the role of visual complexity in affective reactions to webpages: subjective, eye movement, and...

Short Papers___________________________________________________________________________________________________

The Role of Visual Complexity in AffectiveReactions to Webpages: Subjective, Eye

Movement, and Cardiovascular Responses

Alexandre N. Tuch, Sylvia D. Kreibig,Sandra P. Roth, Javier A. Bargas-Avila,

Klaus Opwis, andFrank H. Wilhelm

Abstract—In this study, we tested whether the visual complexity (VC) of

webpages influences viewer’s affective reactions. In a laboratory experiment,

48 students viewed 36 webpages varying in VC while subjective feelings,

behavioral, and cardiovascular responses were recorded. Less complex

webpages were associated with more positive affect, decreased eye movements

(specifically in the first few seconds of viewing), a triphasic heart rate response,

and increased finger pulse amplitude. Results suggest that affective responses to

webpage viewing differ as a function of VC and that webpage displaying could be

made adaptive to the user’s emotions.

Index Terms—Psychophysiology, affective computing, multicomponent

approach, arousal, valence.

Ç

1 INTRODUCTION

HOW do user interfaces influence the affective experience of theuser? And could user interfaces become sensitive and adaptive touser’s affective reactions?

Interactions between humans and machines have becomecommonplace in our modern society. Webpages, consisting oftext, images, and other media elements, represent one form of auser interface that is increasingly used as a gateway to information,entertainment, and social networking. As server or client-sidesoftware (e.g., JavaScript, AJAX) allows for dynamic creating andupdating of webpages, it would be desirable for such interfaces toalso be sensitive and adaptive to the user’s affective experience.That is, if user responses could be identified that relate to affectiveexperiences during webpage viewing, such information could beused to automatically adapt currently or subsequently viewedwebpages and by this optimize the viewing experience. Thepresent paper seeks to contribute to this idea by demonstrating1) that a perceptual feature of webpages—visual complexity(VC)—influences affective experience and 2) that unobtrusivelymeasured behavioral and physiological responses vary with VC ofwebpages as well as the user’s affective experience.

VC has long been recognized as playing a central role inperceiving visual displays—specifically with respect to the questionof what is visually pleasing to a viewer [1]. VC refers to theamount of details in a display. It is supposed to determine the easeof processing at or before the structural stage of object recognition.VC of a display depends on the amount of constituent elementsand the diversity of these elements. In relation to our use case, thismeans that the more the single elements perceived on a webpage,the higher the subjective impression of complexity of this page [2].

VC has been shown to affect evaluative responses [3], [4], suchas appraisal of novelty, and emotional responses, such as interestor frustration [5]. Emotions are typically conceptualized as multi-component responses, elicited by appraising an event as relevantto an individual’s goals, needs, or values, with coordinated effectson subjective feeling, expressive behavior, and physiologicalresponses that evolve over time and may thus exhibit a time-varying response [6], [7]. Emotions, in the context of webpageperception, primarily fall under what Scherer [8] calls aestheticemotions—emotions that are elicited by the aesthetic appreciationof the intrinsic qualities of a visual or auditory stimulus, based onforms and relationships. Aesthetic emotions depend on thesubjective success of the information processing and are oftenpositive, such as pleasure or interest, but can also be negative incase of unsatisfactory processing, such as frustration or annoyance[9]. Aesthetic emotions are suggested to be of lower intensity andcharacterized by an absence of appraisals concerning goalrelevance and coping potential [8]. Still, as for other emotions,specific subjective feelings, expressive behavior, and physiologicalresponses can be identified for aesthetic emotions [10].

In the following, we review effects of VC on the three responselevels of emotions: For subjective feelings, we address aestheticappraisals of perceived pleasantness, arousal, and interest/appeal;for expressive behavior, we focus on active interaction with thestimulus, as indicated by eye movement behavior; and forphysiological responses, we discuss cardiovascular activity as anindicator of affective reactions to webpages.

1.1 Subjective Feelings

Berlyne proposed that VC and aesthetic appraisals are related inform of an inverted U-shaped curve: Stimuli of moderate VC areperceived as most pleasant, whereas stimuli of low or high VC areperceived as unpleasant [3], [4], [11]. In contrast, an information-processing stage model of aesthetic perception [9] suggests aninverse relation between VC and positive affective responses: Asless complex visual stimuli are processed more fluently, ease ofinformation processing will result in more positive aestheticjudgments toward simpler stimuli [12], [13]. This relation has alsobeen demonstrated in photographs in a study by Ochsner [14],where a negative relation of perceived VC with subjectivepleasantness and a positive relation with subjective arousal hadbeen found.

But also, VC of human-machine interfaces can have aninfluence on the viewers’ affective state. An early set of studies[15], [16] demonstrated an influence of webpage complexity onusers’ attitudes toward the webpage. The authors found a directnegative effect of webpage VC such that viewers have a morenegative attitude toward more complex webpages and an indirectpositive effect such that the page’s interestingness was found toincrease with increasing webpage VC, and interestingness, inturn, had a positive impact on attitudes toward the site.

More recent research reports evidence both for an inversecurvilinear relationship or a negative relation between webpageVC and affective responses: Geissler et al. [2] demonstrated thatconsumers respond more favorably (as indicated by a morepositive attitude and higher interestingness ratings) in response

230 IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. 2, NO. 4, OCTOBER-DECEMBER 2011

. A.N. Tuch, S.P. Roth, J.A. Bargas-Avila, and K. Opwis are with theDepartment of Cognitive Psychology and Methodology, Institute ofPsychology, University of Basel, Missionsstrasse 62A, Basel CH-4055,Switzerland.E-mail: {a.tuch, sandra.roth, javier.bargas, klaus.opwis}@unibas.ch.

. S.D. Kreibig is with the Department of Psychology, Stanford University,450 Serra Mall, Bldg 420, Stanford, CA 94305-2130.E-mail: [email protected].

. F.H. Wilhelm is with the Department of Clinical Psychology, Psychother-apy, and Health Psychology, Institute of Psychology, University ofSalzburg, Hellbrunnerstrasse 34, Salzburg A-5020, Austria.E-mail: [email protected].

Manuscript received 7 July 2010; revised 8 Mar. 2011; accepted 30 May 2011;published online 12 July 2011.Recommended for acceptance by H. Prendinger.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log NumberTAFFC-2010-07-0051.Digital Object Identifier no. 10.1109/TAFF-C.2011.18.

1949-3045/11/$26.00 � 2011 IEEE Published by the IEEE Computer Society

to webpages that fall in a moderate range of perceived VC. Incontrast, Pandir and Knight [17] and Tuch et al. [18] foundnegative relations between webpage VC and pleasantness ratings.

Thus, although there appears to exist a relation between VC andviewer’s affective responses, the exact nature of this relation remainsunclear, specifically in the domain of webpage perception. Theplurality of findings based on self-report measures suggests a needfor other, potentially more objective, behavioral and physiologicalindicators of affective responses, to which we now turn.

1.2 Expressive Behavior

Eye movements reflect moment-to-moment cognitive processes inthe interaction with visual stimuli and are influenced by the VCand informativeness of a visual stimulus [19]. Research has shownthat increased VC leads to increased eye movements, as indicatedby decreased mean fixation duration on individual objects [20],[21]. Increased VC has furthermore been found to lead todecreased mean saccade amplitude, meaning that the eye travelsshorter distances during a movement [21].

There is further evidence for changes in eye movement behaviorover time [22], [23]: During an initial orienting or inspection period,physically salient features of the image are scanned; in asubsequent period of scrutiny or fixation, the eyes are fixated onthe more informative areas. This suggests that informative regionsof the image are not fixated immediately, but only after an orientingperiod during which the entire picture is scanned. This is expressedin an increase of fixation duration over viewing time [22], [24] ,plateauing after 3.4 sec [21]. This plateau is reached later under highVC (4.3 sec). Average saccade size similarly decreases over time,but plateaus earlier, on average after 2 sec.

Relations between eye movements and affect remain elusive.Neither an effect of affective picture content on eye movements [25]nor a correlation between eye movements and ratings of subjectivevalence and arousal [26] has been found in prior research. Still, theremay be a link between eye movements and affective experience asexperimentally increased eye movements, such as through astimulus tracing task, reduce subjective ratings of negative emo-tional valence and decrease electrodermal activity [27], [28].

1.3 Cardiovascular Responses

Heart rate (HR) and finger pulse amplitude (FPA) are measures ofcardiovascular functioning. HR reflects changes both in sympa-thetic and parasympathetic nervous system functioning, whereasFPA is an index of blood flow, and decreases reflect vasoconstric-tion. Early research related vasoconstrictory and dilatory re-sponses to affective information processing [29]: Vasoconstrictionat the finger and vasodilation at the forehead mark the orientingresponse (OR), whereas vascoconstriction at both the finger andforehead mark the defense response (DR). The OR guides attentionand is said to enhance information intake of low to moderatelyintense stimuli, whereas the DR prepares the organism for a fightor flight response to high-intensity stimuli.

Subsequent research identified additional response compo-nents, such as HR responses. Because peripheral vasomotorchanges are a less reliable index of OR and DR than the HRresponse [30], other studies have focused on the HR response inaffective picture viewing tasks [31], [32], [33]. In these studies, HRacceleration has been found to be positively related to pleasantnessratings [34]. Positive versus negative affective responses arefurthermore marked by a time-varying HR response: The HRresponse to unpleasant pictures is characterized by a large initialdeceleration that is sustained for the 6-sec viewing interval,whereas a triphasic response characterized viewing of pleasantpictures (consisting of a deceleration during the first 2 sec, anacceleration for the subsequent 1-2 sec, and a second decelerationduring the final 3 sec [35]). The vascular response shows a mild

initial vasoconstriction (0-4sec), which is independent of affectivevalence, that continues to increase for negative affective stimuli, butmay turn into vasodilation for positive affective stimuli (5-10 sec)[36], [37]. HR reactivity during a 6-sec period following a brief500 ms picture presentation has been found to show a triphasicwaveform that is independent of picture valence, consisting of aninitial deceleration (first sec), subsequent acceleration during thenext 3-4 sec, and a second deceleration during the last 2 sec [38].

The magnitude of the OR has also been related to VC. Complexvisual stimuli evoke stronger HR deceleration than simple stimuli[39] and HR deceleration correlates positively with ratings ofpicture as attention grabbing, interesting, and unpleasant [32].Accordingly, Jennings et al. [40] concluded that an increase ofattention (e.g., complexity, uncertainty, conflict, and/or novelty)results in HR deceleration.

1.4 Aim of the Present Study

The present study examined whether VC influences affectiveresponses to webpages. We took a multicomponent approach toaffective measurement: 1) Subjective ratings regarding valence,arousal, appeal, and annoyance of webpages tap into the feelingcomponent of emotion and were measured to study the relationbetween feelings and other affective response components. 2) Eyemovement, measured with electrooculography (EOG), tracks abehavioral component of emotion that is of relevance because ofthe visual processing in this research question. 3) Assessment ofcardiovascular reactivity allows a window into the physiologicalcomponent of emotion. HR and FPA were selected because theyhave been commonly used in prior research on affective percep-tion, they are sensitive to change on a short time scale, and they arenoninvasive and easy to measure, which contributes to subjectcomfort and generalizability beyond the laboratory.

Affective responses were recorded during viewing of real-lifewebpages that differed in VC. A first set of analyses tested theinfluence of VC on feeling ratings that related to the overall viewingexperience. We predicted that increased VC of webpages would beassociated with lower pleasantness and higher arousal ratings.

Based on dynamic emotion models [6], [7], a second set ofanalyses addressed the time course of behavioral and cardiovas-cular reactivity, for which responses of webpage viewing weresubdivided into 4-sec bins. This allowed us to test whether andwhen eye movements and cardiovascular responses were asso-ciated with VC as well as with feeling ratings. We predicted highereye movement activity for the first than the second interval as wellas increased eye movement activity for high-VC webpages.

For cardiovascular reactivity, we defined an early, late, andoffset response (each of 4-sec duration). We expected a mild initialand stronger secondary HR deceleration for more complexwebpages, whereas stimulus offset should lead to HR acceleration.Vasoconstriction, as indicated by decreased FPA, was predicted inresponse to webpage onset; examination of secondary and offsetresponses were exploratory for FPA.

This paper presents further results from a study described inTuch et al. [18]. Whereas the previous study focused on usabilityaspects, the present work presents analyses of affective reactions towebpages based on subjective ratings (appeal and annoyance),behavioral (eye movements), and physiological (finger pulseamplitude) measures that were not previously reported andfocuses on the time course of these physiological responses duringwebpage perception.

2 METHOD

2.1 Participants

Forty-eight undergraduate psychology students participated inthis experiment. The sample consisted of 17 men (M ¼ 25:5 years,SD ¼ 7:7; range ¼ 20-52) and 31 women (M ¼ 21:2 years,SD ¼ 2:2; range ¼ 19-29).

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. 2, NO. 4, OCTOBER-DECEMBER 2011 231

2.2 Stimuli

Thirty-six different screenshots of existing webpages were used inthe experiment. We ensured high ecological validity of theexperiment by using real webpages. VC of webpage screenshotswas quantified as the number of bytes of the image file preservedafter digital image compression using JPEG [41], M ¼ 584 KB,SD ¼ 158 KB, range ¼ 272-864 KB. All screenshots were taken atthe same resolution (1;280� 1;024 pixels).

First, 160 webpages with a wide range of VC were selected fromthe World Wide Web according to the subjective impressions of theauthors. Selection criteria were: the content of the webpage must1) be business, news, or science related, 2) be written in German orEnglish, 3) not be familiar to Swiss students. A full screenshot ofeach webpage was made without Internet browser elements (seeFig. 1 for sample webpages).

Next, on the basis of JPEG file size and subjective impression ofthe authors, 36 of the 160 webpage screenshots were selected forthe experiment in order to obtain a wide range of VC. To validatethe JPEG file size as a proxy for subjectively perceived VC, anonline survey was implemented. In this online survey, selectedwebpage screenshots were presented to participants one afteranother. Participants were asked to rate each of the webpagesimmediately on three different complexity items (visually complex[item1], well organized [item2, inversely scored], overloaded[item3]) by means of Likert-type scales ranging from 1 (not at all)to 7 (extremely). A total of 57 participants completed the onlinesurvey. Complexity ratings for each webpage and item wereaveraged over participants so that every page finally had threeaveraged complexity scores (one for every item). All complexityitems were significantly correlated with JPEG file size (ritem1 ¼0:76, ritem2 ¼ �0:75, ritem3 ¼ 0:85). The internal consistency of thethree-item scale (item1 � item2 þ item3) was high (Cronbach� ¼ 0:80) and correlated r ¼ 0:80 with JPEG file size. These resultsindicate that JPEG file size is a good proxy for VC in our study.

2.3 Procedure

2.3.1 Setting

The experiment took place in a laboratory of the Department ofPsychology at the University of Basel. Participants were seated infront of a computer monitor. They controlled the progress of theexperiment using a computer mouse with their right hand. Allinstructions were displayed in written form on the screen. Eachparticipant was tested separately, and room temperature was heldconstant between 21 and 23�C.

The participants were first given a short introduction to the laband physiological measurements. After physiological sensors had

been attached to the face, the torso, and the left hand, theexperimenter retired to the adjacent control room, where he couldcommunicate with the participants by intercom and observe themthrough a camera that was unobtrusively placed. Sensors wereattached to the fingers of the left hand so that the participantscould use their right hand to perform tasks with the computermouse. All left-handed participants (n ¼ 3) indicated that they alsoused their right hand to manage a computer mouse.

2.3.2 Tasks

Initially, a 3-minute baseline measurement was conducted whereparticipants were instructed to sit quietly in the chair and look atthe blank monitor, followed by a short calibration for the EOGmeasurements (participants had to follow a moving fixation-crosswith their eyes). After that, the actual experiment started. Itconsisted of two main phases. In the first phase, the 36 webpagescreenshots were presented in random order. Each screenshot wasshown for 8 sec, followed by a blank screen (8 sec). Participantswere instructed to look at the pages to get a visual overview.During the entire phase, physiological responses were recorded.

In the second phase, the 36 webpage screenshots were shownagain in random order and participants rated each on the valenceand arousal scale of the Self-Assessment Manikin (SAM [42]) aswell as for “general appeal” and “annoyance” on a visual analoguescale ranging from 1 (does not apply at all) to 9 (applies strongly). TheSAM is designed to assess the dimensions valence and arousaldirectly by means of two sets of graphical manikins. It has beenused extensively in research on emotion (e.g., [43]) and UserExperience [44].

2.4 Data Collection

Ratings were recorded with the software E-Prime 1.1 (PsychologySoftware Tools). Physiological channels were recorded withBIOPAC hardware and AcqKnowledge software (Biopac Systems,Inc., Goleta, CA), sampled at 1,000 Hz. The horizontal EOG wasmeasured with 6 mm Ag/AgCl miniature electrodes filled withelectrolyte attached at the left and right temples. HR was measuredwith a standard Lead-II electrocardiogram (ECG) using threedisposable electrodes. FPA was assessed with a finger plethysmo-graph on the index finger of the left hand.

2.5 Data Preparation

Psychophysiological data were reduced using ANSLAB software[45]. The raw EOG signal (in millivolts, mV) was 0.5 Hz high-passfiltered. The raw ECG signal was 40 Hz low-pass filtered and 0.5 Hzhigh-pass filtered to aid automatic R-wave identification. R-waveswere then edited for artifacts, false-positive, or false-negativeR-waves, based on which interbeat intervals were quantified thatwere subsequently transformed to HR (in beats per minute, bpm).The raw finger plethysmograph signal was 20 Hz low-pass filteredand 0.13 Hz high-pass filtered. FPA (in arbitrary units, AU) wascalculated by means of automatic minima and maxima identifica-tion of the pulse wave.

For the EOG signal, an 8 sec interval was extracted for eachstimulus. It was split into the first interval response (FIR; seconds 0-4of stimulus presentation) and the second interval response (SIR;seconds 4-8). The mean SD for each EOG response interval wascalculated (SD EOGFIR and SD EOGSIR), which reflects theamplitudes of the saccades (large eye movements).

HR and FPA data were extracted from 2 sec before to 12 secafter stimulus onset. Mean values for each channel were extractedfor 14 1-sec intervals. The 2-sec period before stimulus onset wasused as a stimulus-related baseline in order to compute the changein HR and FPA for stimulus exposure and stimulus offset periods.Baseline adjusted HR and FPA values were then aggregated overparticipants for each webpage, resulting in one HR and one FPA


Fig. 1. Examples of webpage screenshots used in the experiment. The degree ofvisual complexity is indicated for each screenshot in JPEG file size (KB).

change score per webpage (�HR and �FPA). Next, change scores

were split into three response intervals: the first interval response

(FIR; seconds 0-4 of stimulus presentation), the second interval

response (SIR; seconds 4-8), and a stimulus offset response (SOR;

seconds 8-12, i.e., after stimulus offset). This results in three

response intervals for HR (�HRFIR, �HRSIR, and �HRSOR) and

for FPA (�FPAFIR, �FPASIR, �FPASOR).

2.6 Statistical Analysis

A first set of analyses tested the influence of VC on feeling ratings

of the overall viewing experience. To this end, all subjective ratings

were aggregated for each webpage so that every webpage had a

single score for each scale. To assure linearity of correlations,

scatter plots were examined and alternative regression models

(quadratic and cubic) were calculated. The alternative models did

not provide significantly better fits; thus, Pearson Product Moment

Correlations were used for analyzing the degree of linear

relationship.A second set of analyses involved two tests for each of the

behavioral and cardiovascular measures: One test addressed the

time course of reactivity over 4-sec averages. EOG responses were

tested with a related samples t-test (FIR versus SIR). HR and FPA

responses were tested with repeated measures ANOVAs with

response interval (FIR versus SIR versus SOR) as an independent

variable, using Greenhouse-Geisser adjustment to correct viola-

tions of sphericity.The second test analyzed whether and when eye movements

and cardiovascular responses were associated with VC as well as

with feeling ratings. To this end, correlation analyses were used.

Similarly to the self-report analysis, all subjective ratings and all

computed baseline differences were aggregated for each webpage

so that each webpage had a single score for each scale and

physiological response interval. As above, linear models provided

the best fit; thus, Pearson Product Moment Correlations were

calculated. An alpha level of 0.05 was used for all statistical tests.

3 RESULTS

3.1 Relationship between Visual Complexity andSubjective Ratings

VC was strongly associated with all subjective ratings (Table 1).

More complex webpages were rated as being more arousing

(r ¼ 0:74, p < 0:001) and more annoying (r ¼ 0:63, p < 0:001). Less

complex webpages were considered as being more pleasant

(r ¼ �0:61, p < 0:001) and more appealing (r ¼ �0:57, p < :001).

3.2 EOG Response

3.2.1 Time Course Analysis

The related samples t-test revealed a significant decrease in SD

EOG over time, tð1; 35Þ ¼ 3:107, p ¼ 0:004. There were more eye

movements in the FIR than in the SIR (Fig. 2).

3.2.2 Influence of Visual Complexity and Subjective Ratings

The EOG response during stimulus exposure was significantly

related to VC (rFIR ¼ 0:42, p ¼ 0:005; rSIR ¼ 0:30, p ¼ 0:037), with

more complex webpages provoking more eye movements. The

EOG response was also related to subjective ratings of valence

(rFIR ¼ �0:44, p ¼ 0:004) and arousal (rFIR ¼ 0:40; p ¼ 0:007) dur-

ing the first half of stimulus exposure as well as ratings of appeal

(rFIR ¼ �0:30, p ¼ 0:036) and annoyance (rFIR ¼ 0:29, p ¼ 0:044).

For the SIR, only the correlation between EOG and VC reached

significance (Table 2).

3.3 Heart Rate Response


The �HR responses differed significantly over time,

F ð2; 70Þ ¼ 45:94, p < 0:001, with a complex response pattern of

almost no change in HR during FIR, slightly decreased HR during

FIR, and increased HR during SOR (Fig. 2). Posthoc comparisons

between response intervals showed that all intervals differ

significantly from each other.


In all three intervals, HR was negatively related to the degree of

webpage complexity (Table 2): VC was significantly correlated with

�HRFIR (r ¼ �0:31; p ¼ 0:033), �HRSIR (r ¼ �0:36, p ¼ 0:017),

and �HRSOR (r ¼ �0:31, p ¼ 0:034). More complex webpages

provoked a more pronounced decrease in HR than less complex

webpages for the SIR, respectively, less pronounced increase for

the SOR. Regarding the subjective ratings, only arousal was

correlated with �HRFIR (r ¼ �0:35, p ¼ 0:020) and �HRSIR

(r ¼ �0:29, p ¼ 0:043), but there was no significant correlation for

the SOR (r ¼ �0:19, p ¼ 0:140).


TABLE 1Correlation Coefficients for Visual Complexity

versus Ratings of the Webpages

Fig. 2. Mean standard deviation across all complexity levels of the electrooculogram (SD EOG, left), mean baseline-related change in heart rate (�HR, middle), andmean baseline-related change in finger pulse amplitude (�FPA, right) for the first interval response (FIR, 0-4 s), the second interval response (SIR, 4-8 s), and thestimulus offset response (SOR,; 8-12 s). Whiskers indicate one standard error of the mean.

3.4 Finger Pulse Amplitude Response


�FPA increased constantly over time (Fig. 2). The repeatedmeasures ANOVA showed a significant main effect for time,F ð1:54; 54:01Þ ¼ 10:86, p < 0:001. Posthoc comparisons betweenresponse intervals showed that all intervals differ significantlyfrom each other.


FPA response was positively correlated with VC and arousalratings during SIR and SOR, but not during FIR (Table 2).Specifically, VC was significantly correlated with �FPASIR

(r ¼ 0:34, p ¼ 0:021) and �FPASOR (r ¼ 0:28, p ¼ 0:047). Thus,there was a more pronounced increase in FPA for more complexwebpages during SIR and SOR. Similarly as for HR, only arousalwas correlated with �FPASIR (r ¼ 0:36, p ¼ 0:014) and �FPASOR

(r ¼ 0:42, p ¼ 0:005), but there were no significant correlations forthe FIR. Notably, correlations between both measures increasedfrom SIR to SOR.

4 DISCUSSION

This study tested whether different levels of VC of webpagesinfluence affective reactions of users during a simple viewing task.Results show 1) that VC of webpages influences affectiveexperience and 2) that unobstrusively measured behavioral andphysiological responses vary with VC of webpages as well as theuser’s affective experience.

4.1 Visual Complexity and Subjective Feelings

Our findings of a negative relation between VC and ratings ofvalence and appeal as well as a positive relation between VC andratings of arousal and annoyance suggest that less complexwebpages are more positively perceived. These findings areconsistent with previous studies: Ochsner [14] found the samepattern of VC and arousal and valence ratings using photographsas stimuli. Pandir and Knight [17] also reported a negativecorrelation between VC and pleasure ratings of webpages. Thesefindings are more consistent with the information-processing stagemodel of aesthetic perception [9] that proposes a negative relationbetween VC and aesthetic judgment than the inverse curvilinearrelation between VC and aesthetic perception proposed by Berlyne[3]. Thus, on a level of consciously accessible feelings, less complexwebpages, as indexed by a measure of digital file compression, aremore favorably judged.

4.2 Visual Complexity and Behavioral Expression

Eye movements differed as a function of VC. Bigger saccadic eyemovements were observed while viewing more complex webpagescompared to less complex webpages. This effect was strongerduring the first half (4 sec) of the viewing task. These resultsemphasize the importance of the first impression of a webpage [46].

In the first few seconds, many physiological and cognitive responsestake place and may decide whether attention will remain on thecurrently viewed webpage or whether something new will comeinto the focus of attention. Attention and eye movements are closelyrelated, as extensive research shows (e.g., [19]). The higher eyemovement activity observed during viewing of high-VC webpages,particularly in the first half, is consistent with distinctions into aninitial orienting period and a subsequent fixation period [22], [23],the latter being delayed for more complex stimuli [21].

Eye movements also differed as a function of subjectivefeelings. Viewing of webpages judged as more pleasant and asmore appealing was associated with less eye movement, suggest-ing longer fixation on one area. Similarly, viewing of webpagesjudged as more arousing and more annoying was associated withmore eye movement. These findings are contrary to studies byBarrowcliff et al. [27] and Lee and Drummond [28], which showedthat increased eye movements reduce subjective ratings ofnegative emotional valence. Rather, we here show for the firsttime that less complex webpages are perceived as more pleasantand were associated with decreased eye movements, suggestingincreased fixation.

4.3 Visual Complexity and Cardiovascular Response

Our cardiovascular data also proved sensitive to the time courseand VC of webpage viewing. Over all webpages, HR firstremained relatively unchanged, then showed a deceleration, andchanged to acceleration after stimulus offset. Assuming thatwebpages in general tend to elicit rather positive emotions, themarginal change during the first viewing interval could be basedon the summed effects of the typically observed initial decelerationand subsequent acceleration; the HR deceleration observed duringthe second viewing interval could be based on the secondarydeceleration [35]; and the HR acceleration found in response towebpage offset could be based on the acceleratory response topicture offset [38]. Moreover, HR decreased more after stimulusonset during viewing of high-VC webpages than during viewingof low-VC webpages. After stimulus offset, HR increased moreafter viewing webpages of low VC. These results are in line withearlier findings that attending to more complex stimuli elicits astronger OR, i.e., stronger HR deceleration, whereas less complexand more pleasant material led to a more pronounced accelatoryHR response [32], [39], [40].

The FPA response was inconsistent with our predictions of aninitial decrease, i.e., vasoconstriction, as we observed practically nochange during the first viewing interval and increased FPA, i.e.,vasodilation, during the second viewing interval as well as inresponse to webpage offset. However, as previously noted in theliterature, findings on peripheral vasoconstriction during OR havebeen difficult to replicate [30]. Similarly as for the HR response, ourfindings could be based on a mild initial decrease and relativeincrease in FPA during the first viewing interval. The increasedFPA during the second interval is consistent with the biphasicpattern of vasoconstriction and subsequent vasodilation found for


TABLE 2Correlation Coefficients of Visual Complexity, Feeling Ratings, and Psychophysiological Indices for All Response Intervals

forehead pulse amplitude [36], [37]. FPA was sensitive to webpageVC during the second and offset intervals.

Taken together, these results suggest that HR and FPA aresensitive to evaluative appraisals of VC and pleasantness and arein support of process models of emotion that propose a dynamicdevelopment of the emotion response over time [6], [7].

4.4 Limitations

The present study has several limitations. The study was carriedout in the highly controlled context of a laboratory setting and witha young and healthy student sample. Future studies need toinvestigate whether similar effects can be found in participants’normal environments using, e.g., wearable physiological recordingdevices [47]. Future studies will also need to address whether ourfindings will generalize to different age groups and different socialclasses, among other sociodemographic variables.

The present study was also constrained with respect to whataspects of emotion could be measured. For a more comprehensiveassessment of the affective response elicited by webpage viewing,it would be desirable to also assess discrete emotional feelings(e.g., aesthetic emotions) [8], other behavioral expressions, such asfacial microexpressions that communicate pleasure or displeasure(e.g., activation of the corrugator supercilii) [48], and furtherphysiological indices that may trace emotion on the level of thecentral or autonomic nervous system [49].

Finally, in the present study we focused on aesthetic perceptionof webpages that varied in VC. Because we focused on aperceptual attribute, we did not address different categories orcontents of webpages. Further, we restricted our stimuluspresentation to screen shots of webpages, rather than requiringparticipants to interact with the webpage content. The way weinstructed participants might have motivated them to focus onintrinsic aesthetic aspects of the webpages. It will be an importantdirection for future research to test if giving participants the goal ofactively searching out information would lead to different affectiveresponses [1], [8].

5 CONCLUSION

This study demonstrated that VC is associated with a user’saffective responses during webpage perception. Three levels ofaffective responding—subjective feelings, behavioral expressions,and physiological responses—varied as a function of webpage VC.Thus, complexity plays an important role in affective perception ofwebpages.

Further research is needed to clarify what elements inwebpage design cause an increase in VC and how these elementsinfluence affective reactions of the user. We believe that VCprovides a meaningful feature for the evaluation of websites, withcompressed file size (JPEG) as an easily assessable proxy forperceived VC.

Moreover, even though psychophysiological methods in thefield of affective computing face a variety of difficulties [50], thepresent study shows that in a controlled experimental setting,physiological responses to moderately intense affective stimuli,like webpages, can be identified. New methods are continuouslybeing developed that allow the detection and recognition ofemotional responses using psychophysiological measurements[51], [52], [53]. The combination of such methods with technolo-gical advances in software and hardware [54], [55] should make itpossible in the future to dynamically create and update webpagesaccording to the user’s affective experience during web browsing.

REFERENCES

[1] D.C. Donderi, “An Information Theory Analysis of Visual Complexity andDissimilarity,” Perception, vol. 35, pp. 823-835, 2006.

[2] G.L. Geissler, G.M. Zinkhan, and R.T. Watson, “The Influence of HomePage Complexity on Consumer Attention, Attitudes, and Purchase Intent,”J. Advertising, vol. 35, no. 2, pp. 69-80, 2006.

[3] D. Berlyne, “Complexity and Incongruity Variables as Determinants ofExploratory Choice and Evaluative Ratings,” Canadian J. Psychology, vol. 17,pp. 274-290, 1963.

[4] D. Berlyne and S. Peckham, “The Semantic Differential and Other Measuresof Reaction to Visual Complexity,” Canadian J. Psychology, vol. 20, no. 2,pp. 125-135, 1966.

[5] P.J. Silvia, “What Is Interesting? Exploring the Appraisal Structure ofInterest,” Emotion, vol. 5, pp. 89-102, 2005.

[6] P.J. Lang and M.M. Bradley, “Emotion and the Motivational Brain,”Biological Psychology, vol. 84, pp. 437-450, 2010.

[7] K.R. Scherer, “The Dynamic Architecture of Emotion: Evidence for theComponent Process Model,” Cognition and Emotion, vol. 23, pp. 1307-1351,2009.

[8] K.R. Scherer, “Which Emotions Can Be Induced by Music? What Are theUnderlying Mechanisms? and How Can We Measure Them?” J. New MusicResearch, vol. 33, no. 3, pp. 239-251, 2004.

[9] H. Leder, B. Belke, A. Oeberst, and D. Augustin, “A Model of AestheticAppreciation and Aesthetic Judgments,” British J. Psychology, vol. 95, no. 4,pp. 489-508, 2004.

[10] O. Grewe, F. Nagel, R. Kopiez, and E. Altenmuller, “Emotions over Time:Synchronicity and Development of Subjective, Physiological, and FacialAffective Reactions to Music,” Emotion, vol. 7, pp. 774-788, 2007.

[11] P. Vitz, “Preference for Different Amounts of Visual Complexity,”Behavioral Science, vol. 11, pp. 105-114, 1966.

[12] F. Attneave, “Physical Determinants of the Judged Complexity of Shapes,”J. Experimental Psychology, vol. 53, no. 4, pp. 221-227, 1957.

[13] R. Reber, N. Schwarz, and P. Winkielman, “Processing Fluency andAesthetic Pleasure: Is Beauty in the Perceiver’s Processing Experience?”Personality and Social Psychology Rev., vol. 8, no. 4, pp. 364-382, 2004.

[14] K.N. Ochsner, “Are Affective Events Richly Recollected or SimplyFamiliar? the Experience and Process of Recognizing Feelings Past,”J. Experimental Psychology: General, vol. 129, no. 2, pp. 242-261, 2000.

[15] G.C. Bruner and A. Kumar, “Web Commercials and AdvertisingHierarchy-of-Effects,” J. Advertising Research, vol. 40, no. 1, pp. 35-42, 2000.

[16] J.S. Stevenson, G.C. Bruner, and A. Kumar, “Webpage Background andViewer Attitudes,” J. Advertising Research, vol. 40, no. 1, pp. 29-34, 2000.

[17] M. Pandir and J. Knight, “Homepage Aesthetics: The Search for PreferenceFactors and the Challenges of Subjectivity,” Interacting with Computers,vol. 18, pp. 1351-1370, 2006.

[18] A.N. Tuch, J.A. Bargas-Avila, K. Opwis, and F.H. Wilhelm, “VisualComplexity of Websites: Effects on Users’ Experience, Physiology,Performance, and Memory,” Int’l J. Human-Computer Studies, vol. 67,pp. 703-715, 2009.

[19] K. Rayner, “Eye Movements in Reading and Information Processing:20 Years of Research,” Psychological Bull., vol. 124, no. 3, pp. 372-422, 1998.

[20] G. Robinson, D. Erickson, G. Thurston, and R. Clark, “Visual Search byAutomobile Drivers,” Human Factors: The J. Human Factors and ErgonomicsSoc., vol. 14, pp. 315-23, 1972.

[21] P. Unema, S. Pannasch, M. Joos, and B. Velichkovsky, “Time Course ofInformation Processing during Scene Perception: The Relationship betweenSaccade Amplitude and Fixation Duration,” Visual Cognition, vol. 12, no. 3,pp. 473-494, 2005.

[22] G. Buswell, How People Look at Pictures. Univ. of Chicago Press, 1935.[23] B. Karpov, A. Luria, and A. Yarbuss, “Disturbances of the Structure of

Active Perception in Lesions of the Posterior and Anterior Regions of theBrain,” Neuropsychologia, vol. 6, no. 2, pp. 157-166, 1968.

[24] J. Antes, “The Time Course of Picture Viewing,” J. Experimental Psychology,vol. 103, no. 1, pp. 62-70, 1974.

[25] P.J. Lang, M.M. Bradley, J.R. Fitzsimmons, B.N. Cuthbert, J.D. Scott, B.Moulder, and V. Nangia, “Emotional Arousal and Activation of the VisualCortex: An fMRI Analysis,” Psychophysiology, vol. 35, no. 2, pp. 199-210,1998.

[26] R. Lane, P. Chua, and R. Dolan, “Common Effects of Emotional Valence,Arousal and Attention on Neural Activation during Visual Processing ofPictures,” Neuropsychologia, vol. 37, no. 9, pp. 989-997, 1999.

[27] A. Barrowcliff, N. Gray, T. Freeman, and M. MacCulloch, “Eye-MovementsReduce the Vividness, Emotional Valence and Electrodermal ArousalAssociated with Negative Autobiographical Memories,” J. Forensic Psy-chiatry & Psychology, vol. 15, no. 2, pp. 325-345, 2004.

[28] C. Lee and P. Drummond, “Effects of Eye Movement versus TherapistInstructions on the Processing of Distressing Memories,” J. AnxietyDisorders, vol. 22, no. 5, pp. 801-808, 2008.

[29] E.N. Sokolov, Perception and Conditioned Reflex. Pergamon Press, 1963.[30] E.N. Sokolov and J.T. Cacioppo, “Orienting and Defense Reflexes: Vector

Coding the Cardiac Response,” Attention and Orienting: Sensory andMotivational Processes, P.J. Lang, R.F. Simon, and T. Balaban, eds., pp. 1-22, Erlbaum, 1997.

[31] M.M. Bradley, M.K. Greenwald, and A.O. Hamm, “Affective PictureProcessing,” The Structure of Emotion, pp. 48-68, Hogrefe & Huber, 1993.

[32] W. Libby Jr, B. Lacey, and J. Lacey, “Pupillary and Cardiac Activity duringVisual Attention,” Psychophysiology, vol. 10, no. 3, pp. 270-294, 1973.

[33] W.M. Winton, L.E. Putnam, and R.M. Krauss, “Facial and AutonomicManifestations of the Dimensional Structure of Emotion,” J. ExperimentalSocial Psychology, vol. 20, pp. 195-216, 1984.


[34] P.J. Lang, M.M. Bradley, and B.N. Cuthbert, “Emotion, Motivation, andAnxiety: Brain Mechanisms and Psychophysiology,” Biological Psychiatry,vol. 44, pp. 1248-1263, 1998.

[35] M. Bradley, M. Codispoti, B. Cuthbert, and P. Lang, “Emotion andMotivation I: Defensive and Appetitive Reactions in Picture Processing,”Emotion, vol. 1, no. 3, pp. 276-298, 2001.

[36] R. Hare, K. Wood, S. Britain, and J. Shadman, “Autonomic Responses toAffective Visual Stimulation,” Psychophysiology, vol. 7, pp. 408-417, 1971.

[37] R.D. Hare, “Response Requirements and Directional Fractionation ofAutonomic Responses,” Psychophysiology, vol. 9, pp. 419-427, 1972.

[38] M. Codispoti, M.M. Bradley, and P.J. Lang, “Affective Reactions to BrieflyPresented Pictures,” Psychophysiology, vol. 38, no. 3, pp. 474-478, 2001.

[39] M. Fredrikson and A. Ohman, “Heart-Rate and Electrodermal OrientingResponses to Visual Stimuli Differing in Complexity,” ScandinavianJ. Psychology, vol. 20, pp. 37-41, 1979.

[40] J. Jennings, J. Averill, E. Opton, and R. Lazarus, “Some Parameters of HeartRate Change: Perceptual versus Motor Task Requirements, Noxiousness,and Uncertainty,” Psychophysiology, vol. 7, no. 2, pp. 194-212, 1970.

[41] E. Riglis, “Modeling Visual Complexity in Image Architectures,” technicalreport, Heriot-Watt Univ., 1998.

[42] P.J. Lang, “Behavioral Treatment and Bio-Behavioral Assessment: Compu-ter Applications,” Technology in Mental Health Care Delivery Systems,J.B. Sidowski, J.H. Johnson, and T.A. Williams, eds., pp. 119-137, AblexPublishing, 1980.

[43] P.J. Lang, M.M. Bradley, and B.N. Cuthbert, “International AffectivePicture System (IAPS): Affective Ratings of Pictures and InstructionManual,” technical report, Univ. of Florida, 2008.

[44] J. Bargas-Avila and K. Hornbæk, “Old Wine in New Bottles or NovelChallenges? a Critical Analysis of Empirical Studies of User Experience,”Proc. Ann. Conf. on Human Factors in Computing Systems, pp. 2689-2698, 2011.

[45] F.H. Wilhelm and P. Peyk, “Autonomic Nervous System Laboratory,”http://www.sprweb.org, 2005.

[46] G. Lindgaard, G. Fernandes, C. Dudek, and J. Brown, “Attention WebDesigners: You Have 50 Milliseconds to Make a Good First Impression!,”Behaviour and Information Technology, vol. 25, no. 2, pp. 115-126, 2006.

[47] F.H. Wilhelm and P. Grossman, “Emotions beyond the Laboratory:Theoretical Fundaments, Study Design, and Analytic Strategies forAdvanced Ambulatory Assessment,” Biological Psychology, vol. 84,pp. 552-569, 2010.

[48] J.T. Cacioppo, L.K. Bush, and L.G. Tassinary, “Microexpressive FacialActions as a Function of Affective Stimuli,” Personality and Social PsychologyBull., vol. 18, pp. 515-526, 1992.

[49] S.D. Kreibig, “Autonomic Nervous System Activity in Emotion: A Review,”Biological Psychology, vol. 84, pp. 394-421, 2010.

[50] R.D. Ward and P.H. Marsden, “Affective Computing: Problems, Reactionsand Intentions,” Interacting with Computers, vol. 16, no. 4, pp. 707-713, 2004.

[51] J.N. Bailenson, E.D. Pontikakis, I.B. Mauss, J.J. Gross, M.E. Jabon, C.A.C.Hutcherson, C. Nass, and O.P. John, “Real-Time Classification of EvokedEmotions Using Facial Feature Tracking and Physiological Responses,” Int’lJ. Human-Computer Studies, vol. 66, pp. 303-317, 2008.

[52] V. Kolodyazhniy, S.D. Kreibig, W.T. Roth, J.J. Gross, and F.H. Wilhelm, “AnAffective Computing Approach to Physiological Emotion Specificity:Toward Subject-Independent and Stimulus-Independent Classification ofFilm-Induced Emotions,” Psychophysiology, vol. 48, no. 7, pp. 908-922, July2011.

[53] S.D. Kreibig, G. Schaefer, and T. Brosch, “Psychophysiological ResponsePatterning in Emotion: Implications for Affective Computing,” Blueprint forAffective Computing: A Sourcebook, K.R. Scherer, T. Baenziger, and E. Roesch,eds., pp. 105-130, Oxford Univ. Press, 2010.

[54] M.-Z. Poh, D.J. McDuff, and R.W. Picard, “Non-Contact, AutomatedCardiac Pulse Measurements Using Video Imaging and Blind SourceSeparation,” Optics Express, vol. 18, pp. 10762-10774, 2010.

[55] P. Zimmermann, S. Guttormsen, B. Danuser, and P. Gomez, “AffectiveComputing - A Rationale for Measuring Mood with Mouse and Keyboard,”Int’l J. Occupational Safety and Ergonomics, vol. 9, pp. 539-551, 2003.

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