A vast space of compact strategies for effective decisions.

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  • Author(s): Ma T;Ma T; Hermundstad AM; Hermundstad AM
  • Source:
    Science advances [Sci Adv] 2024 Jun 21; Vol. 10 (25), pp. eadj4064. Date of Electronic Publication: 2024 Jun 21.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: American Association for the Advancement of Science Country of Publication: United States NLM ID: 101653440 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2375-2548 (Electronic) Linking ISSN: 23752548 NLM ISO Abbreviation: Sci Adv Subsets: MEDLINE
    • Publication Information:
      Original Publication: Washington, DC : American Association for the Advancement of Science, [2015]-
    • Subject Terms:
    • Abstract:
      Inference-based decision-making, which underlies a broad range of behavioral tasks, is typically studied using a small number of handcrafted models. We instead enumerate a complete ensemble of strategies that could be used to effectively, but not necessarily optimally, solve a dynamic foraging task. Each strategy is expressed as a behavioral "program" that uses a limited number of internal states to specify actions conditioned on past observations. We show that the ensemble of strategies is enormous-comprising a quarter million programs with up to five internal states-but can nevertheless be understood in terms of algorithmic "mutations" that alter the structure of individual programs. We devise embedding algorithms that reveal how mutations away from a Bayesian-like strategy can diversify behavior while preserving performance, and we construct a compositional description to link low-dimensional changes in algorithmic structure with high-dimensional changes in behavior. Together, this work provides an alternative approach for understanding individual variability in behavior across animals and tasks.
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    • Publication Date:
      Date Created: 20240621 Date Completed: 20240621 Latest Revision: 20240623
    • Publication Date:
      20240623
    • Accession Number:
      PMC11192086
    • Accession Number:
      10.1126/sciadv.adj4064
    • Accession Number:
      38905348