But What Does It All Mean? Understanding Eye-Tracking Results (Part 5)
Part V: Time and the Heatmap
Part 2: What can you learn from eye tracking data?
Part 3: What is a heatmap... really?
Part 4: What is a scan path?
Part 5: Time and the heatmap
In my previous post, I mentioned that heatmaps do not have a time component. Several people have asked me to discuss this topic in a little more detail, so here we go.
Important point #1:
A heatmap represents which content was seen by a group of participants. A heatmap helps to answer the questions “Where did users look?” and “Where didn’t users look?”
Important point #2:
If you are analyzing a heatmap, you are momentarily saying “Ok, in this instant we don’t care how long people looked at things, we just want to know what they saw.”
A heatmap does not answer the questions:
• How long did someone look at my page or page element?
• Did users look away and look back?
To further clarify these points, here’s a general overview of how a heatmap is created (click illustrations for a larger view):
Step 1: Collect data A single participant views a web page. We record her eye movements as she browses. This gives us data which can be represented in a gaze replay animation. Now imagine that the animation is composed of a stack of stills, just like a flip book. Each page of our eye tracking “flipbook” contains the X,Y, and Time coordinates of a single fixation. |
Step 2: Collapse individual data sets along the t-dimension Keep imaging our eyetracking data as a flipbook (3D data structure). Now we will collapse the stack along the time axis. In its most basic form, the calculation will take the area of a web page and note every location where the participant fixated. By collapsing the time dimension, we remove it from all further calculations (poof!)
This means that someone who fixated the center of a page for 100ms will end up with the same fixation summary as someone who stared at the center of the page for an extended period of time.
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Of course, the algorithm for doing this computation is more complex than the example I just presented. Any heatmapping algorithm should take into account peripheral vision, microsaccades, blinks, fixation duration, “bad data”, ocular drift, dynamic page behavior, etc. For now let’s continue with the simplified example. Steps #1 and #2 are completed for, oh, say 15 people total. This gives us 15 individual fixation summary plots. |
Step #3: Compute an average viewing value for each pixel of the webpage Again the algorithm which handles this step is more complex than what is presented here, but the basic idea is… For every pixel of a web page, the system asks “how many people saw this pixel?” If 10 people saw the pixel out of a group of 15, then the algorithm says “66% percent of people saw this pixel… color it yellow on the plot.” |
This averaging algorithm outputs a heatmap showing what percentage of participants saw each page element. Handling the data in this way keeps any single individual from biasing heatmap percentages. For example, imagine that 14 people only looked at the center of a page, and 1 person looked at the entire area of the page. Even though 1 participant viewed a much larger area than anyone else (and probably spend a goodly amount of time doing it), she still only represents 6.7% of the 15 person group. The resulting heatmap would then show that 100% of people fixated the center of the screen, and less than 10% looked elsewhere. |
Other questions that have come up:
If you make a heatmap from only the first 10 seconds of viewing, doesn’t that add a time component to the heatmap?
The answer is no. The method for computing a heatmap is the same no matter how large the time sample used. When you slice for specific time intervals, you are just selecting a specific group of fixations to include in the calculation. The resulting heatmap still has no time component.
Are heatmaps created from time slices more valid or informative than those created from full experiment sessions?
I suppose that depends on the kind of information you want to get from the plot. If you want to see where people looked in the first 10 seconds, a time slice heatmap is appropriate. If you are trying to understand an order for page element viewing, a heatmap is probably the wrong analysis tool. If you want to see where people are looking over their entire experience with the page, a full session heatmap is the way to go.
This is the last planned installment of our “But what does it all mean?” series. However, the discussion is still open, so if anyone is interested in other topics, just let me know.
Article by Teresa Hernandez - Eyetools, Inc.
Illustrations by Boyd Richard - Eyetools, Inc.