Brain activity in, virality out.
A working model distilled from a decade of fMRI research. Move the dials on six brain systems — reward, self-reference, mentalizing, arousal, narrative sync — and watch a predicted virality score emerge, with every weight backed by a published study.
Run a full Mind Scan on your draft.
First we map your draft to 6 brain regions. Then 50 simulated personas read it like a real feed and tell you what they'd like, share, skip — and why. You get a shareable report link at the end.
Advisor · evidence-based tips
Click Predict brain activity for a prioritized list of edits that lift the neural predictors most strongly linked to real-world sharing — each bullet anchored to a primary fMRI study, with live citation counts.
🐟 Audience Simulator
100 personas · 10 creditsTeste vor 100 KI-Zuschauern, bevor die echte Welt urteilt. Du bekommst Like/Share/Skip-Verteilung, Top-Einwände und O-Töne aus dem Schwarm.
Input · simulated activation
% BOLD signal (normalized)Value integration — converts content into a personal 'is this worth sharing?' signal
Reward anticipation — fires for content the brain expects to be rewarding to consume or pass on
Self-referential processing — 'does this say something about me?' drives forwarding
Mentalizing — modeling who else would care about this content
Arousal & salience — emotional intensity that compels engagement
Neural synchrony — how similarly different brains process the same video; universal appeal
Creator profile · authority multiplier
self-reported · applied to viralityInverse-U follower curve peaks around 10k–100k (Wies 2022). Niche fit adds a topical-expertise lift (Hubner 2021). Platform factor reflects structural virality (Goel 2016).
How the model works
- 01 · StimulusParticipants watch ads, films or read articles inside an fMRI scanner. BOLD signal is recorded across the whole brain.
- 02 · Neural featuresAverage activation is extracted from six theory-driven ROIs and from inter-subject correlation maps measuring how synchronous brains are.
- 03 · Population predictionThe same content's real-world spread is regressed on those neural features — and repeatedly out-predicts what people say they will share.
Limitations: this explorer is a pedagogical linear approximation. Published models use ROI time series, not single scalars, and weights vary across content domains. Always cite the primary studies linked above.