Optimizing the human learnability of abstract network representations.

Qian, William; Lynn, Christopher W; Klishin, Andrei A; Stiso, Jennifer; Christianson, Nicolas H; Bassett, Dani S

Qian, William; Lynn, Christopher W; Klishin, Andrei A; Stiso, Jennifer; Christianson, Nicolas H; Bassett, Dani S.

Afiliação

Qian W; Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104.
Lynn CW; Initiative for the Theoretical Sciences, Graduate Center, City University of New York, New York, NY 10016.
Klishin AA; Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544.
Stiso J; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544.
Christianson NH; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.
Bassett DS; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.

Proc Natl Acad Sci U S A ; 119(35): e2121338119, 2022 08 30.

Article em En | MEDLINE | ID: mdl-35994661

Assuntos

Simulação por Computador; Conhecimento; Aprendizagem; Modelos Psicológicos; Humanos; Idioma; Música; Reforço Psicológico

Palavras-chave

complex networks; graph learning; maximum entropy

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Simulação por Computador / Conhecimento / Aprendizagem / Modelos Psicológicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2022 Tipo de documento: Article

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