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levandyck.bsky.social
Lenny van Dyck
@levandyck.bsky.social
150 followers 200 following 14 posts
PhD candidate in CogCompNeuro at JLU Giessen Exploring brains, minds, and worlds 🧠💭🗺️ https://levandyck.github.io/
levandyck.github.io
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
How is high-level visual cortex organized?

In a new preprint with @martinhebart.bsky.social & @kathadobs.bsky.social, we show that category-selective areas encode a rich, multidimensional feature space 🌈

www.biorxiv.org/content/10.1...
#neuroskyence

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www.biorxiv.org
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Reposted by Lenny van Dyck
dkaiserlab.bsky.social
Kaiser Lab @dkaiserlab.bsky.social · 23h
From line drawings to scene perception — our new review argues for moving beyond experimenter-driven manipulations toward participant-driven approaches to reveal what’s in our internal models of the visual world. 👁️✍️🛋
royalsocietypublishing.org/doi/10.1098/...
Characterizing internal models of the visual environment | Proceedings of the Royal Society B: Biological Sciences
Despite the complexity of real-world environments, natural vision is seamlessly efficient. To explain this efficiency, researchers often use predictive processing frameworks, in which perceptual effic...
royalsocietypublishing.org
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Reposted by Lenny van Dyck
jbrendanritchie.bsky.social
J. Brendan Ritchie @jbrendanritchie.bsky.social · Aug 29
Our target discussion article out in Cognitive Neuroscience! It will be followed by peer commentary and our responses. If you would like to write a commentary, please reach out to the journal! 1/18 www.tandfonline.com/doi/full/10.... @cibaker.bsky.social @susanwardle.bsky.social
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Aug 11
Really looking forward to #CCN2025!

On Tuesday, I'm presenting new work with @kathadobs.bsky.social on segregated vs. integrated face & body processing in visual cortex 😊🧍🧠

Using DNNs & fMRI, we test competing hypotheses, finding both distinct & shared selectivity.

Come by Poster A64 for more.
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Reposted by Lenny van Dyck
martinhebart.bsky.social
Martin Hebart @martinhebart.bsky.social · Aug 11
Very much looking forward to #CCN2025! Would love to see you at our lab's talks and posters, and meet me at the panel discussion in the Algonauts session on Wednesday!
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Reposted by Lenny van Dyck
davidecortinovis.bsky.social
Davide Cortinovis @davidecortinovis.bsky.social · Aug 7
New preprint out! We propose that action is a key dimension shaping the topographic organization of object categories in lateral occipitotemporal cortex (LOTC)—and test whether standard and topographic neural networks capture this pattern. A thread:

www.biorxiv.org/content/10.1...

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Investigating action topography in visual cortex and deep artificial neural networks
High-level visual cortex contains category-selective areas embedded within larger-scale topographic maps like animacy and real-world size. Here, we propose action as a key organizing factor shaping vi...
www.biorxiv.org
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Reposted by Lenny van Dyck
vivianpaulun.bsky.social
Vivian Paulun @vivianpaulun.bsky.social · Aug 1
Super excited to share our new article: “Dissociable cortical regions represent things and stuff in the human brain” with @nancykanwisher.bsky.social, @rtpramod.bsky.social and @joshtenenbaum.bsky.social

Video abstract: www.youtube.com/watch?v=B0XR...

Paper: authors.elsevier.com/a/1lWxv3QW8S...
Things and Stuff: How the brain distinguishes oozing fluids from solid objects
YouTube video by McGovern Institute
www.youtube.com
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Reposted by Lenny van Dyck
davidtyt.bsky.social
Yingtian (David) Tang @davidtyt.bsky.social · Jul 30
🧠 NEW PREPRINT
Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective
www.biorxiv.org/content/10.1...
Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective
How the human brain supports diverse behaviours has been debated for decades. The canonical view divides visual processing into distinct "what" and "where/how" streams – however, their origin and inde...
www.biorxiv.org
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Reposted by Lenny van Dyck
martinhebart.bsky.social
Martin Hebart @martinhebart.bsky.social · Jun 23
What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results.

www.nature.com/articles/s42...
Dimensions underlying the representational alignment of deep neural networks with humans - Nature Machine Intelligence
An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual ...
www.nature.com
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Reposted by Lenny van Dyck
irisgroen.bsky.social
Iris Groen @irisgroen.bsky.social · Jun 16
In these tumultuous times, still happy to report a scientific achievement: our preprint on affordance perception was just published in PNAS!

www.pnas.org/doi/10.1073/...

Using behavior, fMRI and deep network analyses, we report two key findings. To recapitulate (preprint 🧵lost on other place):
Representation of locomotive action affordances in human behavior, brains, and deep neural networks | PNAS
To decide how to move around the world, we must determine which locomotive actions (e.g., walking, swimming, or climbing) are afforded by the immed...
www.pnas.org
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
Thanks so much, Nick! I just read your latest preprint the other day and already noted it for the next version. Super relevant work :)
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
This work resonates with recent proposals by @meenakshikhosla.bsky.social, @taliakonkle.bsky.social, @jacob-prince.bsky.social, @cibaker.bsky.social, @jbrendanritchie.bsky.social, @olivercontier.bsky.social and many others.

Check out the preprint for details. We’d love to hear your thoughts!

11/11
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
This multidimensional framework supports both discrete category selectivity and continuous feature integration, offering a unified account of high-level visual cortex organization.

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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
So here's our takeaway.

When analyzed in a data-driven manner, the two views aren't mutually exclusive but rather complementary. Individual dimensions form sparse feature-selective clusters but also contribute to distributed maps across cortex.

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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
Individually, they followed a striking topography.

📍 Distinct subclusters within category-selective areas
🌐 But sparsely distributed maps across cortex

Local specialization meets global distribution.

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Functional tuning maps of individual dimensions from category-selective areas across cortex.
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
Collectively, the dimensions from each area explained activity both within their area but also across broader regions of visual cortex.

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Prediction performance of dimensions from category-selective areas across cortex.
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
These dimensions captured diverse information.

🎯 Many aligned with each area’s preferred category (e.g., bodies in EBA)
🧩 Others encoded finer subcategory features (e.g., body parts)
🔄 Some even reflected cross-category distinctions (e.g., food vs. text)

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Interpretability of dimensions in category-selective areas.
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
We found that each area encoded multiple interpretable dimensions, consistent across individuals and primarily tuned to high-level semantic content.

Strikingly, even the most category-selective voxels showed this multidimensional tuning.

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Consistency of dimensions in category-selective areas.
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
To test this, we analyzed fMRI responses to thousands of natural images within classical category-selective areas using a data-driven decomposition approach.

Would the resulting organization look modular, continuous, or like something in between?

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Data-driven fMRI voxel decomposition of category-selective areas.
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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
On the other side, a dimensional view argues that it is organized by a continuous feature space with distributed maps spanning across cortex.

Can these seemingly opposing views be reconciled?

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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
During my studies, I got interested in a long-standing debate in visual neuroscience.

On one side, a categorical view holds that high-level visual cortex is composed of discrete modules specialized for domains like faces, bodies, and scenes.

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levandyck.bsky.social
Lenny van Dyck @levandyck.bsky.social · Jun 18
How is high-level visual cortex organized?

In a new preprint with @martinhebart.bsky.social & @kathadobs.bsky.social, we show that category-selective areas encode a rich, multidimensional feature space 🌈

www.biorxiv.org/content/10.1...
#neuroskyence

🧵 1/n
www.biorxiv.org
1 30 74
Reposted by Lenny van Dyck
nblauch.bsky.social
Nick Blauch @nblauch.bsky.social · Jun 16
What shapes the topography of high-level visual cortex?

Excited to share a new pre-print addressing this question with connectivity-constrained interactive topographic networks, titled "Retinotopic scaffolding of high-level vision", w/ Marlene Behrmann & David Plaut.

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Reposted by Lenny van Dyck
neurosteven.bsky.social
Steven Scholte @neurosteven.bsky.social · May 28
There are 2 PhD positions in my lab in Amsterdam (collaboration with Sander Bohte, @tessamdekker.bsky.social and Ingmar Visser) on NeuroAI of developmental vision.

academicpositions.nl/ad/centrum-w...
2 PHD-STUDENTS IN NEUROAI OF DEVELOPMENTAL VISION (M/F/X) - Academic Positions
Join an interdisciplinary team to research developmental vision in NeuroAI. Requires a strong computational background, deep learning skills, and a Master's ...
academicpositions.nl
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Reposted by Lenny van Dyck
hannesmehrer.bsky.social
Hannes Mehrer @hannesmehrer.bsky.social · May 27
Announcement: Workshop at #CCN2025
🧠 Modeling the Physical Brain: Spatial Organization & Biophysical Constraints
🗓️ Monday, Aug 11 | 🕦 11:30–18:00 CET | 📍 Room A2.07
🔗 Register: tinyurl.com/CCN-physical...
#NeuroAI @cogcompneuro.bsky.social
Home
tinyurl.com
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Reposted by Lenny van Dyck
gretatuckute.bsky.social
Greta Tuckute @gretatuckute.bsky.social · May 23
What are the organizing dimensions of language processing?

We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals
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