good morning!! it's day 2 of #cogsci2026, and we're so excited to hear about new work from Seo-young Lee, Jess Mankewitz, Polina Tvilodub, Jinyi Kuang, and Karla Perez!

If you’re at #cogsci2026, please come see presentations by some of the great folks collaborating with the Language and Cognition Lab at Stanford!

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!

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

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

Framing the Problem — Workshop Series

A workshop series on representation construction in cognitive science and AI. CogSci 2026 (Rio) and CCN 2026 (NYU).

framing-the-problem.github.io

One of the first studies from my PhD is out now in JEP:G 🥳We tested whether people can infer the truth from teachers who were either helpful, misleading, or randomly sampling. With Keith Ransom and @perfors.net psycnet.apa.org/fulltext/202...

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 🧵

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

Excited to share our new publication, “Measuring Naturalistic Speech Comprehension in Real Time”! ➡️ rdcu.be/fa3hk #psynomBRM w/ @kriesjill.bsky.social, Shiven Gupta, Maria Papworth Burrel, & @lauragwilliams.bsky.social 🧵1/11

Now up as a reviewed @elife.bsky.social preprint: "Continuous developmental changes in word recognition support language learning across early childhood" elifesciences.org/reviewed-pre... Using data from ~2000 kids ages 1-6, we quantify links between word recognition and early vocabulary growth!

Now out in Cognition, work with the great @gershbrain.bsky.social @tobigerstenberg.bsky.social on formalizing self-handicapping as rational signaling! 📃 authors.elsevier.com/a/1lo8f2Hx2-...

Yang Xiang@yangxiang.bsky.social · 2y ago

Really excited about this project, and thanks so much to my wonderful collaborators @gershbrain.bsky.social @tobigerstenberg.bsky.social for making this happen! Some main takeaways in thread 🧵 (1/5)

How do we predict what others will do next? 🤔 We look for patterns. But what are the limits of this ability? In our new paper at CCN 2025 (@cogcompneuro.bsky.social), we explore the computational constraints of human pattern recognition using the classic game of Rock, Paper, Scissors 🗿📄✂️

My final project from grad school is out now in Dev Psych! Mombasa County preschoolers were more accurate on object-based than picture-based vocabulary assessments, whereas Bay Area preschoolers were equally accurate on object-based and picture-based assessments. psycnet.apa.org/doiLanding?d...

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:

When people form conventions in reference games, how easy are they for outsiders to interpret? (for values of "outsider" that include naïve humans and vision-language models) Check out @vboyce.bsky.social's poster today at #CogSci2025 to find out. paper: escholarship.org/uc/item/16c4...

Idiosyncratic but not opaque: Linguistic conventions formed in reference games are interpretable by naïve humans and vision–language models

Author(s): Boyce, Veronica; Prystawski, Ben; Tan, Alvin Wei Ming; Frank, Michael C. | Abstract: When are in-group linguistic conventions opaque to non-group members (teen slang like "rizz") or general...

escholarship.org

How can we use modern NLP methods to get lots of granular data from think-aloud experiments? Watch @danielwurgaft.bsky.social explain how in the Reasoning session at 4pm this afternoon at #CogSci2025 paper: arxiv.org/abs/2505.23931

Scaling up the think-aloud method

The think-aloud method, where participants voice their thoughts as they solve a task, is a valuable source of rich data about human reasoning processes. Yet, it has declined in popularity in contempor...

arxiv.org

How do people trade off between speed and accuracy in reasoning tasks without easy heuristics? Come to my talk, "Thinking fast, slow, and everywhere in between in humans and language models," in the Reasoning session this afternoon #CogSci2025 to find out! paper: escholarship.org/uc/item/5td9...

Thinking fast, slow, and everywhere in between in humans and language models

Author(s): Prystawski, Ben; Goodman, Noah | Abstract: How do humans adapt how they reason to varying circumstances? Prior research has argued that reasoning comes in two types: a fast, intuitive type ...

escholarship.org

🚨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/

How can we combine the process-level insight that think-aloud studies give us with the large scale that modern online experiments permit? In our new CogSci paper, we show that speech-to-text models and LLMs enable us to scale up the think-aloud method to large experiments!

Daniel Wurgaft@danielwurgaft.bsky.social · last yr.

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)🧵

Delighted to announce our CogSci '25 workshop at the interface between cognitive science and design 🧠🖌️! We're calling it: 🏺Minds in the Making🏺 🔗 minds-making.github.io June – July 2024, free & open to the public (all career stages, all disciplines)

the functional form of moral judgment is (sometimes) the nash bargaining solution new preprint👇

Despite the world being on fire, I can't help but be thrilled to announce that I'll be starting as an Assistant Professor in the Cognitive Science Program at Dartmouth in Fall '26. I'll be recruiting grad students this upcoming cycle—get in touch if you're interested!

Super excited to submit a big sabbatical project this year: "Continuous developmental changes in word recognition support language learning across early childhood": osf.io/preprints/ps...

Hello bluesky world :) excited to share a new paper on data visualization literacy 📈 🧠 w/ @judithfan.bsky.social, @arnavverma.bsky.social, Holly Huey, Hannah Lloyd, @lacepadilla.bsky.social! 📝 preprint: osf.io/preprints/ps... 💻 code: github.com/cogtoolslab/...

AI models are fascinating, impressive, and sometimes problematic. But what can they tell us about the human mind? In a new review paper, @noahdgoodman.bsky.social and I discuss how modern AI can be used for cognitive modeling: osf.io/preprints/ps...

1/13 New Paper!! We try to understand why some LMs self-improve their reasoning while others hit a wall. The key? Cognitive behaviors! Read our paper on how the right cognitive behaviors can make all the difference in a model's ability to improve with RL! 🧵

New paper in Psychological Review! In "Causation, Meaning, and Communication" Ari Beller (cicl.stanford.edu/member/ari_b...) develops a computational model of how people use & understand expressions like "caused", "enabled", and "affected". 📃 osf.io/preprints/ps... 📎 github.com/cicl-stanfor... 🧵

What counts as in-context learning (ICL)? Typically, you might think of it as learning a task from a few examples. However, we’ve just written a perspective (arxiv.org/abs/2412.03782) suggesting interpreting a much broader spectrum of behaviors as ICL! Quick summary thread: 1/7

The broader spectrum of in-context learning

The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervised few-shot learning...

arxiv.org