autospice 0.1.0

SPICE: Sparse and Interpretable Cognitive Equations

Computational Discovery of Sparse and Interpretable Cognitive Equations (SPICE)

SPICE is a framework for automating scientific practice in cognitive science and is based on a two cornerstones:

1. A task-specific RNN is trained to predict human behavior and thus learn implicitly latent cognitive mechanisms.

2. Sparse Identification of nonlinear Dynamics (SINDy; an equation discovery algorithm) is used to obtain mathematically interpretable equations for the learned cognitive mechanisms.

The resulting model with the neural-network architecture but with equations instead of RNN modules is called SPICE model. An overview is given in Figure 1.

This README file gives an overview on how to install and run SPICE as a scikit-learn estimator. To learn how to use SPICE in more comprehensive scenarios, you can go to tutorials.

Installation

You can install SPICE using pip:

pip install -e .

Features

• Scikit-learn compatible estimator interface
• Customizable network architecture for identifying complex cognitive mechanisms
• Participant embeddings for identifying individual differences

Quick Start

from spice.estimator import SpiceEstimator
import numpy as np

# Create and configure the model
spice_estimator = SpiceEstimator(
    hidden_size=8,
    epochs=128,
    n_actions=2,
    n_participants=10,
    learning_rate=1e-2,
    sindy_optim_threshold=0.03,
    verbose=True
)

# Generate example data
conditions = np.random.rand(10, 100, 5)  # (n_participants, n_trials, n_features)
targets = np.random.randint(0, 2, size=(10, 100, 2))  # (n_participants, n_trials, n_actions)

# Fit the model
spice_estimator.fit(conditions, targets)

# Make predictions
pred_rnn, pred_sindy = model.predict(conditions)

# Get learned features
features = model.get_sindy_features()

Requirements

See requirements.txt for a complete list of dependencies.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this package in your research, please cite:

@software{spice2025,
  title = {SPICE: Sparse and Interpretable Cognitive Equations},
  year = {2025},
  author = {Weinhardt, Daniel},
  url = {https://github.com/whyhardt/SPICE}
}