ATTENTION INTELLIGENCE
Understanding attention. Informing design.
Research overview · Serdar Şenel, PhD
Attention is limited. Understanding how it is earned requires more than counting how often a message appears. In Serdar Şenel’s Stopper research, Attention Intelligence connects visual design, observed human responses and computational analysis to examine what people notice and how they engage with visual communication.
The work grows from Şenel’s doctoral research on the stopping power of advertising images in social media. The Stopper Effect examines the moment a visual interrupts scrolling. Attention Intelligence extends this inquiry into a framework for measuring, interpreting and predicting attention.
A connected view of human attention
The methodology brings together behavioral observation, eye tracking, EEG, galvanic skin response and AI-supported visual analysis. Each contributes a different kind of evidence. Gaze shows where people look; behavioral data records what they do; physiological signals add context to their responses. These measures require interpretation together.
Stopper AI examines visual features such as composition, hierarchy, typography, contrast and negative space. Its predictions are intended to be compared with measured human responses. A predicted heatmap is an analytical aid, not direct evidence of what a person saw, understood or remembered.
From research to communication decisions
For brands, designers and institutions, the practical question is how attention research can improve the choices behind a message. The framework can support investigations across advertising, branding, packaging, digital interfaces, retail, education and media. Its purpose is to make creative judgment better informed by evidence.
The methodology report below sets out this research approach and the relationship between Stopper Theory, Stopper Lab, Stopper AI and Stopper Analyze. It presents a framework for inquiry and validation, rather than a universal score for human attention.
FULL METHODOLOGY REPORT
Stopper Attention Intelligence Methodology: Measuring, Understanding and Predicting Human Attention
Serdar Şenel, PhD · 2026 · Version 1.0
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