Drift-Diffusion Model (DDM)

Purpose

This skill encodes expert knowledge for applying drift-diffusion models (DDMs) to two-choice reaction time data. DDMs decompose observed accuracy and RT distributions into latent cognitive processes — evidence accumulation rate, response caution, and non-decision time. This skill guides researchers through model variant selection, parameter fitting, and result evaluation, encoding domain-specific judgment that requires specialized training in computational cognitive modeling.

When to Use This Skill

• Designing a study where two-alternative forced choice (2AFC) RT data will be collected and you want to decompose behavior into latent cognitive components
• Choosing between DDM variants (classic DDM, full DDM, EZ-diffusion, HDDM, LBA) for a given dataset and research question
• Setting up model fitting: selecting fitting method, preparing data, configuring software tools
• Evaluating model fit quality: checking parameter recovery, running posterior predictive checks, comparing nested models
• Interpreting DDM parameters in terms of cognitive processes (e.g., drift rate as evidence quality, boundary separation as response caution)
• Troubleshooting fitting problems: convergence failures, implausible parameter estimates, poor fits to RT quantiles

When NOT to Use This Skill

• Tasks with more than two response options require multi-accumulator models (see Racing Diffusion Model or LBA in references/model-variants.md)
• Go/No-Go tasks violate the two-boundary assumption; use single-boundary models or SSP models instead (Ratcliff et al., 2018)