How do you feel?
EQ in AI
How do you feel?
It is the oldest question in art and the newest question in technology. MindReader takes your content and simulates, region by region, how a brain responds to it.
Built on Meta FAIR · TRIBEv2 Read the research →
Attention Dorsal attention network
Where the reader locks focus. Activity here rises on novelty, urgency, and anything that breaks the rhythm of what came before.
Personal Attention Effort Gut Memory Social Language
In the real world Super Bowl ad testing · Nielsen / Neuro-Insight · $8M per airing
See it in action
Watch it read every word.
Maya's discovery call, played back at the speed of thought. The model reads each line and shows which words pulled attention, felt personal, or fired a gut reaction.
View Maya's preloaded result → Open a finished report without waiting for a new run.
Reading call
Attention · 0.89
Usually when those two things happen together, someone is trying to ramp reps faster without making managers listen to every call manually. Is that close?
Three systems · live
01:04
01 · Attention01:04
Did it grab focus?
Maya named the exact manager burden: listening to every call manually.
02 · Gut Reaction02:08
Did it feel real?
The value prop stayed tied to concrete call friction instead of generic product language.
03 · Personal Resonance02:29
Did it feel familiar?
Named sales tools made the listener map the question to their own workflow.
Mapped systems
One cortex. Seven systems.
Every run returns seven readings — one per cortical system. Click a system to see where it lives. The same surface shows up in your own results.
Personal Attention Effort Gut Memory Social Language
Attention
Dorsal attention network
Where the reader locks focus. Activity here rises on novelty, urgency, a sharp cut, a personal stake — anything that breaks the rhythm of what came before.
Corbetta & Shulman, 2002
The science
Peer-reviewed. Population-tested.
720+
people's fMRI behind every prediction — TRIBE v2's training set
20,484
brain surface points individually modeled per run — every region, every time
1,000+
hours of fMRI in the model (Meta FAIR)
Anchored in peer-reviewed research
Falk, Berkman & Lieberman · 2012 Psychological Science Huth et al. · 2016 Nature Fedorenko et al. · 2010 J. Neurophysiology Knutson et al. · 2007 Neuron Hasson et al. · 2008 J. Neuroscience Tang et al. · 2025 TRIBE v2