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Temporal and state abstractions for efficient learning, transfer, and composition in humans.

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Humans learn transferable strategies by creating hierarchical options, which are reusable multi-step policies. This enables faster learning and knowledge transfer in novel situations.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Humans excel at generalizing knowledge to new tasks using prior experience.
  • Understanding the mechanisms of knowledge structuring for rapid generalization remains a challenge.
  • Previous work suggested hierarchical state abstraction aids generalization of simple rules.

Purpose of the Study:

  • To investigate if humans learn and transfer multi-step strategies, termed options, in sequential decision-making.
  • To test the role of temporal abstractions in hierarchical learning and transfer.
  • To develop and validate a computational model of human option learning.

Main Methods:

  • Developed a novel sequential decision-making protocol across four experiments.
  • Compared human performance against flat and hierarchical reinforcement learning models.
  • Extended the options framework to integrate temporal and state abstractions.

Main Results:

  • Observed significant transfer effects at multiple hierarchical levels.
  • Demonstrated that flat RL models and hierarchical models without temporal abstractions could not explain the findings.
  • Developed a quantitative model integrating temporal and state abstractions that accurately captured human transfer effects.

Conclusions:

  • Humans construct and compose hierarchical options, utilizing them for exploration and knowledge transfer.
  • Temporal abstractions are crucial for human generalization in complex, multi-step tasks.
  • The proposed model provides a quantitative account of how humans leverage hierarchical options for efficient learning.