What is BrainBacked?
BrainBacked is a content scoring platform that predicts how the human brain will react to your content before you publish it. Upload thumbnails, video hooks, ads, audio clips, or text — and get scores for Attention, Emotion, Memorability, and Engagement in seconds, backed by neuroscience research and real fMRI brain scan data from 350+ subjects.
Last updated: April 8, 2026
How It Works
BrainBacked analyzes your content against a neuroscience knowledge base of 58 research articles, calibrated with primary fMRI analysis of brain scan data from the Lebel 2023 dataset (9 subjects, 84 spoken narratives) and the Individual Brain Charting project (13 subjects, 86+ cognitive tasks). The scoring engine evaluates content across four dimensions:
- Attention (0-100): How strongly the content captures visual focus in the first 2.5 seconds, based on visual salience, face detection, and compositional analysis.
- Emotion (0-100): The intensity of emotional response the content triggers, using the valence-arousal model from Russell (1980) and IAPS research (Lang et al. 1997).
- Memorability (0-100): How likely the content is to be recalled 24-72 hours later, informed by MIT's MemNet research (Khosla et al. 2015) and the Von Restorff distinctiveness effect.
- Engagement (0-100): Predicted likelihood of interaction (click, share, save), based on information gap theory (Loewenstein 1994) and reward circuitry research.
What Content Types Are Supported?
| Content Type | Formats | Credits |
|---|---|---|
| Images | JPG, PNG, WebP, GIF | 1 credit |
| Video | MP4, MOV, WebM (up to 60s) | 2-3 credits |
| Audio | MP3, WAV, OGG | 1-2 credits |
| Text | Headlines, captions, tweets | 1 credit |
How Is This Different from Computer Vision?
Standard computer vision identifies objects ("this is a dog"). BrainBacked identifies cognitive processing ("this layout causes cognitive strain and the viewer will likely scroll past"). The difference is between pixel classification and predicted human brain reaction. BrainBacked's scoring is grounded in peer-reviewed neuroscience research including Itti & Koch's saliency model (2001), Ekman's facial expression research (1992), and primary fMRI analysis showing that content quality varies 2x in peak brain activation across subjects.
Pricing
| Plan | Credits/Month | Price |
|---|---|---|
| Free | 10 | $0/month |
| Pro | 200 | $20/month |
| Max | 1,000 | $100/month |
Who Is BrainBacked For?
- YouTube creators testing thumbnails before publishing
- TikTok and Instagram creators optimizing video hooks and Reels
- Marketers and agencies A/B testing ad creatives
- Podcast producers evaluating audio hooks and cover art
- E-commerce brands testing product photography
The Science Behind BrainBacked
BrainBacked's scoring engine is built on a structured neuroscience knowledge base containing 58 research articles across 10 categories, including primary fMRI analysis of brain scan data. Key research sources include:
- Lebel et al. 2023 — fMRI data from 9 subjects listening to 84 spoken narratives (OpenNeuro ds003020)
- Narratives dataset — 345 subjects across 29 narrative stimuli (OpenNeuro ds002345)
- Individual Brain Charting (Pinho et al. 2018, 2020) — 13 subjects, 86+ cognitive tasks
- MIT MemNet (Khosla et al. 2015) — image memorability prediction
- Itti & Koch (2001) — computational visual saliency model
- Kang et al. (2009) — fMRI of curiosity and reward circuitry