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An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective

Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology an...

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Detalles Bibliográficos
Autores principales: Hoppe, Dorothée B., Hendriks, Petra, Ramscar, Michael, van Rij, Jacolien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579095/
https://www.ncbi.nlm.nih.gov/pubmed/35032022
http://dx.doi.org/10.3758/s13428-021-01711-5

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