Fun! 🎉

Don’t forget to try our interactive widget on the project website. Test some of the encoding models in the paper and visualize brain predictivity right in your browser 🤗🧠

We also highlight pitfalls & controls:
🚩 Shuffled folds inflate scores due to autocorrelation
✅ Contiguous + trimmed folds give realistic benchmarks
⚠️ Head motion reliably reduces predictivity

📊 Replicating past results

1️⃣ Language models outperform baselines, embeddings, and speech models in predicting the language network
2️⃣ Larger models yield higher predictivity
3️⃣ Downsampling and FIR choices substantially shape results

🚨 Paper alert:
To appear in the DBM Neurips Workshop

LITcoder: A General-Purpose Library for Building and Comparing Encoding Models

📄 arxiv: arxiv.org/abs/2509.091...
🔗 project: litcoder-brain.github.io