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International Journal of Human–Computer Interaction

ISSN: 1044-7318 (Print) 1532-7590 (Online) Journal homepage: http://www.tandfonline.com/loi/hihc20

Perceived Visual Complexity of In-VehicleInformation Display and Its Effects on GlanceBehavior and Preferences

Seul Chan Lee, Hwan Hwangbo & Yong Gu Ji

To cite this article: Seul Chan Lee, Hwan Hwangbo & Yong Gu Ji (2016) PerceivedVisual Complexity of In-Vehicle Information Display and Its Effects on Glance Behavior andPreferences, International Journal of Human–Computer Interaction, 32:8, 654-664, DOI:10.1080/10447318.2016.1184546

To link to this article: http://dx.doi.org/10.1080/10447318.2016.1184546

Accepted author version posted online: 04May 2016.Published online: 04 May 2016.

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Perceived Visual Complexity of In-Vehicle Information Display and Its Effects onGlance Behavior and PreferencesSeul Chan Lee, Hwan Hwangbo, and Yong Gu Ji

Department of Information and Industrial Engineering, Yonsei University, Seoul, Korea

ABSTRACTDespite enhancements in the visual complexity of in-vehicle information display in recent years, fewstudies have examined the effects of such increased complexity. We conducted this study with thefollowing objectives: (1) to suggest a framework for predicting the perceived visual complexity (PVC) ofin-vehicle information display; (2) to examine the effects of PVC on the visual behavior of humanoperators; (3) to investigate the relationship between preferences and PVC. A theoretical frameworkto evaluate PVC was developed, and a survey study was used to collect participants’ perceptions onvisual complexity. A regression analysis was employed to find the relationship between each of threefactors and PVC. Two of the factors—quantity and variety—showed a positive correlation with PVC,whereas the third factor, relation, exhibited a negative correlation. Visual search experiments wereconducted to test the effects of PVC on the performance of visual search tasks and glance behavior.The results showed that the high level of PVC leads to more time-on-task and number of fixations. Wealso found that preference for in-vehicle information displays was inversely proportional to PVC. Theresults enable us to predict how human operators perceive visual complexity and explain the influenceof PVC on human behavior.

1. Introduction

Advances in information technology have led to the develop-ment of a wide variety of in-vehicle information systems(IVIS) for drivers in recent years (Horrey, Wickens, &Consalus, 2006). These systems provide information relatedto driving, vehicle status, navigation, and entertainment.However, numerous studies have shown that visual demandson the driver influence driving performance (Engström,Johansson, & Östlund, 2005; Harbluk, Noy, Trbovich, &Eizenman, 2007; Horrey et al., 2006; Kim, Lim, Jo, & Kim,2015). Due to limited human capacity for information proces-sing, the increased amount of information in IVIS can distractdrivers (Marois & Ivanoff, 2005). According to Leder et al.’smodel (Leder, Belke, Oeberst, & Augustin, 2004), visual com-plexity is a particularly crucial part of how visual stimuli areperceived. Therefore, it is significant to examine and managethe level of visual complexity of in-vehicle information interms of human–computer interaction (HCI).

Past literature on visual complexity in HCI have beenstudied based on perceived visual complexity (PVC) (Dong,Ling, & Hua, 2007; Geissler, Zinkhan, & Watson, 2006).PVC represents the level of subjective awareness of visualcomplexity. However, these studies had some limitations.They used a concept of visual complexity that did notconsider the theoretical basis of such complexity.Moreover, most studies have dealt with the perception of

visual information in static task domains, such as webpagecomplexity (Tuch, Bargas-Avila, Opwis, & Wilhelm, 2009;Tuch, Presslaber, Stöcklin, Opwis, & Bargas-Avila, 2012)and software complexity (Goldberg, 2014). Few studieshave attempted to investigate factors affecting visual com-plexity during interactions with a dynamic task domain(Cummings, Sasangohar, Thornburg, Xing, & D’Agostino,2010a, 2010b; Xing, 2004, 2007; Xing & Manning, 2005).

Furthermore, the level of visual complexity increases asever more advanced IVIS are used. Therefore, it is necessaryto define visual complexity based on complexity theory andthe theory of human information processing for in-vehicleinformation displays.

In this article, we propose a framework to predict thePVC of in-vehicle information displays, and examine therelationship between PVC and behaviors of human opera-tor, such as visual search task performances and eye move-ment behavior.

In the following, we first review related studies on visualcomplexity, and then derive the proposed framework to pre-dict PVC. Based on this, we report how we design experi-ments to verify the reliability of the framework for predictingPVC and to find relationships between PVC and the visualbehavior of human operators. We also present the results ofsurvey questionnaires, visual search tasks, and the glancebehavior of human drivers. We close with a discussion andconclusion.

CONTACT Yong Gu Ji yongguji@yonsei.ac.kr Department of Information and Industrial Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu,Seoul 120-749, Korea.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/hihc.

INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION2016, VOL. 32, NO. 8, 654–664http://dx.doi.org/10.1080/10447318.2016.1184546

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2. Literature Review

2.1. Definition of Visual Complexity

Even though numerous previous studies have investigatedvisual complexity, it remains a difficult concept to define. Itsdefinition can differ according to the focus of any study.Edmonds (1995) defined complexity as “that property of alanguage expression which makes it difficult to formulate itsoverall behavior, even when given almost complete informa-tion about its atomic components and their inter-relations.”However, this is too general a definition to explain visualcomplexity. Heaps and Handel (1999) and Oliva et al.(2004) proposed defining visual complexity as “the degree ofdifficulty in providing a verbal description of an image.” Thisdefinition can explain the visual complexity of general visualstimuli, such as pictures, paintings, or images, but has limitedvalue in defining visual complexity in terms of HCI.

The definition of visual complexity in HCI should includethe perspective of human information processing. Moreover,the level of visual complexity can differ according to each user’sphysical or cognitive characteristics. Edmonds’s (1995) defini-tion of complexity makes sense only when considered relativeto a given observer. Grassberger (1991) showed that the level ofvisual complexity is not directly proportional to visual com-plexity. In general, definitions of complexity are built based onkey characteristics related to the research domain because it isdifficult to incorporate the concept of complexity into a mean-ingful single sentence (Liu & Li, 2012). Thus, we also definecomplexity by including such key characteristics.

2.2. Framework for Assessing PVC

Several researchers have investigated the general concept anddefinitions of visual complexity. Heylighen (1999) related theperception of complexity to a diversity of visual stimuli, and avisual pattern is seen as complex insofar as its parts aredifficult to identify and distinguish from other things.However, when the parts are treated as a whole, the level ofcomplexity decreases. The perspective of Drożdż, Kwapień,Speth, and Wojcik (2002) is similar to that of Heylighen(1999). They considered complexity to be “the middle pointbetween coherence and chaos,” and it means that the conceptof complexity is situated at somewhere between coherenceand chaos. Various studies have dealt with visual complexityusing a similar viewpoint. Tullis (1983) selected four aspects—overall density, local density, grouping, and layout complexity—as factors of display complexity. Edmonds (1995) consid-ered it “the difficulty to formulate an overall behavior withgiven atomic components and their inter-relations,” andLangton (1992) called it a “level of mutual information,which measures the correlation between information at sitesseparated by time and space.” Halford, Wilson, and Phillips(1998) described relational complexity as “the number ofinteracting variables that must be presented in parallel toperform a process entailed in a task.”

Numerous studies have also suggested measurements ofcomplexity. Michailidou, Harper, and Bechhofer (2008)selected the number of images, visible links, words, and variety

of colors as measurements of webpage complexity. Oliva et al.(2004) proposed the quantity of objects, detail, and color ascomplexity measures of a visual scene. Cummings et al. (2010b)suggested that the complexity of a human–system interfaceconsists of the number of displays, icons, alarms, and sharedcontrol devices, the variety of fonts, icons, colors, alarms, anddisplays, clutter, etc. Many other studies have shown a correla-tion between the number of information objects, the variety ofcharacteristics, and visual complexity (Dong et al., 2007;Geissler, Zinkhan, & Watson, 2001; Harper, Michailidou, &Stevens, 2009; Ivory, Sinha, & Hearst, 2001). Relational aspectssuch as layout, density, and clutter are also associated withvisual complexity (Dong et al., 2007; Lindgaard, Fernandes,Dudek, & Brown, 2006; Rosenholtz, Li, & Nakano, 2007). Wehave reviewed general concepts, definitions, theories, and mea-sures from several types of complexity studies. Although thesehave focused on different aspects of complexity, they shareseveral conceptions. Fundamentally, each definition of visualcomplexity is associated with three core factors: quantity, vari-ety, and relation. Therefore, we define visual complexity as thelevel of a human operator’s awareness of complexity based onquantitative visual factors, variety-based visual factors, andrelational visual factors, and propose a framework for PVCusing these factors.

Quantity: Visual information consists of objects perceivedby humans. Humans cannot perceive visual information with-out basic visual objects, and a greater number of objects ofinformation increase the level of visual complexity. Therefore,the quantity of basic visual objects is an essential factoraffecting visual complexity.

Variety: Visual complexity relates to variety in terms ofHCI, and is often also called diversity. It is defined as thedegree of similarity and difference in the characteristics ofvisual objects. Irrespective of the number of objects, increas-ing variety makes it harder to perceive visual information.According to Treisman and Gelade (1980), visual perceptionoccurs in two stages. In the first stage, such characteristics ofvisual objects as number, shape, and color are separatelycoded, and are combined in the second stage. Therefore,increasing variety makes it more difficult for human operatorsto perceive visual information.

Relation: The relationship among visual objects also affectsvisual perception. In other words, the form of information isrelated to visual perception. For example, a group of visualobjects is sometimes perceived as one organized object(Palmer, 1999; Wertheimer, 1938). The organizational formatof visual objects, such as density, grouping, and layout, influ-ences the perception of visual information (Tullis, 1983).

We are not the first researchers to analyze visual complex-ity as a function of quantity, variety, and relation. Xing (2004,2007) and Xing and Manning (2005) proposed 3 × 3 matricesof information complexity composed of three factors (quan-tity, variety, relation) and three stages of information proces-sing (perception, cognition, action). However, Xing (2004,2007) and Xing and Manning (2005) did not focus on thebehavior of human operators. Hence, the relation betweenPVC and behavior has not yet been investigated in the litera-ture. Therefore, our study seeks to explore this relationship

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and its applicability to visual information displays, especiallyinstrument displays.

2.3. Effect of Visual Complexity on Preference

Various studies have dealt with the effect of PVC on aspectsof human emotions. In particular, they have focused on PVCas a factor influencing perceived usability, preference, or aes-thetics. An example of this is Berlyne’s (1974) finding thatpeople prefer a moderate level of visual complexity. Based onthese results, many researchers have investigated the relation-ship between visual complexity and perceived aesthetic pre-ference (Geissler et al., 2006; Kaplan, Kaplan, & Wendt, 1972;Michailidou et al., 2008; Pandir & Knight, 2006; Reinecke &Gajos, 2014; Reinecke et al., 2013; Tuch, Bargas-Avila, &Opwis, 2010). However, as stated earlier, most studies havefocused on static situations, such as the complexity of awebpage in a desktop environment. According to Böhm(2003), emotional responses change according to the environ-mental risk. Therefore, it is expected that the effect of visualcomplexity on preference also differs according to the taskenvironment. However, very few studies have investigated thelevel of preference of PVC in the context of IVIS, thus makingthis an area where further research is needed.

3. Methodology

The experiment was designed to achieve the following objec-tives: (1) identifying the role of three factors (quantity, variety,relation) on the PVC of an instrument cluster; (2) examiningthe relationships among PVC, visual search performance, andeye movement data; and (3) identifying the effects of the PVCof an instrument cluster on user preferences.

3.1. Stimuli

We collected 56 cluster images via Google Images by usingcombinations of terms such as “cluster,” “instrument panel,”and “dashboard.”We then selected 30 of these images accordingto the following criteria: high resolution, not overlap in informa-tion, no highly similar forms, and containing various featuresand objects. In order to control for the effects of familiarity, weincluded images from 20 different motor companies. The

collected images were converted to JPEG format and equalizedto 928 × 354 pixels. We selected high-resolution images, so thatwe could resize them without blurriness or loss of contour.Examples of the visual stimuli are shown in Figure 1.

3.2. Apparatus

An eye-tracking system (SMI RED-m device, SensoMotoricInstruments (SMI), Teltow, Germany) was used to record eyemovement data. We used the SMI Experiment Center soft-ware (SMI, Teltow, Germany) to present visual stimuli andcollect raw data for visual search performance (efficiency andaccuracy), and SMI BeGaze software (SMI, Teltow, Germany)to analyze experimental data. The eye tracker was attached toa 24-inch monitor on which visual stimuli were presented. Itwas connected to a laptop to obtain data, and manipulate anddesign the experiment. The monitor was located at an appro-priate distance, similar to that in a standard vehicle. Another24-inch monitor and a laptop computer were used to collectquestionnaire information.

3.3. Questionnaires

In this study, we designed questionnaires to collect informa-tion to assess the PVC of visual stimuli. Such questionnaireshave been used in past research to assess visual complexity,and we developed our questionnaire with reference to thiswork (Heaps & Handel, 1999; Ling, Lopez, & Shehab, 2013;Tuch et al., 2012; Xing, 2008). We conducted the question-naire in Korean because all participants were Korean.A double-check process was conducted to validate theKorean version of the questionnaire. Two researchers firsttranslated an English questionnaire into Korean. Anotherresearcher then retranslated the Korean questionnaire intoEnglish. We confirmed that the meanings of all items wereidentical with minor differences in wording.

The questionnaire contained 4 parts (overall PVC, quan-tity, variety, and relation) divided across 13 items rated ona 7-point Likert scale (3 items for each part and 1 questionfor preference), and was used to assess visual stimuli(Appendix A). A higher score indicated a greater level ofPVC for each factor. Of the 13 items, 2 reverse-scoreditems were used to detect untruthful responses.

Figure 1. Examples of visual stimuli used in experiment.

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3.4. Participants

Fifty-four participants (24 males, 30 females) between 20 and71 years of age (M = 40.5, SD = 14.1) with driving experiencetook part in our study. There were 14 participants in their 20s,12 participants in their 30s and 40s, 10 participants in their50s, and 6 participants in their 60s. All participants hadnormal or corrected-to-normal vision, and the average fre-quency of driving was 3.01 times per week (SD = 1.8).

3.5. Experimental Design of Visual Search Task

Visual search tasks were conducted to identify the relationshipbetween human behavior and PVC. Our experiment wasdesigned based on past research (Tuch et al., 2009; Yoon,Lim, & Ji, 2015a, 2015b). The relevant studies had used astar-shaped figure as the search target. However, we used acircled digit range (numbered from 1 to 5) instead (Figure 1).According to Oliva et al. (2004), the perception of imagecomplexity is related to familiarity with a given scene andknowledge of objects inside the scene. Therefore, a star-shaped figure can affect the level of complexity because it isnot part of an ordinary cluster display, but a circled digit issimilar to the information expected therein.

Participants were asked to find the target within the visualstimuli. Each participant performed 90 trials (3 trials perstimulus) to prevent contamination from aspects of visualsearch other than those related to visual complexity factors.Every target number appeared 18 times, and was embedded inthe stimulus in areas balanced for the left side, the center, andthe right side. To prevent ordering effects, the 90 stimuli werepresented in random order. Visual search task performanceand eye-movement data were collected.

We had several reasons to design the experimental settingswithout considering actual driving context. First, we wantedparticipants to evaluate visual complexity based on their per-ception process. However, we doubted that if we considerdriving context, the results of evaluation on PVC can beinfluenced by high cognitive load from driving task. Second,although the digitization of in-vehicle display reduces con-straints on the presentation of information, it allows thedesigner to organize the information format more freelythan otherwise. However, the layout did not continuouslychange during the search task in our experiment. Therefore,we used static images for the visual search task. Third, wewere concerned that if we had used actual information iconsfor the search target, the results could have been influenced bythe expectations of the participants. Therefore, we wanted tocontrol for these negative effects by using the task of finding acircle number as the search task. We expected to be able toobserve the participants’ visual search behavior in response tothe in-vehicle information display, though this task was notidentical to the actual task.

We used time-on-task as performance measure, and fixa-tion and entry time were measured for glance behavior. Weexpected time-on-task to increase according to increasinglevels of visual complexity. However, we did not use a successrate for the visual search task because the percentage ofcorrect answers was more than 99%. We only used a success

rate to check for any unreliable participants. The number offixations was used to evaluate glance behavior when partici-pants were required to find the search target, and entry timewas used to check the strategies of participants to perform thetask. Entry time was defined as the time taken to record thefirst fixation.

3.6. Procedure

A pilot study involving four participants was conducted to testthe entire experimental procedure. Based on the pilot study,several minor aspects of the overall procedure and tasks weremodified. The experiment consisted of three parts and lastedapproximately 2 hr. Before the start of the experiment, weexplained the overall procedure and checked the qualificationsof the participants. Half the participants performed the visualsearch task first and the other half completed the question-naire first in order to prevent ordering effects among theparts.

The participants received an explanation of the visualsearch task and completed a practice session prior to themain session, including the calibration of the eye tracker.Following the 30th and 60th trials, the participants took abreak for 1 min to prevent eye fatigue while the eye-trackerwas recalibrated. They were asked to push the spacebar on thekeyboard as soon as they found the target within the stimulus,upon which an answering page was shown to select the targetnumber (Figure 2).

For the questionnaire, the participants responded to 13items for each stimulus. Thirty stimuli were presented inrandom order to prevent ordering effects. After completingthe 10th and 20th stimuli, the participants took a 1 min break.

4. Results

4.1. Questionnaire Results

A total of 1,620 data points for each item of the questionnairewere collected. We first checked the reliability of the ques-tionnaire items for quantity, variety, and relation. Internalconsistency was estimated for every questionnaire item usingCronbach’s alpha coefficient, and yielded the followingresults: 0.929 for quantity items, 0.920 for variety items,0.836 for relation items, and 0.930 for overall PVC items. Ingeneral, an alpha value of 0.7 or above is considered accep-table (Nunnally, Bernstein, & Berge, 1967); thus, these valueswere sufficiently high to establish the reliability of ourquestionnaire.

Based on the average score of the overall PVC items, wedivided the visual stimuli into three groups (high, medium,and low) according to level of PVC (LoC). Results for PVClevels showed that the average value for the low group was2.42, that for the medium group was 3.46, and the averagevalue for the high group was 4.83. The results of a one-wayanalysis of variance (ANOVA) showed that there were sig-nificant differences between groups (df = 2, F = 444.309,p < 0.01).

We analyzed the perceived level of quantity, variety, andrelation as well as differences in these levels according to

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gender and age; furthermore, we investigated the relationshipbetween PVC level and these three factors.

4.2. Model for Predicting PVC

The results of the correlation analysis showed that the per-ceived level of quantity, variety, and relation were significantlycorrelated with PVC (p < 0.01). Therefore, we propose anestimation model to predict PVC that was composed of thesethree factors. All factors were highly significant in the estima-tion model (Table 1). The variance inflation factor values werelower than 10 (3.649, 3.933, and 2.218); hence, collinearity wasnot a problem between the independent variables. The R2

value was 0.770, which meant that 77% of the variation inPVC was explained by our estimation model of quantity,variety, and relation. Thus, we propose the following estima-tion model to predict PVC using regression analysis:Perceived visual complexity = 3.603 + 0.315 × (quantity) +0.226 × (variety) − 0.491 × (relation)

4.3. Effect of Gender and Age on Level of PVC

A two-way ANOVA was conducted to analyze quantity,variety, relation, and PVC according to gender and age.The overall results are shown in Table 2. Gender affectedquantity and variety scores, whereas age influenced only the

quantity score. However, a significant interaction effect ofage and gender was found for each variable (Table 3).

4.4. Visual Search Task Results

A visual search experiment was designed to examine the effectof overall PVC on visual search task performance and glancebehavior. Participants were asked to find the circled digitnumber in a visual stimulus with varying levels of visualcomplexity.

We did not set a time limit during the visual search task,because of which the rate of correct answers was higher than99%. We checked the answer rate to detect unreliable partici-pants. Given that a high answer rate was obtained, we con-firmed that most participants accurately performed the visualsearch task. However, several unexpected problems arose.Participants sometimes found the search target without hesi-tation because it happened to be within the location of theirfirst glance behavior at the stimulus. At other times, partici-pants were unable to find the target even though their gazeshad passed over it. Therefore, we excluded from the analysisseveral cases where it took less than 1 s or more than 14.87 sto find the target. The threshold value (14.87 s) was selectedbased on mean time plus three standard deviations. We alsoremoved three stimuli because instant detection and oversightof the stimulus occurred more frequently with these threevisual stimuli.

4.5. Relationship between PVC and Time-on-Task

We first conducted a correlation analysis between PVC andtime-on-task, followed by a regression analysis. The resultsof the correlation analysis showed that the following threefactors were significantly correlated with time-on-task:quantity (r = 0.771, p < 0.01), variety (r = 0.708,p < 0.01), and relation (r = –0.779, p < 0.01).

Figure 2. Experimental environment.

Table 1. Regression model for PVC.

Factors

Unstandardizedcoefficient

Standardizedcoefficient t pBeta

Standarderror

Intercept 3.603 0.155 23.253 0.001***Quantity 0.315 0.022 0.326 14.302 0.001***Variety 0.226 0.023 0.229 9.698 0.001***Relation −0.497 0.021 −0.407 −22.881 0.001***

***p < 0.001.

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Accordingly, these factors were reliable for predicting thetime-on-task. However, the results of the regression analysisshowed that these three factors did not directly account forthe time-on-task due to multicollinearity problems.Therefore, time-on-task was directly explained by thePVC, and not by the three factors. The level of PVC wasstrongly correlated with time-on-task (r = 0.764, p < 0.01).

4.6. Effects of Gender, Age, and PVC on Time-on-Task

A three-way ANOVA was conducted using the three inde-pendent variables of gender, age, and level of PVC groups. Asshown in Table 4, significant differences were found accord-ing to gender and age. The average length of time-on-task was4.18 s for men and 4.54 s for women. The average value oftime-on-task was 2.99 s for people in their 20s, 3.27 s forpeople in their 30s, 4.32 s for people in their 40s, 5.65 s forpeople in their 50s, and 7.85 s for people in their 60s(Figure 3).

4.7. Glance Behavior Results

We were mainly interested in the effect of level of PVC onglance behavior. Therefore, we analyzed several eye move-ment metrics, such as number of fixations, entry time, andscan path. Data from three participants were excluded due tolow eye-tracking ratios (below 90%).

4.8. Effect of Gender, Age, and Level of PVC on Numberof Fixations

A three-way ANOVA with Scheffé’s post-hoc test was alsoconducted to analyze the effects of gender, age, and level ofPVC on the number of fixations (Table 5, Figure 4). Theresults showed that gender differences did not significantlyaffect number of fixations, but the main effects of age andlevel of PVC were found to be significant. The interactioneffects of gender and age were also significant. The averagevalue for each group was 11.13 for people in their 20s, 12.23for people in their 30s, 16.47 for people in their 40s, 18.07 forpeople in their 50s, and 24.70 for people in their 60s. Theresult of Scheffé’s post-hoc test revealed that an increasinglevel of PVC significantly influenced the number of fixations.Three different groups were found using Scheffé’s post hoctest. The first group included people in their 20s and 30s, thesecond group included people in their 40s and 50s, and the

Table 3. Results of two-way ANOVA of the three factors and PVC.

DV Factors SS df MS F p η2

Quantity Gender 19.25 1 19.25 6.58 0.010* 0.04Age 41.53 4 10.38 3.55 0.007** 0.09Gender × Age 29.27 4 7.32 2.50 0.041* 0.06

Variety Gender 25.48 1 25.48 9.04 0.003** 0.06Age 16.04 4 4.01 1.42 0.224 0.04Gender × Age 38.55 4 9.64 3.42 0.009** 0.08

Relation Gender 0.69 1 0.69 1.52 0.218 0.02Age 2.28 4 0.57 1.25 0.287 0.05Gender × Age 4.31 4 1.08 2.37 0.05* 0.25

PVC Gender 2.46 1 2.46 0.90 0.343 0.01Age 15.64 4 3.91 1.43 0.221 0.04Gender × Age 33.36 4 8.34 3.05 0.016* 0.08

*p < 0.05, **p < 0.01.

Table 2. Descriptive statistics of the three factors and PVC.

20sMean (SD)

30sMean (SD)

40sMean (SD)

50sMean (SD)

60sMean (SD)

OverallMean (SD)

Quantity scoreMale 4.14 (1.60) 4.01 (1.71) 3.83 (1.92) 3.92 (1.76) 3.17 (1.81) 3.91 (1.77)Female 4.00 (1.68) 4.17 (1.67) 3.87 (1.78) 4.21 (1.70) 3.99 (1.46) 4.05 (1.68)Overall 4.06 (1.64) 4.09 (1.69) 3.85 (1.85) 4.10 (1.73) 3.72 (1.63) 3.99 (1.72)

Variety scoreMale 3.86 (1.55) 3.78 (1.67) 3.74 (1.89) 3.72 (1.78) 3.21 (1.59) 3.74 (1.72)Female 3.69 (1.61) 3.96 (1.71) 3.78 (1.69) 4.22 (1.66) 4.02 (1.58) 3.91 (1.66)Overall 3.76 (1.58) 3.87 (1.69) 3.76 (1.79) 4.02 (1.73) 3.75 (1.62) 3.83 (1.69)

Relation scoreMale 4.00 (0.66) 4.14 (0.66) 4.11 (0.78) 4.16 (0.66) 4.26 (0.61) 4.11 (0.69)Female 4.19 (0.62) 4.19 (0.71) 4.25 (0.58) 4.10 (0.72) 4.17 (0.70) 4.18 (0.66)Overall 4.11 (0.64) 4.16 (0.68) 4.18 (0.69) 4.13 (0.70) 4.20 (0.67) 4.15 (0.68)

PVC scoreMale 3.74 (1.57) 3.59 (1.71) 3.71 (1.96) 3.58 (1.72) 3.41 (1.50) 3.64 (1.73)Female 3.54 (1.56) 3.49 (1.75) 3.14 (1.43) 3.83 (1.65) 3.61 (1.55) 3.52 (1.60)Overall 3.63 (1.56) 3.54 (1.73) 3.42 (1.74) 3.73 (1.68) 3.54 (1.53) 3.57 (1.66)

Table 4. Results of three-way ANOVA of time-on-task.

DV Factors SS df MS F p η2

Time-on-task

Gender 71.436 1 71.436 7.156 0.008*** 0.00Age 3053.112 4 763.278 76.465 0.001*** 0.16LoC 432.044 2 216.022 21.641 0.001*** 0.03Gender × Age 399.083 4 99.771 9.995 0.001*** 0.03Gender × LoC 13.195 2 6.597 0.661 0.517 0.00Age × LoC 58.512 8 7.314 0.733 0.663 0.00Gender × Age ×LoC

14.607 8 1.826 0.183 0.993 0.00

Note. LoC: Level of complexity.***p < 0.001.

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last group included people in their 60s. Participants’ numberof fixations increased with age.

4.9. Effect of Area of Interest on Number of Fixations andEntry Time

Nine equal-sized areas of interest (AOIs) were set accordingto space-based divisions in visual stimuli. Figure 5 shows thenames of the AOIs (left), and examples of the scanpath(right). A one-way ANOVA with Scheffé’s post-hoc test wasperformed to analyze the effect of the AOI. The results

showed that the number of fixations (F = 258.139,p < 0.001) and entry time (F = 56.079, p < 0.001) weresignificantly different according to the AOI (Table 6).

4.10. Preference

We analyzed the effect of PVC for IVIS on user preferenceusing regression analysis (Figure 6). Preference was found tohave a strong negative correlation with PVC (r = 0.776,p < 0.001), and 90.4% of the variance was explained by thismodel.

Preference ¼ �0:78� PVCð Þ þ 6:44; R2 ¼ 0:904

5. Discussion

The objectives of this study were to develop a framework forpredicting PVC in vehicle displays, investigate the relationshipbetween PVC and a human operator’s actual visual behavior,and determine the effects of PVC on preference.

First, we found that PVC can be explained by the proposedmodel, which includes quantity, variety, and relation. Most

Figure 3. Time-on-task based on gender, age, and PVC.

Table 5. Results of three-way ANOVA of number of fixations.

DV Factors df F p

Number of fixations Gender 1 1.010 0.315Age 4 52.276 0.001***LoC 2 62.494 0.001***Gender × Age 4 7.904 0.001***Gender × LoC 2 0.562 0.570Age × LoC 8 1.622 0.113Gender × Age × LoC 8 0.325 0.957

Note. LoC: Level of complexity.***p < 0.001.

Figure 4. Number of fixations based on gender, age, and PVC.

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previous studies have focused on the correlation between eachcomplexity metric and overall visual complexity. The resultsof this study corresponded with those of past research (Donget al., 2007; Geissler et al., 2006; Harper et al., 2009; Ivoryet al., 2001; Michailidou et al., 2008; Oliva et al., 2004).However, we did not merely select complexity factors, butalso suggested an estimation model for predicting the levelof PVC. As expected, we found a significant relationshipbetween PVC and the three factors. The perceived levels ofquantity and variety were proportional to the level of PVC,whereas relation factors were inversely proportional to thelevel of PVC. These results are related to information proces-sing in human operators (Palmer, 1999; Treisman & Gelade,

1980). Furthermore, these findings indicate that visual com-plexity theories are applicable not only to general images orwebpages but also to IVIS.

Second, our findings confirmed that PVC significantly affectsvisual search tasks and glance behavior. As the PVC increased,participants had greater difficulty in finding the target. Theseresults were in close agreement with a previous study by Tuch etal. (2009). It took a long time to find the target when the level ofvisual complexity was high, especially for older participants ofour experiment. Thus, the risk of traffic accidents can increase ifdrivers need more time to find the information they seek. It hasbeen widely recognized that driver distraction leads to trafficaccidents (Horberry, Anderson, Regan, Triggs, & Brown, 2006;Sagberg, 2001; Strayer & Johnston, 2001). Therefore, the effi-ciency of visual search tasks is directly related to driving safety,which highlights the importance of designing IVIS well. PVCvaries with organization strategies, even when displays have asimilar amount of information and variability in characteristics,based on the result that the factor of relation is negativelycorrelated to PVC.

The results of eye movement analysis indicated the following.First, the fixation behavior of human operators was influencedby level of PVC. These results were similar to those of priorstudies (Harbluk et al., 2007; Tuch et al., 2012). Second, thevisual search pattern we identified in human operators wasalso similar to those in previous studies. The direction used

Figure 6. Scatterplot of the relationship between PVC and preference.

Table 6. Results of one-way ANOVA of number of fixations and entry time.

AOI

Dependent variables

Number of fixations Entry time (ms)

U1 0.82b 630.79b

U2 1.21c 755.87b

U3 0.58a 814.67c

M1 2.41e 538.89a

M2 2.59e 587.04a

M3 1.90d 1002.89d

D1 1.89d 837.14c

D2 1.91d 949.41c

D3 1.71d 1033.22d

Note. For each area of interest (AOI), values with different alphabet superscriptswere significantly different at p < 0.05.

Figure 5. AOI settings (left) and examples of scanpath (right).

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when searching for information is related to reading habits(Chokron & De Agostini, 1995). All participants of our experi-ment were Korean. Korean is read from left to right and top tobottom. Therefore, the operators mainly scanned informationfrom the upper-left side to the lower-right side. These findingssuggest several design considerations for positioninginformation.

Third, many researchers have examined the relationshipsbetweenPVCandpreference. There are two types of results relatedto this issue. Many studies have documented that people prefer abalanced level of visual complexity (Böhm, 2003; Geissler et al.,2006; Kaplan et al., 1972;Michailidou et al., 2008; Pandir&Knight,2006; Reinecke & Gajos, 2014; Reinecke et al., 2013; Tuch et al.,2010). These studies, which found an inverted U-shaped relation-ship between visual complexity and preference, mainly focused onstatic task environments. However, the results of several studies,including ours, are inconsistent with these results (Tuch et al.,2012). Our findings indicated that level of PVC was negativelyproportional to preference. Reinecke et al. (2013) explained thatthese controversial results are due to differences in domain char-acteristics. According to Reinecke et al. (2013), it is necessary toidentify the characteristics that contribute to perceived complexity.Therefore, the different results were due to the characteristics ofIVIS. In static situations, people tend to prefer a moderate levelover a high level of visual complexity because it is more aestheti-cally attractive, and moderate complexity is related to perceivedusability, trust, credibility, and impression (De Angeli, Sutcliffe, &Hartmann, 2006; Hassenzahl & Monk, 2010; Robins & Holmes,2008; Schenkman & Jönsson, 2000). However, preference formoderate complexity changes when people perform tasks indynamic environments, such as driving. In such cases, the effi-ciency of the task is directly related to safety issues. Therefore, cardashboard cluster instruments should be designed as simply aspossible.

6. Conclusion

In summary, this study was conducted to evaluate IVIS byapplying the theoretical concept of visual complexity. Theresults of our study have several academic and practical impli-cations. First, we developed a framework to predict PVC basedon the underlying theory of complexity, thereby contributing toHCI, cognitive engineering, and visual display research.Second, we analyzed the relationship between PVC andhuman behavior by providing findings from collected eye-tracking data. Considering the importance of understandingthe interaction between the driver and an IVIS, it is meaningfulto analyze the impact of complexity on glance behavior.Moreover, we analyzed the influence of gender and age, andthe results showed that their effects are significant. Therefore,not only do these findings make academic contributions, theyalso have practical uses, such as guiding information displaydesign. A designer should consider the fact that the level ofPVC is correlated with a driver’s glance behavior.

In this study, participants were asked to find a searchtarget. Even though visual search performance significantlydiffered between groups, we think that the task was quite easy.Therefore, further studies are needed to understand the

impact of PVC on drivers in actual driving situations inorder to elaborate on the results of this study.

Funding

This work was supported by Mid-Career ResearcherProgram through NRF grant funded by the MSIP (Ministryof Science, ICT, and Future Planning) (grant number NRF-2013R1A2A2A03014150).

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About the Authors

Seul Chan Lee is a PhD candidate in the Department of Information andIndustrial Engineering at Yonsei University, Korea. His research interestsinclude HCI and human factor issues in vehicle environment and smartdevices.

Hwan Hwangbo is a PhD candidate in the Department of Information andIndustrial Engineering at Yonsei University, Korea. His research interestsinclude user experience, user interface design, and human factors.

Yong Gu Ji is a professor in the Department of Information and IndustrialEngineering at Yonsei University, Korea, where he directs the InteractionDesign Laboratory. He received his PhD in industrial engineering fromPurdue University. His research interests include usability/user experiencein ICT, and HCI issues in smart devices and self-driving vehicles.

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Appendix A

Questionnaire Items

Dimension Items Reliability

Quantity Q1. The amount of visual elements (texts, symbols, icons, graphical patterns, etc.) makes me distracted and confused. α ¼ 0:929Q2. Instrumental display has too many visual elements (texts, symbols, icons, graphic patterns, etc.).Q3. It will be less complicated if the instrumental display reduces the amount of visual elements.

Variety V1. The variety of visual features (colors, shape, size, etc.) makes me distracted and confused. α ¼ 0:920V2. Instrumental display has too many visual features.V3. It will be less complicated if the instrumental display uses similar visual features.

Relation R1. The organization of visual information helps me to find information. α ¼ 0:836R2. Visual elements of instrumental display are arranged without principles.R3. Visual information of instrumental display is well organized.

Overall PVC O1. Overall, instrumental display has a lot of various visual information with disordered arrangement. α ¼ 0:930O2. Overall, instrumental display looks very complicated.O3. Overall, instrumental display makes me distracted and confused.

Preference P. I prefer this instrumental display. –

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