Tobias Gerstenberg

🚀 Postdoc Alert! Are you passionate about social learning & cultural evolution? @dominikdeffner.bsky.social & I have a 3-year position with freedom to develop your research and work on cutting-edge multiplayer and immersive experiments. Apply by March 30! hmc-lab.com/SocialLearni... Pls share 🙏

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.

1/3 AI is getting smarter, but is it getting wiser? 🤔 Thrilled to share our new @cp-trendscognsci.bsky.social paper on building wise machines, co-authored with Sam Johnson, @amirhkarimi.bsky.social, @yoshuabengio.bsky.social , @sydneylevine.bsky.social , @melaniemitchell.bsky.social, & more!

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...

Book cover. A silhouette of a person's head filled with colorful geometric shapes—perhaps symbolizing cognitive resources or deployment thereof. The style is attractive and modern, if generic. text: The Rational Use of Cognitive Resources Falk Lieder, Frederick Callaway, Thomas L. Griffithts

I'm excited to announce that I had my first (co-authored) book published today! "The Rational Use of Cognitive Resources" with Falk Lieder and Tom Griffiths (@cocoscilab.bsky.social ). You can read it for free! (see thread)

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...

Scientists no Longer Find Twitter Professionally Useful, and have Switched to Bluesky Synopsis. Social media has become widely used by the scientific community for a variety of professional uses, including networking and public outreach. For

Bluesky is the new science Twitter, new study by @whysharksmatter.bsky.social and Julia Wester concludes!

"Results show that for every reported professional benefit that scientists once gained from Twitter, scientists can now gain that benefit more effectively on Bluesky than on Twitter."

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/...

YouTube video by Johns Hopkins University Apes Share Human Ability to Imagine

Imagination in bonobos!

I am thrilled to share a new paper w/ Amalia Bastos, out now in @science.org

We provide the first experimental evidence that a nonhuman animal can follow along a pretend scenario & track imaginary objects. Work w/ Kanzi, the bonobo, at Ape Initiative

youtu.be/NUSHcQQz2Ko

Hackathon page screenshot

This July, we are holding a virtual hackathon to explore LEVANTE data!

If you're interested in data analysis, development, and cross-cultural variability, please join us!

First week is open, second week is by application with mentorship on group projects.

levante-hackathon-2026.github.io

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...

With some trepidation, I'm putting this out into the world:
gershmanlab.com/textbook.html
It's a textbook called Computational Foundations of Cognitive Neuroscience, which I wrote for my class.

My hope is that this will be a living document, continuously improved as I get feedback.

Theory of Minds: Early Understanding of Interacting Minds The idea that we understand others’ actions in terms of their underlying mental states has shaped decades of developmental research on social cognition. Existing work, however, has primarily focused o...

Officially out! In this review, Aaron Chuey and I discuss how existing work on ToM mostly focused on a single individual’s mental states (e.g., what Sally thinks). Extending ToM, we argue for ToMS—an understanding of how multiple individuals communicate and influence each others’ minds. t.ly/u4rtb

Another fun project from @yangxiang.bsky.social. She asks the question: do people assign responsibility to personality traits in the same way that they assign reponsibility to people? The answer: sort of!

osf.io/preprints/ps...

Excited that this is now out in @nathumbehav.nature.com 🎉

David Rose (davdrose.github.io) led this project on how children's understanding of causal language develops.

📃 (preprint): osf.io/preprints/ps...
📎: github.com/davdrose/cau...

Cracking the code of why, when some choose to ‘self-handicap’ — Harvard Gazette New research also offers hints for devising ways to stop students from creating obstacles to success.

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...

🚨🚨🚨
Our 52nd Annual Meeting will be held from June 18–20, 2026 at Johns Hopkins University in Baltimore, MD, with a pre-conference on Mental Control and Agency held at JHU on June 17
🚨🚨🚨

We are currently inviting submissions of papers (talks and posters)!

fantastic!! congratulations 🎉

a red building on UPENN's campus photographed during the fall

the Philadelphia skyline, with clear skies and autumn trees

starting fall 2026 i'll be an assistant professor at @upenn.edu 🥳

my lab will develop scalable models/theories of human behavior, focused on memory and perception

currently recruiting PhD students in psychology, neuroscience, & computer science!

reach out if you're interested 😊

Officially out in the current issue of Trends in Cognitive Sciences:

"Physics versus graphics as an organizing dichotomy in cognition"

www.cell.com/trends/cogni...

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. 🎱⬅️➡️🤔💭💡

I am accepting graduate students for the UCI Cognitive Sciences PhD program for Fall 2026. Check out my lab website - www.relcoglab.org for our recent themes. Our funded work focuses on combinatorial reasoning, moral decision-making, and conceptual cognition in humans and large language models.

These results suggest that people build internal causal models that abstract away irrelevant information. Through surprise tests, we gain insights into what these models look like, finding that they shape memory, prediction, and generalization.

w Steven Shin, Chuqi Hu & @paulm-k.bsky.social

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.