Fit a Drift-Diffusion Model

Estimate the parameters of a drift-diffusion model (DDM) from reaction time and accuracy data, evaluate model fit against observed quantiles, compare candidate model variants, and validate estimation quality through parameter recovery simulation.

When to Use

• Modeling binary decision-making with reaction time data
• Estimating cognitive parameters (drift rate, boundary separation, non-decision time) from experimental data
• Comparing sequential sampling model variants for a decision task
• Validating that a DDM fitting pipeline recovers known parameter values
• Decomposing speed-accuracy tradeoff effects into latent cognitive components

Inputs

• Required: Reaction time data with accuracy labels (correct/error) per trial
• Required: Subject and condition identifiers for each trial
• Required: Choice of DDM variant (basic 3-parameter, full 7-parameter, or hierarchical)
• Optional: Prior distributions for Bayesian estimation (default: weakly informative)
• Optional: Number of simulated datasets for parameter recovery (default: 100)