Keith S. Karn

eyePatterns: software for identifying patterns and similarities across fixation sequences

Fixation sequence analysis can reveal the cognitive strategies that drive eye movements. Unfortunately this type of analysis is not as common as other popular eye movement measures, such as fixation duration and trace length, because the proper tools for fixation sequence analysis are not incorporated into most popular eye movement software. This paper describes eyePatterns, a new tool for discovering similarities in fixation sequences and identifying the experimental variables that may influence their characteristics.

The hunt for usability: tracking eye movements

Usability testing methods have not changed significantly since the origins of the practice. Usability studies typically address human performance at a readily observable task-level, including measures like time to complete a task, percentage of participants succeeding, type and number of errors, and subjective ratings of ease of use [3]. Certain types of questions are difficult to answer efficiently with these techniques. Imagine, for example, that we observe users spending longer than expected looking at a particular dialog of a software application or web page without making the appropriate selection to complete the task. Participants often have difficulty reporting their behavior and the experimenter is clueless about what went wrong. Is it because the user is overlooking the control? Is the user distracted by another element in the interface -- perhaps an animated graphic? Is the user seeing the control, but failing to comprehend its meaning? Different answers to these questions would clearly lead to different recommendations. If overlooking the control is a problem, increasing its salience is appropriate. If confusion of the controls function is a problem, changing the graphic or text label may be appropriate. If distraction is a problem, decreasing the salience of other stimuli may help. Without answers to these questions, design recommendations have to be implemented by trial and error. Recording the fixation pattern of the participants eyes can offer additional information to help answer these questions. While this concept is not new, it has been confined primarily to military aircraft cockpit issues [2,4]. Only recently has eye tracking technology advanced to make it practical in the broader usability community. Usability studies of human-computer systems that have included eye tracking, e.g., [1] are beginning to show benefits of these techniques. However, important challenges remain.

“Saccade pickers” vs. “fixation pickers”: the effect of eye tracking instrumentation on research

Users of most video-based eye trackers apply proximity algorithms to identify fixations and assume that saccades are what happen in between. Most video-based eye trackers sample at 60 Hz., a rate which is too low to reliably find small saccades in an eye position record. We propose to call these slower eye trackers and their typical proximity analysis routines “fixation pickers.” Systems such as dual-Purkinje-image (DPI) trackers, coil systems, and electro-oculography permit higher sampling rates, typically providing the 250 Hz or greater sampling frequency necessary to detect most saccades. Researchers using these types of eye trackers typically focus on identifying saccades using velocity based algorithms and assume that fixations are what happen in between these movements. We propose to call these faster eye trackers and their velocity-based analysis routines “saccade pickers.” Given that fixation pickers and saccade pickers extract different things from an eye position record, it is no wonder that the two systems yield different results. This discrepancy has become a problem in eye movement research. A study of cognitive processing conducted with one eye tracking system is likely to give results which cannot be easily compared to a study conducted with another eye tracker. Imagine that two investigators are both interested in studying visual search. Both choose the number of saccades as one of their dependent variables to measure search performance. One investigator chooses a video-based fixation picker. The other investigator chooses a DPI-based saccade picker. Because the saccade picker is tuned to identify smaller saccades, the investigator with the DPI tracker reports more saccades and fixations during an equivalent visual search task compared to the investigator with the video-based tracker. Both investigations are likely to produce valid results, but results which are not comparable to the other investigators.

SIMPLIFIED EYE TRACKING ENHANCES PROBLEM UNDERSTANDING AND SOLUTION DISCOVERY IN USABILITY TESTING

Identifying problems and generating recommendations for product user interface redesign are primary goals of usability testing. Typical methods seem inadequate for the deep understanding of usability problems needed for developing effective solutions. Sporadically over the past 50 years, usability teams have tracked user eye movements to achieve this deeper understanding, but high cost and complexity have prevented the widespread use of this technology. We investigated whether simplified eye tracking techniques, in combination with traditional usability testing methods, could enhance problem discovery and understanding. These techniques included: using a video-based eye tracking system, tracking only a few participants, and encoding gaze durations (not individual fixations) on only a few areas of interest. For each of three interface versions, we studied twelve participants with traditional usability testing techniques and eye tracked just two. Eye tracking yielded discovery of additional usability problems and detailed characterizations which led to more focused and appropriate solutions.