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Hi Arkitaip, glad you asked - (I'm one of co-founders of EyeQuant.) In a nutshell, EyeQuant's predictions use a model of human attention that is based on hund
by fab1an 16y ago
Hi Arkitaip,
glad you asked - (I'm one of co-founders of EyeQuant.)
In a nutshell, EyeQuant's predictions use a model of human attention that is based on hundreds of empirical eye-tracking studies, which were conducted at the Neurobiopsychology Lab at the University of Osnabrueck, Germany - also, we're cooperating with Caltech and USC.
The models only represent the cultural background of tracked subjects - it's strictly data-driven. Our current model is representative for western subjects; it can't predict, e. g., people who usually read Farsi.
It also only predicts the first 3-5 seconds.
Here's the method:
In the eye-tracking studies, we present subjects with websites from different categories and track their fixations - more importantly, we also analyze the statistical features of the fixated spots (around 50 of them to date): color contrasts, luminance, shapes and location, to name a few of the very basic ones.
In the next step we build models based on the weight of these features and evaluate them with other eye-tracking studies as a gold standard. One eye-tracking study typically predicts another one with 90-95% accuracy for the first few seconds (ROC). With EyeQuant, and after 5 years of foundational research, we achieve around 84% predictive accuracy - that's pretty close to what you'll get with an empirical study, but the results are available within seconds.
Let me know if you have more questions!
- arkitaip 16y agoThanks for providing more information about EyeQuant. One thing that I wonder about is how sensitive your system is wrt the customer's users. If I am running a gaming site where most users are men between ages 12-39, will you only use data from test subjects that share similar characteristics?
- deleted 16y ago[deleted]
- fab1an 16y agoGreat question. When we started to researching the models, we expected to build a lot of different ones for different demographics and target groups. However, it turned out that - for the first few seconds of viewing behaviour - we couldn't identify statistically significant differences between a lot of groups - students vs. elder people, male vs. female etc. Note that is only valid for the majority of "normal" websites - we haven't started tracking adult stuff (maybe we should...:) How do we infer whether viewing behaviour between different groups is different / not so different? We take one eye-tracking study with only males and another one with only females, e.g., and try to predict the results of the first with the other (and vice-versa) - if the predictive accuracy remains stable, as compared to the results we get within one group itself, the difference isn't large enough to build another model. We did find differences between two groups, however: people who spent a lot of time online vs. people who barely go online. We're woking on a new model for the "newbies". Also, we're about to roll out a model for males with color vision deficiency - that's 10% of all males who simply won't see the big RED button! :)