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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 682-692 |
| Number of pages | 11 |
| Journal | Nature Human Behaviour |
| Volume | 2 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 1 2018 |
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All Science Journal Classification (ASJC) codes
- Social Psychology
- Experimental and Cognitive Psychology
- Behavioral Neuroscience
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Knowledge gaps in the early growth of semantic feature networks. / Sizemore, Ann E.; Karuza, Elisabeth; Giusti, Chad; Bassett, Danielle S.
In: Nature Human Behaviour, Vol. 2, No. 9, 01.09.2018, p. 682-692.Research output: Contribution to journal › Article
TY - JOUR
T1 - Knowledge gaps in the early growth of semantic feature networks
AU - Sizemore, Ann E.
AU - Karuza, Elisabeth
AU - Giusti, Chad
AU - Bassett, Danielle 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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UR - http://www.scopus.com/inward/citedby.url?scp=85053001984&partnerID=8YFLogxK
U2 - 10.1038/s41562-018-0422-4
DO - 10.1038/s41562-018-0422-4
M3 - Article
VL - 2
SP - 682
EP - 692
JO - Nature Human Behaviour
JF - Nature Human Behaviour
SN - 2397-3374
IS - 9
ER -