Neuro testing

Skill DojoCodingLabs/GTM-Engineering-Command-Center/skills/neuro-testing

Neural pre-testing with Meta Tribe v2 — brain response prediction for ad creatives, scoring algorithm, ROI mapping, and interpretation guideFrom its SKILL.md

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npx -y skills add DojoCodingLabs/GTM-Engineering-Command-Center --skill neuro-testing

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SKILL.md

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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