WARN-D machine learning competition is live » Eiko Fried If you share one single thing of our team in 2026—on social media or per email with your colleagues—please let it be this machine learning competition. It was half a decade of work to get here, especi...

After 5 years of data collection, our WARN-D machine learning competition to forecast depression onset is now LIVE! We hope many of you will participate—we have incredibly rich data.

If you share a single thing of my lab this year, please make it this competition.

eiko-fried.com/warn-d-machi...

Excited to announce that this is now out in eLife as a reviewed preprint 🚀

doi.org/10.7554/eLif...

Huge thanks to the eLife editors, reviewers and our fantastic collaborators: Katharina Wille, @nicoschuck.bsky.social, Matt Nassar, Radoslaw Cichy, Peter Dayan and @rasmusbruckner.bsky.social

Also, huge thanks to my amazing collaborators: Katharina Wille, Matt Nassar, Radoslaw Cichy, @nicoschuck.bsky.social, Peter Dayan, and @rasmusbruckner.bsky.social.

@ccnberlin.bsky.social

Surprisingly, despite robust evidence of adaptive learning across participants, we consistently did not find any evidence of impaired learning under internalizing or trait anxiety in all these experiments. Stay tuned for a tweetprint, and I’d love to hear your thoughts in the meantime!

We investigated how internalizing symptoms and trait anxiety affect learning under uncertainty - across 8 experiments, 2 tasks, and 820 participants (online + in-person).

Absence of Systematic Effects of Internalizing Psychopathology on Learning Under Uncertainty Difficulties in adapting learning to meet the challenges of uncertain and changing environments are widely thought to play a central role in internalizing psychopathology, including anxiety and depres...

🚨 New preprint alert!

Our project investigating the effects of internalizing and trait anxiety on learning under uncertainty is finally online 🐆🐅

www.biorxiv.org/content/10.1...