User interfaces are visually rich and complex. Consequently, it is difficult for designers to predict which locations will be attended to first within a display. Designers currently depend on eye tracking data to determine fixated locations, which are naturally associated with the allocation of attention. A computational saliency model can make predictions about where individuals are likely to fixate. Thus, we propose that the saliency model may facilitate successful interface development during the iterative design process by providing information about an interface's stimulus-driven properties. To test its predictive power, the saliency model was used to render 50 web page screenshots; eye tracking data were gathered from participants on the same images. We found that the saliency model predicted fixated locations within web page interfaces. Thus, using computational models to determine regions high in visual saliency during web page development may be a cost effective alternative to eye tracking.
INTRODUCTION Some visual designs guide users to the locations of important information, while others mislead users. Visual saliency, inherent in a complex interface, cues users to certain spatial regions over others. If employed correctly by designers, salient cues may reduce information search times and facilitate task completion [cf. 18] by implicitly Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are Pnreo-tpmrocaedeedionrgsdiosftrthibeu5ttehdInftoerrnpartioofniatloWrcoorkmshmoperocniaMl oaddevlaDnrtiavgeen aDnedvetlhoaptmceonptioefs Advanced User Interfaces (MDDAUI 2010): Bridging between User Experience and UobIreEarnregptihnuiebselrinisnohgt,i,coteorgaapnnoidzsettdhoeantftuhselelr2vc8eittrhastAiooCrnMtooCnortenhdfeeirsfetinrricsbetuoptneagHteou.mlTiasontsFc,aocrpteoyqrusoiitrnheesrwpriisoer, Computing Systems (CHI 2010), Atlanta, Georgia, USA, April 10, 2010. specific permission and/or a fee.
CMopDyDrigAhUt©I22001100,foAr pthreili1nd0i,v2id0u1a0l,paAptelrasnbtya,thGeepoarpgeiras,' aUuSthAor.s. Copying permitted
foCroppriyvraitgehatn2d0a0c9adAemCiMcpu9rp7o8s-e1s-.6R0e5-p5u8b-l2ic4a6ti-o7n/0o9f/m0a4t.e.r.$ia5l .f0ro0m. this volume
requires permission by the copyright owners. This volume is published by its editors.
communicating to users where they ought to start their
visual search [
It is no surprise then, that designers often monitor eye
movements to evaluate a web page’s saliency, or entry
points. Eye tracking systems allow designers to test whether
their web pages actually guide users' fixations to important
locations. However, eye tracking has a number of
recognized costs. Eye tracking systems are often expensive,
not easily accessible, time consuming to employ and they
gradually lose calibration [
Stimulus and Goal Driven Searches
In this article we investigate the influence of
stimulusdriven saliency on attention within the context of a web
page. Stimulus-driven saliency guides attention quickly and
without explicit intention, thus some might question its role
during a purposeful search on a web page. There is ample
evidence to suggest that goals do influence the guidance of
attention. For example, web page eye tracking research has
shown that changing the task (or goal) during a search, or
seeking navigational or informational indicators, changes
observers’ fixation patterns [3]. Additional research has
shown that, given enough time, expectations can cause a
consistent pattern of fixations – F-shaped pattern or reading
patterns (e.g., left-right/top-bottom) [
Testing a Saliency Model within Web Pages Designers recognize the need to predict and identify where users’ attention will be guided on a web page. For example, it is well known that one should avoid using poor designs that increase the likelihood of users missing important interface features such as branding, navigational or informational symbols. But, using an eye tracking system to monitor guidance of attention – as is traditional – can be expensive, difficult to employ and time consuming within the context of a practical iterative design process. Thus, we investigated the utility of a computational saliency model in predicting the guidance of attention in web page screenshots. This new method is benchmarked and compared to another set of data in which participants’ eye movements were tracked while they viewed the same web page screenshots.
METHOD Participants The data from eight undergraduate participants are examined. All participants reported extensive web site experience.
Stimuli and Equipment The images were 50 screenshots of various web pages. Each participant saw each screenshot only once.
Participants' eye movements were recorded by an ASL eye tracker with a sampling rate of 120 Hz. Screenshots were shown on a Samsung LCD monitor, which had a viewing area of approximately 38.0 cm × 30.0 cm. A chin rest maintained a viewing distance of approximately 80 cm. Images subtended approximately 26.70 x 21.20 visual angle. Procedure Participants first read and signed an informed consent document, and were then seated in front of the monitor with their chin in the chin rest. The experiment began and concluded with a 9-point calibration sequence to calibrate the eye tracker and estimate the amount of tracking error. Participants were told that they would view a series of web page screenshots, and that they should, "look around the image like you normally would if you were surfing the internet." A fixation cross was presented at the center of the screen to signal the beginning of a trial. After a delay of approximately 1 second, a randomly selected web page screenshot was presented for 5 seconds. The fixation cross then reappeared to signal the beginning of the next trial. The experiment took complete.
approximately minutes to Saliency maps were created using the algorithms developed by Itti, Koch, and Niebur (1998). The model was run on each image individually and the output was normalized by dividing all values by the maximum value for that map, and multiplying all values by 100. To simplify data analysis, the size of the saliency maps was increased to be identical to the size of the screenshots (1024 x 768 pixels). As described in the Introduction, these saliency maps are 2-D representations of areas in the screenshot that show the relative saliency of locations in the image. Figure 1 shows an example of two web page screenshots and their corresponding saliency maps. Low values (dark areas in the image) indicate regions of the image that are low in saliency, while high values (light areas in the image) indicate regions high in saliency.
RESULTS We used a similar technique to Parkhurst, Law, and Niebur (2002) to determine whether salient regions in web pages were fixated more often than would be expected by chance. Specifically, the values of the saliency map at the location of each participant's first ten fixations were extracted. For example, the x, y coordinates of the first fixation for each participant was determined for every screenshot and the value at the same location in the corresponding saliency map was extracted. This process was repeated for fixations two through ten. These values formed the Observed Distribution of participant responses (Figure 2).
To determine the likelihood that salient regions would be fixated by chance, we repeated the process used to find the Observed Distribution after rearranging the fixations and saliency maps for all screenshots. For example, the values from the saliency map for screenshots 2 to 50 were extracted at the fixated locations from screenshot 1. The saliency values of all other screenshots were extracted at the location of the first ten fixations for all subjects for each screenshot. These values formed the Shuffled Distribution. The method used to create this distribution controls for spatial biases that may inflate correlations between fixations and salient regions. If the values of the Shuffled Distribution are larger than those of the Observed Distribution, it would indicate that participants fixated on regions that are lower in saliency than what is expected by chance. If, however, the values of the Observed Distribution are larger than those in the Shuffled Distribution, it would indicate that participants fixated regions that are higher in saliency than what is expected by chance.
Figure 2 shows the means for the Observed and Shuffled Distributions of the first ten fixations for each screenshot. An analysis of variance was conducted with fixation number (1-10) as a within-subjects variable and distribution (observed, shuffled) as a between-subjects variable, to determine whether any differences between the distributions varied as a function of fixation number. The main effect of fixation number was reliable, F(9, 882) = 6.39, MSE = 19.03, p < .001. Pairwise comparisons revealed that the values for the first fixation were higher than all other values, and that the values of the tenth fixation were lower than all other values. This indicates that early fixations tend to occur at regions of higher salience than those of later fixations. More importantly, the main effect of distribution was also reliable, F(1, 98) = 4.86, MSE = 397.95, p < .05, indicating that the values of Observed Distribution were larger than those of the Shuffled Distribution. This difference confirms that participants fixated regions higher in saliency than would be expected by chance, showing that the saliency model is effective at predicting fixations. Distribution x Fixation number was not significant, F < 1. Eye tracking is a commonly employed method for examining the guidance of overt attention within interfaces (e.g., web pages). However, it has several drawbacks. We propose that a web page’s saliency, stimulus-driven properties, may be revealed through the use of a computational saliency model. Therefore, we compared the performance of the model to eye tracking data collected from human observers. We were able to demonstrate that, indeed, the saliency model predicts the deployment of overt attention within a web page interface.
Previous research has shown a modest correlation between
saliency and eye fixations in natural and artificial scenes
[
Our data suggest that saliency maps alone can provide reasonable predictions of overt attention. In addition, saliency maps can be generated quickly, and require no additional equipment or participants. Even with these positive attributes, one may be hesitant to abandon eye tracking altogether. Our recommendation to designers is to choose the method most appropriate for your project given your constraints and needs. It is often the case that developing effective interfaces requires many levels of analysis. For example, during the early formative testing process it would be appropriate to begin by using the saliency model to ensure that regions identified as being important are also visually salient. Then, during the ‘final’ prototype development stage, employ the eye tracking method to verify that your participants are actually looking at the critical elements in the design.