Читать книгу Eye Tracking the User Experience - Aga Bojko - Страница 26

Eye tracking measures allow you to make comparisons between stimuli along performance-related dimensions, such as search efficiency, ease of information processing, and cognitive workload. While it is true that you can also use measures such as time on task or task completion rate to identify performance-related differences between interfaces, eye tracking data can help detect differences more subtle and difficult to observe in a lab environment using more conventional methods.

If they are so subtle and almost invisible, why are these differences important? These seemingly small differences could turn into something much larger and seriously impact performance under other, more real-world circumstances, where task complexity, fatigue, or distraction levels may be increased. Think of busy retail environments, longdistance truck driving, or even combat situations—you can’t always replicate the complicated, fast-moving world that users have to deal with when using a product or an interface.

The challenge is that you will often not know if more conventional measures will be able to detect any existing differences or if you should use a more sensitive instrument such as eye tracking. Based on the participant sample size, past experience with similar research, as well as knowledge of the environment where the tested object is typically used, you can only try to make an educated guess.

If negative implications of poor performance are high, you should consider supplementing the research with eye tracking to make sure all your bases are covered. Even if more conventional measures manage to reveal performance differences, the eye tracking data can then be used to support these other measures. For example, number of fixations can be used to support time-on-task data, and pupil diameter can be used to support results obtained with subjective workload assessment tools such as the NASA Task Load Index (NASA-TLX).

The “Prescription Drug Labels” case study provides an example of how eye tracking helped detect differences in search efficiency and ease of information processing between drug label designs.