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Optimizing the human learnability of abstract network representations

Qian, William and Lynn, Christopher W. and Klishin, Andrei A. and Stiso, Jennifer and Christianson, Nicolas H. and Bassett, Dani S. (2022) Optimizing the human learnability of abstract network representations. Proceedings of the National Academy of Sciences of the United States of America, 119 (35). Art. No. e2121338119. ISSN 0027-8424. PMCID PMC9436382. doi:10.1073/pnas.2121338119.

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Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To investigate these questions, we study the optimization of network learnability in a computational model of human learning. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core–periphery structure, we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.

Item Type:Article
Related URLs:
URLURL TypeDescription CentralArticle
Lynn, Christopher W.0000-0002-6487-2671
Klishin, Andrei A.0000-0002-5740-8520
Stiso, Jennifer0000-0002-3295-586X
Christianson, Nicolas H.0000-0001-8330-8964
Bassett, Dani S.0000-0002-6183-4493
Additional Information:We thank Christopher Kroninger for feedback on earlier versions of this manuscript. We also thank Pixel Xia for useful discussions on Sierpiński graph construction. This work was supported by the Army Research Office (DCIST-W911NF-17-2-0181) and the National Institute of Mental Health (1-R21-MH-124121-01). D.S.B. acknowledges additional support from the John D. and Catherine T. MacArthur Foundation, the Institute for Scientific Interchange Foundation, NSF CAREER Award PHY-1554488, and the Center for Curiosity. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. Data, Materials, and Software Availability. Previously published data were used for this work from GitHub ( All code and data used in analyses have been made publicly available at (60).
Funding AgencyGrant Number
Army Research Office (ARO)W911NF-17-2-0181
John D. and Catherine T. MacArthur FoundationUNSPECIFIED
Institute for Scientific Interchange FoundationUNSPECIFIED
Center for CuriosityUNSPECIFIED
Issue or Number:35
PubMed Central ID:PMC9436382
Record Number:CaltechAUTHORS:20221122-564647900.18
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117994
Deposited By: Research Services Depository
Deposited On:07 Dec 2022 17:52
Last Modified:07 Dec 2022 17:55

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