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PLoS computational biology, 2024-03, Vol.20 (3), p.e1011978-e1011978
2024

Details

Autor(en) / Beteiligte
Titel
Learning environment-specific learning rates
Ist Teil von
  • PLoS computational biology, 2024-03, Vol.20 (3), p.e1011978-e1011978
Ort / Verlag
United States: Public Library of Science
Erscheinungsjahr
2024
Link zum Volltext
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
  • People often have to switch back and forth between different environments that come with different problems and volatilities. While volatile environments require fast learning (i.e., high learning rates), stable environments call for lower learning rates. Previous studies have shown that people adapt their learning rates, but it remains unclear whether they can also learn about environment-specific learning rates, and instantaneously retrieve them when revisiting environments. Here, using optimality simulations and hierarchical Bayesian analyses across three experiments, we show that people can learn to use different learning rates when switching back and forth between two different environments. We even observe a signature of these environment-specific learning rates when the volatility of both environments is suddenly the same. We conclude that humans can flexibly adapt and learn to associate different learning rates to different environments, offering important insights for developing theories of meta-learning and context-specific control.
Sprache
Englisch
Identifikatoren
ISSN: 1553-734X
eISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1011978
Titel-ID: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10990245

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