We're looking for a brilliant postdoc to work with Roger Levy Nidhi Seethapathi and me on an exciting new project at the intersection of computational cognition, language, and motor control! Please share with anyone who might be interested. More info here: academicjobsonline.org/ajo/jobs/27294

Princeton is hiring postdocs interested in projects focused on comparing AI systems to human cognition or using ideas from cognitive science to better understand those systems as part of a new initiative studying Natural and Artificial Minds. Apply at puwebp.princeton.edu/AcadHire/posit…

🚨New preprint!🚨Excited to share my 1st PhD project with Bonan Zhao / 赵博囡 Griffiths Computational Cognitive Science Lab Natalia Vélez (natvelali.bsky.social @ 🦋) Teaching is a powerful way to pass on "tools for thought" to solve new problems. How well do existing teaching models of teaching capture this process? Link: osf.io/preprints/psya…

great opportunity to do a #PhD in #Europe in #ML #AI 🚨🚨🚨 I'll hire 2 students via #ELLIS this year, reach out if you want to do research in: - #reliable and #efficient ML in the wild - scalable #neurosymbolic #nesy AI - #lowrank representations - #tractable inference 🚨🚨🚨

Are you interested in interdisciplinary work on explanation and understanding in natural and artificial minds? Check out this new postdoc opportunity through Princeton's NAM initiative: puwebp.princeton.edu/AcadHire/apply…

The new Princeton AI Lab has positions for AI Postdoctoral Research Fellows for three research initiatives: AI for Accelerating Invention, Natural and Artificial Minds, and Princeton Language and Intelligence. More information here: ai.princeton.edu/ai-lab/employm…

Yilun Du and I are planning to hire a postdoc next year to work at the intersection of natural and artificial intelligence: academicpositions.harvard.edu/postings/14244

I’ll be considering applications for a PhD student to start in Fall 2025! I’ve been having a blast starting up the Exploration, Learning, and Mind Lab at University of New Hampshire this fall, and I’m excited to grow our lab community. App review begins 1/15. Learn more: liquinlab.github.io

I’m recruiting PhD students to join the Computational Minds and Machines Lab at the University of Washington in Seattle! Join us to work at the intersection of computational cognitive science and AI with a broad focus on social intelligence. (Please reshare!)

*Call for Abstracts* Submit your abstracts on "learning and decision-making over time to achieve a goal" to #RLDM2025 Successful applications will be selected for poster or oral presentation to an interdisciplinary audience. Deadline: Jan 15 Learn more: rldm.org/call-for-abstr…

Can LLMs be used to discover interpretable models of human and animal behavior?🤔 Turns out: yes! Thrilled to share our latest preprint where we used FunSearch to automatically discover symbolic cognitive models of behavior. 1/12

1/n LLMs learn to represent numbers by predicting tokens in text. This poses a challenge: depending on context, the same set of digits can be treated as a number or a string. Given this duality, we ask what is a number in the eyes of an LLM? Is it a string or an integer? Or both?

New pre-print! In this work, we explore the extent to which different inductive biases can be instantiated among disparate neural architectures, specifically Transformers, CNNs, MLPs, and LSTMs. Link: arxiv.org/abs/2502.20237 (1/4)

📢 Abstract deadline extended (to rolling) for our conference on the Mathematics of Neuroscience and AI (Split, 27-30 May) Thank you to everyone that has submitted so far! The quality is exceptional, and we are really excited to hear the latest results and theories! We know

Our paper on the logic of guesses is now out (w/ Neil Bramley and Chris Lucas). We provide a new information-theoretic perspective on many phenomena (old and new) in judgment under uncertainty. 🧵

It turns out that a lot of the most interesting behavior of LLMs can be explained without knowing anything about architecture or learning algorithms. Here we predict the rise (and fall) of in-context learning using hierarchical Bayesian methods.

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…

New review on computational approaches to studying human planning out now in Trends in Cognitive Sciences! Really enjoyed having the opportunity to write something broader about the field with the help of Evan Russek Marcelo Mattar Wei Ji Ma and Griffiths Computational Cognitive Science Lab cell.com/trends/cogniti…

Can an AI model predict perfectly and still have a terrible world model? What would that even mean? Our new ICML paper formalizes these questions One result tells the story: A transformer trained on 10M solar systems nails planetary orbits. But it botches gravitational laws 🧵

Centaur may have learned a shortcut that explains away psychological tasks. Hanbo Xie Link to preprint: osf.io/preprints/psya…