@summerfieldlab.bsky.social and I are very happy to share this paper! Building on work by @scychan.bsky.social, we show that how people learn depends on the distribution of examples they see, and changes in a way that’s very similar to transformer models.

I think that if you hypothesize that learning may dominate (aspects of) what the system acquires, then they can be useful as models of that portion of the process — bearing in mind that like any model (organism), they are wrong. They offer a way of testing hypotheses about 1/3

new: Eric Bigelow @ericbigelow.bsky.social suggests the 2 main ways of controlling LLMs (prompting & steering) can be understood as changing model beliefs (as in Bayesian belief updating) "Belief Dynamics Reveal the Dual Nature of In-Context Learning & Activation Steering" arxiv.org/pdf/2511.00617

The Harvard Gazette has a nice story on my student @yangxiang.bsky.social and her work with @tobigerstenberg.bsky.social news.harvard.edu/gazette/stor...

What aspects of human knowledge do vision models like CLIP fail to capture, and how can we improve them? We suggest models miss key global organization; aligning them makes them more robust. Check out LukasMuttenthaler's work, finally out (in Nature!?) www.nature.com/articles/s41... + our blog! 1/3

In LLMs, concepts aren’t static: they evolve through time and have rich temporal dependencies. We introduce Temporal Feature Analysis (TFA) to separate what's inferred from context vs. novel information. A big effort led by @ekdeepl.bsky.social, @sumedh-hindupur.bsky.social, @canrager.bsky.social!

Humans and LLMs think fast and slow. Do SAEs recover slow concepts in LLMs? Not really. Our Temporal Feature Analyzer discovers contextual features in LLMs, that detect event boundaries, parse complex grammar, and represent ICL patterns.

Excited to have this out! This was a fun project that started with YingQiao and I discussing whether VLMs can do mental simulation of physics like people do, and it culminated in a new method where we prompted image generation models to simulate a series of images frame-by-frame.

🚨 NEW PREPRINT: Multimodal inference through mental simulation. We examine how people figure out what happened by combining visual and auditory evidence through mental simulation. Paper: osf.io/preprints/ps... Code: github.com/cicl-stanfor...

🚨New paper out w/ @gershbrain.bsky.social & @fierycushman.bsky.social from my time @Harvard! Humans are capable of sophisticated theory of mind, but when do we use it? We formalize & document a new cognitive shortcut: belief neglect — inferring others' preferences, as if their beliefs are correct🧵

*Sharing for our department’s trainees* 🧠 Looking for insight on applying to PhD programs in psychology? ✨ Apply by Sep 25th to Stanford Psychology's 9th annual Paths to a Psychology PhD info-session/workshop to have all of your questions answered! 📝 Application: tinyurl.com/pathstophd2025

In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested in this question, check out our new commentary! Thread:

Super excited to have the #InfoCog workshop this year at #CogSci2025! Join us in SF for an exciting lineup of speakers and panelists, and check out the workshop's website for more info and detailed scheduled sites.google.com/view/infocog...

Submit your latest and greatest papers to the hottest workshop on the block---on cognitive interpretability! 🔥

Excited to announce the first workshop on CogInterp: Interpreting Cognition in Deep Learning Models @ NeurIPS 2025! 📣 How can we interpret the algorithms and representations underlying complex behavior in deep learning models? 🌐 coginterp.github.io/neurips2025/ 1/4

A bias for simplicity by itself does not guarantee good generalization (see the No Free Lunch Theorems). So an inductive bias is only good to the extent that it reflects structure in the data. Is the world simple? The success of deep nets (with their intrinsic Occam's razor) would suggest yes(?)

Really nice analysis!

🚨New paper! We know models learn distinct in-context learning strategies, but *why*? Why generalize instead of memorize to lower loss? And why is generalization transient? Our work explains this & *predicts Transformer behavior throughout training* without its weights! 🧵 1/

Excited to share a new CogSci paper co-led with @benpry.bsky.social! Once a cornerstone for studying human reasoning, the think-aloud method declined in popularity as manual coding limited its scale. We introduce a method to automate analysis of verbal reports and scale think-aloud studies. (1/8)🧵