iiinova — where your digital self keeps evolving

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Robert Hawkins @rdhawkins.bsky.social · 24/07/2026

good morning!! it's day 2 of #cogsci2026, and we're so excited to hear about new work from Seo-young Lee, Jess Mankewitz, Polina Tvilodub, Jinyi Kuang, and Karla Perez!

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Mike Frank @mcxfrank.bsky.social · 23/07/2026

If you’re at #cogsci2026, please come see presentations by some of the great folks collaborating with the Language and Cognition Lab at Stanford!

LangCog lab talks and posters at cogsci2026

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Ben Prystawski @benpry.bsky.social · 23/07/2026

How do people learn abstract knowledge over generations in a crafting game with rich structure? Come by my talk in the cultural evolution session on Friday morning at #CogSci2026 to find out!

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Ben Prystawski @benpry.bsky.social · 26/06/2026

New preprint! AI agents have shown impressive scientific automation capabilities. Can we apply them to psychology research, *including* human data collection? We introduce auto-psych, a framework that proposes cognitive models and uses them to design and run human experiments. 1/

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Samah Abdelrahim | سماح @samahrahim.bsky.social · 24/06/2026

1st published paper alert ⚠️ in which @mcxfrank.bsky.social and I ask: How robust is the “shape bias”? a phenomenon that has been central to theories of early word learning: If a child hears a new word for an object, they often extend it to other objects with the same shape. tinyurl.com/JCL-shapebias

cambridge.org

Examining the Robustness and Generalizability of the Shape Bias: A Meta-Analysis | Journal of Child Language | Cambridge Core

Examining the Robustness and Generalizability of the Shape Bias: A Meta-Analysis

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Akshay K. Jagadish @akjagadish.bsky.social · 22/06/2026

1/ 🚨 New opinion piece: "Can we automatize scientific discovery in the cognitive sciences?" We lay out a vision for a fully automated, in-silico science of the mind, where modern AI systems run every stage of the scientific discovery cycle in cognitive science 🧵 #AutomatedDiscovery #AI4Science

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Mark Ho @markkho.bsky.social · 17/06/2026

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.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).

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Manikya Alister @manikyaalister.bsky.social · 09/06/2026

One of the first studies from my PhD is out now in JEP:G 🥳We tested whether people can infer the truth from teachers who were either helpful, misleading, or randomly sampling. With Keith Ransom and @perfors.net psycnet.apa.org/fulltext/202...

Title of paper: When a Helpful Bias Is Unhelpful: Limitations in Reasoning
About Random and Deliberately Misleading Evidence
Abstract: Social information aids learning: By making assumptions about other people’s knowledge and intentions,
people can draw strong and accurate inferences from limited data. In this study, we systematically tested
people’s ability to reason from information providers with different intentions. The task was an adaptation of
Shafto et al.’s (2014) rectangle game, where learners guessed a rectangle’s size and location based on
provided clues. We examined reasoning based on information from four types of providers: a helpful
provider, a provider who sampled randomly, and two misleading providers (who could mislead but not lie).
We also varied whether people were given a cover story describing the provider in advance or whether they
could infer how helpful a provider was based on what the provider shared. Participants learned efficiently
from helpful providers, aligning closely with the predictions of a normative Bayesian model, even without a
cover story. However, while people usually recognized unhelpful providers, they struggled to identify and
respond appropriately to misleading strategies. Overall, our results suggest a helpful bias: In our task,
participants assumed helpful intent unless given explicit feedback, and even then, they did not fully adjust in
line with Bayesian predictions. People also struggled to overcome this bias when learning from randomly
sampled information, especially when they had experience being an information provider themselves
(Experiment 3).

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Linas Nasvytis @linasnasvytis.bsky.social · 08/06/2026

1/ New preprint! Reasoning models often require hundreds of task examples and thousands of rollouts to improve on a task. How can they learn more from much less? Introducing CORE: contrastive self-reflection for rapid, sample-efficient, and interpretable self-improvement 🧵

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Linas Nasvytis @linasnasvytis.bsky.social · 19/05/2026

1\ Can you make this Roman-numeral equation true by moving exactly one matchstick?

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Irmak Ergin @irmakergin.bsky.social · 01/04/2026

Excited to share our new publication, “Measuring Naturalistic Speech Comprehension in Real Time”! ➡️ rdcu.be/fa3hk #psynomBRM w/ @kriesjill.bsky.social, Shiven Gupta, Maria Papworth Burrel, & @lauragwilliams.bsky.social 🧵1/11

rdcu.be

Measuring naturalistic speech comprehension in real time

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Mike Frank @mcxfrank.bsky.social · 05/01/2026

Now up as a reviewed @elife.bsky.social preprint: "Continuous developmental changes in word recognition support language learning across early childhood" elifesciences.org/reviewed-pre... Using data from ~2000 kids ages 1-6, we quantify links between word recognition and early vocabulary growth!

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Yang Xiang @yangxiang.bsky.social · 19/09/2025

Now out in Cognition, work with the great @gershbrain.bsky.social @tobigerstenberg.bsky.social on formalizing self-handicapping as rational signaling! 📃 authors.elsevier.com/a/1lo8f2Hx2-...

kwnsfk27.r.eu-west-1.awstrack.me

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Erik Brockbank @erikbrockbank.bsky.social · 12/08/2025

How do we predict what others will do next? 🤔 We look for patterns. But what are the limits of this ability? In our new paper at CCN 2025 (@cogcompneuro.bsky.social), we explore the computational constraints of human pattern recognition using the classic game of Rock, Paper, Scissors 🗿📄✂️

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Rebecca Zhu @rebeccazoo.bsky.social · 07/08/2025

My final project from grad school is out now in Dev Psych! Mombasa County preschoolers were more accurate on object-based than picture-based vocabulary assessments, whereas Bay Area preschoolers were equally accurate on object-based and picture-based assessments. psycnet.apa.org/doiLanding?d...

psycnet.apa.org

APA PsycNet

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Andrew Lampinen @lampinen.bsky.social · 05/08/2025

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 in this question, 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

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Ben Prystawski @benpry.bsky.social · 01/08/2025

How do people trade off between speed and accuracy in reasoning tasks without easy heuristics? Come to my talk, "Thinking fast, slow, and everywhere in between in humans and language models," in the Reasoning session this afternoon #CogSci2025 to find out! paper: escholarship.org/uc/item/5td9...

escholarship.org

Thinking fast, slow, and everywhere in between in humans and language models

Author(s): Prystawski, Ben; Goodman, Noah | Abstract: How do humans adapt how they reason to varying circumstances? Prior research has argued that reasoning comes in two types: a fast, intuitive type ...

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Daniel Wurgaft @danielwurgaft.bsky.social · 28/06/2025

🚨New paper! We know models learn distinct in-context learning strategies, but *why*? Why generalize instead of memorize to lower loss? And why is generalization transient? Our work explains this & *predicts Transformer behavior throughout training* without its weights! 🧵 1/

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Ben Prystawski @benpry.bsky.social · 25/06/2025

How can we combine the process-level insight that think-aloud studies give us with the large scale that modern online experiments permit? In our new CogSci paper, we show that speech-to-text models and LLMs enable us to scale up the think-aloud method to large experiments!

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Junyi Chu @junyi.bsky.social · 06/06/2025

Delighted to announce our CogSci '25 workshop at the interface between cognitive science and design 🧠🖌️! We're calling it: 🏺Minds in the Making🏺 🔗 minds-making.github.io June – July 2024, free & open to the public (all career stages, all disciplines)

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xavier roberts-gaal @xrg.bsky.social · 20/05/2025

the functional form of moral judgment is (sometimes) the nash bargaining solution new preprint👇

figure 2 from our preprint, reporting the results from two experiments

we measure moral judgments about dividing money between two parties and manipulate the degree of asymmetry in the outside options each party has

we find that moral judgments track predictions from rational bargaining models like the nash bargaining solution and the kalai-smorodinsky solution in a negotiation context

by contrast, in a donation context, moral intuitions completely reverse, instead tracking redistributive and egalitarian principles

preprint link: https://osf.io/preprints/psyarxiv/3uqks_v1

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Fred Callaway @fredcallaway.bsky.social · 07/05/2025

Despite the world being on fire, I can't help but be thrilled to announce that I'll be starting as an Assistant Professor in the Cognitive Science Program at Dartmouth in Fall '26. I'll be recruiting grad students this upcoming cycle—get in touch if you're interested!

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Mike Frank @mcxfrank.bsky.social · 14/04/2025

Super excited to submit a big sabbatical project this year: "Continuous developmental changes in word recognition support language learning across early childhood": osf.io/preprints/ps...

title of paper (in text) plus author list

Time course of word recognition for kids at different ages.

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Erik Brockbank @erikbrockbank.bsky.social · 07/03/2025

Hello bluesky world :) excited to share a new paper on data visualization literacy 📈 🧠 w/ @judithfan.bsky.social, @arnavverma.bsky.social, Holly Huey, Hannah Lloyd, @lacepadilla.bsky.social! 📝 preprint: osf.io/preprints/ps... 💻 code: github.com/cogtoolslab/...

osf.io

OSF

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Mike Frank @mcxfrank.bsky.social · 06/03/2025

AI models are fascinating, impressive, and sometimes problematic. But what can they tell us about the human mind? In a new review paper, @noahdgoodman.bsky.social and I discuss how modern AI can be used for cognitive modeling: osf.io/preprints/ps...

Figure 1. A schematic depiction of a model-mechanism mapping between a human learning system (left side) and a cognitive model (right side). Candidate model mechanism mappings are pictured as mapping between representations but also can be in terms of input data, architecture, or learning objective.

Figure 2. Data efficiency in human learning. (left) Order of magnitude of LLM vs. human training data, plotted by human age. Ranges are approximated from Frank (2023a). (right) A schematic depiction of evaluation scaling curves for human learners vs. models plotted by training data
quantity.

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Kanishk Gandhi @gandhikanishk.bsky.social · 04/03/2025

1/13 New Paper!! We try to understand why some LMs self-improve their reasoning while others hit a wall. The key? Cognitive behaviors! Read our paper on how the right cognitive behaviors can make all the difference in a model's ability to improve with RL! 🧵

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Tobias Gerstenberg @tobigerstenberg.bsky.social · 12/02/2025

New paper in Psychological Review! In "Causation, Meaning, and Communication" Ari Beller (cicl.stanford.edu/member/ari_b...) develops a computational model of how people use & understand expressions like "caused", "enabled", and "affected". 📃 osf.io/preprints/ps... 📎 github.com/cicl-stanfor... 🧵

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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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Isabel Papadimitriou @isabelpapad.bsky.social · 18/11/2024

Do you want to understand how language models work, and how they can change language science? I'm recruiting PhD students at UBC Linguistics! The research will be fun, and Vancouver is lovely. So much cool NLP happening at UBC across both Ling and CS! linguistics.ubc.ca/graduate/adm...

Aerial picture of the UBC campus, with an arrow pointing to a building and text asking "Your PhD lab?"

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Mike Frank @mcxfrank.bsky.social · 18/10/2024

If you try to replicate a finding so you can build on it, but your study fails, what should you do? Should you follow up and try to "rescue" the failed rep, or should you move on? Boyce et al. tried to answer this question; in our sample, 5 of 17 rescue projects succeeded. osf.io/preprints/ps...

osf.io

OSF

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Natalia Vélez @natvelali.bsky.social · 13/09/2024

Preprint alert! After 4 years, I’m super excited to share work with @thecharleywu.bsky.social @gershbrain.bsky.social and Eric Schulz on the rise and fall of technological development in virtual communities in #OneHourOneLife #ohol doi.org/10.31234/osf...

A promotional image of One Hour One Life, showing a character growing up from a baby, to a child, to an adult, to an old woman, to a pile of bones. This work is not affiliated with One Hour One Life; we are grateful to Jason Rohrer, the game's developer, for making the game open data and open source.

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Andrew Lampinen @lampinen.bsky.social · 23/05/2024

How well can we understand an LLM by interpreting its representations? What can we learn by comparing brain and model representations? Our new paper highlights intriguing biases in learned feature representations that make interpreting them more challenging! 1/

Clear clusters in model representations driven by some features (plot colors) but neglecting other more complex ones (plotted as shapes) which are mixed within the color clusters.

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Mike Frank @mcxfrank.bsky.social · 06/05/2024

When a replication fails, researchers have to decide whether to make another attempt or move on. How should we think about this decision? Here's a new paper trying to answer this question, led by Veronica Boyce and featuring student authors from my class! osf.io/preprints/ps...

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