Attention Insight Technology

Our AI-powered platform can produce eye tracking heatmaps that are up to 96% accurate without any human participants involved. Previously we collected data from our own commissioned eye tracking studies and from open-source eye tracking datasets. By feeding this data to our deep learning algorithm, we trained it to perceive various designs and images just like humans do.

60 sec

to get a result

96 %



participants needed


image datasets from eye-tracking studies

The technology
behind our heatmaps

Eye tracking studies

Data set

Accuracy validation

Algorithm training


Eye tracking studies

Eye tracking is a process of measuring eye movement, determining where the person’s gaze is directed.

The result of an eye tracking study is an Attention Heatmap that shows how a group of participants viewed an image or video. Such eye movement data was needed to train our deep learning algorithms.

To carry out the eye tracking studies, we hired a neuromarketing lab which is a member of the International Neuromarketing Science and Business Association (NMSBA).

Data set

The Attention Insight algorithm is trained with approximately 70,000 individual participants’ data sets from eye tracking studies.

The image datasets we use are both open-source and proprietary.

Data set statistics:

  • Participant attention duration: 4 seconds;
  • Average gender distribution: 58% women and 42% of men;
  • Average participant age distribution: varies from 7 years old to about 60+ years old. However, most participants fall into the 21–30 age bracket.
  • Participants are from the USA and Europe

Algorithm training

Our heatmaps are generated by a deep learning algorithm called Convolutional Neural Network (CNN). It is a computing system that has an architecture inspired by the biological brain and mimics how neuron layers work.

A CNN consists of multiple layers of nodes that are connected with different weight connections. These weights determine how much one node impacts the following node.

Before the training, a CNN has random weight connections resulting in inaccurate heatmaps. The difference between the generated heatmap and the “ground truth” (actual eye tracking heatmaps) is called the error. During the many training cycles, these weights between layers are adjusted so that the error is reduced. After the training, our generated heatmaps match the “ground truth” closely.

We regularly add new data sets to make sure that our algorithm meets industry standards and stays the most accurate algorithm in the market.

Accuracy validation

To measure the accuracy of our generated heatmaps, we submitted our results to the Massachusetts Institute of Technology (MIT) / Tuebingen saliency benchmark.

They sent us 300 test images, and we sent them back our model results on their testing data set of those 300 images.

After evaluating our results, MIT scientists concluded that our heatmaps match actual eye tracking heatmaps with 92.5% accuracy for general images. Across all types of designs, our heatmap accuracy is up to 96%

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Real Eye Tracking vs Predictive Eye Tracking
Eye Tracking
Predictive Eye Tracking

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