HSSM: New Python Toolbox Simplifies Hierarchical Bayesian Modeling in Cognitive Neuroscience
Researchers have released HSSM (Hierarchical Sequential Sampling Model), an open-source Python toolbox designed to make advanced neuro-cognitive process modeling accessible to a wider scientific audience. Built on PyMC and Bambi, it uses simulation-based inference and neural network surrogate likelihoods to enable parameter estimation for models that lack closed-form solutions. The tool aims to accelerate the cycle from computational model development to empirical testing by lowering technical barriers for both theorists and experimentalists.
The HSSM ecosystem is a newly introduced Python toolbox that expands access to hierarchical Bayesian inference for neuro-cognitive modeling, a domain previously limited to a narrow set of analytically tractable models. By leveraging simulation-based inference through likelihood surrogates—neural networks trained to approximate intractable likelihoods—HSSM enables fast parameter estimation across a broad range of sequential sampling models. The toolbox is built atop established probabilistic programming frameworks PyMC and Bambi, offering a user-friendly formula syntax for specifying hierarchical mixed-effects regressions on model parameters. Researchers can incorporate trial-by-trial neural or physiological covariates, making it suitable for integrating neuroimaging or psychophysiological data with behavioral models. The ecosystem also includes utilities for model simulation, training data generation, and deployment of surrogate likelihood networks via HuggingFace, facilitating community-wide sharing of trained models. The authors frame HSSM as a community resource, designed so that contributions by individual researchers organically benefit the broader field. The preprint is hosted on bioRxiv and has not yet undergone formal peer review.
What's missing
As a preprint, this work has not yet undergone peer review, so independent validation of the toolbox's performance claims—such as speed and accuracy of surrogate likelihoods relative to gold-standard methods—is pending.
What different sources said
- bioRxivCenter
HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neuro-cognitive Modeling
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