Dear colleagues,

We are excited to announce re:vision, a community-driven initiative for replicating and generalizing findings in image-related neuroscience and we'd love for you to take part.

Condition-rich fMRI datasets have transformed how we study visual representations, but they rest on two assumptions: that their stimuli sample natural images broadly, and that findings generalize across that distribution. These assumptions are rarely tested directly. re:vision sets out to change that.

The initiative is built on the new LAION-fMRI dataset: densely-sampled 7T fMRI responses to 25,000+ natural images, designed to cover the space of natural images at exceptional scale and diversity. Participants pick a published finding, replicate it using LAION-fMRI, and test whether it holds and generalizes. No new data collection is required, all data, preprocessed betas, and annotations are provided through our Python package. 

Anyone with an interest in neuroscience can participate in teams of 1 - 3 researchers. From the PI that wants to get their hands dirty again to the master's student looking for a thesis project. We advise that at least one member of your replication team has experience with fMRI analysis.

Why participate?

Key dates:

You can learn more, browse suggested studies, and sign up at re-vision-initiative.org.

Please feel free to forward this to colleagues or students who might be interested.

Kind regards
the re:vision team

re:vision team:

Luca Kämmer, Justus-Liebig-University Gießen & Max Planck Institute CBS

Josefine Zerbe, Justus-Liebig-University Gießen & Max Planck Institute CBS

Johannes Roth, Justus-Liebig-University Gießen & Max Planck Institute CBS

Alessandro Gifford, Freie Universität Berlin

Apurva Ratan Murty, Georgia Institute of Technology
Iris Groen, Amsterdam University

Michael Bonner, Johns Hopkins University

Margaret Henderson, Carnegie Mellon University

Radoslaw Cichy, Freie Universität Berlin

Martin Hebart, Justus-Liebig-University Gießen & Max Planck Institute CBS