reaction-time-analysis
CommunityTurn RT data into DDM parameters
Education & Research#data visualization#cognitive modeling#reaction-time#ex-gaussian#drift diffusion model#behavioral data#Bayesian modeling
Authorxjtulyc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill streamlines reaction-time (RT) analysis by cleaning noisy behavioral trials, modeling RT distributions, and extracting cognitive process parameters that explain speed–accuracy tradeoffs.
Core Features & Use Cases
- RT data quality control: removes implausible and statistical outlier trials and reports cleaning summaries.
- Distribution modeling & diagnostics: fits an ex-Gaussian model to RTs and generates Q-Q and density checks, plus supports Vincentile and delta plots for condition comparisons.
- DDM parameter estimation: computes fast EZdiff DDM parameters (v, a, Ter) and supports Bayesian DDM workflow ideas using PyMC.
Quick Start
Use the reaction-time-analysis skill to clean your trial-level RT dataset and then fit ex-Gaussian distributions and EZdiff DDM parameters per experimental condition.
Dependency Matrix
Required Modules
scipynumpypandasmatplotlibpymcpytensor
Components
assets
💻 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: reaction-time-analysis Download link: https://github.com/xjtulyc/awesome-rosetta-skills/archive/main.zip#reaction-time-analysis Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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