This. But I’d take it further. Most humans are most often substantially less logical and more biased than frontier models. Humans are still much smarter in many crucial ways, but internal consistency is not one of them.

Andrew Lampinen@lampinen.bsky.social · 20h ago

Yet another (popular) article on human/LM reasoning that (as usual) I'm deeply disappointed to see casually presupposes that humans do something called "reasoning" that's implied to be linking together steps that logically follow without any qualification, then proceeds to be dismissive 1/

like us! No one is doing computer science and math with their naked brains. Pen & paper, math notation, computer language—these are our neurosymbolic add ons! @lampinen.bsky.social , @blaiseaguera.bsky.social , @kmahowald.bsky.social all get it. Not sure why this isn’t more obvious to folks

Andrea Lathrop@cabernet.bsky.social · 4d ago

This is a very good point. It's an LLM-system, at this point, not a pure LLM, so theoretical critiques about what an LLM can or cannot do should not be taken to be predictions of what an LLM-plus-a-bunch-of-stuff can or cannot do.

So thrilled to be joining the Psychology Department at @columbiauniversity.bsky.social as an assistant prof next summer! I'll be recruiting for PhD/postdoc to start in Autumn 2027, so if you're interested in using computational approaches to studying human cognition please get in touch!

Tired of doom-scrolling bluesky? Try mitpuzzles.com!

Sam Cheyette@samcheyette.bsky.social · last mo.

are you a puzzle-enjoyer, or would you like to become one? try out a game or two at mitpuzzles.com! we're trying to figure out the factors that separate novice from expert problem solving, and what changes with experience, using self-motivated puzzle solving

So proud of Ted @languagemit.bsky.social! <3

We make flexible choices in new situations by knitting together information from separate relevant memories. But what governs which memories are retrieved and when? In a new preprint, we captured how people build decision variables from different memories by tracking their gaze on a blank screen.

Flexible decisions arise from resource-rational memory sampling

Flexible decision making depends on retrieving and recombining memories. Yet because this process unfolds covertly, its governing principles remain unknown. Here we use gaze reinstatement to uncover t...

biorxiv.org

How do human minds make sense of big, messy problems? 😵‍💫🌀 How do we distill complexity into something simple enough to solve? 🤔💡 We’ll be tackling these questions (and more!) at two workshops on task representations, abstractions, and construals #CogSci2026 #CCN2026 🧵 framing-the-problem.github.io

Framing the Problem — Workshop Series

A workshop series on representation construction in cognitive science and AI. CogSci 2026 (Rio) and CCN 2026 (NYU).

framing-the-problem.github.io

Pleased to share that this work is now published in TMLR! openreview.net/forum?id=RuW...

Andrew Lampinen@lampinen.bsky.social · 11mo ago

Why does AI sometimes fail to generalize, and what might help? In a new paper (arxiv.org/abs/2509.16189), we highlight the latent learning gap — which unifies findings from language modeling to agent navigation — and suggest that episodic memory complements parametric learning to bridge it. Thread:

I'm making a website for my new lab (!!!). Who should I steal ideas from? (Self-nominations welcome)

This is an actual line that was added to the official system prompt for Codex for GPT-5.5 by OpenAI. Usually the system prompt is as minimal as possible, so I assume it would otherwise mention goblins a lot. AIs are weird.

Good read. I'd go further and suggest that scientific evidence against behaviorism is impossible in principle. Behaviorism simply says that you can model behavior as p(behavior | experience, genes). Cognitivism just adds a latent state, r (representation): p(b|e,g) = ∫ p(b|r) p(r|e,g) dr. (1/4)

Stefano Palminteri@stepalminteri.bsky.social · 4mo ago

The more I study the issue and the original texts, the more the scientific arguments against behaviorism (and, conversely, in favor of the cognitive “revolution”) seem weaker to me. This is one of my favorite papers across all fields (and very convincing IMHO) www.academia.edu/75630727/The...

New preprint from my lab! We study how reinforcement learning & selective attention interact. To do so, we built a set of models describing different ways that value & reward prediction error can modulate top-down attention. We compare model outcomes to monkey data from a color value learning task

bioRxiv Neuroscience@biorxiv-neursci.bsky.social · 4mo ago

Modulation of feature attention by reward prediction error explains value learning behavior https://www.biorxiv.org/content/10.64898/2026.04.10.717847v1

📢📢 Announcing this year's conference on the Mathematics of Neuroscience & AI (Rome, 9-12th June). We’ve got a stellar line-up and venue, and invite everyone to join: www.neuromonster.org

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)

Hybrid neural–cognitive models reveal how memory shapes human reward learning

Hybrid neural–cognitive models reveal how memory shapes human reward learning

Nature Human Behaviour, Published online: 05 February 2026; doi:10.1038/s41562-025-02324-0Using artificial neural networks applied to human data, Eckstein et al. show that good models of reinforcement learning require memory components that track representations of the past.

dlvr.it

Convincing evidence that people recall individual past experiences to inform decisions—specifically when an easier incremental learning strategy isn’t available. Also, a masterclass in experimental design.

Jonathan Nicholas@jonathannicholas.bsky.social · 6mo ago

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.

With some trepidation, I'm putting this out into the world: gershmanlab.com/textbook.html It's a textbook called Computational Foundations of Cognitive Neuroscience, which I wrote for my class. My hope is that this will be a living document, continuously improved as I get feedback.

Writing is thinking Outsourcing the entire task of writing to LLMs will deprive us of the essential creative task of interpreting our findings and generating a deeper theoretical understanding of the world.

How do we achieve few-shot generalization? New work led by @fabianrenz.bsky.social dives into the role of replay in learning and using structure to generalize reward. Dream team effort with Shany Grossman @nathanieldaw.bsky.social Peter Dayan & @doellerlab.bsky.social www.biorxiv.org/content/10.6...

Now out in JEP: General, "How working memory and reinforcement learning interact when avoiding punishment and pursuing reward concurrently" psycnet.apa.org/record/2026-... Preprint with final version: osf.io/preprints/ps... 1/n

We're excited to announce that Cognitive Science at Dartmouth is recruiting PhD students to work collaboratively with me, Steven Frankland, and Fred Callaway. Come study the principles and mechanisms that enable us to understand, plan, and act in the world! Info: sites.dartmouth.edu/cogscigrad/

Yes! @upenn.edu declines signing The Compact. I'm proud of this decision.

Nicole Rust@nicolecrust.bsky.social · 10mo ago

I’m proud of @upenn.edu‘s first steps regarding The Compact.

1st Sharp Lab preprint! 🚨 We tested how anxiety affects task generalization—not how people generalize threat stimuli, but how they reuse action-outcome structures when planning in new contexts. Worry makes people avoid reusing actions that co-occurred w/ threat! 📄: osf.io/preprints/ps... 🧵 1/12

Forget modeling every belief and goal! What if we represented people as following simple scripts instead (i.e "cross the crosswalk")? Our new paper shows AI which models others’ minds as Python code 💻 can quickly and accurately predict human behavior! shorturl.at/siUYI%F0%9F%...

New in @pnas.org: doi.org/10.1073/pnas... We study how humans explore a 61-state environment with a stochastic region that mimics a “noisy-TV.” Results: Participants keep exploring the stochastic part even when it’s unhelpful, and novelty-seeking best explains this behavior. #cogsci #neuroskyence

We often hear from reviewers: "what about demand effects?" So we developed a method to eliminate them. Something weird happened during testing: We couldn’t detect demand effects in the first place! (1/8)

Happy to share "The Dynamics of Caregiver Unpredictability Shape Moment-to-Moment Infant Looking During Dyadic Interaction," out now in Child Development thanks to a large team of people I worked on this with! srcd.onlinelibrary.wiley.com/doi/pdfdirec...

<em>Child Development</em> | SRCD Journal | Wiley Online Library

Cognitive development is associated with how predictable caregivers are, but the mechanisms driving this are unclear. One possibility is caregiver predictability initially shapes how infants gather i...

srcd.onlinelibrary.wiley.com

Our new paper is out in PNAS: "Evolving general cooperation with a Bayesian theory of mind"! Humans are the ultimate cooperators. We coordinate on a scale and scope no other species (nor AI) can match. What makes this possible? 🧵 www.pnas.org/doi/10.1073/...

Evolving general cooperation with a Bayesian theory of mind | PNAS

Theories of the evolution of cooperation through reciprocity explain how unrelated self-interested individuals can accomplish more together than th...

pnas.org

Centaur's performance may be largely driven by learning a good model of behavioral auto-regression—independent of the task. An important lesson for cognitive modelers: higher likelihood ≠ better account of behavior. osf.io/preprints/ps...