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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 17/07/2026

If you are heading to the Cognitive Science Society conference next week, you might enjoy the most recent episodes of the Cognition Project podcast: Donald Norman, Eleanor Rosch, and George Lakoff talk about the first time the conference was held (among many other things!)

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 27/06/2026

New preprint explores how using AI to discover psychological theories and propose experiments to test them can create an automated cognitive scientist

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 27/06/2026

Large language models can memorize patterns they see in text, but that backfires when a task deviates from a common pattern. We demonstrate this phenomenon using riddles: when something looks like a riddle but has a simple answer AI systems make surprising mistakes.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 17/03/2026

Current AI models are trained on human behavior -- the words we produce. New preprint explores the idea that we might be able to address some of the gaps in these systems by training on the latent variables behind that behavior: human cognition.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 16/03/2026

New preprint shows how ideas from distributed computing can be used to understand the performance of teams of language model agents on different kinds of tasks

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Reposted by Griffiths Computational Cognitive Science Lab

Fred Callaway @fredcallaway.bsky.social · 18/02/2026

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)

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

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/12/2025

We will be releasing edited versions of the interviews in a podcast format over the next few months.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/12/2025

The book is based in part on interviews conducted over the last decade with researchers including Jerome Bruner, Nick Chater, Noam Chomsky, Susan Dumais, Jeﬀ Elman, Geoﬀ Hinton, Daniel Kahneman, Jay McClelland, Steven Pinker, Eleanor Rosch, Roger Shepard, and Barbara Tversky.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/12/2025

It explains three major approaches to formalizing thought—rules and symbols, neural networks, and probability and statistics—introducing each idea through the stories of the people behind it.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/12/2025

The book takes the 19th century idea that there are Laws of Thought just as there are Laws of Nature — mathematical principles that explain how minds work — and traces that idea through the Cognitive Revolution of the 20th century to a 21st century perspective on what those Laws might be.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/12/2025

Links: Macmillan: us.macmillan.com/books/978125... Bookshops: bookshop.org/p/books/the-... Amazon: www.amazon.com/Laws-Thought...

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/12/2025

Excited to announce a new book telling the story of mathematical approaches to studying the mind, from the origins of cognitive science to modern AI! The Laws of Thought will be published in February and is available for pre-order now.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 16/12/2025

Princeton's AI Lab is advertising positions for AI Postdoctoral Fellows in two areas: studying natural and artificial minds, and designing, understanding or engineering large AI models. We are also searching for a Lead Research Software Engineer! ai.princeton.edu/ai-lab/emplo...

ai.princeton.edu

Employment Opportunities

Find and learn more about our open positions.Join our team

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 15/08/2025

Our new preprint explores how advances in AI change how we think about the role of symbols in human cognition. As neural networks show capabilities once used to argue for symbolic processes, we need to revisit how we can identify the level of analysis at which symbols are useful.

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Reposted by Griffiths Computational Cognitive Science Lab

Tom McCoy @rtommccoy.bsky.social · 20/05/2025

🤖🧠 Paper out in Nature Communications! 🧠🤖 Bayesian models can learn rapidly. Neural networks can handle messy, naturalistic data. How can we combine these strengths? Our answer: Use meta-learning to distill Bayesian priors into a neural network! www.nature.com/articles/s41... 1/n

A schematic of our method. On the left are shown Bayesian inference (visualized using Bayes’ rule and a portrait of the Reverend Bayes) and neural networks (visualized as a weight matrix). Then, an arrow labeled “meta-learning” combines Bayesian inference and neural networks into a “prior-trained neural network”, described as a neural network that has the priors of a Bayesian model – visualized as the same portrait of Reverend Bayes but made out of numbers. Finally, an arrow labeled “learning” goes from the prior-trained neural network to two examples of what it can learn: formal languages (visualized with a finite-state automaton) and aspects of English syntax (visualized with a parse tree for the sentence “colorless green ideas sleep furiously”).

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Reposted by Griffiths Computational Cognitive Science Lab

Sev Harootonian @harootonian.bsky.social · 19/05/2025

🚨 New preprint alert! 🚨 Thrilled to share new research on teaching! Work supervised by @cocoscilab.bsky.social, @yaelniv.bsky.social, and @markkho.bsky.social. This project asks: When do people teach by mentalizing vs with heuristics? 1/3 osf.io/preprints/os...

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Reposted by Griffiths Computational Cognitive Science Lab

Rachit Dubey @rachitdubey.bsky.social · 17/04/2025

🚨 New in Nature Human Behavior! 🚨 Binary climate data visuals amplify perceived impact of climate change. Both graphs in this image reflect equivalent climate change trends over time, yet people consistently perceive climate change as having a greater impact in the right plot than the left. 👇1/n

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 26/03/2025

New preprint shows that ideas from distributed systems can be used to predict when agents will adopt specialized strategies when working together to perform a task

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 10/12/2024

The new AI Lab at Princeton has positions for AI Postdoctoral Research Fellows for three research initiatives: AI for Accelerating Invention, Natural and Artificial Minds, and Princeton Language and Intelligence. Deadline is 12/31. More information here: ai.princeton.edu/ai-lab/emplo...

ai.princeton.edu

Employment Opportunities

Find and learn more about our open positions.Join our team

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Reposted by Griffiths Computational Cognitive Science Lab

Carlos G. Correa @cgcorrea.bsky.social · 03/12/2024

My paper on hierarchical plans is out in Cognition!🎉 tldr: We ask participants to generate hierarchical plans in a programming game. People prefer to reuse beyond what standard accounts predict, which we formalize as induction of a grammar over actions. authors.elsevier.com/a/1kBQr2Hx2x...

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/11/2024

(4/5) Here's the table of contents. An Open Access version of the book is available through the MIT Press website.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/11/2024

(3/5) That same perspective is valuable for understanding modern AI systems. In particular, Bayesian models highlight the inductive biases that make it possible for humans to learn from small amounts of data, and give us tools for building machines with the same capacity.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/11/2024

(2/5) Bayesian models start by considering the abstract computational problems intelligent systems have to solve and then identifying their optimal solutions. Those solutions can help us understand why people do the things we do.

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/11/2024

(1/5) Very excited to announce the publication of Bayesian Models of Cognition: Reverse Engineering the Mind. More than a decade in the making, it's a big (600+ pages) beautiful book covering both the basics and recent work: mitpress.mit.edu/978026204941...

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Reposted by Griffiths Computational Cognitive Science Lab

Declan Campbell @thisisadax.bsky.social · 15/11/2024

(1) Vision language models can explain complex charts & decode memes, but struggle with simple tasks young kids find easy - like counting objects or finding items in cluttered scenes! Our 🆒🆕 #NeurIPS2024 paper shows why: they face the same 'binding problem' that constrains human vision! 🧵👇

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 11/01/2024

We are advertising a new postdoctoral position in computational cognitive science, with specific interest in applications of large language models in cognitive science and use of Bayesian methods and metalearning to understand human cognition and AI systems. www.princeton.edu/acad-positio...

princeton.edu

Application for Postdoctoral Research Associate

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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 01/12/2023

First post! Does the success of deep neural networks in creating AI systems mean Bayesian models are no longer relevant? Our new paper argues the opposite: these approaches are complementary, creating new opportunities to use Bayes to understand intelligent machines arxiv.org/abs/2311.10206

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