lqg 0.1.6

(Inverse) optimal control for linear quadratic Gaussian systems

Inverse optimal control for continuous psychophysics

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

Straub, D., & Rothkopf, C. A. (2021). Putting perception into action: Inverse optimal control for continuous psychophysics. bioRxiv.

CCN 2022 tutorial

For our tutorial at CCN 2022, you can follow along in the Jupyter notebook CCN_2022_Tutorial.ipynb. To run the notebook, you can either install the lqg package locally (see below) or open it in the browser on Google Colab.

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 preprint:

@article{straub2021putting,
  title={Putting perception into action: Inverse optimal control for continuous psychophysics},
  author={Straub, Dominik and Rothkopf, Constantin A},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}