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/
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.
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.
August 11, 2025 at 1:25 PM
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.
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.
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