Wonderful keynote by @hyogweon.bsky.social at #cogsci2026 on "Distinctively human intelligence: Thinking, learning, and communicating about the self". Hyo argues that better understanding how people think about the self is a key ingredient for uncovering what makes us intelligent.

The Causality in Cognition Lab is pumped for #CogSci2026 🇧🇷

Now out in Noûs: A communication-first account of explanation, with Jacqueline Harding (jacquelineharding.github.io) and Thomas Icard (stanford.edu/~icard/). Paper: onlinelibrary.wiley.com/doi/10.1111/... Preprint: arxiv.org/abs/2505.03732

Very proud Doktorvater moment from a few weeks back. Pictured with Dr Sarah Wu and Dr David Rose (and with a large hat thanks to University College London's regalia). Sarah will postdoc with @tomerullman.bsky.social. David with @shaunnichols.bsky.social and @davidpierce.xyz. I will miss you!! ♥️

New paper in Cognitive Psychology on how people combine visual 👀, auditory 👂, and haptic information 🖐️ to make causal judgments. Led by Elyse Chase (www.elysechase.com), with Kevin Smith, and Sean Follmer. 📜 authors.elsevier.com/a/1nHnI2Hxok...

It was such an honor and so much fun to participate in Phil Johnson-Laird's 90th birthday conference. Thank you @ruthbyrne.bsky.social and Sunny Khemlani for organizing the event, and the folks at NYU for hosting it. Here is Phil asking whether people are smarter than logic. Phil certainly is!

Congratulations to Dr. Dr. David Rose! 🥳 David (davdrose.github.io) first got a PhD in Philosophy, and now added another one in Psychology.

Congratulations Dr. Sarah Wu 🎓🎉 (sarahawu.github.io)

From Susan Gelman's talk (who met John thrice 📖☎️👨‍🏫): 1) Don't be afraid to ask questions. 2) "Research is hard (and fun!)" 3) Appearances can be deceiving. From Alison Gopnik's comments: John showed us that it's possible to be a good man and a good scientist.

Some notes from the wonderful celebration of John Flavell's life and work at #CDS2026. "Age early and get it over with." (John on his looks not having changed in many years.) "Use the simplest word to get the idea across." "If I could only step into a child's mind even if just for a moment."

Excited (and cold) for my first ever #CDS2026! I'll be presenting work by the 'Dropping Things' dream team in the workshop on "Counterfactual thinking: What is it good for?" Paper sneak peek: cicl.stanford.edu/papers/rose2...

The Causality in Cognition Lab -- a supportive, bluesky-colored team -- is looking for a predoc to join us! Here are infos about the lab (cicl.stanford.edu) and the position (careersearch.stanford.edu/jobs/iriss-p...). The application deadline is May 1st. Please share, thank you 🙏

Congratulations @judithfan.bsky.social on winning the Lila R. Gleitman Prize for early-career contributions to Cognitive Science 🥳 Amazing!! cognitivesciencesociety.org/gleitman-pri...

Thanks Arthur Le Pargneux (arthurlepargneux.wixsite.com/arthurleparg...) for your talk "Contractualist moral cognition: From fair divisions to the emergence of rules via implicit agreements". A novel take on morality backed by clever experiments + elegant models 👍 📃 psycnet.apa.org/fulltext/202...

Thank you Yoonseo Zoh (zohyos7.github.io) for sharing your work with us on "Intuitive Theories in Moral Cognition". Intuitive theories structure how people represent dilemmas, how they generalize to new contexts, and how they switch between representations based on resource-rational constraints.

Thanks @maxtaylordavies.bsky.social for sharing your work with us on "Using the information bottleneck to study social cognition". Max develops resource-rational models that elegantly unify existing theories for various phenomena such as stereotyping and ToM development. 📃 osf.io/preprints/ps...

I'm very sad to have learned today that Joe Halpern passed away. Joe was a giant who knew no scientific boundaries. He loved science with a contagious, child-like enthusiasm. He was wonderful and I'm so grateful that I got to learn from him. Thank you Joe 🙏 www.bangsfuneralhome.com/obituaries/j...

Thanks @katenuss.bsky.social for sharing your work with us! Kate studies how people learn about the world through external exploration (acting) and internal exploration (imagining). Modeling & experiments reveal that people learn by simulating counterfactuals! 📃 elifesciences.org/articles/84260

Thank you @ltreiman.bsky.social for sharing your work with us on how people act differently when they know that their behavior is used to train AI. In the ultimatum game, people are more likely to reject disadvantageous offers when an AI is watching and learning. 📃 www.pnas.org/doi/10.1073/...

Thanks @miriam-hauptman.bsky.social for sharing your work with us! How people learn about the visual world from language is mediated through causal models. Both sighted and blind people infer how many colors an object has based on how color ➡️ function. 📃 m-hauptman.github.io/files/Hauptm...

Thank you Aniket Vashishtha (aniketvashishtha.github.io) for sharing your work on counterfactual reasoning in LLMs with us! Most current benchmarks don't assess genuine counterfactual reasoning. Aniket's work does, showing that LLMs struggle and what to do about that. 📃 arxiv.org/abs/2510.015...

I'm very excited about this work led by @nbalamur.bsky.social Inspired by the classic "Spot the ball ⚽" task, we develop a benchmark for visual social inference. We find that human participants perform much better than vision-language models. Try it here: v0-new-project-9b5vt6k9ugb.vercel.app

It was great to hear from Brian Leahy (brianleahy.net) in the devo lunch at Stanford today!! He presented a beautiful set of studies that suggest that many 4-year-old children have a minimal concept of possibility: they simulate only once and treat the outcome as a fact. 🎱⬅️➡️🤔💭💡

We develop a causal abstraction that infers a causal story of how the data was generated, paying more attention to factors that mattered for the prediction task. This model captures participants' generalization judgments better than a feature-based model, despite having many fewer parameters.

This time, we also asked participants to predict what would happen in novel situations. For example, we showed them two familiar cubes on a novel ramp. These generalization trials also featured ramps that were facing the opposite direction from what they had seen before.

In Exp 3, participants either viewed forward-facing ramps, or backward-facing ones. The cubes always ended up on the right side. Again, after having learned to predict which cubes cross the finish line, we surprised and asked them where exactly the cubes would be. Ps made similar errors as in Exp 2.

Exp 2: Participants predict whether a cube on ramp will cross a finish line. Either cube color or ramp color is diagnostic. A surprise question about exactly where the cube will end up reveals systematic errors: they knew on which side of the line the cube would end up, but not the exact location.

Exp 1: Participants learn whether color or shape matter for turning on a machine. In a surprise test, we ask them what they saw last. They frequently misremember (e.g., choosing a differently colored object when only the shape mattered). Only happens with enough evidence to learn the rule!

When people are asked to predict what happens next, do they learn simple feature-outcome mappings, or do they learn causal models that capture the underlying generative process? If they do learn causal models, how can we tell? We ran 3 experiments (N=1080) using two paradigms to find out.

🚨 New preprint 🚨 How do people's mental models shape memory, prediction, and generalization? We find that people spontaneously construct goal-dependent causal abstractions that compress experience to privilege relevant information. 📃 osf.io/preprints/ps... 🔗 github.com/cicl-stanfor...