Lenny van Dyck
@levandyck.bsky.social
PhD candidate in CogCompNeuro at JLU Giessen
Exploring brains, minds, and worlds 🧠💭🗺️
https://levandyck.github.io/
Exploring brains, minds, and worlds 🧠💭🗺️
https://levandyck.github.io/
Thanks so much, Nick! I just read your latest preprint the other day and already noted it for the next version. Super relevant work :)
June 18, 2025 at 2:11 PM
Thanks so much, Nick! I just read your latest preprint the other day and already noted it for the next version. Super relevant work :)
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
Check out the preprint for details. We’d love to hear your thoughts!
11/11
June 18, 2025 at 12:28 PM
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
Check out the preprint for details. We’d love to hear your thoughts!
11/11
This multidimensional framework supports both discrete category selectivity and continuous feature integration, offering a unified account of high-level visual cortex organization.
10/n
10/n
June 18, 2025 at 12:28 PM
This multidimensional framework supports both discrete category selectivity and continuous feature integration, offering a unified account of high-level visual cortex organization.
10/n
10/n
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.
9/n
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.
9/n
June 18, 2025 at 12:28 PM
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.
9/n
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.
9/n
Individually, they followed a striking topography.
📍 Distinct subclusters within category-selective areas
🌐 But sparsely distributed maps across cortex
Local specialization meets global distribution.
8/n
📍 Distinct subclusters within category-selective areas
🌐 But sparsely distributed maps across cortex
Local specialization meets global distribution.
8/n
June 18, 2025 at 12:28 PM
Individually, they followed a striking topography.
📍 Distinct subclusters within category-selective areas
🌐 But sparsely distributed maps across cortex
Local specialization meets global distribution.
8/n
📍 Distinct subclusters within category-selective areas
🌐 But sparsely distributed maps across cortex
Local specialization meets global distribution.
8/n
Collectively, the dimensions from each area explained activity both within their area but also across broader regions of visual cortex.
7/n
7/n
June 18, 2025 at 12:28 PM
Collectively, the dimensions from each area explained activity both within their area but also across broader regions of visual cortex.
7/n
7/n
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)
6/n
🎯 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)
6/n
June 18, 2025 at 12:28 PM
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)
6/n
🎯 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)
6/n
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.
5/n
Strikingly, even the most category-selective voxels showed this multidimensional tuning.
5/n
June 18, 2025 at 12:28 PM
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.
5/n
Strikingly, even the most category-selective voxels showed this multidimensional tuning.
5/n
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?
4/n
Would the resulting organization look modular, continuous, or like something in between?
4/n
June 18, 2025 at 12:28 PM
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?
4/n
Would the resulting organization look modular, continuous, or like something in between?
4/n
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?
3/n
Can these seemingly opposing views be reconciled?
3/n
June 18, 2025 at 12:28 PM
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?
3/n
Can these seemingly opposing views be reconciled?
3/n
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.
2/n
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.
2/n
June 18, 2025 at 12:28 PM
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.
2/n
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.
2/n