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Updated: Jun 23, 2026

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HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neurocognitive Modeling.

Alexander Fengler, Yang Xu, Krishn Bera

    Biorxiv : the Preprint Server for Biology
    |June 22, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    The Hierarchical Sequential Sampling Model (HSSM) ecosystem offers a Python toolbox for advanced cognitive neuroscience modeling. It enables faster parameter estimation for complex models, benefiting both researchers and the scientific community.

    Area of Science:

    • Cognitive Neuroscience
    • Computational Neuroscience
    • Bayesian Inference

    Background:

    • Computational models are crucial in cognitive neuroscience but often limited to simple models due to analytical constraints.
    • Rigorous application of complex models to experimental data is challenging.

    Purpose of the Study:

    • Introduce the Hierarchical Sequential Sampling Model (HSSM) ecosystem, a Python toolbox.
    • Democratize access to a wide range of neurocognitive process models.
    • Facilitate rigorous empirical testing of computational models.

    Main Methods:

    • Utilize hierarchical Bayesian inference and simulation-based inference with likelihood surrogates.
    • Employ PyMC and Bambi for user-friendly formula syntax in hierarchical mixed-effects regressions.

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  • Incorporate trial-by-trial neural or physiological covariates.

    • Main Results:

    • Enable fast parameter estimation for models lacking closed-form likelihoods.
    • Facilitate rapid model simulation and training data generation.
    • Provide utilities for training neural networks to deploy surrogate likelihoods.

    Conclusions:

    • The HSSM ecosystem accelerates the development and empirical testing cycle for computational models.
    • Bridge the gap between computational theorists and experimentalists in cognitive neuroscience.
    • Foster community-wide benefits through accessible and extensible modeling tools.