When learners surpass their models: mathematical modeling of learning from an inconsistent source

DOI10.1007/s11538-014-9990-2zbMath1305.91203arXiv1402.4678OpenAlexW2593979997WikidataQ44526098 ScholiaQ44526098MaRDI QIDQ467753

Natalia L. Komarova, Yelena Mandelshtam

Publication date: 5 November 2014

Published in: Bulletin of Mathematical Biology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1402.4678

zbMATH Keywords

Markov chainsregularizationlearningcultural transmission

Mathematics Subject Classification ID

Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Memory and learning in psychology (91E40) Linguistics (91F20)

Related Items (2)

Spatial evolution of regularization in learned behavior of animals ⋮ Mathematical modeling of learning from an inconsistent source: a nonlinear approach

Cites Work

• Stochastic dynamic models of response time and accuracy: A foundation primer
• Markov processes and learning models
• Evolution of Universal Grammar

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