What can cognitive science learn from AI? In infinitefaculty.substack.com/p/what-cogni... I outline how AI has found that scale and richness of learning experiences fundamentally change learning & generalization — and how I believe we should rethink cognitive experiments & theories in response.

New post! Last week I shared why I thought cognitive (neuro)science hasn’t contributed as much as one might hope to the design of AI systems; this week I'm sharing my thoughts on how methods and principles from these fields *have* been useful in my work. infinitefaculty.substack.com/p/how-cognit...

Why isn’t modern AI built around principles from cognitive science or neuroscience? Starting a substack (infinitefaculty.substack.com/p/why-isnt-m...) by writing down my thoughts on that question: as part of a first series of posts giving my current thoughts on the relation between these fields. 1/3

I'm more and more convinced that low-dimensional manifolds in the brain are just an artifact of the experimental designs and analyses we use... 🧠📈 🧪

Dimensionality reduction may be the wrong approach to understanding neural representations. Our new paper shows that across human visual cortex, dimensionality is unbounded and scales with dataset size—we show this across nearly four orders of magnitude. journals.plos.org/ploscompbiol...

Heading to NeurIPS this week! Let me know if you want to chat about science of what models learn, cognitive science, interpretability, what models learn in context vs. from their training data, etc. A few things I'm involved in:

Amazing opportunity to work with a brilliant researcher and all-around wonderful person — definitely apply if you're interested in memory & perception at the intersection of AI & cognitive (neuro)science!

Very important point! We've made arguments from a computational perspective that low-variance features can be computationally relevant (bsky.app/profile/lamp...), but it's much cooler to see it demonstrated on a model of real neural dynamics

Great work by Andrea & co, now out with more datasets, models, and analyses!

I was honored to speak at Princeton’s symposium on The Physics of John Hopfield: Learning & Intelligence this week. I sketched out a perspective that ties together some of our recent work on ICL vs. parametric learning, and some possible links to hippocampal replay: 1/

Can't tell you how great it is to finally be able to release and talk about this work, SIMA 2, the next step toward embodied intelligence in rich, interactive 3D worlds! deepmind.google/sima

Today in Nature Machine Intelligence, Kazuki Irie & I discuss 4 classic challenges for neural nets — systematic generalization, catastrophic forgetting, few-shot learning, & reasoning. We argue there is a unifying fix: the right incentives & practice. rdcu.be/eLRmg

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

New work to appear @ TACL! Language models (LMs) are remarkably good at generating novel well-formed sentences, leading to claims that they have mastered grammar. Yet they often assign higher probability to ungrammatical strings than to grammatical strings. How can both things be true? 🧵👇

Apologies for being quiet on here lately — been focusing on the more important things in life :)

Pleased to share that our survey "Getting aligned on representational alignment" — on representational alignment across cognitive (neuro)science and machine learning — is now published in TMLR! openreview.net/forum?id=Hiq... Kudos to @sucholutsky.bsky.social @lukasmut.bsky.social for leading this!

🚨 New preprint alert! 🧠🤖 We propose a theory of how learning curriculum affects generalization through neural population dimensionality. Learning curriculum is a determining factor of neural dimensionality - where you start from determines where you end up. 🧠📈 A 🧵: tinyurl.com/yr8tawj3

Why does AI sometimes fail to generalize, and what might help? In a new paper (arxiv.org/abs/2509.16189), we highlight the latent learning gap — which unifies findings from language modeling to agent navigation — and suggest that episodic memory complements parametric learning to bridge it. Thread:

How can an imitative model like an LLM outperform the experts it is trained on? Our new COLM paper outlines three types of transcendence and shows that each one relies on a different aspect of data diversity. arxiv.org/abs/2508.17669

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:

Get ready to enter the simulation... Genie 3 is a new frontier for world models: its environments remain largely consistent for several minutes, with visual memory extending as far back as 1min. These limitations will only decrease with time. Welcome to the future.🙌 deepmind.google/discover/blo...

Looking forward to attending CogSci this week! I'll be giving a talk (see below) at the Reasoning Across Minds and Machines workshop on Wednesday at 10:25 AM, and will be around most of the week — feel free to reach out if you'd like to meet up!

This summer my lab's journal club somewhat unintentionally ended up reading papers on a theme of "more naturalistic computational neuroscience". I figured I'd share the list of papers here 🧵:

Does vision training change how language is represented and used in meaningful ways?🤔The answer is a nuanced yes! Comparing VLM-LM minimal pairs, we find that while the taxonomic organization of the lexicon is similar, VLMs are better at _deploying_ this knowledge. [1/9]

Quick thread on the recent IMO results and the relationship between symbol manipulation, reasoning, and intelligence in machines and humans:

Excited to share a new project spanning cognitive science and AI where we develop a novel deep reinforcement learning model---Multitask Preplay---that explains how people generalize to new tasks that were previously accessible but unpursued.

Exciting new preprint from the lab: “Adopting a human developmental visual diet yields robust, shape-based AI vision”. A most wonderful case where brain inspiration massively improved AI solutions. Work with @zejinlu.bsky.social @sushrutthorat.bsky.social and Radek Cichy arxiv.org/abs/2507.03168

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/s415...

Experimentology is out today!!! A group of us wrote a free online textbook for experimental methods, available at experimentology.io - the idea was to integrate open science into all aspects of the experimental workflow from planning to design, analysis, and writing.

Really nice analysis!

Humans and animals can rapidly learn in new environments. What computations support this? We study the mechanisms of in-context reinforcement learning in transformers, and propose how episodic memory can support rapid learning. Work w/ @kanakarajanphd.bsky.social : arxiv.org/abs/2506.19686

I'm excited about this TICS Opinion with @yngwienielsen.bsky.social, challenging the view that structural priming—the tendency to reuse a recent syntactic structure—provides evidence for the psychological reality of grammar-based constituent structure. authors.elsevier.com/a/1lIFK4sIRv... 🧵1/4

What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results. www.nature.com/articles/s42...

LLMs excel at finding surprising “needles” in very long documents, but can they detect when information is conspicuously missing? 🫥AbsenceBench🫥 shows that even SoTA LLMs struggle on this task, suggesting that LLMs have trouble perceiving “negative spaces”. Paper: arxiv.org/abs/2506.11440 🧵[1/n]

New and improved version of our recent preprint on how to build generalizable models & theories in cognitive science by (in part) drawing on lessons and tools from AI!

ACL paper alert! What structure is lost when using linearizing interp methods like Shapley? We show the nonlinear interactions between features reflect structures described by the sciences of syntax, semantics, and phonology.

A string may get 17 times less probability if tokenised as two symbols (e.g., ⟨he, llo⟩) than as one (e.g., ⟨hello⟩)—by an LM trained from scratch in each situation! Our new ACL paper proposes an observational method to estimate this causal effect! Longer thread soon!

Transformer-based neural networks achieve impressive performance on coding, math & reasoning tasks that require keeping track of variables and their values. But how can they do that without explicit memory? 📄 Our new ICML paper investigates this in a synthetic setting! 🎥 youtu.be/Ux8iNcXNEhw 🧵 1/13

🎥 The recording of the first ELLISxUniReps Speaker Series session with Andrew Lampinen @lampinen.bsky.social and Jack Lindsey is now available here: www.youtube.com/watch?v=eXE0... Next appointment: 20th June 2025 – 16:00 CEST on Zoom 🔵 Keynote: Mariya Toneva (MPI) 🔴 Flash: Lenka Tětková (DTU)

🚨 New pre-print! (Well, new & much improved version in any case.) 🚨 If you're interested in LLM post-training techniques and in how to make LLMs better "language users", read this thread, introducing the "LM Playpen".

📣 Starting in less than one hour! Join at: ethz.zoom.us/j/66426188160 at 4:30 pm CEST

New preprint! 🚨→ Determinants of Visual Ambiguity Resolution. A new work with @ortiztudela.bsky.social @jvoeller.bsky.social @martinhebart.bsky.social and @gonzalezgarcia.bsky.social We created ~2k images and collected ~100k responses to study visual ambiguity. www.biorxiv.org/content/10.1...

What are the organizing dimensions of language processing? We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals

New preprint alert! We often prompt ICL tasks using either demonstrations or instructions. How much does the form of the prompt matter to the task representation formed by a language model? Stick around to find out 1/N

Excited to share a new preprint w/ @michael-lepori.bsky.social & Michael Franke! A dominant approach in AI/cogsci uses *outputs* from AI models (eg logprobs) to predict human behavior. But how does model *processing* (across layers in a forward pass) relate to human real-time processing? 👇 (1/12)

🎉 The first @ellis.eu × Unireps Speaker Series (Reading Group associated with Ellis PhD & Postdoc Program) session is happening soon! 🔜 🗓️ When: 29th May 2025 – 16:30 CET 📍 Where: ethz.zoom.us/j/66426188160 🎙️ Who: Andrew Lampinen @lampinen.bsky.social (Keynote) & Jack Lindsey (Flash Talk)

It was an honor to speak at the @kempnerinstitute.bsky.social on how some of our recent works can be interpreted as an application of rational analysis to language model behaviors — seeing both their successes and failures through the lens of adaptation to the properties of their training data.

How do language models generalize from information they learn in-context vs. via finetuning? In arxiv.org/abs/2505.00661 we show that in-context learning can generalize more flexibly, illustrating key differences in the inductive biases of these modes of learning — and ways to improve finetuning. 1/

Had fun talking at the Spurious Correlations & Shortcut Learning at ICLR! One example I brought up, which I think provides an uncommon perspective: a case where spurious shortcuts can improve generalization... even to out-of-distribution sets where the spurious feature doesn't generalize! Thread:

Some important points here — the way I'd put it is that the gap between even 1) AI being better than many experts at certain well-specified math/AI research tasks and 2) progress in the field accelerating by orders of magnitude is still huge.