Dong Hee Lee

1/11 Excited to share our @Naturestudy led by @Leon_Oo1 @csabaorban @ZShaoshi doi.org/10.1038/s41586… It is well-known that AI performance scales with logarithm of sample size (Kaplan, McCandlish 2020), but in many domains, sample size can be # participants or # measurements...

The interoceptive origin of reinforcement learning Review by Lilian Weber (@lilwebian), Debbie Yee (@debyeeneuro), Dana Small (@danamsmall), & Frederike Petzschner(@rikepetzschner) tinyurl.com/mj8c5v8p

Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s41586…

🧠264 pages and 1416 references chart the future of Foundation Agents. Our latest survey dives deep into agents—covering brain-inspired cognition, self-evolution, multi-agents, and AI safety. Discover the #1 Paper of the Day on Hugging Face👇: huggingface.co/papers/2504.01… 1/3

New survey on predictive representations in reinforcement learning, covering AI, cognitive, and neuroscience perspectives: arxiv.org/abs/2402.06590 Joint work with @cogscikid, @momchiltomov, @willdecothi, @caswellcaswell

I am absolutely stoked to share our new study on multiscale neural dynamics across species and behaviour + network simulations exploring their benefits! The best lab going around! Captained by THE @jmacshine 🙌

Now in #PAIN: “Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models” by @dong_hee_lee et al. bit.ly/3CkhByN

I'm pleased to share our lab’s collective effort: ‘Pain Neuroimager Manifesto: Towards Person-Centered Neuroscience of Pain.’ This manifesto reflects our commitment to a person-centered, pluralistic, and nuanced approach to pain neuroimaging research. cocoanlab.github.io/manifesto/