We broke Weber's law. By manipulating the prior (ie the frequencies of small and large magnitudes), we changed how people's accuracy depended on magnitude. When large magnitudes were more frequent, subjects became more precise about them. This points to efficient coding, dynamically implemented.

Sam Gershman@gershbrain.bsky.social · 5d ago

@arthurpr4t.bsky.social has a new preprint with important results on a famous psychophysical law (Weber's law). It isn't, in fact, a law, because it can be broken. A more fundamental principle (efficient coding) shows when and why Weber's law holds true. www.biorxiv.org/content/10.6...

📣Algorithmic Grammar of Flexible Cognition: A Walk through Latent Operations osf.io/preprints/ps... Flexible behavior moves adaptively btwn cognitive modes. But units of analysis remain representations & few operations. Proposal: formalize open-ended cognition as walk over latent operations 1/n

New perspective piece out in @cp-neuron.bsky.social with Zaid Zada, @adelegoldberg.bsky.social, and Uri Hasson! We try to articulate some of our excitement about LLMs and discuss what kinds of insights they might provide into the neural computations supporting natural language in the human brain.

New position piece w/ @tylerbonnen.bsky.social out now in COBS! We suggest that data augmentation is a useful framework for understanding hippocampal contributions to generalization, and offers a path towards more precise modeling: 1/

Cool work by @amirzur.bsky.social, @rdhawkins.bsky.social and their collaborators replicating our finding on causal implicatures from correlational language, and showing that this can arise from rational pragmatic inference: escholarship.org/content/qt60...

Deciding when to jump in and help someone—and when to hold back and let them work through it—is something humans navigate constantly. How do AI assistants handle this tradeoff? We introduce Int-Bench, a framework for evaluating interventions during problem-solving tasks.

Version 2 of Theory of Contravariance w/ @dyamins.bsky.social is out! New material on contravariance for Transformers, and the theory of Representational Similarity Analysis (RSA) and centered kernel analysis (CKA)/Procrustes.

Aran Nayebi@anayebi.bsky.social · last mo.

1/6 Why have deep neural networks aligned so strongly with brains for the past 15 years? What explains it? @dyamins.bsky.social & I make progress on this question in our new paper👇 In a nutshell, we *prove* that for sufficiently hard tasks, the choice of alignment metric does *not* matter.

Hello world! 👋 We’re Minds, Machines, and Brains (MMB) 👤🤖🧠 a new open access journal from @mitpress.bsky.social exploring the principles of intelligence and cognition across natural and artificial minds. Submissions open this Fall! 🔗 direct.mit.edu/mmb

How do people learn abstract knowledge over generations in a crafting game with rich structure? Come by my talk in the cultural evolution session on Friday morning at #CogSci2026 to find out!

go check out @benpry.bsky.social talking about our think aloud work and automated discovery of cognitive models!

Charley Wu@thecharleywu.bsky.social · last mo.

The framing-the-problem.github.io/cogsci2026.h... workshop at #cogsci2026 is kicking off with an intro from @hanqizhou.bsky.social Come find us in Galvea B

The framing-the-problem.github.io/cogsci2026.h... workshop at #cogsci2026 is kicking off with an intro from @hanqizhou.bsky.social Come find us in Galvea B

I've posted a preprint of a philosophical paper I've written on the relationships between understanding, prediction, and compression, based on a talk I gave at SPAN 2025. Now in press at Synthese. philarchive.org/archive/POLUPA

Incredibly excited about this direction: automating scientific discovery in cognitive science, with agents designing targeted experiments, collecting human data, and using the results to refine their theories. We show that this loop can lead to models that better predict human behavior!

Ben Prystawski@benpry.bsky.social · 2mo ago

New preprint! AI agents have shown impressive scientific automation capabilities. Can we apply them to psychology research, *including* human data collection? We introduce auto-psych, a framework that proposes cognitive models and uses them to design and run human experiments. 1/

Fun story about this paper, which I'm really excited about! This came out of a classic nerdswipe by @noahdgoodman.bsky.social - we were teaching together this winter and he said "is it time for robots to do psychology?"

Ben Prystawski@benpry.bsky.social · 2mo ago

New preprint! AI agents have shown impressive scientific automation capabilities. Can we apply them to psychology research, *including* human data collection? We introduce auto-psych, a framework that proposes cognitive models and uses them to design and run human experiments. 1/

Really proud to be part of this dream team! We make a strong case for end-to-end systems that can not only propose new cognitive theories but *validate* them by collecting human data! ✨ Check out project lead Ben’s thread for the highlights and a link to our preprint!

Ben Prystawski@benpry.bsky.social · 2mo ago

New preprint! AI agents have shown impressive scientific automation capabilities. Can we apply them to psychology research, *including* human data collection? We introduce auto-psych, a framework that proposes cognitive models and uses them to design and run human experiments. 1/

New preprint! AI agents have shown impressive scientific automation capabilities. Can we apply them to psychology research, *including* human data collection? We introduce auto-psych, a framework that proposes cognitive models and uses them to design and run human experiments. 1/

1st published paper alert ⚠️ in which @mcxfrank.bsky.social and I ask: How robust is the “shape bias”? a phenomenon that has been central to theories of early word learning: If a child hears a new word for an object, they often extend it to other objects with the same shape. tinyurl.com/JCL-shapebias

1/ 🚨 New opinion piece: "Can we automatize scientific discovery in the cognitive sciences?" We lay out a vision for a fully automated, in-silico science of the mind, where modern AI systems run every stage of the scientific discovery cycle in cognitive science 🧵 #AutomatedDiscovery #AI4Science

Join us at both conferences this summer! Our #CogSci2026 workshop features a series of invited talks on problem representations and abstractions. Our #CCN2026 community event opens a broader discussion on naturalistic problem solving: frameworks, tasks, methods, and where the field should go next.

Mark Ho@markkho.bsky.social · 2mo ago

How do human minds make sense of big, messy problems? 😵‍💫🌀 How do we distill complexity into something simple enough to solve? 🤔💡 We’ll be tackling these questions (and more!) at two workshops on task representations, abstractions, and construals #CogSci2026 #CCN2026 🧵 framing-the-problem.github.io

🚨New preprint!🚨 We know that LM representations can be used to predict brain responses to language. But what *features* of these representations underlie this alignment? We use SAEs to find out!

1/ New preprint! Reasoning models often require hundreds of task examples and thousands of rollouts to improve on a task. How can they learn more from much less? Introducing CORE: contrastive self-reflection for rapid, sample-efficient, and interpretable self-improvement 🧵

We've updated the preprint of our Naturalistic Computational Cognitive Science paper (arxiv.org/abs/2502.20349) — we've tried to clarify and streamline the arguments, and added some new examples: 1/5

Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior

How can cognitive science build generalizable theories that span the full scope of natural situations and behaviors? We argue that progress in Artificial Intelligence (AI) offers timely opportunities ...

arxiv.org

🤖🧠NEW PAPER🧠🤖 Children & neural networks can learn syntax from linear strings of words. How do they do it? Our hypothesis: Word co-occurrence statistics provide cues to syntax! (I.e., a new type of bootstrapping to consider!) Paper: arxiv.org/abs/2605.20529 1/n

1/ New preprint with @dyamins.bsky.social + team! Ventral visual representations within areas evolve over the course of the response along the same hierarchical complexity axis that distinguishes the visual areas, potentially driven by local recurrence.

bioRxiv Neuroscience@biorxiv-neursci.bsky.social · 3mo ago

A hierarchical computational motif unifies neural dynamics across the ventral visual stream https://www.biorxiv.org/content/10.64898/2026.05.18.726101v1

1\ Can you make this Roman-numeral equation true by moving exactly one matchstick?

What is a psychological theory? Here's our take on this tricky and controversial question in this week's Experimentology chapter summary. Many things called "theories" in psychology aren't actually theories — they're frameworks. 🧵 experimentology.io

Thrilled to be sharing my latest work at #CogSci2026! Many of the spaces we move through, from kitchens to airports, were designed with specific uses in mind. How do people create such environments, and how do users figure out what they were designed for? 📃 osf.io/preprints/ps...

Many of us were taught experiments are for testing hypotheses. In Ch 1 of Experimentology, our free, open methods textbook, my coauthors and I argue differently: experiments are for estimating the magnitude of causal effects. This reframing has important consequences. 🧵 experimentology.io

New opinion piece on the interface between research on concepts and categories in minds vs. in neural network LMs! I take the position that there is much to be learned from this interface (e.g., learning about concepts from language alone) and outline some directions for future.

Goodfire released a megapost of all the random feature geometry stuff they're finding, and it's worth a read

The World Inside Neural Networks

How neural geometry will unlock understanding and control of AI

goodfire.ai