Griffiths Computational Cognitive Science Lab
@cocosci_lab
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Tom Griffiths' Computational Cognitive Science Lab. Studying the computational problems human minds have to solve.
Princeton, NJ
Joined August 2020
(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: https://t.co/5dnLpcMQzu
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Princeton also has a postdoc position associated with this multi-institution project focused on modeling human decision-making and human-AI trust. The link to apply for the Princeton position is:
We’re hiring! @sucholutsky and I are seeking a postdoc and RA for a project on trust in AI systems with folks at NYU, Princeton, BU, and Cornell Positions open until filled. Apply soon! Please share 🔁 postdoc: https://t.co/iARVtYrMLN RA:
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Paper now out in @CognitionJourn shows that people make suboptimal decisions about how to make investments to mitigate existential risks (as well as what the optimal decision rule looks like!)
🚨🚨New paper in Cognition (with Adam Elga and @cocosci_lab) on how people assess existential risk. 🚨🚨 A thread🧵 (1/10)
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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.
🤖🧠 NEW PAPER ON COGSCI & AI 🧠🤖 Recent neural networks capture properties long thought to require symbols: compositionality, productivity, rapid learning So what role should symbols play in theories of the mind? For our answer...read on! Paper: https://t.co/VsCLpsiFuU 1/n
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A short 📹 explainer video on how LLMs can overthink in humanlike ways 😲! had a blast presenting this at #icml2025 🥳
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A hallmark of human intelligence is the capacity for rapid adaptation, solving new problems quickly under novel and unfamiliar conditions. How can we build machines to do so? In our new preprint, we propose that any general intelligence system must have an adaptive world model,
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🤔 Feel like your AI is bullshitting you? It’s not just you. 🚨 We quantified machine bullshit 💩 Turns out, aligning LLMs to be "helpful" via human feedback actually teaches them to bullshit—and Chain-of-Thought reasoning just makes it worse! 🔥 Time to rethink AI alignment.
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New review on computational approaches to studying human planning out now in @TrendsCognSci! Really enjoyed having the opportunity to write something broader about the field with the help of @evanrussek @marcelomattar @weijima01 and @cocosci_lab
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Our paper is out today at @NatureHumBehav. We used machine learning to uncover what makes economic games complex for people.
nature.com
Nature Human Behaviour - Zhu et al. use machine learning to reveal complex insights into human strategic decision-making.
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Video games are a powerful tool for assessing the inductive biases of AI systems, as they are engineered based on how humans perceive the world and pursue their goals. This new benchmark evaluates the ability of vision language models using some challenging classic video games.
Can GPT, Claude, and Gemini play video games like Zelda, Civ, and Doom II? 𝗩𝗶𝗱𝗲𝗼𝗚𝗮𝗺𝗲𝗕𝗲𝗻𝗰𝗵 evaluates VLMs on Game Boy & MS-DOS games given only raw screen input, just like how a human would play. The best model (Gemini) completes just 0.48% of the benchmark! 🧵👇
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In this new preprint we use methods from cognitive science to explore how well large language models make inferences from observations and construct interventions for understanding complex black-box systems that are analogous to those that scientists seek to understand
Using LLMs to build AI scientists is all the rage now (e.g., Google’s AI co-scientist [1] and Sakana’s Fully Automated Scientist [2]), but how much do we understand about their core scientific abilities? We know how LLMs can be vastly useful (solving complex math problems) yet
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New preprint shows that training large language models to produce better chains of thought for predicting human decisions also results in them producing better psychological explanations.
1/14 Can we build an AI that thinks like psychologists or economists? 🤔Our new preprint shows how reinforcement learning (RL) can train LLMs to explain human decisions—not just predict them! That is, we're pushing LLMs beyond mere prediction into explainable cognitive models.
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This paper uses metalearning to distill a Bayesian prior into a set of initial weights for a neural network, providing a way to create networks with interpretable soft inductive biases. The resulting networks can learn just as quickly as a Bayesian model when applied to new data.
🤖🧠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! https://t.co/vmOkilhMxJ 1/n
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(1/11) Evolutionary biology offers powerful lens into Transformers learning dynamics! Two learning modes in Transformers (in-weights & in-context) mirror adaptive strategies in evolution. Crucially, environmental predictability shapes both systems similarly.
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🚨 New preprint alert! 🚨 Thrilled to share new research on teaching! Work supervised by @cocosci_lab, @yael_niv, and @mark_ho_. This project asks: When do people teach by mentalizing vs with heuristics? 1/3 https://t.co/EnFTJrlOSz
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New preprint! In-context and in-weights learning are two interacting forms of plasticity, like genetic evolution and phenotypic plasticity. We use ideas from evolutionary biology to predict when neural networks will use each kind of learning.
(1/11) Evolutionary biology offers powerful lens into Transformers learning dynamics! Two learning modes in Transformers (in-weights & in-context) mirror adaptive strategies in evolution. Crucially, environmental predictability shapes both systems similarly.
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Every ChatGPT query costs more energy than the entire life of a fruit fly.
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New paper: Language models have “universal” concept representation – but can they capture cultural nuance? 🌏 If someone from Japan asks an LLM what color a pumpkin is, will it correctly say green (as they are in Japan)? Or does cultural nuance require more than just language?
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We are looking for a new lab manager, shared with the Concepts and Cognition Lab of @TaniaLombrozo. Apply here:
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Happy to share my first first-authored work at @cocosci_lab. Determining sameness or difference between objects is utterly trivial to humans, but surprisingly inaccessible to AI. Meta-learning can help neural networks overcome this barrier. Link: https://t.co/ID8DfXOImj (1/5)
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Many studies suggest AI has achieved human-like performance on various cognitive tasks. But what is “human-like” performance? Our new paper conducted a human re-labeling of several popular AI benchmarks and found widespread biases and flaws in task and label designs. We make 5
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