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!)

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

Akshay K. Jagadish@akjagadish.bsky.social · last mo.

1/ 🚨 New preprint: "Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist" Introducing AutoCog 🤖 — a fully autonomous AI system that runs the entire scientific discovery cycle in cognitive science to surface novel theories of human behavior 🧵

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.

Bella Fascendini@bellafascendini.bsky.social · last mo.

New paper! w/ @cocoscilab.bsky.social🧵Can large language models reason flexibly, or have they learned what reasoning looks like? We introduce a new paradigm to test this question—the riddle riddle—and find that humans and LLMs show opposite patterns of performance. 📜

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.

Sean Escola@seanescola.bsky.social · 5mo ago

We know about cosmological dark matter despite being unable to measure it because, without it, galaxies would fall apart. By analogy, let's talk about "cognitive dark matter" (CDM): brain functions that meaningfully shape behavior but are hard to infer from behavior alone. New paper! 🧵👇

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

Elizabeth Mieczkowski@emiecz.bsky.social · 5mo ago

🚨New preprint! LLM teams are being deployed at scale, yet we lack the tools to predict when they’ll succeed, fail, or how to design them. Distributed computing faced the exact same questions and figured out how to answer them. We show those insights apply directly to LLMs 🧵👇

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)

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.

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...

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.

Tom McCoy@rtommccoy.bsky.social · 12mo ago

🤖 🧠 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: arxiv.org/abs/2508.05776 1/n

🤖🧠 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

🚨 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...

🚨 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

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

Elizabeth Mieczkowski@emiecz.bsky.social · last yr.

We often assume that specialized roles improve performance in multi-agent systems, but when does specialization emerge based on a given task and environment? 🧵👇 ⭐️ New preprint w/ Ruaridh Mon-Williams, @neilbramley.bsky.social, Chris Lucas, @natvelali.bsky.social & @cocoscilab.bsky.social

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...

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...

(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...

(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! 🧵👇

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...

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