Inverse optimal control for continuous psychophysics

This repository contains the official JAX implementation of the inverse optimal control method presented in the paper:

Straub, D., & Rothkopf, C. A. (2022). Putting perception into action with inverse optimal control for continuous psychophysics. eLife, 11, e76635.

Installation

The package can be installed via pip

python -m pip install lqg

although I recommend cloning the repository to get the most recent version and installing locally with a virtual environment

python -m venv env
source env/bin/activate
python -m pip install -e .

Usage examples

• main.py shows how to simulate data and infer parameters using the LQG model of the tracking task.

• notebooks/01-HowTo.ipynb explains the model and its parameters in more detail, including the extension to subjective internal models.

• notebooks/02-Data.ipynb fits the ideal observer and bounded actor model to the data from Bonnen et al. (2015) to reproduce Fig. 4A from our paper.

Citation

If you use our method in your research, please cite our paper:

@article{straub2022putting,
  title={Putting perception into action with inverse optimal control for continuous psychophysics},
  author={Straub, Dominik and Rothkopf, Constantin A},
  journal={eLife},
  volume={11},
  pages={e76635},
  year={2022},
  publisher={eLife Sciences Publications Limited}
}

Signal-dependent noise

This implementation supports the basic LQG framework. For the extension to signal-dependent noise (Todorov, 2005), please see our NeurIPS 2021 paper and its implementation.