Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: a meta-analytical approach using diffusion decision modeling

Reinforcement learning (RL) models describe how humans and animals learn by trial-and-error to select actions that maximize rewards and minimize punishments. Traditional RL models focus exclusively on choices, thereby ignoring the interactions between choice preference and response time (RT), or how...

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Detalles Bibliográficos
Autores principales: Fontanesi, Laura, Palminteri, Stefano, Lebreton, Maël
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2019
Materias:
Special Issue/Reward Systems, Cognition, and Emotion
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598978/
https://www.ncbi.nlm.nih.gov/pubmed/31175616
http://dx.doi.org/10.3758/s13415-019-00723-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598978/
https://www.ncbi.nlm.nih.gov/pubmed/31175616
http://dx.doi.org/10.3758/s13415-019-00723-1

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