Understanding expoze Accuracy
Overview
expoze is an AI-powered attention prediction platform that calculates human visual focus on static designs with 95 percent accuracy compared to physical eye-tracking labs. Built on a neural network trained on eye-tracking data gathered by alpha.one, expoze scores 0.87 on the standardized MIT benchmark. This allows teams to validate visual hierarchy instantly without conducting lab studies. For analyzing video and audio assets, we offer a dedicated platform called junbi.
Strategic Value of Predictive Accuracy
Traditional lab-based eye-tracking research provides high accuracy, but recruiting participants and managing lab equipment requires substantial budget and time. Predictive eye-tracking replaces physical testing with algorithmic intelligence. This gives you scientific confidence to validate visual hierarchy continuously throughout your creative workflow.
How expoze Processes Your Visuals
The platform converts static designs into predictive heatmaps through a clear processing flow. Here is the flow:
- Select your static artwork and click Upload in your expoze workspace.
- The neural network scans structural visual features, analyzing elements such as color contrast, layout density, and focal points.
- The system compares these visual features against eye-tracking datasets collected by alpha.one.
- Click Generate to create your visual heatmap and quantifiable attention metrics.
Measuring Accuracy: The MIT Benchmark
Predictive eye-tracking must match real human behavior to be reliable. MIT established a benchmark specifically to score attention prediction models. expoze scores 0.87 on this benchmark. Compared to traditional eye-tracking research conducted in a physical lab, this performance translates to a 95 percent accuracy rate.
Watch the explanation below to see how our model compares to physical lab data.
💡 Pro Tip:
Test multiple variations of your static creative during the early layout phase. Comparing draft iterations in expoze allows you to fix visual hierarchy issues long before final export. Simple as that.