Adaptive learning under expected and unexpected uncertainty.
Nat Rev Neurosci
; 20(10): 635-644, 2019 10.
Article
em En
| MEDLINE
| ID: mdl-31147631
ABSTRACT
The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning depends on whether they are representative of a typically experienced range of outcomes or signal a change in the reward environment. Successful learning and decision-making therefore require the ability to estimate expected uncertainty (related to the variability of outcomes) and unexpected uncertainty (related to the variability of the environment). Understanding the bases and effects of these two types of uncertainty and the interactions between them - at the computational and the neural level - is crucial for understanding adaptive learning. Here, we examine computational models and experimental findings to distil computational principles and neural mechanisms for adaptive learning under uncertainty.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Encéfalo
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Adaptação Biológica
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Incerteza
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Aprendizagem
Tipo de estudo:
Prognostic_studies
Limite:
Animals
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Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article