In summary, this shared representational geometry provides a powerful framework to study the brain's functional organization and trace how information is routed through the cortex.

We welcome your questions!

💻 Code: github.com/memory-forma...

(8/8)

Diving deeper into the LOTC hub's social vs non-social component:

Alignment across brains along the lateral stream (EVC→LOTC) is present only when viewing social scenes (with people or animals).

This supports its proposed role as a specialized "third visual pathway" for social perception.

⬇️ (7/8)

So what information does each hub actually encode?

Using KMCCA, we studied the primary dimension that organizes each hub's information:

👁️ EVC: Low-level visual features
🏞️ Ventral Hub: Scene & object structure
👨‍👩‍👧‍👦 LOTC Hub: Social vs. non-social content

⬇️ (6/8)

Vision DNNs capture this shared geometry, with each brain hub showing a different layer alignment profile:

🧠 Early visual ↔️ Shallow DNN layers
🧠 Ventral hub ↔️ Mixed DNN layers
🧠 LOTC ↔️ Deep DNN layers

Language Models only align with the high-level LOTC hub.

⬇️ (5/8)

This shared representational geometry is so consistent across people that we could map a whole-brain connectivity network based on it, revealing interactions between visual, memory and prefrontal areas.

⬇️ (4/8)

We identified 3 cortical hubs with highly consistent representations across all individuals:
📍 Early visual cortex (V1–V4)
📍 Ventral hub (scene/object areas ~PPA)
📍 LOTC Hub (hMT+/TPOJ)

These hubs form two pathways:
- Classical ventral stream (EVC → Ventral)
- Lateral stream (EVC → LOTC)

⬇️ (3/8)

Using Representational Similarity Analysis (RSA) on fMRI data from people viewing diverse scenes, we measure:

- Inter-subject RSA: Are visual representations shared across individuals?
- Brain-Model RSA: Is this shared information low-level (visual) or high-level (semantic)?

Methods ⬇️ (2/8)