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Knowledge gaps in the early growth of semantic feature networks

Nature Human Behaviour volume 2, pages 682–692 (2018)Cite this article

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Abstract

Understanding language learning and more general knowledge acquisition requires the 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 correspond to shared features, and then by characterizing the structure of strongly interrelated 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 in the growing semantic feature network of toddlers. We detect topological cavities of multiple dimensions and find that, despite word order variation, the 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.

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Fig. 1: Knowledge gaps manifesting as topological cavities in the growing semantic feature network.
Fig. 2: Persistent homology detects longevity of topological cavities in n-order complexes.
Fig. 3: Persistent homology distinguishes random from structured generative models of n-order complexes.
Fig. 4: Topological cavities form and die in the semantic feature network with a pattern that is resistant to random node reordering.
Fig. 5: Global semantic feature network architecture is consistent across maternal education levels despite local variations.
Fig. 6: Number of persistent cycles killed correlates with topological properties instead of lexical features.

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Acknowledgements

The authors thank L. Torres, T. Eliassi-Rad and B. Klein for discussions. This work was supported by the National Science Foundation CAREER PHY-1554488 to D.S.B. The authors also acknowledge support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Paul G. Allen Foundation, the Army Research Laboratory through contract number W911NF-10-2-0022, the Army Research Office through contract numbers W911NF-14-1-0679 and W911NF-16-1-0474, the National Institute of Health (2-R01-DC-009209-11, 1R01HD086888-01, R01-MH107235, R01-MH107703, R01MH109520, 1R01NS099348 and R21-M MH-106799), the Office of Naval Research and the National Science Foundation (BCS-1441502, CAREER PHY-1554488, BCS-1631550 and CNS-1626008). The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Ann E. Sizemore & Danielle S. Bassett

  2. Department of Psychology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Elisabeth A. Karuza

  3. Department of Mathematical Sciences, University of Delaware, Newark, DE, USA

    Chad Giusti

  4. Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  5. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  6. Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

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  1. Ann E. Sizemore
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A.E.S. and D.S.B. devised the experiments. A.E.S. performed the data analysis and crafted the initial draft. All authors revised the manuscript and approved the final version.

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Correspondence to Danielle S. Bassett.

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Sizemore, A.E., Karuza, E.A., Giusti, C. et al. Knowledge gaps in the early growth of semantic feature networks. Nat Hum Behav 2, 682–692 (2018). https://doi.org/10.1038/s41562-018-0422-4

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