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A flexible and generalizable model of online latent-state learning
Many models of classical conditioning fail to describe important phenomena, notably the rapid return of fear after extinction. To address this shortfall, evidence converged on the idea that learning agents rely on latent-state inferences, i.e. an ability to index disparate associations from cues to...
Enregistré dans:
| Publié dans: | PLoS Comput Biol |
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| Auteurs principaux: | , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Public Library of Science
2019
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6762208/ https://ncbi.nlm.nih.gov/pubmed/31525176 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pcbi.1007331 |
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