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The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
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Credit assignment in hierarchical option transfer.

Jing-Jing Li1, Liyu Xia2, Flora Dong3

  • 1Helen Wills Neuroscience Institute, University of California, Berkeley.

Cogsci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference
|December 19, 2022
PubMed
Summary

Humans flexibly learn new strategies by creating and reusing hierarchical options, even when faced with new situations. This demonstrates precise credit assignment in hierarchical learning, unaffected by option similarity.

Keywords:
credit assignmenthierarchical reinforcement learningthe options frameworktransfer learning

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Machine Learning

Background:

  • Humans excel at structuring knowledge for rapid generalization.
  • Hierarchical options (strategy chunks) aid learning in complex tasks.
  • The credit assignment problem in hierarchical learning remains unclear.

Purpose of the Study:

  • Investigate how humans assign credit to new versus old options in novel contexts.
  • Determine if option similarity influences credit assignment.
  • Test the flexibility and precision of human hierarchical learning.

Main Methods:

  • Two groups of participants (n=124, n=104) learned hierarchically structured options.
  • Participants experienced negative transfer in a new option context.
  • Behavioral analysis assessed reuse of old options and creation of new ones.

Main Results:

  • Old options were reused without interference; new options were created and credited appropriately.
  • Credit assignment was independent of the similarity between new and old options.
  • The Option Model accurately captured observed behavioral results.

Conclusions:

  • Humans exhibit flexible and precise credit assignment in hierarchical learning.
  • Learned hierarchical options are robustly reused and recomposed in new contexts.
  • Findings support the role of option learning and transfer in human cognition.