Interdisciplinary approaches connecting cognitive science and machine learning to study and evaluate metacognition are especially welcome. https://t.co/9ywc5Be5ms

We are organizing a workshop on Metacognition in Generative AI at @EurIPSConf in Copenhagen later this year. Submission deadline for short papers is on October 17th.

@milenamr7 @evanrussek @TankredSaanum @lucaschubu @nishadsinghi @xin__sui @fabian_theis @vishaal_urao @KozzyVoudouris @NRDlab @WitteKristin @shuchen_wu @dirkuwulff @HuaDongXiong @cpilab plus the missing ones that I couldn't find here.

Huge thanks to the team and collaborators who made this possible. @elifakata @MatthiasBethge @FraBraendle @callfredaway @juliancodaforno @can_demircann @eckstein_maria @EltetoNoemi @cocosci_lab @HaridiSusanne @akjagadish @Ji_An_Li @sreejan_kumar @modirshanechi @surabhisnath

You can also explore the model via our @huggingface space:

More information on the project landing page:

We also present a case study showing how Centaur can support scientific discovery. An updated version of this approach is available in our new preprint:

Centaur can also be adapted to predict secondary measurements like neural activity and response times -- despite never being trained to do so.

We find that Centaur generalizes to unseen experiments and accurately predicts human behavior under modified cover stories, problem structures, and even in entirely novel domains.

Centaur was trained on Psych-101, a new dataset with trial-by-trial data from 160 psychological experiments, containing over 60,000 participants and 10,000,000 choices.

Paper available at

Excited to see our Centaur project out in @Nature. TL;DR: Centaur is a computational model that predicts and simulates human behavior for any experiment described in natural language.

New short-form preprint in which we use Centaur to identify gaps in interpretable cognitive models and revise them accordingly using Qwen3 -- fully automated and without a human-in-the-loop. https://t.co/z1GoaGNFWT

1/ 🚨 Updated preprint: “Generating Computational Cognitive Models using Large Language Models” 👥 Co-led by @milenamr7 with Marvin Mathony, Tobias Ludwig & @cpilab 📄 Check full paper here: https://t.co/DrvSmqOjHV

AI in Science: Ethical and Practical Challenges 🧠 🎤Interview with Dr. Marcel Binz, Postdoctoral Researcher and Deputy Head of the Helmholtz Institute for Human-Centered AI (HCAI) at #HelmholtzMunich. 👉 https://t.co/37Nn76yGBU The integration of Large Language Models (LLMs)

We are looking for two PhD students at our institute in Munich. Both postions are open-topic, so anything between cognitive science and machine learning is possible. More information: https://t.co/wBE0mau7p3 Feel free to share broadly!

The German Cognitive Science Society is organizing a PhD symposium in Tuebingen in April. If you are a PhD student in the vicinity, you should definetely register (by February 28th) -- it will be fun! https://t.co/luse38EmqX

Deadline: March 31th More details:

We are currently building the largest, cross-domain data set of human behavior as part of an open collaborative project. Contributions of any form are welcome, but especially experiments with meta-data from developmental, cross-cultural, or clinical studies.

Our paper (with @elifakata, @MatthiasBethge, @cpilab) on visual cognition in multimodal large language models is now out in @NatMachIntell. We find that VLMs fall short of human capabilities in intuitive physics, causal reasoning, and intuitive psychology.