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Reliability of Self-Reported Awareness Measures Based on Eye Tracking

William Albert and Donna Tedesco

Journal of Usability Studies, Volume 5, Issue 2, Feb 2010, pp. 50 - 64

Article Contents

Response Outcomes

A more rigorous examination of the reliability of self-reported awareness was made by categorizing each response relative to gaze duration. The following were the four types of outcomes in this analysis (see Table 2):

False alarms and misses could be collectively thought of as the overall error rate, because participants either remembered an element they didn’t see (false alarm) or didn’t remember an element they did fixate on (miss). Conversely, a success rate was based on the combination of hits and correct rejections.

It should be noted here that there is varying research regarding appropriate parameters of gaze duration to measure attention. The “eye-mind hypothesis” (Just & Carpenter, 1980) suggested that people process words and other information instantaneously upon seeing them (a 0 ms duration), while other research has used or suggested up to 250 ms for text (Guan et al., 2006; Johansen & Hansen, 2006; Rayner & Pollatsek, 1989) and 100 ms for pictures, graphs, or numerical data (Guan et al., 2006). In the categorization scheme above, the criteria used to define each outcome were somewhat arbitrary, but with these researched parameters as guidance. For this study, we adopted a liberal definition of what we considered a success (250 ms cutoff), and a conservative definition of what we considered an error (up to 500 ms for a miss). Essentially, we wanted to give the participants the benefit of the doubt wherever possible.

Table 2. Categorization of Error and Success Types for Experiments 1 and 2

Table 2

For Experiment 1, there was an overall error rate of 15% (see Table 3). There was roughly twice the number of false alarms (10.2%) as misses (4.8%). The success rate in Experiment 1, approximately split between correct rejections and hits, was 55.2%.

Table 3. Response Outcomes for Experiment 1

Table 3

The overall error rate in Experiment 2 was 17.4% (see Table 4). Similar to Experiment 1, participants made about half as many false alarms (4.8%) as misses (12.6%). The overall success rate was 33.8%, with roughly more than twice as many correct rejections (22.1%) as hits (11.7%).

Table 4. Response Outcomes for Experiment 2

Table 4

Short of performing an in-depth Signal Detection Theory analysis, we derived some important themes from this data. Roughly 5% to 10% of the time, participants said they definitely saw or spent a long time looking at an element when, in fact, they did not see it at all (false alarms). Participants indicated that they did not see the element, or spent no time looking at something, when in fact they did 4.8% (Experiment 1) or 12.6% (Experiment 2) of the time. Taken together, there was an overall error rate of 15% (Experiment 1) to 17% (Experiment 2).

Even though words and images may be easily encoded into memory in less than 500 ms, it was possible that participants fixated with little or no attention. To be even more conservative in how we classified errors, we adjusted the threshold for misses up to 1,000 ms. For Experiment 1, the miss rate dropped from 4.8% down to 1.1%. For Experiment 2, the miss rate dropped from 12.6% down to 5.8%. When adopting this highly conservative approach to classifying errors, the overall error rate was 11.3% in Experiment 1 and 10.6% in Experiment 2.

A substantial decrease in the miss rate was not surprising because the greater the fixation time, the less likely a participant reported definitely not noticing an element (Experiment 1) or spending no time looking at an element (Experiment 2). Essentially, the longer you spend looking at something, the more likely you are to notice it. Of course, it was still possible that participants looked at an element for 500 ms or even a full second and did not fully process the most basic characteristics.

Element Types

Is it possible that participants can be trusted with their self-reported awareness for just certain types of elements? To answer this question we decided to look at the following three specific types of elements:

Experiment 1 showed a roughly equal error rate (false alarms + misses) for each element type (12% to 14% overall error) (see Figure 9). One interesting point was that functional elements tended to have a much greater proportion of false alarms compared to misses.

Figure 9

Figure 9. Overall error rates by element type for Experiment 1

Experiment 2 showed greater variability in the overall rate across the three element types (see Figure 10). Navigation elements had an overall rate just under 6%, while functional elements were about 11%. In both Experiments, error rates for functional elements were the highest.

Figure 10

Figure 10. Overall error rates by element type for Experiment 2

Memory Test

A memory test was also used to test the reliability of self-reported awareness. By asking participants if they saw an element that did not exist, we were able to accurately determine the reliability of their responses. We treated the memory test as a separate analysis from the other elements. Taking the responses to the seven new elements used across both the ET and NET groups, we simply derived the percentage of the time participants claimed that they definitely saw it (Experiment 1) or spent a long time looking at it (top 2 box for Experiment 2).

The results of the memory test are shown in Table 5. Experiment 1 resulted in 27% of the responses saying that they definitely saw an element that did not exist in the study slide. Experiment 2 produced a lower false alarm rate; 9% of the responses said that they spent a significant amount of time looking at an element (top 2 box response) that did not exist.

Table 5. Response for Memory Test (ET and NET Groups Combined)

Table 5

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