A proprietary research framework combining AI, behavioral analytics, and cognitive science to model and accelerate expert performance across domains.
Expert Vision is the foundational research program behind my work at the intersection of technology and human performance. It asks a simple question with complex implications: what do experts see that novices miss, and can we model it well enough to teach?
Simple to say. Hard to answer without getting lost in metaphor or hand-wavy “intuition.”
The core hypothesis
Expertise is not only knowledge. It is perception plus procedure — where you look, in what order, what you ignore, what you anchor on, how you recover when the picture changes. If that choreography is observable, it is modelable. If it is modelable, it is trainable.
That is the bet Expert Vision makes.
How the framework works
We integrate multiple signal streams rather than trusting any single measure. Eye-tracking reveals visual foraging — powered in my research by Eyegaze systems and design practices I learned working there, and Interactive Minds hardware and NYAN analysis software for rigorous study design.
The visual reference above walks through remote eye-tracking, pupil center corneal reflection (PCCR), fixations, saccades, and heatmaps — the measurement chain behind Expert Vision.
Behavioral analytics capture choices under time pressure. Bio-monitoring adds physiological context when tasks are high-stakes. Performance outcomes ground the story in results, not vibes.
Statistical modeling looks for patterns that transfer — not “this expert on this dashboard,” but decision sequences that show up in documentation review, incident response, clinical workflows, competitive gaming, and complex engineering tasks.
Then — and this part matters — we translate findings into systems. Training interfaces. Agent retrieval strategies. UI hierarchies. Organizational playbooks. Research that stops at a paper is incomplete in my book.
Why organizations care
Expertise does not scale by cloning your best person. It scales by extracting patterns without flattening nuance. Expert Vision is my attempt to do that ethically and practically — augment experts, accelerate novices, and design AI that respects how humans actually think.
What keeps surprising me
The same structural bias appears again and again: experts hunt scaffolding first. Novices dive into detail and drown. Our tools — especially knowledge retrieval systems — often optimize for the novice path because it is easier to implement.
Fixing that is not just UX polish. It is a competitive advantage in any domain where decisions are complex and time is finite.
Where this goes next
I am extending the framework into longitudinal studies — how expertise develops when AI tools are present from day one, not bolted on later. Does collaboration change scan paths for better or worse? Can we design agents that reinforce expert perception instead of replacing it with generic summaries?
I do not have final answers. I have a method, early findings, and a stubborn belief that human potential is trainable when we study the right signals.
That is Expert Vision. Not mysticism. Measurement in service of respect.