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Data and Code for: On agentic behavioral modeling

Author(s) / Creator(s)

Ostwald, Dirk

Abstract / Description

This repository accompanies a manuscript introducing agentic behavioral modeling (ABM), a framework that links theoretical neuroscience, decision theory, and probabilistic inference by treating artificial agents as generative models of human cognition. The repository contains both the experimental datasets and the code used to analyze them.

Persistent Identifier

Date of first publication

2026-04-30

Publisher

PsychArchives

Citation

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  • Author(s) / Creator(s)
    Ostwald, Dirk
  • PsychArchives acquisition timestamp
    2026-04-30T08:54:38Z
  • Made available on
    2026-04-30T08:54:38Z
  • Date of first publication
    2026-04-30
  • Abstract / Description
    This repository accompanies a manuscript introducing agentic behavioral modeling (ABM), a framework that links theoretical neuroscience, decision theory, and probabilistic inference by treating artificial agents as generative models of human cognition. The repository contains both the experimental datasets and the code used to analyze them.
    en
  • Review status
    unknown
  • Table of contents
    Code: Python code for the Gabor contrast discrimination and Symmetric bandit learning ABMs Data: Simulated and experimental data for the Gabor contrast discrimination and Symmetric bandit learning ABMs For a full table of contents, please refer to Table-of-Contents.pdf in the main folder
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/17261
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.21896
  • Language of content
    eng
  • Publisher
    PsychArchives
  • Dewey Decimal Classification number(s)
    150
  • Title
    Data and Code for: On agentic behavioral modeling
    en
  • DRO type
    researchData
  • DRO type
    code
  • DRO type
    other