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Simon Schug

@smonsays.bsky.social
986 followers 244 following 28 posts

postdoc @princeton computational cognitive science ∪ machine learning smn.one

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Simon Schug @smonsays.bsky.social · 17/09/2026
Human thought is thought to be systematic: Do reasoning models have systematicity of thought? With @brendenlake.bsky.social we study this question in our new preprint arxiv.org/abs/2609.13948 Thread 🧵
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Tom McCoy @rtommccoy.bsky.social · 20/08/2026
Since many are starting grad school soon, let me re-share my One Big Tip™️ for research! Research involves many skills - collaborating, writing, presenting, etc. But many of these skills can be unified under a single overarching ability: theory of mind Blog post link in reply
Illustration of the blog post's main argument, summarized as: "Theory of Mind as a Central Skill for Researchers: Research involves many skills.If each skill is viewed separately, each one takes a long time to learn. These skills can instead be connected via theory of mind – the ability to reason about the mental states of others. This allows you to transfer your abilities across areas, making it easier to gain new skills."
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Naomi Saphra @nsaphra.bsky.social · 09/08/2026
I've been unsettled lately when reading messages and papers. It feels like I'm dissociating. Everything seems a bit alien, even if it's completely human. I've had a realization: When our simulations finally exited the Uncanny Valley, they brought the Uncanny with them.
nsaphra.net
Life on the Uncanny Precipice | Naomi Saphra
We were wrong about the Uncanny Valley.
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Simon Schug @smonsays.bsky.social · 09/04/2026
LLM agents are a serious problem for online experiments. It is very easy to use them and very hard to spot them. What can researchers do? With @brendenlake.bsky.social, we suggest detecting LLMs based on their lack of human cognitive constraints in our #CogSci2026 paper: arxiv.org/abs/2604.00016
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Minas Karamanis @minaskar.bsky.social · 30/03/2026
Hey, I wrote a thing about AI in astrophysics ergosphere.blog/posts/the-ma...
ergosphere.blog
The machines are fine. I'm worried about us.
On AI agents, grunt work, and the part of science that isn't replaceable.
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Mark Histed @markhisted.org · 29/03/2026
The hardest part of science is posing the right question, not answering it.
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Kai Sandbrink @ackaisa.bsky.social · 26/03/2026
Excited that my paper on metacontrol in humans and neural networks with @summerfieldlab.bsky.social and @lhuntneuro.bsky.social is out in PNAS! We examine the way that predictive representations of control enable behavioral adaptation across settings, and pathologies: www.pnas.org/doi/10.1073/...
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Sam Gershman @gershbrain.bsky.social · 14/03/2026
I think Fodor & Pylyshyn's 1988 paper is possibly the most mischaracterized paper in the history of cognitive science. It's often cited as arguing that neural networks cannot achieve systematicity, compositionality, and productivity. But that's not what they actually argue...
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Jonathan Nicholas @jonathannicholas.bsky.social · 23/01/2026
Our experiences have countless details, and it can be hard to know which matter. How can we behave effectively in the future when, right now, we don't know what we'll need? Out today in @nathumbehav.nature.com , @marcelomattar.bsky.social and I find that people solve this by using episodic memory.
nature.com
Episodic memory facilitates flexible decision-making via access to detailed events - Nature Human Behaviour
Nicholas and Mattar found that people use episodic memory to make decisions when it is unclear what will be needed in the future. These findings reveal how the rich representational capacity of episod...
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C. Thi Nguyen @add-hawk.bsky.social · 22/01/2026
Last term I tried an experiment: I walked into my Tech and Design Ethics class, admitted that I had *no idea* what to do about ChatGPT - so I would let them figure it out. As in: their first project was to decide and write the ChatGPT policy for the class. Here's what happened:
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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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Erin Grant @eringrant.me · 06/12/2025
Thrilled to start 2026 as faculty in Psych & CS @ualberta.bsky.social + Amii.ca Fellow! 🥳 Recruiting students to develop theories of cognition in natural & artificial systems 🤖💭🧠. Find me at #NeurIPS2025 workshops (speaking coginterp.github.io/neurips2025 & organising @dataonbrainmind.bsky.social)
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Paul Masset @paulmasset.bsky.social · 19/11/2025
I am recruiting graduate students for the experimental side of my lab @mcgill.ca for admission in Fall 2026! Get in touch if you're interested in how brain circuits implement distributed computation, including dopamine-based distributed RL and probabilistic representations.
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Brenden Lake @brendenlake.bsky.social · 03/11/2025
Checking out the Princeton trails on our lab retreat
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Simon Schug @smonsays.bsky.social · 04/11/2025
Does scaling lead to compositional generaliztation? Our #NeurIPS2025 Spotlight paper suggests that it can -- with the right training distribution. 🧵 A short thread:
Plots showing how scaling model size and data size leads to compositional generalizationA generated image composition of a clock inside a treasure chest inside a transparent cube.
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Brenden Lake @brendenlake.bsky.social · 12/06/2025
I'm joining Princeton University as an Associate Professor of Computer Science and Psychology this fall! Princeton is ambitiously investing in AI and Natural & Artificial Minds, and I'm excited for my lab to contribute. Recruiting postdocs and Ph.D. students in CS and Psychology — join us!
Nassau Hall. Photo credit to Debbie and John O'Boyle
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Simon Schug @smonsays.bsky.social · 25/04/2025
Are transformers smarter than you? Hypernetworks might explain why. Come checkout our Oral at #ICLR tomorrow (Apr 26th, poster at 10:00, Oral session 6C in the afternoon). openreview.net/forum?id=V4K...
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Taylor Webb @taylorwwebb.bsky.social · 10/03/2025
LLMs have shown impressive performance in some reasoning tasks, but what internal mechanisms do they use to solve these tasks? In a new preprint, we find evidence that abstract reasoning in LLMs depends on an emergent form of symbol processing arxiv.org/abs/2502.20332 (1/N)
arxiv.org
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models
Many recent studies have found evidence for emergent reasoning capabilities in large language models, but debate persists concerning the robustness of these capabilities, and the extent to which they ...
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Konrad Kording @kordinglab.bsky.social · 14/02/2025
New blog post: The principle of neuroscience. medium.com/@kording/the...
medium.com
The Principle of Neural Science
I first encountered Principles of Neural Science as a young student of neuroscience. The book was filled with delightful narratives…
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Marine Schimel @marineschimel.bsky.social · 31/01/2025
For my first Bluesky post, I'm very excited to share a thread on our recent work with Mitra Javadzadeh, investigating how connections between cortical areas shape computations in the neocortex! [1/7] www.biorxiv.org/content/10.1...
biorxiv.org
Dynamic consensus-building between neocortical areas via long-range connections
The neocortex is organized into functionally specialized areas. While the functions and underlying neural circuitry of individual neocortical areas are well studied, it is unclear how these regions op...
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Guillaume Bellec @bellecguill.bsky.social · 08/01/2025
Pre-print 🧠🧪 Is mechanism modeling dead in the AI era? ML models trained to predict neural activity fail to generalize to unseen opto perturbations. But mechanism modeling can solve that. We say "perturbation testing" is the right way to evaluate mechanisms in data-constrained models 1/8
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Mark D Humphries @markdhumphries.bsky.social · 30/12/2024
Cutting it a bit fine, but here’s my review of the year in neuroscience for 2024 The eighth of these, would you believe? We’ve got dark neurons, tiny monkeys, the most complete brain wiring diagram ever constructed, and much more… Published on The Spike Enjoy! medium.com/the-spike/20...
medium.com
2024: A Review of the Year in Neuroscience
Feeling a bit wired
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Kris Jensen @kristorpjensen.bsky.social · 21/12/2024
I wrote an introduction to RL for neuroscience last year that was just published in NBDT: tinyurl.com/5f58zdy3 This review aims to provide some intuition for and derivations of RL methods commonly used in systems neuroscience, ranging from TD learning through the SR to deep and distributional RL!
tinyurl.com
An introduction to reinforcement learning for neuroscience | Published in Neurons, Behavior, Data analysis, and Theory
By Kristopher T. Jensen. Reinforcement learning for neuroscientists
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Ben Recht @beenwrekt.bsky.social · 20/12/2024
Stitching component models into system models has proven difficult in biology. But how much easier has it been in engineering? www.argmin.net/p/monster-mo...
argmin.net
Monster Models
Systems-level biology is hard because systems-level engineering is hard.
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Badr AlKhamissi @bkhmsi.bsky.social · 19/12/2024
🚨 New Paper! Can neuroscience localizers uncover brain-like functional specializations in LLMs? 🧠🤖 Yes! We analyzed 18 LLMs and found units mirroring the brain's language, theory of mind, and multiple demand networks! w/ @gretatuckute.bsky.social, @abosselut.bsky.social, @mschrimpf.bsky.social 🧵👇
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Blake Richards @tyrellturing.bsky.social · 16/12/2024
1/ Okay, one thing that has been revealed to me from the replies to this is that many people don't know (or refuse to recognize) the following fact: The unts in ANN are actually not a terrible approximation of how real neurons work! A tiny 🧵. 🧠📈 #NeuroAI #MLSky
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Razvan Pascanu @razvan-pascanu.bsky.social · 15/12/2024
For my first post on Bluesky .. I'll start by announcing our 2025 edition of EEML which will be in Sarajevo :) ! I'm really excited about it and hope to see many of you there. Please follow the website (and Bluesky account) for more details which are coming soon ..
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Markus Meister @mameister4.bsky.social · 14/11/2024
Have you had private doubts whether we'll ever understand the brain? Whether we'll be able explain psychological phenomena in an exhaustive way that ranges from molecules to membranes to synapses to cells to cell types to circuits to computation to perception and behavior?
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Andrew Lampinen @lampinen.bsky.social · 10/12/2024
What counts as in-context learning (ICL)? Typically, you might think of it as learning a task from a few examples. However, we’ve just written a perspective (arxiv.org/abs/2412.03782) suggesting interpreting a much broader spectrum of behaviors as ICL! Quick summary thread: 1/7
arxiv.org
The broader spectrum of in-context learning
The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervised few-shot learning...
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Kai Sandbrink @ackaisa.bsky.social · 03/12/2024
Thrilled to share our NeurIPS Spotlight paper with Jan Bauer*, @aproca.bsky.social*, @saxelab.bsky.social, @summerfieldlab.bsky.social, Ali Hummos*! openreview.net/pdf?id=AbTpJ... We study how task abstractions emerge in gated linear networks and how they support cognitive flexibility.
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Blake Richards @tyrellturing.bsky.social · 13/11/2024
Great thread from @michaelhendricks.bsky.social! Reminds me of something Larry Abbott once said to me at a summer school: Many physicists come into neuroscience assuming that the failure to find laws of the brain was just because biologists aren't clever enough. In fact, there are no laws. 🧠📈 🧪
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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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Simon Schug @smonsays.bsky.social · 13/11/2024
To help find people at the intersection of neuroscience and AI. Of course let me know if I missed someone or you’d like to be added 🧪 🧠 #neuroskyence go.bsky.app/CAfmKQs
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Simon Schug @smonsays.bsky.social · 28/10/2024
Neural networks used to struggle with compositionality but transformers got really good at it. How come? And why does attention work so much better with multiple heads? There might be a common answer to both of these questions.
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