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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2022
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| 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 |