Dragonfly AI simulates the 'bottom up' processing approach to how we as humans perceive visual information. This phase of vision happens when we first glance at something, but before we are aware of what we are looking at.

Bottom up processing is biological process that is universal across all humans, regardless of their demographic such age, race, religion, gender, family size, ethnicity, income, and education.

Behaviour research has shown that human attributes such as gender, age, or experience have little effect on where people will initially look (assuming equivalent visual acuity and other visual processing capabilities). However, once the visual system has completed the initial surveillance process (usually 2–3 seconds), 'top-down' processing influences related to personal interest, experience and task will play a more significant role in where people will look.

The Dragonfly AI model looks at several visual elements that decades of science have proven attract our first glance attention, including Edges, Intensity, Red/Green Colour Contrast, Blue/Yellow Colour Contrast, Texture, orientation and scale etc.

While the Dragonfly solution can analyse visual content and output relevant saliency maps and scores, it requires human (user) interaction to validate and interpret these outputs in the context of the objectives for the task in hand.

Dragonfly AI predicts the areas and elements that are likely to attract attention at first glance, to provide end users with objective and unbiased metrics they can use to make data informed decisions.

It's recommended that you interpret Dragonfly AI analyses with the priority of the visual elements in mind.

For example, does the analysis predict that the priority elements are achieving high saliency scores and will attract attention?

When looking at your priority elements, consider identifying up to 5 key visual elements you would like to prioritise to attract the most attention. What priority should these be in order of importance to the objective and how can you optimise your content to increase the saliency of these elements individually and collectively within the scene.

For example, use an element with high saliency to attract attention, and place it near important copy, calls to action, etc. to maximise the opportunity your priority element will get noticed.

When running your analysis, it's important to understand that Dragonfly AI does not predict Post Attentive vision. Dragonfly only replicates what grabs visual attention.

For example, Dragonfly AI cannot predict what a viewer will do once the viewer knows what they are looking at as they will be influenced by mission, brand loyalty, level of planning, category pre-disposition, price & promo sensitivity etc.

Need more information? Please contact [email protected] or any member of the Dragonfly AI team.