iiinova — where your digital self keeps evolving

Reposted by Khai Loong Aw

Mike Frank @mcxfrank.bsky.social · 25/09/2026

We've just released a new version of childes-db, my lab's interface to the CHILDES database of child language transcripts. It lets you work with CHILDES data from R through a versioned, reproducible API. A few updates 🧵 childes-db.stanford.edu/

Screenshot of the childes-db website: 'A flexible and reproducible interface to CHILDES.' childes-db 2026.1 contains 56,579 transcripts from 9,151 children across 437 corpora, with panels for an R API tutorial and interactive visualizations of mean length of utterance by child age.

12913

Reposted by Khai Loong Aw

Michelle Greene @mgreenephd.bsky.social · 25/09/2026

🚨New paper alert! 🚨 The visual system tunes itself to the statistics of its input. How do individual experiences differently tune vision? In this work, @bjbalas.bsky.social and I examined how one individual difference—height—changes the visual diet and how this affects perception. 1/

29432

Reposted by Khai Loong Aw

Stanford Vision and Perception Neuroscience Lab @stanfordvpnl.bsky.social · 04/08/2026

VPNL is at #CCN2026 !! Check out our lab members' presentations this week :)

0176

Reposted by Khai Loong Aw

Anna (Anya) Ivanova @neuranna.bsky.social · 03/08/2026

The language, intelligence & thought (LIT) lab is presenting at #CCN2026 on Tuesday! 1/

LIT lab @ CCN 2026

Kamya Hari - Independent-component-based encoding models of brain activity during story comprehension
Poster B41

Taha Binhuraib - Divergent scaling patterns in the auditory cortex vs. other brain regions reveal distinct sources of LLM-brain alignment
Poster B78

Anya Ivanova - Is NeuroAI adopting the right methods and theoretical frameworks?
GAC

1213

Reposted by Khai Loong Aw

Nancy Kanwisher @nancykanwisher.bsky.social · 03/08/2026

Kanwisher Lab loves #CCN2026

0384

Reposted by Khai Loong Aw

Badr AlKhamissi @bkhmsi.bsky.social · 03/08/2026

Excited to be at #CCN2026 in NYC! I'll be presenting our spotlight poster, Topo-Omni, with @hannesmehrer.bsky.social 🧠 📍 Poster F14 — Session F 🗓️ Thursday, Aug 6 Come by if you're around, we'd love to talk about how our multimodal model can discover functionally selective brain regions!

1256

Khai Loong Aw @khaiaw.bsky.social · 04/08/2026

Will be presenting our work on building a Universal Vision-Language World Model. It uses visual abstractions as streams of thought, e.g., camera pose, depth, optical flow, point tracks, text. Our model solves vision-language tasks using world modeling and inverse dynamics, unlike standard VLMs.

020

Reposted by Khai Loong Aw

Khai Loong Aw @khaiaw.bsky.social · 04/08/2026

Our Stanford NeuroAI Lab is presenting at the Cognitive Computational Neuroscience conference #CCN2026 🥳 Come say hi to us!! Imran, @norcalneuro.bsky.social, @dyamins.bsky.social, Khaled, @ynshah.bsky.social @seojinlee.bsky.social , @clionaod.bsky.social , Khai

085

Khai Loong Aw @khaiaw.bsky.social · 04/08/2026

085

Khai Loong Aw @khaiaw.bsky.social · 24/07/2026

nice review! amazing that there has been more than a decade of work mapping AI vision & language model representations to human brain responses (yamins et al 2014, wehbe et al 2014)

020

Reposted by Khai Loong Aw

Mariya Toneva @mtoneva.bsky.social · 23/07/2026

This review began with the great Peter Hagoort asking after one of my talks: "What have we actually learned about the brain from brain–DNN comparisons?" 🔥 So eye-opening to survey NeuroAI work across vision and language--many questions have been answered in one field but remain open in the other!

0207

Reposted by Khai Loong Aw

Dota Tianai Dong @dotadotadota.bsky.social · 21/07/2026

1/5 Over a decade of comparing deep neural networks to the human brain—but what have we actually learned? Our new @cp-trendscognsci.bsky.social Feature Review synthesizes a decade of brain–DNN comparisons, asking what they reveal about brain function across vision and language.

14421

Reposted by Khai Loong Aw

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

12713

Reposted by Khai Loong Aw

Vlad Ayzenberg @vayzenb.bsky.social · 22/07/2026

Excited to share our review in @cp-neuron.bsky.social with @lauriebayet.bsky.social and @mickbonner.bsky.social! We describe how implementing principles from child development can advance the mechanistic plausibility and capacities of AI models We packed A LOT into this review, here's a quick 🧵

16829

Reposted by Khai Loong Aw

Tobias Gerstenberg @tobigerstenberg.bsky.social · 20/07/2026

The Causality in Cognition Lab is pumped for #CogSci2026 🇧🇷

05814

Reposted by Khai Loong Aw

Dongyan Lin @dongyanl1n.bsky.social · 26/06/2026

(1/n) Thrilled to share my first paper at Meta FAIR! "EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data" 👶 Human infants learn language from sparse, noisy multimodal input. Today's VLMs can't. We built a benchmark + challenge to close that gap. 🧵

13916

Reposted by Khai Loong Aw

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/

2427

Reposted by Khai Loong Aw

Mike Frank @mcxfrank.bsky.social · 26/05/2026

If a student reads a typical psychology paper, picks an experiment, and tries to replicate it, they have roughly a coin-flip chance of success. That's the punchline of a decade of metascience, and it's the focus of Ch 3 of Experimentology. 🧵 experimentology.io

4336

Reposted by Khai Loong Aw

Josh Wilson @norcalneuro.bsky.social · 22/05/2026

1/ New preprint with @dyamins.bsky.social + team! Ventral visual representations within areas evolve over the course of the response along the same hierarchical complexity axis that distinguishes the visual areas, potentially driven by local recurrence.

1164

Reposted by Khai Loong Aw

Mike Frank @mcxfrank.bsky.social · 18/05/2026

What is a psychological theory? Here's our take on this tricky and controversial question in this week's Experimentology chapter summary. Many things called "theories" in psychology aren't actually theories — they're frameworks. 🧵 experimentology.io

312752

Khai Loong Aw @khaiaw.bsky.social · 16/05/2026

Exciting analysis of objects in kids' everyday visual input (BabyView). Today's best categorization models still train on curated photos — not the long-tailed categories and non-canonical viewpoints kids actually see. A difference in kind, not just quantity, pointing to a fundamental algorithmic gap

0111

Reposted by Khai Loong Aw

jane-yang.bsky.social @jane-yang.bsky.social · 16/05/2026

Children acquire object category representations from their everyday experiences in the first few years of life. What do the inputs to this learning process actually look like? New preprint! arxiv.org/abs/2605.14990

arxiv.org

Characterizing the visual representation of objects from the child's view

Children acquire object category representations from their everyday experiences in the first few years of life. What do the inputs to this learning process look like? We analyzed first-person videos ...

39832

Reposted by Khai Loong Aw

Dota Tianai Dong @dotadotadota.bsky.social · 14/05/2026

What is the function of function words ⁉️Using head-mounted eye-tracking and machine learning, we show how tiny words like THIS and THAT actively power word learning 👶🤖. Check our preprint, we’d love to hear your feedback!!!

1154

Reposted by Khai Loong Aw

Mike Frank @mcxfrank.bsky.social · 27/04/2026

For a year and a half, @carorowland.bsky.social, @lehersingh.bsky.social, Marisa Casillas, Shanley Allen, and I have been meeting to discuss whether innateness is still a useful concept to think about in studying language acquisition. Here's our take: osf.io/preprints/ps...

The origins of language have been a persistent object of philosophical and scientific inquiry, in part
because they offer a window into the origins of thought. Classic innatist theories of language have argued
that languages share universal elements that arise because language acquisition is guided by rich,
biologically specified structures in the form of a universal grammar. However, different versions of this
hypothesis that posit substantial amounts of innate content are not consistent with recent evidence. We
review new evidence from language diversity, human interaction, and large language models (LLMs), all
of which challenge classical innatist views. Yet we believe there is still value in asking what elements of
language development are innate. In this Perspective, we integrate evidence from these three areas
towards a broader conception of innateness, one that seeks to explain both consistency and variation in
language acquisition. On this view, innateness remains a key orienting principle for understanding the
origins of language.

66128

Reposted by Khai Loong Aw

Mike Frank @mcxfrank.bsky.social · 20/04/2026

Just wrote a new blogpost trying to summarize my thoughts on the question of how and whether to use AI for research in psychology and cognitive science: babieslearninglanguage.blogspot.com/2026/04/usin...

babieslearninglanguage.blogspot.com

Using AI to improve (not automate away) academic research

Blog about fatherhood, langauge, developmental psychology, and cognitive science.

55428

Reposted by Khai Loong Aw

Somewhat Pointless 🐈 @somewhatpointless.eurosky.social · 15/04/2026

There's a lot to like here! - Very smart way to use a masked autoencoder (unsupervised technique!) to build a world model from visual data. This makes other visually based world models I've seen seem clumsy in comparison. 1/4

111

Reposted by Khai Loong Aw

Bria Long @brialong.bsky.social · 14/04/2026

Beautiful use of the BabyView dataset to train a visual learning model!

041

Reposted by Khai Loong Aw

Dan Yamins @dyamins.bsky.social · 14/04/2026

I think we finally made really significant progress on the biggest unsolved "developmental AI" problem: learning from human-scale data. Key idea: zero-shot world models that support concept extraction via approximate causal inference. amazing collab w/ @mcxfrank.bsky.social @khaiaw.bsky.social

14111

Reposted by Khai Loong Aw

Mike Frank @mcxfrank.bsky.social · 14/04/2026

So excited about this work using our data of children’s first-person experiences to train efficient, flexible visual learning models!

071

Reposted by Khai Loong Aw

Khai Loong Aw @khaiaw.bsky.social · 14/04/2026

Children exhibit visual understanding from limited experience, orders of magnitude less than our best models. We introduce the Zero-shot World Model (ZWM). Trained on a single child's visual experience, BabyZWM rapidly generates competence across diverse benchmarks with no task-specific training. 🧵

15724

Khai Loong Aw @khaiaw.bsky.social · 14/04/2026

15724

Reposted by Khai Loong Aw

Mete @mismayil.bsky.social · 13/04/2026

LLMs can retrieve knowledge — but can they connect it in *creative* ways to solve problems? Introducing CresOWLve 🦉, a new benchmark that evaluates creative problem-solving over real-world knowledge, using puzzles that require multiple creative thinking strategies.👇

132

Reposted by Khai Loong Aw

Dan Yamins @dyamins.bsky.social · 16/09/2025

Here is our best thinking about how to make world models. I would apologize for it being a massive 40-page behemoth, but it's worth reading. arxiv.org/pdf/2509.09737

arxiv.org

27118

Reposted by Khai Loong Aw

Dawei Bai @daweibai.bsky.social · 09/10/2025

Happy to share that our BBS target article has been accepted: “Core Perception”: Re-imagining Precocious Reasoning as Sophisticated Perceiving With Alon Hafri, @veroniqueizard.bsky.social, @chazfirestone.bsky.social & Brent Strickland Read it here: doi.org/10.1017/S014... A short thread [1/5]👇

79839