DriftLab

A browser-based battery of cognitive psychophysics tasks (Stroop, Flanker, N-back, and more) paired with a Python analysis pipeline that uses drift-diffusion modeling (DDM) to separate processing speed from decision threshold.

🚧 Work in progress — Fase 1 (MVP). Stroop and a simple RT task are playable; DDM fitting comes in a later phase.

Play the demo

Browsers block fetch() on file:// for CORS reasons, so serve the repo over a local static server instead of opening index.html directly:

python -m http.server 8000

Then open http://localhost:8000/ and pick a task.

Run the analysis pipeline

python -m venv venv
venv\Scripts\activate          # Windows
# source venv/bin/activate     # macOS/Linux
pip install -r analysis/requirements.txt
pip install -e analysis        # installs driftlab_analysis in editable mode

Run the tests:

pytest analysis/tests/

Run the notebook:

jupyter notebook analysis/notebooks/01_stroop_basic_analysis.ipynb

It uses real data from analysis/data/raw/ if any is present (exported from the browser tasks), and falls back to a versioned synthetic sample otherwise — so it runs out of the box without playing the task first.

Architecture

Each task lives in its own self-contained folder under experiments/, using a shared data export module (shared/js/data-export.js) so any task's CSV export can be loaded by the analysis pipeline without task-specific code. See each task's own README.md for what it measures and its parameters.

License

MIT — see LICENSE.