Cognitive Strategies Regulate Fictive, but not Reward Prediction Error Signals in a Sequential Investment Task

Computational models of reward processing suggest that foregone or fictive outcomes serve as important information sources for learning and augment those generated by experienced rewards (e.g. reward prediction errors). An outstanding question is how these learning signals interact with top-down cog...

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Detalles Bibliográficos
Autores principales: Gu, Xiaosi, Kirk, Ulrich, Lohrenz, Terry M, Montague, P Read
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BlackWell Publishing Ltd 2014
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4105325/
https://www.ncbi.nlm.nih.gov/pubmed/24382784
http://dx.doi.org/10.1002/hbm.22433

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4105325/
https://www.ncbi.nlm.nih.gov/pubmed/24382784
http://dx.doi.org/10.1002/hbm.22433

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