Princeton neuro prof. But Twitter is an absurd platform for professional communication so I strive to use it most unprofessionally.
Princeton, NJ
Joined September 2010
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Nathaniel Daw retweeted
Princeton is seeking an AI Postdoctoral Research Fellow in Natural and Artificial Minds. An amazing program! Deadline is Feb. 28
apply.interfolio.com/194534
Nathaniel Daw retweeted
1/ 🚨 New preprint: "Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist"
Co-led w/ Younes Strittmatter
Co-mentored by @suyoghc and @cocosci_lab
In collaboration w/ @kachergis, @norijacoby, @nathanieldaw, & @cpilab
Introducing AutoCog — a fully autonomous AI system that runs the entire scientific discovery cycle in cognitive science to surface novel theories of human behavior 🧵
#CognitiveScience #AI4Science #LLMs
ALT AUTOCOG runs as a closed discovery loop iterated over Ncycles: Experimental Design → Behavioral Data Collection → Analysis & Arbitration → Theory Revision , after which a revised theory re-enters the loop.
Nathaniel Daw retweeted
First paper since joining @GoogleDeepmind! We present 🌍ATLAS (Active Theory Learning for Automated Science), a pipeline that generates interpretable mechanistic models from data and optimizes experiments to test them.
Thread below
Nathaniel Daw retweeted
New Annual Review with @nathanieldaw. We argue that the planning machinery of the brain is mostly used for learning from simulated experience, and that thinking prospectively at decision time is just one special case of this more general process.
annualreviews.org/content/jo…
Nathaniel Daw retweeted
Thrilled that my paper is out in the
@Nature . We explored how the brain builds complex tasks by compositionally combining simpler sub-task representations.
nature.com/articles/s41586-0…
Nathaniel Daw retweeted
New paper gives insights into the linguistics of therapy: Found that LLMs estimate psychological distance better than dictionary-based methods used in prior work (pnas.org/doi/10.1073/...), also finding that therapists *encouraging* distance produce bigger symptom changes!
Nathaniel Daw retweeted
Two weeks with 30 super smart early career researchers and over 24 established leaders in @SocforNeuroecon. Thanks to @INSEAD, Paul Glimcher, @HPlassmann, Nathaniel Daw, Joe Kable, and TAs Camilla van Geen and Shemal Doshi! Looking forward to a repeat in two years!
Nathaniel Daw retweeted
New paper with @nathanieldaw in Nature Communications: an RL model that builds a successor map compositionally: it plans as well as the best models, and links components of the map used for planning to neural codes in the medial entorhinal cortex.
rdcu.be/eAofi
Nathaniel Daw retweeted
In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested, check out our new commentary! Thread:
![What do representations tell us about a system? Image of a mouse with a scope showing a vector of activity patterns, and a neural network with a vector of unit activity patterns
Common analyses of neural representations: Encoding models (relating activity to task features) drawing of an arrow from a trace saying [on_____on____] to a neuron and spike train. Comparing models via neural predictivity: comparing two neural networks by their R^2 to mouse brain activity. RSA: assessing brain-brain or model-brain correspondence using representational dissimilarity matrices](https://pbs.twimg.com/media/GxmCufKbUAAoj9k.jpg?name=orig)
ALT What do representations tell us about a system? Image of a mouse with a scope showing a vector of activity patterns, and a neural network with a vector of unit activity patterns Common analyses of neural representations: Encoding models (relating activity to task features) drawing of an arrow from a trace saying [on_____on____] to a neuron and spike train. Comparing models via neural predictivity: comparing two neural networks by their R^2 to mouse brain activity. RSA: assessing brain-brain or model-brain correspondence using representational dissimilarity matrices
Nathaniel Daw retweeted
thrilled that we'll be presenting this paper as a spotlight at #ICML2025 . come by our poster in vancouver to chat with us about the use of LLMs for advancing neuroscience!
here's the camera-ready version:
openreview.net/forum?id=dhRX…
Nathaniel Daw retweeted
How do you make a decision in a situation you’ve never encountered? Your brain may use your memories to do much of the thinking long before you have to make the choice. Read more by @DShohamy @jon_d_nicholas @nathanieldaw in @NatureComms zuckermaninstitute.columbia.…
#memory #decisions #neurosciences
ALT Brain scans like these helped scientists classify how study participants made decisions.
Nathaniel Daw retweeted
if you're at #COSYNE2025 today, come by poster 3-090 to talk to @neuro_kim @kevinjmiller10 and me about this work.
see you then!
Nathaniel Daw retweeted
New pre-print out of our lab, "Children leverage predictive representations for flexible, value-guided choice," from former lab members @lice_zhang @katenuss and collaborators @nathanieldaw @ari_e_kahn! @cate_hartley
osf.io/preprints/psyarxiv/y3…
Nathaniel Daw retweeted
In a dynamic reinforcement learning task, human players adaptively employ a mix of decision strategies, including model-based learning but also the successor representation. @nathanieldaw @ari_e_kahn
nature.com/articles/s44271-0…
Nathaniel Daw retweeted
Devastatingly, we have lost a bright light in our field. Felix Hill was not only a deeply insightful thinker -- he was also a generous, thoughtful mentor to many researchers. He majorly changed my life, and I can't express how much I owe to him.
Even now, Felix still has so much to give -- to all of us. Below, I've compiled some of his writings that I think would be inspiring to anyone in the field. They reflect his unusual perspective, his incisive writing, and his irreverent, witty sense of humor. It's a pleasure and an epiphany to read any of these.
I hope this is a small way to help ensure that Felix's light continues to shine, as it deserves to
--
1. Why do transformers work so well on language? (bit.ly/4h1IbeR) Atypically few jokes in this one, but starting around 0:32:00 Felix answers a fundamental question that I've never seen addressed so well. He ties the architectural properties of the transformer 1:1 to properties of language. I think this perspective is so illuminating and incredibly underappreciated.
2. What deep learning can teach us about linguistics and science (bit.ly/4gGFmzZ). It starts with a pretty entertaining refutal of Chomskian linguistics, and then dives into an optimistic and pretty irrefutable argument about why neural networks can teach us about language.
3. How to write a paper (bit.ly/40698qO). One of many things I learned from Felix -- he taught me to put together a research story from the beginning, including imagined results. This narrative helps guide the research, even if the story might change with each new set of experiments.
4. The bittersweet lesson (bit.ly/3BLsEky). Felix wrote this a few weeks before he died, and it's a unique, insightful take on the bitter lesson. He asked for feedback but sadly I got overwhelmed with other things and it stayed on my todo list for too long.. I didn't get to tell him what a beautiful essay I thought it was, and how much he still had to contribute...
5. Mental health in (AI) research (bit.ly/3BT3lwW). If you are struggling, please do reach out for help, and please do hold on. There may not always be a complete fix, but there are always ways to make things significantly better. I believe this from the bottom of my heart
Nathaniel Daw retweeted
I’m really sad that my dear friend @FelixHill84 is no longer with us. He had many friends and colleagues all over the world - to try to ensure we reach them, his family have asked to share this webpage for the celebration of his life: pp.events/felix
Nathaniel Daw retweeted
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/1kBQr…
Nathaniel Daw retweeted
New preprint with @DaniSBassett and @nathanieldaw!
How do humans learn predictive representations? We propose a trial-by-trial learning rule that incorporates trace updating to learn the SR.
biorxiv.org/content/10.1101/…
Nathaniel Daw retweeted
It took a while, but this paper with @nathanieldaw is now published in @NatureComms. Reviewers had many good comments, so lots of new stuff here, especially on the computational mechanisms of how people dissociate volatility from stochasticity:
nature.com/articles/s41467-0…
New preprint with @nathanieldaw: Humans can distinguish volatility (rate of change or diffusion noise) & moment-to-moment stochasticity (or observation noise) and adjust their learning adaptively. As theoretically expected, these have opposite effects on humans' learning rate.
