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The successor representation in human reinforcement learning
Theories of reward learning in neuroscience have focused on two families of algorithms, thought to capture deliberative vs. habitual choice. “Model-based” algorithms compute the value of candidate actions from scratch, whereas “model-free” algorithms make choice more efficient but less flexible by s...
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| Publicado no: | Nat Hum Behav |
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| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6941356/ https://ncbi.nlm.nih.gov/pubmed/31024137 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41562-017-0180-8 |
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