Drift-Diffusion Model
CommunityGuidance for 2AFC drift-diffusion modeling.
AuthorHaoxuanLiTHUAI
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This skill provides expert guidance on selecting, fitting, and evaluating drift-diffusion models for two-choice reaction-time data in cognitive science, enabling researchers to decompose observed RT and accuracy into latent cognitive components.
Core Features & Use Cases
- Model selection across DDM variants (classic, full, EZ-diffusion, HDDM) for two-choice tasks.
- Parameter interpretation and linking: drift rate, boundary separation, non-decision time, starting point.
- Comprehensive fitting workflow guidance: data preparation, model fitting strategies, convergence checks, and model comparison.
- Practical examples: planning experiments, diagnosing fit issues, and interpreting parameter changes across conditions.
Quick Start
Run a guided walkthrough to fit a drift-diffusion model to two-choice RT data.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: Drift-Diffusion Model Download link: https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/archive/main.zip#drift-diffusion-model Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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