Fit Drift Diffusion Model
Skill Verified ActiveFit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
To provide a robust and validated method for estimating cognitive parameters from behavioral data, enabling researchers to model binary decision-making processes.
Features
- Parameter estimation for DDM variants
- Model comparison using BIC
- Parameter recovery validation
- Data cleaning and preparation for DDM
- Visual and statistical model fit assessment
Use Cases
- Modeling binary decisions with reaction time data
- Estimating cognitive parameters like drift rate and boundary separation
- Comparing different sequential sampling models
- Validating the reliability of DDM fitting pipelines
Non-Goals
- Fitting models other than drift-diffusion variants
- Advanced statistical analyses beyond DDM evaluation
- Experimental design for data collection (though related skills exist)
Installation
/plugin install agent-almanac@pjt222-agent-almanacQuality Score
VerifiedTrust Signals
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