Computational models are central to cognitive neuroscience, but their rigorous application to experimental datasets is often constrained to a narrow set of canonical models that afford tractable analytical computations. We introduce the HSSM (Hierarchical Sequential Sampling Model) ecosystem, a Python toolbox that democratizes access to a broad, extensible array of neuro-cognitive process models through hierarchical Bayesian inference. Naturally leveraging simulation-based inference via likelihood surrogates, HSSM enables fast parameter estimation for models lacking closed-form likelihoods. Built atop PyMC and Bambi, HSSM provides a user-friendly formula syntax for specifying hierarchical mixed-effects regressions on model parameters, incorporating trial-by-trial neural or physiological covariates. The ecosystem allows fast model simulation and training data generation, as well as the neural network training utilities to deploy surrogate likelihood networks via HuggingFace. Contributions are designed to benefit not only the single researcher working on a problem, but organically, the entire research community. Together, the tools in the HSSM ecosystem bridge the interests of computational theorists as well as experimentalists, accelerating the cycle from model development to rigorous empirical testing.
Fengler, A., Xu, Y., Bera, K., Paniagua, C., Omar, A., Frank, M. J.
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