The code for the CPC Zurich is now available online: bitbucket.org/fpetzschner/cp…. Check out the VBA-Toolbox by Lionel Rigoux , hBayes by Woo-Young (Young) Ahn and Active Inference Philipp Schwartenbeck, HGF is soon to come...#CPC2018 #opensource @tnuzurich

IN SILICO is now available for streaming internationally via Vimeo On Demand: vimeo.com/ondemand/insil… It's a story told over 10 years about an attempt to simulate a brain on supercomputers. Now for more about the movie's plot and its subjects, via GIFs... 🧵👇

When in doubt, leave it out? This simple strategy explains how we make the best of our cognitive limitations when tracking the uncertain source of noisy sensory signals. New study led by Julie Drevet, out now in Nature Human Behaviour. 🎉 Summary thread below: 👇 rdcu.be/cV7sU

New preprint ! 📢📢 Very happy to share some recent work looking at the holy trinity of transdiagnostic symptom dimensions (anxious-depression, compulsivity and social withdrawal) and how we can optimise their measurement. psyarxiv.com/q83sh Some key points below… 1/n

A while ago we published this #RegisteredReport in Nature Communications - but was this format of pre-registration really useful? Find some answers in this Q&A with us and one of the reviewers: nature.com/articles/s4146…

Postdoc position in Boston ⭐️ Great place and amazing person to work with !

Wanna try out a (cool🦙) alternative to GPT?

Congratulations to my academic sibling @AlisaLoosen for those (very) well-deserved three shiny balloons

In our lab's latest paper, we introduce a novel modeling approach using RNNs to reveal the cognitive algorithms behind animal decision-making. Check out our preprint, led by UCSD PhD student Ji-An Li and co-authored by Marcus Benna: biorxiv.org/content/10.110…

Preprint alert🚨! In this new paper we study how humans decompose dynamical subprocesses and leverage the abstracted subprocesses for compositional reuse of experience in new situations. psyarxiv.com/sxn4a/ Tweeprint to follow soon!

Paper out in PNASNews! A 'cognitive mapping' lens on language in psychosis, using word embedding models, computational modelling, and MEG. A hint of what's to come at Oxford Psychiatry and UCL Brain Sciences... With Dan McNamee, Yunzhe Liu, Ray Dolan. pnas.org/doi/abs/10.107…

1/ New paper in Nature shows model collapse as successive model generations models are recursively trained on synthetic data. This is an important result. While many researchers today view synthetic data as AI philosopher’s stone, there is no free lunch. Read more 👇

I am excited to share that our article *Navigating the European Union Artificial Intelligence Act for Healthcare* has just been published in npj Digital Medicine🚀 #AIRegulation #DigitalHealth #EUAIAct #MedicalDevices #Innovation #npjDigitalMedicine #AIinHealthcare

Excited to share our brand-new work shedding some light on the neural mechanisms behind one of human’s coolest cognitive feats: compositional generalization of structural knowledge! A Tweeprint-Thread 🧵 1/n

🧵 Today we’re publishing our first Research Agenda – a detailed outline of the most urgent questions we’re working to answer as AI capabilities grow. It’s our roadmap for tackling the hardest technical challenges in AI security.

New paper introducing a framework to better quantify uncertainty in LLM evaluations (led by Lennart Luettgau🙌). A beta Python package (developed by Harry Coppock🚀) is available if you want to try it out. ➡️Get in touch if you have any Qs/feedback! Paper: arxiv.org/abs/2505.05602

Hawthorne effect describes how study participants modify their behavior if they know they are being observed In our paper 📢, we study if LLMs exhibit analogous patterns🧠 Spoiler: they do⚠️ 🧵1/n

We're excited to announce the Call for Papers for SaTML 2026, the premier conference on secure and trustworthy machine learning SaTML Conference We seek papers on secure, private, and fair learning algorithms and systems. 👉 satml.org/call-for-paper… ⏰ Deadline: Sept 24

In a new paper, we examine recent claims that AI systems have been observed ‘scheming’, or making strategic attempts to mislead humans. We argue that to test these claims properly, more rigorous methods are needed.

Evaluating AI models is essential for improving their performance and understanding their risks. Increasingly, researchers are using “autograders” – having Large Language Models (LLMs) grade model outputs. But how do we know if these autograders are reliable? 🧵