The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs

Here, the authors show that rats’ performance on olfactory decision tasks is best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs....

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Autores principales: André G. Mendonça, Jan Drugowitsch, M. Inês Vicente, Eric E. J. DeWitt, Alexandre Pouget, Zachary F. Mainen
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/cdd0caa3d0de4532b42841435d503f3e
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Sumario:Here, the authors show that rats’ performance on olfactory decision tasks is best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs.