💡Can LLMs like GPT-4 reason creatively? Excited to share our latest research on AI and creativity! 🚀 Introducing MacGyver: a new playground for everyday innovation and physical reasoning --we collect problems to trigger unconventional usage of objects and innovative solutions.

Being able to collect large amounts of behavioral data allows us to revisit classic results in cognitive psychology established with simple laboratory stimuli to see whether they hold with more naturalistic stimuli. Shepard's law is just as beautiful for real images.

Do Large Language Models (LLM) have leadership? Together with cognitive scientists Tom Griffiths Griffiths Computational Cognitive Science Lab and Natalia Velez Natalia Vélez (natvelali @ 🧵/🟦/fediscience.org) at Princeton University, we created a multi-LLM-agent system to test if agents work better with a leader. It turns out that LLMs do have leadership,

In this fun new paper we use a programming game to explore people's preferences for the structure of hierarchical plans.

New paper! Nudging has been a popular way to guide people's decisions, but it doesn't always work. We present a framework that allows us to predict when nudges will be effective and test them in a controlled setting. We then use this framework to optimize for effective nudges.

Come join us! 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.

princeton.edu/acad-positions…

New preprint w/ Fred Callaway Griffiths Computational Cognitive Science Lab

osf.io/preprints/psya…

We show that simulated data from cognitive models can be used to pretrain neural networks to infer an individual's preferences from their fixations.

Introducing Gaussian Process Probes (GPP) for probing and measuring uncertainty about concepts represented by AI models. #NeurIPS poster #1504 this Tue at 5pm!

Paper: arxiv.org/abs/2305.18213

Joint work with Alexander Ku, Jason Baldridge, Tom Griffiths Griffiths Computational Cognitive Science Lab and Been Kim

🧵 Excited to share another new paper with Griffiths Computational Cognitive Science Lab, accepted as a spotlight at #NeurIPS2023 ! 🎉 We delve into the intriguing intersection of AI and human cognition, exploring how alignment with human representations impacts few-shot learning tasks.🧠🤖🎓 Let's unpack this!👇

New paper! We use the implicit inductive bias of gradient descent to efficiently implement Bayesian filtering in a way that can scale using modern machine learning tools.

One more week to apply for our joint faculty position in Princeton Neuroscience Institute & Computer Science dept, in neuroAI and intelligent systems, broadly construed. Do you fit? Yes. But feel free to contact me with q's. puwebp.princeton.edu/AcadHire/apply…

Four years in the making, my main PhD work is in Nature Human Behaviour!

Decades of work has shown both benefits and costs to group decision-making.

Can we restructure social networking algorithms so there are fewer costs and more benefits?

Paper: rdcu.be/dquI1

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Reasoning about how others will react to what you say is hard. What if we use LLMs to develop better intuitions? 😲

Introducing our new paper EGS:
💡Explore advice for what to say in any situation
✏️Generate high-quality candidates
🤖Simulate audience reactions using LLMs
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Our new paper uses an experimental simulation of social networks to demonstrate they magnify biases in decision-making, then shows an algorithm from statistics (importance sampling!) can give the benefits of sharing information without increasing biases. Ironically sharing here.

🧵🎉 Our new preprint is up, and we’d love your feedback! We're 'Getting Aligned on Representational Alignment' - the degree to which internal representations of different (biological & artificial) information processing systems agree. 🧠🤖🔬🔍 #CognitiveScience #Neuroscience #AI

New paper explores the implications of alignment of representations between humans and machines for the performance of those machines when learning from small amounts of data. Being aligned with human representations helps, but so does being strongly misaligned... we explain why!

🧵Excited to share our paper 'On the informativeness of supervision signals' that was spotlighted at #UAI2023 ! We delve into the fascinating world of supervised learning and explore how different types of supervision signals contribute to representation-learning performance. 🧠🔍

New preprint (arxiv.org/abs/2309.17363) with incredible PNI second year co-first author, Declan Campbell! In this work, we show how ANNs can yield human behavior on abstract processing of geometric stimuli, providing an alternative to symbolic LoT models in this domain. [1/n]