New in #JNeurosci from Cordeau et al: Breeding and training dogs have altered how they process words and cues to communicate. https://doi.org/10.1523/JNEUROSCI.2174-25.2026

check out @irmakergin.bsky.social's review paper in "Frontiers for Young Minds"! it was such a fun and heartening experience to work with their young reviewers team. highly recommend! kids.frontiersin.org/articles/10....

How Does Your Brain Predict the Future When You Hear Speech?

Thanks to our brains, we can talk to our families and friends and easily understand what they are saying. But did you know that our brains do not just listen—they also try to guess what we are going t...

kids.frontiersin.org

Excited to present soon @cogcompneuro.bsky.social #CCN2026! Come to my talk at 2:35 pm learn about children and teens’ visual experience in natural environments outside of traditional lab settings (“visual diet”) using a new, large-scale, egocentric scene and mobile eye-tracking data 🤓

Stanford Vision and Perception Neuroscience Lab@stanfordvpnl.bsky.social · 7d ago

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

Excited to be at #CCN2026 in New York to present brand-new work with @kathadobs.bsky.social 🥳 By testing hundreds of categories, we found striking differences in functional specialization between visual cortex and DNNs 🧠 Come find me at poster F81 on Thursday 1:45-3:30 p.m. Happy to chat!

excited to be at #CCN2026! come talk to our very new lab during the poster sessions 🌱 we're introducing novel computational frameworks to model human 3D vision + developing theoretical and empirical strategies for understanding these abilities say hello & let us know what you think!

Super excited to be back @cogcompneuro.bsky.social in NYU - this year presenting new exciting work from Project Simsom with @camerontellis.bsky.social, Dan Yamins and the team. Are we ready to build an AI model of infant development? We think so ... come check it out Thursday 10:30 E39 👶🤖

While I'm sad to miss #CCN2026, you don't have to miss out on the cool work coming out of the lab. 😊 Super proud of the diversity of projects this year—from category and unit specialization to inversion effects, shape bias, and memorability in brains and DNNs! Stop by and check them out! #NeuroAI

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

Looking forward to #CCN2026 - check out the presentations from our lab!

Also if you're at #CCN2026 come chat with me about Minds, Machines, and Brains @mmb-journal.bsky.social A new journal we are launching with @mitpress.bsky.social with my co-EICs Kendrick Kay & Ellie Pavlick, and the rest of the wonderful editorial board: direct.mit.edu/mmb/pages/ed...

Minds, Machines, and Brains (MMB)@mmb-journal.bsky.social · last wk.

Hello world! 👋 We’re Minds, Machines, and Brains (MMB) 👤🤖🧠 a new open access journal from @mitpress.bsky.social exploring the principles of intelligence and cognition across natural and artificial minds. Submissions open this Fall! 🔗 direct.mit.edu/mmb

Kanwisher Lab loves #CCN2026

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

Now out in an issue! Children find it easier to represent uncertainty about *what* an object is than *where* an object is, with interesting implications for both early possibility reasoning and object representations ~~ Check it out 👉 direct.mit.edu/opmi/article...

Children’s Representations of Possibility: Uncertainty About “What” Is Easier Than Uncertainty About “Where”

Abstract. Young children struggle in situations involving mere possibility, such as when a target object could be in one of two possible locations. This difficulty is often interpreted as a general li...

direct.mit.edu

Esra Nur Turan-Küçük@esranur.bsky.social · 3mo ago

New paper in Open Mind! 2-to-4-year-olds find it easier to reason about an object's possible identity than its possible location, and we argue this asymmetry traces back to the architecture of early object representations. 🧵

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.

When children start school, how do their brains change to support learning of formal mathematics? Our new paper, with @standehaene.bsky.social, just out in PNAS, followed ~50 children with yearly fMRI scans over 3 years, from preschool to 2nd grade, to see how this network develops. 🧵

check out @xannnimal.bsky.social 's powerful new signal extraction method "Foster's Inverse" for removing noise artifacts in MEG and OPM-MEG!! 🧠 preprint: www.biorxiv.org/content/10.6... code: github.com/xannnimal/fo...

Excited that this work with @serences.bsky.social and @timbrady.bsky.social is now out! Our Gabor-wavelet model better predicted voxel responses in scene regions than 3D models. Does this mean that scene areas aren’t “for” processing 3D scene structure? NO, we argue. 1/ dx.plos.org/10.1371/jour...

A 2D Gabor-wavelet baseline model out-performs a 3D surface model in scene-responsive cortex

Author summary To gain a more complete picture of human visual processing, it is critical to understand the precise format of representations of naturalistic visual scenes. Recent work has approached ...

dx.plos.org

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!

Dota Tianai Dong@dotadotadota.bsky.social · 3w ago

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.

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

Heading to #CogSci2026 in Rio this week? Overview 🧵 thread highlighting work being presented by members of the Cognitive Tools Lab (cogtoolslab.github.io) — come say hi! 🧠🛠️✨

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 🧵

Neuron@cp-neuron.bsky.social · 3w ago

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

Excited to share that our paper is now out in #JNeurosci! We propose a multidimensional framework of high-level visual cortex that reconciles a longstanding debate. Thanks to @kathadobs.bsky.social, @martinhebart.bsky.social, and everyone else for the great discussions along the way. More to come 🧠🌈

Multidimensional feature tuning in category-selective areas of human visual cortex

Two prominent accounts describe the functional organization of human high-level visual cortex. A categorical view emphasizes category-selective areas, while a dimensional view highlights continuous fe...

doi.org

SfN Journals@sfnjournals.bsky.social · 4w ago

#JNeurosci: Using a data-driven analysis of fMRI responses to natural images, @levandyck.bsky.social @martinhebart.bsky.social & @kathadobs.bsky.social identified interpretable dimensions that explain activity in face-, body-, & scene-selective areas https://doi.org/10.1523/JNEUROSCI.0038-26.2026

Now out in Noûs: A communication-first account of explanation, with Jacqueline Harding (jacquelineharding.github.io) and Thomas Icard (stanford.edu/~icard/). Paper: onlinelibrary.wiley.com/doi/10.1111/... Preprint: arxiv.org/abs/2505.03732

🚨 Excited to share our new paper out in Developmental Cognitive Neuroscience! We developed white matter neonatal brain-age models to study brain maturation in full-term and high-risk preterm infants. 🧠👶 Does global brain maturity reveal the toll of clinical complications? Thread 👇 (1/6)

When we consider children in cultural evolution, we tend to cast them as recipients of adult culture. In our BBS target article, @sheinalew.bsky.social & I argued there's more: through 'peer cultures', children actively drive cultural adaptation. Our final paper + a rich set of responses is out now!

New paper with @rebeccasaxe.bsky.social "Expectations of reciprocal generosity are specific to equal relationships," in @openmindjournal.bsky.social. Thread below! Article: doi.org/10.1162/opmi... MIT News: news.mit.edu/2026/would-y...

Would you return a favor? Scientists say it depends on the relationship

People generally only engage in reciprocal generosity with others of equal or unknown status, MIT researchers experimentally demonstrated. During repeated interactions between people of different soci...

news.mit.edu

New preprint! How do children’s drawing abilities develop in contexts where they have relatively fewer drawing opportunities? In a study with 120 Kisumu, Kenya children, we find that children produce more recognizable drawings with age and with drawing experience. osf.io/preprints/ps...

1/ As AI agents become increasingly capable, what must *inevitably* emerge inside them? We prove selection theorems: strong task performance forces world models, belief-like memory and—under task mixtures—persistent variables resembling core primitives associated with emotion.

🚨New dataset just dropped🚨 Introducing Places in the Wild: 67,000 RAW-format photographs (45 mpix) densely sampled from 810 places (260 basic-level categories). This is 11x the number of pixels in ImageNet! Preprint is here: arxiv.org/abs/2606.02481 1/

Places in the Wild: A Large, High-Resolution RAW Photograph Dataset for Ecologically Valid Vision Research

Large image datasets have accelerated progress in cognitive neuroscience and computer vision. However, most datasets are low-resolution, internet-sourced JPEGs with unknown capture conditions and limi...

arxiv.org