| name | neuro-testing |
| description | Neural pre-testing with Meta Tribe v2 — brain response prediction for ad creatives, scoring algorithm, ROI mapping, and interpretation guide |
Neural Pre-Testing with Tribe v2
Pre-test ad creatives, landing pages, email templates, and video ads using Meta's open-source brain prediction model. Predict which creative generates the strongest attention, emotional response, and memory activation BEFORE spending a dollar on deployment.
What Is Tribe v2?
Tribe v2 is Meta FAIR's open-source predictive foundation model that predicts human brain responses (fMRI activation) to visual, auditory, and linguistic stimuli. Trained on 700+ subjects and 1,100+ hours of fMRI data, it produces high-resolution predictions (~20K cortical vertices) with zero-shot generalization to new content.
- Repo: github.com/facebookresearch/tribev2
- Weights: HuggingFace
facebook/tribev2
- License: CC-BY-NC-4.0 (non-commercial)
- Inputs: Video, audio, text (images converted to 3-sec video)
- Outputs: Predicted fMRI activation map (n_timesteps × ~20K vertices)
Brain Region → Marketing Metric Mapping
| Brain Region | Cortical Area | Marketing Metric | Significance |
|---|
| Visual cortex (V1-V4) | Occipital lobe | Attention | Does the creative stop the scroll? |
| Fusiform face area | Temporal lobe | Face Response | Does it leverage human connection? |
| Amygdala region | Medial temporal | Emotional Intensity | Will it trigger sharing/action? |
| Hippocampus region | Medial temporal | Memory Encoding | Will they remember the brand? |
| Wernicke's area | Superior temporal | Comprehension | Is the message understood? |
| Prefrontal cortex | Frontal lobe | Decision Engagement | Are they considering action? |
| Ventral striatum | Subcortical | Reward Response | Does it feel valuable? |
Composite Scoring Formula
composite = (
attention × 0.25 + # Must see it first
emotion × 0.20 + # Must feel something
memory × 0.20 + # Must remember the brand
decision × 0.20 + # Must consider action
comprehension × 0.10 + # Must understand the offer
reward × 0.05 # Must feel it's valuable
)
Weights are default starting values. After campaigns, /gtm-learn correlates neural scores with actual CPA/CTR and saves calibrated weights to .gtm/learnings/neuro-calibration.md.
Decision Matrix
| Composite | Verdict | Action |
|---|
| 70-100 | DEPLOY | Full budget. High confidence this creative performs. |
| 55-69 | TEST | Reduced budget (20%). Include as B-variant to validate. |
| 40-54 | ITERATE | Don't deploy yet. Fix weak dimensions, re-score. |
| 0-39 | SKIP | Kill it. Fundamental creative problems. |
Execution Modes
Mode A: Local Python (preferred)
- Requires: Python 3.11+, tribev2 package, CUDA GPU recommended
- Speed: ~10-30 seconds per creative (GPU) or ~2-5 minutes (CPU)
- Setup:
bash scripts/neuro-setup.sh or pip install tribev2
Mode B: Google Colab (no local GPU)
- Upload creatives, run inference notebook, copy results back
- Speed: ~1-2 minutes per creative (free Colab GPU)
Mode C: Meta AI Demo (quick preview)
- URL: aidemos.atmeta.com/tribev2
- Upload via Playwright/Computer Use, screenshot brain heatmap
- Qualitative only — no numerical scores
Limitations
- CC-BY-NC license — Non-commercial use only. Fine for internal testing, cannot be sold as SaaS.
- Group-average predictions — Predicts average response across 700+ subjects, not individual reactions. Good for general audience, less useful for niche micro-segments.
- GPU recommended — CPU inference works but is 10x slower. Colab provides free GPU fallback.
- Not a replacement for A/B testing — Use as pre-filter to reduce waste (deploy top 2 instead of all 5), not as sole decision maker.
- Image-to-video conversion — Static images need conversion to 3-second video clips. Adds a processing step.
- Subcortical resolution — fMRI surface model has limited resolution for deep brain structures (amygdala, striatum). Scores for these regions are approximations.
Integration Points
/gtm-neurotest — Standalone command to score any creative
/gtm-create Phase 5.5 — Auto-scores after creative generation
/gtm Phase 4.5 — Neural pre-test between Create and Deploy
/gtm-learn — Calibrates scoring weights from actual campaign data
agents/creative-director.md — Uses neuro feedback to iterate on weak creatives
Rules
rules/brain-regions.md — Cortical parcellation and vertex mappings
rules/score-interpretation.md — How to read scores, iterate on weak dimensions
rules/setup-guide.md — Installation, configuration, troubleshooting