TY - JOUR
T1 - Knowledge gaps in the early growth of semantic feature networks
AU - Sizemore, Ann E.
AU - Karuza, Elisabeth A.
AU - Giusti, Chad
AU - Bassett, Danielle S.
N1 - Funding Information:
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.
Publisher Copyright:
© 2018, The Author(s).
PY - 2018/9/1
Y1 - 2018/9/1
N2 - 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.
AB - 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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U2 - 10.1038/s41562-018-0422-4
DO - 10.1038/s41562-018-0422-4
M3 - Article
C2 - 30333998
AN - SCOPUS:85053001984
VL - 2
SP - 682
EP - 692
JO - Nature Human Behaviour
JF - Nature Human Behaviour
SN - 2397-3374
IS - 9
ER -