Overview

PsyNet is a new platform for running advanced behavioral experiments ranging from adaptive psychophysics to simulated cultural evolution. It builds on the virtual lab framework Dallinger. Its goal is to enable researchers to implement and deploy experiments as efficiently as possible, while placing minimal constraints on the complexity of the experiment design.

This website contains a variety of resources to help you learn more about PsyNet. Some particularly useful resources are highlighted below, but see the sidebar for a full list.

• When to use PsyNet?: Learn about the use cases for which PsyNet is optimized.

• Demos: See demos of different PsyNet features.

• Example experiments: See code repositories for real-world PsyNet experiments.

• GitLab repository: Explore PsyNet’s source code.

If you want to refer to PsyNet in your paper, you should for now cite the following reference:

Harrison, P. M. C., Marjieh, R., Adolfi, F., van Rijn, P., Anglada-Tort, M., Tchernichovski, O., Larrouy-Maestri, P., & Jacoby, N. (2020). Gibbs Sampling with People. Advances in Neural Information Processing Systems, 33, 10659–10671. Available at <https://proceedings.neurips.cc/paper_files/paper/2020/file/7880d7226e872b776d8b9f23975e2a3d-Paper.pdf>. Here’s a BibTeX entry:

Here is the citation entry:

@inproceedings{harrison2020psynet,
  title     = {Gibbs Sampling with People},
  booktitle = {Advances in Neural Information Processing Systems},
  author    = {Harrison, Peter M. C. and Marjieh, Raja and Adolfi, Federico and
               {van Rijn}, Pol and Anglada-Tort, Manuel and Tchernichovski, Ofer and
               Larrouy-Maestri, Pauline and Jacoby, Nori},
  date      = {2020},
  volume    = {33},
  url       = {https://arxiv.org/abs/2008.02595}
}