User Study 1.1: Distance task

Subjects: Six undergraduate and graduate students performed the distance task. All participants had normal or corrected-to-normal vision and reported normal color sensitivity but were not tested. Participants were compensated for their time.

Stimuli:The participants viewed images of the bump data under the following visualization tech- niques: color mapping, principal curvature texture, and principal curvature texture with shadows.

Design:This user study compares the three visualization techniques for enabling the distance task. Each participant viewed 60 unique, random surface pairs for each of the three visualization techniques for a total of 180 trials.

Participants estimated how closely the two surfaces approached each other within each of a pair of indicated regions and reported which of the pair of regions contained the closer approach (the smaller distance). The region locations were precomputed to guarantee that bumps were present in both surfaces (instead of the background noise) and that the surface intersections themselves were excluded from the regions. The distances between surfaces at the regions were distributed between 0.5 and 12 grid units (approximately 1 mm to 30 mm). The pairs of regions were selected such that differences in

closest approach were uniformly distributed between 0 and 5.5 grid units (approximately 0 mm to 14 mm) in 0.5 grid unit steps. Region locations were otherwise random. Trials were randomly ordered for each participant.

Participants were asked to respond to the following question:

In which circled region do the two surfaces appear to be closer together?

Each session began with a short training exercise. Participants were introduced to the user inter- face, each visualization technique, marking of regions for comparison, and the current task. Partici- pants were then shown 6 example trials, 2 for each visualization technique. Participants were chal- lenged to perform each training task as they would during the recorded trials, then given the correct response to the training trial, and then allowed to further study the trial image.

Hypothesis

The hypothesis is that the percentage of correct participant responses depends on the visualization technique and on the difference between the region distances.

Independent variables: The design directly manipulates two independent variables – the distance difference between regions and the visualization technique. Also recorded are the following predictor variables: a random unique identifier for each participant, the participant’s gender, and the partici- pant’s response time.

Dependent variable: The dependent variable in this experiment is the percentage of correct re- sponses.

I expected participants would be able to compare distance correctly with the color mapping tech- nique because it directly encodes the necessary information. I expected the cast shadows would en- hance the perception of separation between surfaces, yielding better task performance than for the curvature technique without shadows.

User Study 1.1: Distance Task

Color Mapping Curvature Curvature + Shadows Display Technique

Correct (%)

Figure 5.6: This figure shows the overall percentages of correct responses and their 95% confidence intervals for the distance task. Tukey’s HSD test finds that the two principal curvature texture tech- niques are statistically different from the color mapping technique, but not from each other.

Results

Unless otherwise noted, independent variables have no statistical significance (p> .05).

Analysis: ANOVA analysis finds significant main effects for the distance difference between marked regions (p< .01), the visualization technique (p< .001), the participant (p< .001), and participant response time (p< .001). As the distance difference increases, participants give more correct responses. In fact, at a distance difference of 0 participant responses are at chance (as they should be), and the responses eventually plateau (larger differences in distances no longer produce an increased number of correct responses). Longer response times correlated to higher percentages of correct responses; this effect could be from participants rocking the surface to understand the shape,

but no count of the number of times participants’ rocked the surface was recorded so it can not be tested.

Figure 5.6 shows the percentages of correct responses and the 95% confidence intervals by visual- ization technique. Figure 5.6 also shows that the performance of the participants is better than chance. A Tukey’s Honestly Separable Difference (HSD) test3 finds that participants are correct more often with the two curvature techniques than with direct color mapping. The two curvature techniques are not separable from each other.

Responses to the questionnaire show that the average participant found color mapping and prin- cipal curvature texture with cast shadows to show distance with equal clarity and with greater clarity than principal curvature texture alone. However, the average participant preferred principal curvature with shadows over the other two techniques. This is an interesting result, as it shows the participants’ judgment of the strength of the techniques does not match the study findings. The results did not show the expected improvement in perception due to cast shadows for the principal curvature texture technique.

Discussion: The user study results show the principal curvature texture techniques enabled more correct responses to the distance task than directly mapping distance to color. It should be noted that these results do not reveal the precision with which a single estimation of distance could be made, only that the relative magnitudes of distance can be effectively compared.

That the curvature techniques are better at all for distance is somewhat surprising to note be- cause the color mapping visualization directly encodes that information while the curvature techniques present two shapes from which the distances must be inferred.

An interesting result of this experiment comes from the questionnaires. Subjective preference does not reflect objective performance. Participants expressed a preference for color mapping or principal curvature texture with shadows over curvature texture without shadows and a preference for color mapping over curvature with shadows for the distance task. Neither of these preferences can be

3_{In the context of Tukey’s Honestly Separable Difference test,separablemeans that the parameter estimates or each}

level of the factor was found to have statistically significant differences that can be used to group the factor levels into categories. In these experiments, thefactoris the visualization technique.

supported by the performance results. In particular, it is interesting that participants preferred direct encoding of the metric to be estimated over a technique which enabled better task performance.

Shadows showed no benefit. At this point, I believed the effect of shadows to be masked by the rocking animation. This will be discussed further, after considering the results of the other user study in this experiment group.

5.3.5 User Study 1.2: Local shape task