nat Hum Behav. 2018 Sep;2(9):682-692. doi: 10.1038/s41562-018-0422-4. Epub 2018 Sep 7.

Knowledge gaps in the early growth of semantic feature networks.

Author information

1
Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104 USA.
2
Department of Psychology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104 USA.
3
Department of Mathematical Sciences, University of Delaware, DE 19716 USA.
4
Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, PA 19104 USA.
5
Department of Neurology, Perelman School of Medicine, University of Pennsylvania, PA 19104 USA.
6
Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, PA 19104 USA.

Abstract

Understanding language learning, and more general knowledge acquisition, requires characterization of inherently qualitative structures. Recent work has applied network science to this task by creating semantic feature networks, in which words correspond to nodes and connections to shared features, then characterizing the structure of strongly inter-related groups of words. However, the importance of sparse portions of the semantic network - knowledge gaps - remains unexplored. Using applied topology we query the prevalence of knowledge gaps, which we propose manifest as cavities within the growing semantic feature network of toddlers. We detect topological cavities of multiple dimensions and find that despite word order variation, global organization remains similar. We also show that nodal network measures correlate with filling cavities better than basic lexical properties. Finally, we discuss the importance of semantic feature network topology in language learning and speculate that the progression through knowledge gaps may be a robust feature of knowledge acquisition.

PMID:
30333998
PMCID:
PMC6186390
[Available on 2019-03-07]
DOI:
10.1038/s41562-018-0422-4

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