@article{01HQ7VEBHM96KX3FE8GBECHJRT,
  abstract     = {{A key aspect of cognitive flexibility is to efficiently make use of earlier experience to attain one's goals. This requires learning, but also a modular, and more specifically hierarchical, structure. We hold that both are required, but combining them leads to several computational challenges that brains and artificial agents (learn to) deal with. In a hierarchical structure, metadecisions must be made, of which two types can be distinguished. First, a (meta-)decision may involve choosing which (lower-level) modules to select (module choice). Second, it may consist of choosing appropriate parameter settings within a module (parameter tuning). Furthermore, prediction error monitoring may allow determining the right meta-decision (module choice or parameter tuning). We discuss computational challenges and empirical evidence relative to how these two meta-decisions may be implemented to support learning for cognitive flexibility.}},
  articleno    = {{101374}},
  author       = {{Verbeke, Pieter and Verguts, Tom}},
  issn         = {{2352-1546}},
  journal      = {{CURRENT OPINION IN BEHAVIORAL SCIENCES}},
  keywords     = {{ANTERIOR CINGULATE CORTEX,INTEGRATIVE THEORY,UNCERTAINTY,COGNITION,RHYTHMS,MODEL}},
  language     = {{eng}},
  pages        = {{6}},
  title        = {{Reinforcement learning and meta-decision-making}},
  url          = {{http://doi.org/10.1016/j.cobeha.2024.101374}},
  volume       = {{57}},
  year         = {{2024}},
}

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