FaceSim3D 🗿

Testing the effect of dynamic 3D viewing conditions on face similarity perception

Project description

To test the effect of space and time on face similarity judgments, we conducted an online experiment using a triplet odd-one-out task in a static 2D and a dynamic 3D condition. We then trained sparse and deep computational encoding models of human face similarity judgments to investigate the latent representations that underlie their predictions.

How to navigate this project documentation?

🗿 : A brief overview of the experiment can be found in the section Experiment.

: Results are summarized in the form of a Jupyter notebook in the section Results.

: The research code is available as a Python package. For its setup and API reference, see the section Research Code.

Citation

If you use this code or data, please cite the following paper (Hofmann et al. Human-aligned deep and sparse encoding models of dynamic 3D face similarity perception. PsyArXiv. 2024):

@article{hofmannHumanalignedDeepSparse2024,
    title={Human-aligned deep and sparse encoding models of dynamic {3D} face similarity perception},
    author={Hofmann, Simon M. and Ciston, Anthony and Koushik Abhay and Klotzsche, Felix and Hebart, Martin N. and Müller, Klaus-Robert and Villringer, Arno and Scherf, Nico and Hilsmann, Anna and Nikulin, Vadim V. and Gaebler, Michael},
    journal={PsyArXiv},
    doi={10.31234/osf.io/f62pw},
    year={2024},
}

Contributors/Collaborators

Simon M. Hofmann*, Anthony Ciston, Abhay Koushik, Felix Klotzsche, Martin N. Hebart, Klaus-Robert Müller, Arno Villringer, Nico Scherf, Anna Hilsmann, Vadim V. Nikulin, Michael Gaebler

This study was conducted at the Max Planck Institute for Human Cognitive and Brain Sciences as part of the NEUROHUM project.

* corresponding author