8/🖼️ Big Picture

Optimizing to model world dynamics leads to brain-like representations.


🧠 The visual system isn't a patchwork of modules — it’s a unified system built on shared core principles.

7/🧠 Finding 4
We introduce task-based functional localization.

It:
1. Recovers many prior neuroscience results in a unified way
2. Reveals new structure in action understanding pathways

A novel scalable approach to functional brain mapping.

6/🌀 Finding 3
Putting observations together:

• Single-objective models align with all regions and behaviors
• Cortex shows hybrid, smooth representation transitions

💡 A new perspective: the brain may implement a shared feature backbone — reused for diverse tasks, just like a “foundation model”.

5/🌐 Finding 2.2
These two aren’t isolated — they’re:
• Blended across ventral & dorsal streams

• Smoothly mapped across the cortex

So, the visual system isn’t modular — it’s highly distributed, and the classic stream separation theory appears oversimplified.

4/🌐 Finding 2.1

So, what does the brain actually compute during dynamic vision?

Across 10 cognitive tasks (e.g., pose, social cues, action), just two suffice to explain brain-like representations:
• Object form
• Appearance-free motion

3/📊 Finding 1

✅ Dynamic models > static image models > classic vision models

✅ Across both dorsal & ventral regions

✅ Across neural & behavioral alignment

Best match to brain: V-JEPA.
In general, learning world dynamics give alignment to the whole visual system.

2/🧪 Approach

We benchmarked diverse video models, each with a different pretraining objective.


Then: tested how well they predict human fMRI responses to natural movies.

🧠 ~10,000 voxels, whole visual system.

1/🔍 Motivation

The brain is thought to process vision through two streams:

🖼 Ventral — objects, form, identity

🧭 Dorsal — motion, spatial layout, actions

Image models explain ventral well.
But: what about dorsal? Can one model do both?