hBayesDM

hBayesDM applies hierarchical Bayesian modeling in an R package to reinforcement learning and decision-making (RLDM) tasks and social exchange games, estimating individual- and group-level parameters and extracting trial-by-trial latent variables (e.g., prediction errors) for model-based analyses including fMRI and EEG.

Key Features:

• Hierarchical Bayesian Modeling: Employs hierarchical Bayesian techniques to estimate individual- and group-level parameters simultaneously with mutual constraint between levels.
• Support for RLDM Tasks and Social Exchange Games: Provides model implementations for a range of reinforcement learning and decision-making tasks and social exchange games.
• Latent Variable Extraction: Facilitates extraction of trial-by-trial latent variables such as prediction errors for use in model-based analyses with fMRI and EEG.
• Bayesian Model Comparison: Implements procedures for comparing competing computational models using Bayesian approaches.
• Applicability to Multiple Decision Systems: Supports modeling of goal-directed, habitual, and Pavlovian decision-making processes.
• R Package Implementation: Distributed as an R package for computational modeling workflows.

Scientific Applications:

• Computational Psychiatry: Provides quantitative frameworks to decompose psychiatric conditions into fundamental neurocognitive dimensions using RLDM parameters.
• Clinical Assessment: Facilitates assessment and potential diagnosis of psychiatric patients through parameter estimation from RLDM tasks.
• Model-Based Neuroimaging: Supplies trial-by-trial latent variables (e.g., prediction errors) for integration with fMRI and EEG to identify brain substrates of RLDM processes.
• Behavioral Decision-Making Research: Enables investigation of mechanisms and interactions among goal-directed, habitual, and Pavlovian systems.

Methodology:

Hierarchical Bayesian modeling that simultaneously estimates individual- and group-level parameters, allowing group-level information to inform individual estimates and vice versa.