Dear Dr. Roberto,
This is Jiawei, who was in charge of the numerical analysis of human eye gaze data. Good question and I agree with your viewpoint indeed!
I think my supervisor Kun Guo could give you a more explicit answer regarding individual differences when viewing the driving clips. Here is my understanding of the computation.
Regarding the personal preference of scene selections, we classified the driving videos into 10 different conditions based on the motion cues and collected data from 35 participants, averaged them to the "ground truth". I should admit there are unavoidable "outliers" due to participants getting distracted.
Then I guess averaging is sometimes not very precise indeed. To address this I used iterative computation (take an arbitrary participant out of 35 and make the iterative computing). Finally, two distinct properties of human and visual attention models emerged:
1. Humans gaze at different locations under different stimuli presentations (normal or reversed), while visual attention models, even so-called "spatiotemporal visual attention models" predicted the gazing points on the low-level saliency, i.e., there were few difference between normal or reversed video.
2. Central bias in visual attention models is not as obvious as in humans, the gazing predictions of computational vision models (even when deep learning is involved) are simply low-level or mid-level features combined. There are still different gaps between current visual attention models and human observers.
I didn't state this in the article, however, during the data analysis, I found that the participants who have driving experience or no/little driving experience have different eye gaze distributions on the temporal sequences. However, I did not have the time to investigate this further, as this research was part of a PhD in Lincoln which needed to be completed within 3.5 years.
For further understanding, please do not hesitate to contact Kun or Federica. Please cite our paper because it is really very interesting work! Thank you!