iiinova — where your digital self keeps evolving

Yingtian (David) Tang @davidtyt.bsky.social · 07/07/2026

Controlled test: fix a non-naturalistic image anchor (two plasticine discs 🟢🟣), optimize only motion. Target pSTS → faces and coordinated interaction emerge Target MT → pure translation/oscillation, no social content Same anchor. Different content. (8/10)

100

Yingtian (David) Tang @davidtyt.bsky.social · 07/07/2026

Ablations confirm every design choice: ✅ dynamic > static (biggest gap in MT/EBA) ✅ two-stage search > single-stage ✅ V-JEPA 2 > CLIP (0.44 vs 0.35 predictivity) ✅ genetic search > hill-climbing / random ❌ BrainDiVE (gradient-based) collapses entirely in this setting (7/10)

110

Yingtian (David) Tang @davidtyt.bsky.social · 07/07/2026

Beyond fixed ROIs, NEvo runs as a searchlight 🔦 scoring overlapping cortical patches along a continuous cortical trajectory. We recover a clean lateral stream gradient: texture (V1) → motion/interaction (EBA/pSTS) → social scenes (aSTS) (6/10)

100

Yingtian (David) Tang @davidtyt.bsky.social · 07/07/2026

The results recover known selectivity: 🙂 faces → FFA 🏞️ scenes → PPA 🚶 bodies → EBA 🌀 motions → MT/V3A 👥 interaction → pSTS Interactive demo at nevo-project.epfl.ch They *outperform prior stimuli* — reaching 📈 99.8% of Moments in Time responses 📈 95.8% of handcrafted localizers (5/10)

120

Yingtian (David) Tang @davidtyt.bsky.social · 07/07/2026

Two design choices matter: 1️⃣ **Two-stage search**: optimize a static anchor image first, then animate it → much more sample-efficient. 2️⃣ **Multi-block V-JEPA 2 features**: each voxel selects its best-predicting block → hierarchical feature modeling (4/10)

110

Yingtian (David) Tang @davidtyt.bsky.social · 07/07/2026

🚨 NEW PREPRINT Videos strongly shape activity across the visual cortex. But can we design videos that maximally drive specific brain regions? We present NEvo 🧬🧠 — a neural-guided evolutionary framework that synthesizes videos to maximally activate target visual ROIs. (1/10)

13010

Yingtian (David) Tang @davidtyt.bsky.social · 26/06/2026

4/ 🔬 MODEL-BASED DISCOVERY vjepa2-basic has already powered model-based stimulus synthesis, leading to scientific discovery in the lateral visual stream. 💥 Our full model-based synthesis preprint drops next week. Stay tuned.

010

Yingtian (David) Tang @davidtyt.bsky.social · 26/06/2026

2/ 🚀 TWO MODELS vjepa2-basic → Our strong baseline → Based on our previous large-scale study of dynamic vision models: dynamic-vision.epfl.ch vjepa2-enhanced → Our best encoding model → Adds lightweight decoders + bagging for improved prediction performance

110

Yingtian (David) Tang @davidtyt.bsky.social · 30/07/2025

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.

120

Yingtian (David) Tang @davidtyt.bsky.social · 30/07/2025

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.

110

Yingtian (David) Tang @davidtyt.bsky.social · 30/07/2025

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”.

110

Yingtian (David) Tang @davidtyt.bsky.social · 30/07/2025

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.

110

Yingtian (David) Tang @davidtyt.bsky.social · 30/07/2025

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

120

Yingtian (David) Tang @davidtyt.bsky.social · 30/07/2025

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.

110

Yingtian (David) Tang @davidtyt.bsky.social · 30/07/2025

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.

110

Yingtian (David) Tang @davidtyt.bsky.social · 30/07/2025

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?

120