## 4 Discussion  
The measurement of fixation distance shows the viewer's concentration towards the centre of the scene. As reported in the literature, fixation duration is affected by two major factors: the viewer's cognitive abilities to process the scene, and the amount of the time required by the oculomotor system so that it can signal the next eye movement [23]. There is a major difference between the fixation distances from the centre of the screen as well as its X and Y coordinates values, and the saliency values through the GBVS model itself. Fixation distance is actually the subject viewing distance of the video and it may change as the position of the subject changes. But on the other hand, the saliency value is are fixed depending upon the video frame saliency map. It does not change because it is not dependent on the position or the distance of the subject and the screen, although these saliency values are dependent on the saliency maps. Visual saliency models including the GBVS assess the fixation of human observers on real-word images/frames. However how accurately a specific visual saliency model can determine the fixation is still a question that needs to be answered [25]; these computational models provide the data of eye fixation according to spatial distribution which is then compared to the human fixation observed while watching videos, as in this paper. GBVS Map image saliency values were compared with the fixation distance values already provided in the database. Viewers watching the movies may have previous exposure to the scenes, as all the videos are dynamic natural scenes. According to the literature, viewers are more influenced by sad emotions while watching videos containing sad features content and grab less information on peripheral details [18]. Overall the experiment on the fixation distance calculation's result showed that a subject or participant is generally more focused on the central theme of the movie which grabs their attention. This also accounts for the emotions of the students while watching videos and their assessment of the emotional category of the movie while taking the survey. Fixation distance plays a major role in this experiment as it deals with the fixed number of X and Y locations in the video frames. These locations change regularly and so do the video frames, which is also an important aspect of emotional responses. However, the subjects in fixation distance and students in the survey did not respond to peripheral details in scenes. The fixation distance seems to be longer with more varied eye movement patterns while watching a video scene than when viewing images or reading, which is due to fewer changes in the movement of the eye [11]. Overall, people tend to watch more emotional videos than neutral ones. Since the graph of fixation distance shows the attention span dispersed around the centre of the screen, it shows that eventually, the emotional content does not depend on attention. Existing models account for more attention towards the happier scenes as compared to the neutral and sad scenes. The same rule goes for the current model in the experiment, as it is focusing on happy videos and somehow on the neutral ones too but is not capturing sad scenes. GBVS model assigns more weight to the image centre, recognizing these regions as more salient regions than the peripheral regions. This is because the model comprises of two components measuring the local dissimilarity within the image. In addition, the model produces a directed graph between local nodes because it has an intrinsic central tendency [3]. GBVS Map images showed that happier videos are more emotionally salient, with the highest saliency location/regions grabbing more viewer attention and ultimately causing happy emotions. Significance in either in one-way ANOVA or just the bar graphs for all and 125 frames showed variability differences for sad