Masters Thesis - Issues of Saliency and Recognition in the Search for Web Page Bookmarks
5. Data Processing
5.1) Essential tools for processing eye movement data
Recording eye movements generates huge amounts of data which have to go through several levels of processing before they can be understood and analysed (Jacob & Karn, 2003).
The raw data are quite dense (see Appendix F) and is best reviewed using a graphical gaze point viewer, which should be supplied with most eye trackers. This type of software can 'play back' eye movements, superimposed over the image that the person was originally viewing, as shown in Figure 17.
Figure 17
Eye movements can be 'played back' using custom software.
Here, fixations are identified by blue crosses at their centre with blue circles indicating the duration of the fixation. Saccades are represented by the red lines connecting the fixations. The complete superimposed eye movement data is called a 'trace'.
5.2) Error correction
Eye trackers can be notoriously sensitive and subject to error, so it is prudent to check the accuracy of all data. In the present study, a reliable pattern of error was detected in the x,y coordinates of the gaze point location.
Firstly, a 'drift' was found in the absolute gaze point of many of the participants. This was most likely due to errors in re-acquiring the image of the eye after it moved in and out of camera range. Absolute drift is easy to spot since all gaze points on the screen are shifted in the same direction by the same amount, with fixation patterns obviously not matching the objects on screen, as shown in Figure 18.
Absolute drift was corrected in the present study by 'dragging' the eye trace back so that the pattern of fixations and saccades matched the layout of the objects on screen, as shown in Figure 19.
Figure 18
An example of 'absolute drift' in the eye movement record: every gaze point has drifted in the same direction by the same amount.
Figure 19
The same eye movement data from Figure 17, after the 'absolute drift' has been corrected. Note how the pattern of fixations and saccades now matches the layout of the objects on screen.
Errors in relative gaze point location, however, are more difficult to correct. With relative drift, the eye trace is 'warped' so that some fixation clusters appear to be on target, while others 'fall short' of their apparent target or 'over shoot' them. Figure 20 shows an example of relative warp: Here, we can see a cluster of fixations which matches the address bar, but doesn't quite 'reach' it despite the fact that the fixations on the article title are perfectly centered.
Figure 20
An example of 'relative warp' in the eye movement record: most gaze points match the objects on screen, while others miss their apparent target.
When attempting to correct for relative drift, not all the fixation clusters can be re-aligned with their apparent intended targets. In this study, the elements of interest were the name of the site, the title of the article and the section name, so priority was given to adjusting fixation clusters over these regions to maximise data accuracy where it mattered most. Each time the eye trace was corrected, the raw data file was re-written with the new gaze points, ready for the next stage of processing.
In principle the absolute offset should be consistent for each person, meaning that the offset can be corrected once and applied to the rest of the trials for that participant. However, the relative warp was fairly unpredictable, therefore the offsets were corrected manually trial by trial to achieve the best possible accuracy of raw data for the subsequent stages of analysis.
At all times, the eye trace was re-aligned with textual elements so that the first fixation in a phrase fell 3-4 characters to the right of the start of the first word, in accordance with the structure of the perceptual span. In total, 1440 trials were corrected by hand.
5.3) Filtering and analysis
Once the eye movements have been measured, logged and error corrected, the data have to be filtered to enable examination of participants' processing of specific regions of the screen, such as control elements, navigation links and images etc. In the present study the main areas of interest were the site name, section name and article title on both the website and bookmark screens.
The first step in defining the areas of interest was to load the stimuli screenshots into a graphics editing program, then draw to a rectangle around the target element and log the screen coordinates of this rectangle in a data file. The coordinates in this data file were then used by parsing software to count eye movements occurring only in these areas. The x,y screen coordinates in pixels of the top left corner and bottom right corner of the rectangle were recorded as well as it's width and height. Examples of the areas of interest are shown in Figures 21 and 22 below (The yellow borders serve to highlight the location of the areas of interest, and did not appear on the original screenshots).
Figure 21
Areas of interest defined on a web page screenshot (highlighted here by yellow borders).
Figure 22
Areas of interest defined on a bookmark screenshot (highlighted here by yellow borders).
Once these areas had been defined, parsing software was used to extract the corresponding eye movement data and format it so that it could be analysed with standard statistical software such as Excel or SPSS.