HSSM

Jan 31, 2025

Understanding how we make decisions is a core challenge in cognitive science. Sequential Sampling Models (SSMs) offer a powerful way to analyze this process by modeling how we accumulate evidence before making a choice. A new Python toolbox called HSSM (for Hierarchical Sequential Sampling Models) makes exploring these models easier than ever.

Developed by the lab of Dr. Michael Frank, this toolbox focuses on Hierarchical Sequential Sampling Models (HSSMs), particularly those based on drift-diffusion models (DDMs). HSSM builds upon the widely-used HDDM package from the same group, offering an updated and more flexible approach. Leveraging the power of PyMC for model fitting, Bambi for regression modeling, and ArviZ for visualization, HSSM provides a comprehensive suite of tools.

The repository includes extensive documentation and numerous examples demonstrating how to use the toolbox to model decisions within a DDM framework. It also shows how to analyze the relationship between behavioral or neural measures and the parameters of these DDMs. HSSM supports several DDM variants and other models like the Linear Ballistic Accumulator (LBA).

Researchers interested in the cognitive processes underlying decision-making, particularly those seeking to connect neural activity to these processes, will find this toolbox invaluable.

This research tool was created by your colleagues. Please acknowledge the Principal Investigator, cite the article in which the tool was described, and include an RRID in the Materials and Methods of your future publications. RRID:SCR_026356

Access the code!

Software and documentation are available in the GitHub repository.