INTRODUCTION

Many interfaces make use of icons to commu-nicate information. The advantage of using iconslies in the fact that they are thought to be able tocommunicate large amounts of information quick-ly and concisely (e.g., Caplin, 2001) and have thepotential to cut across language barriers (Bocker,1993). Information is represented by icons sema-siographically; they convey semantic informationin a nonverbal manner and do not rely on a set ofclear rules to convey meaning, as do written words(Carr, 1986). The lack of clear conventions toaccess meaning makes ease of interpretation a cru-cial design consideration. Ease of access to mean-ing can be evaluated in a wide variety of ways(Bocker, 1993; Hancock, Rogers, Schroeder, &Fisk, 2004), including open-ended questioning,eliciting production of possible meanings, rating,

ranking, and various forms of multiple choice.Bocker (1993) noted the most realistic approachis one that tries to represent actual usage, “a recog-nition situation in which a user with a certain in-tention is confronted with the controls of a deviceand has to make a choice” (p. 76). In the experi-ment reported here an icon identification task wasused that mimicked this as closely as possiblewhile allowing the use of icons from a wide rangeof applications: Participants were given a functionname (i.e., representing the user’s intention) andasked to choose the appropriate icon from an array(i.e., to make a choice from a set of controls).

From a practical perspective it is important toknow which icon characteristics are of primeimportance in determining performance in theidentification task so that designers can focus onthese aspects in design. Although research to datehas examined a number of icon characteristics,

Icon Identification in Context: The Changing Role of IconCharacteristics With User Experience

Sarah J. Isherwood, University of Leeds, Leeds, UK, Siné J. P. McDougall, University ofWales Swansea, Swansea, UK, and Martin B. Curry, National Air Traffic Control Services,Bournemouth, UK

Objective: This research examined the relative importance of icon characteristics indetermining the speed and accuracy of icon identification. Background: Studies todate have focused on the role of one or two icon characteristics when users first expe-rience an icon set. This means that little is known about the relative importance of iconcharacteristics or how the role of icon characteristics might change as users gain expe-rience with icons. Methods: Thirty participants carried out an icon identification taskover a long series of trials to simulate learning through experience. Icon characteris-tics investigated included semantic distance, concreteness, familiarity, and visualcomplexity. Results: Icon characteristics were major determinants of performance,accounting for up to 69% of the variance observed in performance. However, the im-portance of icon characteristics changed with experience: Semantic distance is cru-cial initially while icon-function relationships are learned, but familiarity is importantlater because it has lasting effects on access to long-term memory representations.Conclusion: These findings suggest that icon concreteness may not be of primaryimportance when identifying icons and that semantic distance and familiarity may bemore important. Application: Designers need to take into account icon characteris-tics other than concreteness when creating icons, particularly semantic distance andfamiliarity. The precise importance of the latter characteristics will vary dependingon whether icons are rarely encountered or frequently used.

Address correspondence to Siné McDougall, Psychology Department, University of Wales Swansea, Swansea, SA2 8PP, UK;s.mcdougall@swansea.ac.uk. HUMAN FACTORS, Vol. 49, No. 3, June 2007, pp. 465–476. DOI 10.1518/001872007X200102.Copyright © 2007, Human Factors and Ergonomics Society. All rights reserved.

466 June 2007 – Human Factors

individual studies have focused on one, or at mosttwo, characteristics (e.g., Arend, Muthig, & Wand-macher,1987; Byrne,1993; McDougall, de Bruijn,& Curry, 2000). This means that little is knownabout the relative importance of icon characteris-tics in design. The study reported here comparedthe importance of a range of icon characteristics indetermining the accuracy and speed of icon iden-tification.

With few exceptions, research has examinedthe role of icon characteristics when users are firstpresented with a new set of icons (but see Green& Barnard, 1990, and McDougall et al., 2000).Although this is clearly relevant for newly creat-ed icon sets, it does not take account of the factthat icon sets are often used on a daily basis. Thisexperiment therefore explored the way in whichthe role of icon characteristics change as usersgain experience with the icon set in order to knowwhich icon characteristics are likely to influenceperformance after regular use.

ICON CHARACTERISTICS

Concreteness

The concreteness of an icon is the extent towhich it depicts real objects, materials, or people(see Figure 1 for examples). Abstract icons, in con-trast, are less pictorial and tend to have less obvi-ous connections with real-world items and makemore use of shapes, arrows, and lines (McDougall,Curry, & de Bruijn, 1999). Concreteness is oftenseen as an icon’s most important property (Moyes& Jordan, 1993). This is because concrete iconsdepict objects, allowing people to use their knowl-edge of the everyday world in order to interpretthem. Although some researchers have shown thatusers respond more quickly and accurately to con-crete icons (Arend et al., 1987; Stammers & Hoff-man, 1991; Stotts, 1998), others have suggestedthat these performance advantages are not longlasting and diminish as users gain experience withicons (Green & Barnard, 1990; McDougall et al.,2000). This highlights the importance of consid-ering user experience, given that the role of con-creteness in determining user performance maychange over time.

Visual Complexity

Complexity refers to the amount of detail orintricacy in an icon. A metric developed byGarcia, Badre, and Stasko (1994) defined this

operationally as the number of lines, letters, andsimple shapes an icon contains. Figure 1b is anexample of a visually complex icon and Figure 1dis an example of a simple icon. Concrete icon setsdeveloped for research have tended to be morevisually complex than abstract icons because ofthe belief that detailed depictions of real-worlditems would allow users to access their existingknowledge about these objects in order to infermeaning (Arend et al., 1987; Garcia et al., 1994;Green & Barnard, 1990). This contrasts withnumerous guidelines that recommend simplicityin icon design (Gittins, 1986; Horton, 1994) be-cause simpler icons can be discriminated more

(a) 'fighter' (b) 'library'

Concrete icons

(c) 'oscillating (d) 'zoom' motor'

Abstract icons

(e) 'print' (f) 'slow' (g) 'hazard' (direct) (implied) (arbitrary)

Icon-Function Relationships Figure 1. Examples of different types of icons. The li-

brary icon comes from The icon book: Visual symbols forcomputer systems and documentation, W. Horton, 1994,John Wiley & Sons, New York. Reprinted with permis-sion of John Wiley & Sons, Inc. The permission toreproduce extracts from the BS ISO 7000 (zoom [ad-justment] and hazard icons) is granted by BSI. BritishStandards can be obtained from BSI Customer Services,389 Chiswick High Road, London W4 4AL. Tel: +44(0)20 8996 9001. All the other icons are in the publicdomain.

ICON IDENTIFICATION IN CONTEXT 467

easily (Byrne, 1993) and are easier to locate invisual search (Byrne, 1993; Scott, 1993). Recentresearch suggests that the visual complexity oficons has an important role to play in search butis not directly involved in icon identification(McDougall et al., 2000). Thus, although visualcomplexity may have a relatively small role toplay in determining icon interpretability in thepresent study, its effects may still be apparentbecause participants were required to search thedisplay for an appropriate icon.

Semantic Distance

Semantic distance refers to the closeness ofrelationship between the icon and the function itrepresents. It is important to note that the functionassigned to an icon by those designing it may bequite different to the meaning attributed to it byusers in practice. The relationship between iconand function is generally classified into three types(see Figure 1, e–g): In the first, there is a close,direct, relationship between the icon and its intend-ed function; the second type requires the use ofinferences in order to ascertain the meaning of theicon; and the third level consists of arbitrary rela-tionships in which the function of the icon isunderstood only if users have previously learnedits meaning (e.g., Familant & Detweiler, 1993;see also Hartshorne & Weiss, 1932). In practiceit is probably better to regard this dimension as acontinuum running from very closely related tovery distantly related (McDougall et al., 1999).The limited evidence available suggests thatsemantic distance has an important role to play indetermining interpretability (Goonetilleke, Shih,On, & Fritsch, 2001; McDougall, Curry & deBruijn, 2001) and that it may be more importantthan concreteness in determining learning andcomprehension (McDougall et al., 2001). Thispossibility was examined in the present study.

Familiarity

Icon familiarity was included as a characteris-tic to be investigated because previous researchhas shown that it is related to icon meaning(McDougall et al., 1999). Familiarity with iconstakes two forms. The first is frequency of use,which relates directly to experience with an iconset. This form of frequency was examined by pre-senting participants with icons over several blocksof trials and is subsequently referred to as experi-ence. The second form is familiarity with objects

depicted; although a person may be familiar withitems in an icon, he or she may not know the icon’sintended meaning (e.g., in Figure 1b, recognizingthe books in the icon does not necessarily lead tounderstanding that “library” is the icon function).The role of this form of familiarity was examinedusing previously obtained ratings of icon famil-iarity, and it is this form of familiarity that is sub-sequently referred to as familiarity. To date noone has investigated the role of familiarity direct-ly; however, evidence from picture-naming (asopposed to icon-naming) research suggests thatfamiliarity is regarded as an index of ease of accessto long-term memory representations (Lambon-Ralph, Graham, Ellis, & Hodges, 1998) and issecond only to semantic distance in its power topredict identification times (Bates et al., 2003).Its effects are also thought to be long lasting andremain after repeated presentation (Jescheniak &Levelt, 1994). On this basis, it was hypothesizedthat familiarity would have important and long-lasting effects on icon identification. However,given the paucity of relevant evidence, it is diffi-cult to predict its importance relative to that ofother icon characteristics.

To summarize, the evidence to date, thoughoften limited, suggests that semantic distance maybe the preeminent predictor of icon identification.Icon concreteness and familiarity may also be im-portant, though the effects of concreteness may beshort lived. The role of visual complexity seemslikely to be restricted to its effect on the visualsearch component of the identification task ratherthan icon identification per se. It was thereforeexpected to have a small, but consistent, effect onperformance.

METHOD

Participants

Atotal of 30 participants took part in this study.All were undergraduate or postgraduate studentsstudying at the University of Wales Swansea whowere paid or given psychology course credits fortheir participation (mean age = 21 years 7 months;26 women and 4 men).

Materials and Apparatus

Aset of 40 icons was selected from a set of 239black-and-white icons for which measures of con-creteness, complexity, familiarity, and semanticdistance had been previously obtained (McDougall

468 June 2007 – Human Factors

et al., 1999; see Table 1). Icons in the original cor-pus were chosen from a broad spectrum of appli-cations, and the function names are those givenfor that use. Rating scales for each icon charac-teristic were as follows:

Concreteness. Icons were rated in accordancewith the extent to which they depicted real objects,materials, or people; those that did not were to beregarded as abstract (1 = definitely abstract, 5 =definitely concrete; see Gilhooly & Logie, 1980,and Paivio, Yuille, & Madigan, 1968, for previoususe of this definition with words).

Complexity. This was defined as the amount ofdetail or intricacy in the icon, after the methodol-ogy adopted by Snodgrass and Vanderwart (1980)for line drawings (1 = very simple, 5 = very com-plex).

Familiarity. Participants provided ratings offamiliarity based on the extent to which they re-garded them as familiar or not (1 = very unfamil-iar, 5 = very familiar).

Semantic distance. Participants provided rat-ings of the closeness of the relationship betweenicon and function (1 = not closely related, 5 =very strongly related; see Keller & Stevens, 2004,for the use of similar ratings). This rating differedfrom the previous ratings in that both the icon andits function were presented for rating, whereas forthe others only the icon was rated.

The reliability of the ratings for the originalcorpus was assessed using split-half reliability, andall were above .90. The validity of these ratingscould not be assessed in comparison with otherwork because no other ratings have been obtained,but complexity ratings were compared with themetric developed by Garcia et al. (1994), and thecorrelation between the two measures was high(rs = .73). The 40 icons selected from this corpusfor this experiment were initially selected random-ly, and then some adjustments were made to ensurethat the full range of each of the characteristicsunder examination were represented (concrete-ness: M = 3.23, SD = 1.12, range =1.65–4.93; com-plexity: M = 2.86, SD = 0.79, range = 1.13–4.15;familiarity: M = 3.00, SD =1.01, range =1.52–4.87;semantic distance: M = 2.78, SD = 1.11, range =1.17–4.88).

A PC equipped with an Intel Pentium III Pro-cessor with 63.0 MB RAM and 450 MHz con-trolled the presentation of the stimuli and recordedparticipants’responses. Participants responded us-ing a mouse set to a default (medium) tracking

speed. Arrays of eight black-and-white icons (each90 × 90 pixels) were displayed on at 14-inch(35.6-cm) monitor set to a resolution of 1024 × by768 pixels.

Experimental Task

In the icon identification task a grid of eighticons was displayed on the computer screen, alongwith a function label in the top left corner of thescreen (see Figure 2). Participants were requiredto mouse click as quickly and accurately as possi-ble on the icon that they thought matched the func-tion label. When participants chose the correcticon, it was highlighted with a dashed border.When they chose the wrong icon, the correct iconwas highlighted with a dotted border. Pilot testingshowed that the two borders were clearly distin-guishable. Participants had 5 s to respond in eachexperimental trial before being shown a newfunction and set of icons.

Each block of experimental trials consisted of40 5-s trials. The effects of increasing user expe-rience were mimicked by presenting participantswith 10 blocks of 40 trials. Icons were presentedrandomly at different positions on the grid. Eachicon from the set of 40 appeared as the target icononce and as a distracter icon seven times in eachblock of 40 trials. Each icon was therefore pre-sented to participants 8 times in each block and80 times across all 10 blocks of trials.

RESULTS

Table 2 shows the mean accuracy scores andresponse times across blocks of experimental tri-als. Both performance measures show dramaticimprovements in initial blocks of trials; accuracyscores appear to reach asymptote in later trials,whereas response times continue to gradually re-duce with experience. The correlations betweeneach icon characteristic and performance acrossblocks of trials are also shown in Table 2. Iconconcreteness, familiarity, and semantic distancewere all closely correlated with accuracy scores ininitial blocks of trials, but correlations decreasedin later blocks of trials once users had gained someexperience with the icons. Visual complexity wasnot significantly correlated with accuracy on anyblock of trials. Strong correlations were also ap-parent between response times and concreteness,familiarity, and semantic distance, and theseremained strong across all 10 blocks of trials.Again, correlations between response times and

Continued on page 471

ICON IDENTIFICATION IN CONTEXT 469

Continued on next page

TABLE 1: Icons and Ratings in Alphabetical Order

Icon Ratings Icon Ratings

Concreteness: 2.27Complexity: 2.80Familiarity: 1.93Sem. dist.: 1.89

Added fabric web width

Concreteness: 1.98Complexity: 2.60Familiarity: 2.05Sem. dist.: 1.98

Air vent

Concreteness: 2.93Complexity: 4.13Familiarity: 1.98Sem. dist.: 1.64

Apple computer

Concreteness: 4.27Complexity: 2.58Familiarity: 1.98Sem. dist.: 4.14

Baggage lockers

Concreteness: 1.80Complexity: 3.88Familiarity: 2.45Sem. dist.: 2.88

Binary File

Concreteness: 4.65Complexity: 1.13Familiarity: 4.33Sem. dist.: 1.35

Blow molding

Concreteness: 4.35Complexity: 2.90Familiarity: 3.93Sem. dist.: 3.14

Cooperate

Concreteness: 3.03Complexity: 3.05Familiarity: 3.48Sem. dist.: 3.95

Dam

Concreteness: 4.32Complexity: 4.00Familiarity: 3.20Sem. dist.: 3.48

Debug

Concreteness: 4.47Complexity: 2.05Familiarity: 3.57Sem. dist.: 2.67

Diagnose

Concreteness: 3.45Complexity: 2.35Familiarity: 2.98Sem. dist.: 3.31

Distressed Vessel

Concreteness: 2.40Complexity: 1.45Familiarity: 3.43Sem. dist.: 2.43

Eject

Concreteness: 4.40Complexity: 3.63Familiarity: 4.45Sem. dist.: 3.48

Electric Transmission

Concreteness: 2.25Complexity: 2.65Familiarity: 1.93Sem. dist.: 1.17

Electrical precipitator

Concreteness: 2.43Complexity: 3.82Familiarity: 3.15Sem. dist.: 2.78

Equipotentials

Concreteness: 4.93Complexity: 3.03Familiarity: 4.53Sem. dist.: 4.83

Football

Concreteness: 4.50Complexity: 1.88Familiarity: 4.33Sem. dist.: 3.69

Fighter

Concreteness: 2.00Complexity: 2.75Familiarity: 1.87Sem. dist.: 1.32

Fixed bed reactor

Concreteness: 2.38Complexity: 3.35Familiarity: 2.42Sem. dist.: 1.86

Iron

Concreteness: 2.13Complexity: 4.15Familiarity: 1.38Sem. dist.: 1.46

Jacketed reactor

470 June 2007 – Human Factors

TABLE 1: continued

Icon Ratings Icon Ratings

Concreteness: 4.13Complexity: 3.30Familiarity: 3.05Sem. dist.: 3.43

Library

Concreteness: 2.08Complexity: 3.18Familiarity: 3.58Sem. dist.: 2.40

Line vessel

Concreteness: 2.23Complexity: 2.98Familiarity: 2.45Sem. dist.: 3.26

Lumber industry

Concreteness: 3.37Complexity: 1.23Familiarity: 4.75Sem. dist.: 2.14

Male

Concreteness: 4.25Complexity: 1.95Familiarity: 3.37Sem. dist.: 3.12

Manual control

Concreteness: 2.15Complexity: 2.73Familiarity: 2.00Sem. dist.: 1.98

Museum

Concreteness: 2.70Complexity: 3.18Familiarity: 3.58Sem. dist.: 2.40

Noise

Concreteness: 1.88Complexity: 2.83Familiarity: 1.85Sem. dist.: 1.26

Oscillating motor

Concreteness: 4.00Complexity: 2.23Familiarity: 3.90Sem. dist.: 3.90

Picnic area

Concreteness: 2.65Complexity: 3.20Familiarity: 2.03Sem. dist.: 1.63

Press tool

Concreteness: 1.65Complexity: 3.40Familiarity: 1.65Sem. dist.: 1.55

Rinse

Concreteness: 3.65Complexity: 3.72Familiarity: 3.30Sem. dist.: 3.15

Risk of explosion

Concreteness: 1.90Complexity: 4.00Familiarity: 1.53Sem. dist.: 1.70

Rotary vacuum cleaner

Concreteness: 4.83Complexity: 3.18Familiarity: 3.90Sem. dist.: 4.62

Safe

Concreteness: 2.12Complexity: 3.03Familiarity: 1.87Sem. dist.: 2.38

Scale of measurement

Concreteness: 2.25Complexity: 3.40Familiarity: 1.65Sem. dist.: 1.55

Spark ignition coil element

Concreteness: 4.88Complexity: 3.10Familiarity: 4.53Sem. dist.: 4.83

Tape cassette

Concreteness: 4.93Complexity: 3.03Familiarity: 3.88Sem. dist.: 4.90

Typewriter

Concreteness: 4.83Complexity: 3.28Familiarity: 3.90Sem. dist.: 4.69

Video camera

Note. Sem. dist. = semantic distance. Permission to reproduce extracts from BS ISO 7000 (added fabric web width, manual control,press tool, scale of measurement [measurement], and spark coil ignition element) is granted by BSI. British Standards can be obtainedfrom BSI Customer Services, 389 Chiswick High Road, London W4 4AL. Tel: +44 (0)20 8996 9001. The Apple computer, binary file(bitmap), cooperate, diagnose, library, and noise icons are reprinted with permission of John Wiley & Sons, Inc. All the other icons arein the public domain. One icon in the set is not included here because permission to reproduce it was not obtained.

ICON IDENTIFICATION IN CONTEXT 471

visual complexity were not significant. Because vi-sual complexity did not correlate with either accu-racy or response times, it was not included in anyfurther analyses.

Accuracy

A repeated measures analysis of covariancewas conducted to examine accuracy performancewith blocks of trials (1–10) as a within-subjectseffect and concreteness, familiarity, and semanticdistance as covariates. By-items analyses ratherthan by-subjects analyses were used because itwas the nature of the icons that was the focus ofinterest. Accuracy scores for each icon, averagedacross participants, were therefore computed priorto these analyses. Accuracy scores increased sig-nificantly as participants gained experience overblocks of trials, F(9, 324) = 80.13, MSE = 0.28,p < .01, ηp

2 = .69. Of the covariates, semantic dis-tance had a significant overall effect on accuracy,F(1, 36) = 9.23, MSE = 0.03, p < .01, ηp

2 = .20, buticon familiarity, F(1, 36) = 2.16, MSE = 0.15, p =.06, and concreteness did not, F(1, 36) = .34,MSE = 0.003, p = .56. The interaction betweenblocks of trials and concreteness was not signif-icant, F(9, 324) = 1.39, MSE = 0.005, p = .19; how-ever, the interactions with semantic distance andfamiliarity were significant, F(9, 324) = 11.57,MSE = 0.04, p < .01, ηp

2 = .24 and F(9, 324) =3.67, MSE = 0.01, p < .01, ηp

2 = .09, respectively.The nature of the relationship between icon

characteristics and accuracy scores was examinedfurther by carrying out regression analyses for

each block of trials. The beta values obtained inthe regressions were identical to the correlationvalues shown in Table 2. Table 3 shows the per-centage of the variance explained at each blockof trials. When considered as the sole predictor ofaccuracy, semantic distance predicts over half thevariance observed in accuracy scores in earlyblocks of trials, although this reduces as partici-pants gain experience with the icons. Familiarityalso explains a remarkable amount of the variance,although it is not as powerful as semantic distance.

At a practical level it is important to knowwhich icon characteristic is the best single predic-tor of identification accuracy because measuresof this characteristic can then be used as an indexof icon usability. Previous research, however, hasshown that ratings of semantic distance, familiar-ity, and icon concreteness are correlated and there-fore overlap in the performance variance they arelikely to explain (McDougall et al., 1999). Furtherregression analyses were therefore carried out inwhich the icon characteristics were entered afterall other icon characteristics in order to find outthe unique variance that each contributed to pre-dicting accuracy scores (see Table 3). This showsthat although there is considerable overlap in thevariance explained by these characteristics, bothsemantic distance and familiarity contribute sig-nificantly to predicting accuracy in early blocks oftrials. Finally, it is important to consider the totalpredictive power of all four covariates when theywere considered together. These are also shownin Table 3 and indicate that when considered

Figure 2. The icon identification task. The noise (bottom row, third from left) icon comes from The icon book: Visualsymbols for computer systems and documentation, W. Horton, 1994, John Wiley & Sons, New York. Reprinted withpermission of John Wiley & Sons, Inc. All the other icons are in the public domain.

472

TAB

LE 2

: Acc

urac

y an

d R

esp

ons

e Ti

mes

, Mea

ns, S

tand

ard

Dev

iatio

ns, a

nd C

orr

elat

ions

With

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n C

hara

cter

istic

s fo

r E

ach

Blo

ck o

f Tr

ials

Blo

cks

of

Tria

ls

12

34

56

78

910

Acc

urac

y (%

co

rrec

t)M

ean

66.9

290

.83

94.4

297

.40

97.0

097

.33

98.5

098

.83

98.6

799

.00

Stan

dar

d d

evia

tio

n24

.56

16.1

310

.61

8.32

6.40

6.37

3.11

3.25

2.24

2.02

Ico

n co

ncre

tene

ss.5

9**

.63*

*.6

2**

.44*

*.4

0*.2

3.1

9.1

7.2

0.2

4V

isua

l co

mp

lexi

ty–.

03–.

14–.

22–.

07–.

09–.

11–.

10.0

3–.

06–.

02Ic

on

fam

iliar

ity

.65*

*.6

4**

.65*

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.41*

*.2

2.1

8.1

7.1

3.3

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man

tic

dis

tanc

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*.6

6**

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.28

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ean

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2243

2026

1834

1748

1713

1724

1690

1668

1627

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452

343

935

932

229

628

625

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322

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ness

–.61

**–.

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–.70

**–.

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–.56

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–.56

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Vis

ual c

om

ple

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.16

.23

.27

.27

.20

.15

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.19

.13

.22

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n fa

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65**

–.77

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77**

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–.62

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63**

–.52

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.05.

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< .0

1.

473

TAB

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: Per

cent

age

of

Varia

nce

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ted

fo

r in

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h B

lock

of

Tria

ls

Blo

cks

of

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ls

12

34

56

78

910

Acc

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man

tic

dis

tanc

e%

Var

ianc

e ex

pla

ined

56.4

**50

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43.9

**29

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18.6

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7.8

8.6

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% U

niq

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exp

lain

ed18

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10.9

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5*9.

5**

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5.3

4.3

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iliar

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% V

aria

nce

exp

lain

ed42

.8**

40.6

**42

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19.7

**16

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% V

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474 June 2007 – Human Factors

together, the covariates predicted up to 62% vari-ance seen in accuracy scores.

Response Times

Identical analyses were carried out for responsetimes. The effect of experience with icons overblocks of trials was significant, F(9, 324) = 80.13,MSE = 1,820,611.85, p < .01, ηp

2 = .68. Of thecovariates, familiarity had a significant overalleffect on response times, F(1, 36) = 9.03, MSE =370,359.26, p < .01, ηp

2 = .20, but semantic distance, F(1, 36) = 4.07, MSE = 167,070.67, p =.051, and concreteness, F(1, 36) = .14, MSE =5,726.18, p = .71, did not. Although semantic distance had no overall effect, it interacted sig-nificantly with blocks of trials, F(9, 324) = 8.55,MSE = 216,317.18, p < .01, ηp

2 = .19. Neitherfamiliarity nor concreteness interacted with expe-rience, F(9, 324) = 1.43, MSE = 36,310.87, p = .17,and F(9, 324) = 0.75, MSE = 18,983.94, p = .66,respectively. The significant effects of familiarityand semantic distance were examined further us-ing regression analyses. Table 3 shows that in thefirst block of trials semantic distance accounted foralmost 60% of the variance in response times. Al-though this reduced considerably as participantsgained experience with the icons, it remained asignificant predictor across all blocks of trials.Familiarity was initially a less powerful predictorthan semantic distance but remained a consistent-ly strong predictor of response times across blocksof trials (hence the lack of a significant interac-tion with blocks of trials in the initial analysis).This pattern of findings is reflected in the uniquevariance that each characteristic predicts, with se-mantic distance predicting significant variance inearly blocks of trials and familiarity being a sig-nificant predictor in most blocks of trials. Whenconsidered together, the covariates explain up to68.6% of the variance observed in early blocks of trials.

DISCUSSION

This experiment revealed that cognitive iconcharacteristics are major determinants of iconcomprehensibility, accounting for up to 69% of thevariance seen in performance. The extent to whicha given icon characteristic predicted icon identi-fication varied depending on whether accuracy orresponse time was considered and also on theamount of experience participants had with the

icon set. The two primary determinants of iconidentification were semantic distance and famil-iarity. Semantic distance was important in earlyblocks of trials, when participants were buildingconnections between icons and functions, but be-came less important once these relationships werelearned. When performance accuracy was con-sidered, familiarity was influential in early blocksof trials, presumably because participants wereable to use their familiarity with items depicted inthe icon in order to ascertain its meaning. Howev-er, familiarity has longer term effects in determin-ing response times. The most likely explanationfor this pattern of findings is that performance isbetter for familiar items because long-term mem-ory representations are initially richer and easierto access (Lambon-Ralph et al., 1998), and thisdifference in the quality of the underlying repre-sentations remains even after repeated presenta-tions (Jescheniak & Levelt, 1994).

Given that concreteness has been regarded bysome researchers to be the most important proper-ty of icons (Moyes & Jordan, 1993), these findingshighlight the need to consider the relative impor-tance of icon characteristics rather than simplyexplore their role in isolation. Our findings alsosuggest that the effects of the visual metaphor areless powerful than is commonly supposed. This isprobably because only a limited number of func-tions can easily be represented pictorially (Rogers& Oborne, 1987; Stammers & Hoffman, 1991).Because many more concepts can be representedabstractly, icon design needs to focus more close-ly on the conceptual mapping between icon andfunction.

It is not clear why visual complexity had littleeffect on performance, particularly as it has beenfound to be important in the visual search of dis-plays (Byrne, 1993; McDougall et al., 2000). Apossible explanation may lie in the nature of thetask given to participants. The effects of visualcomplexity appear to be most apparent in responsetimes when the search aspects of the task are em-phasized – that is, when an icon, rather than afunction label, is presented to participants andthey are required to search the array for that iconas quickly as possible (see McDougall et al.,2000, Experiment 1).

Theoretically, one possible way of explainingthe role of icon characteristics is with referenceto theories of picture naming, on the assumptionthat picture and icon identification are likely to

ICON IDENTIFICATION IN CONTEXT 475

involve similar processing. Johnson, Paivio, andClark’s (1996) theory is typical of theories of thistype, and information processing is thought to pro-ceed in a series of steps:(1) search and perception of the icon (i.e., access to and

computation of a visual representation of the ob-jects from the visual image);

(2) retrieval of a matching representation (i.e., access tostored visual representations);

(3) activation of semantic information (i.e., access toconceptual and functional information associatedwith the object); and

(4) access to the function, or name, via referential con-nections.

Visual complexity appears to determine thespeed with which a display can be searched andan icon located (Bryne, 1993; McDougall et al.,2000) and is associated with Processing Step 1.This did not appear to be a major feature of theicon identification task used in this study. Famil-iarity with items depicted in the icon determinesthe ease with which stored visual representationscan be retrieved (i.e., Processing Step 2), and bet-ter long-term memory representations for famil-iar icons also act as more efficient retrieval cues,enabling quicker and more effective access tolong-term memory representations for the con-ceptual and functional information associatedwith the item (i.e., Processing Step 3; see Lambon-Ralph et al., 1998). Semantic distance, because itis a measure of the degree to which icon and func-tion labels are related, is an index of the close-ness, or strength, of the referential connectionsbetween the semantic information accessed atStep 3 and the function label accessed at Step 4.

Johnson et al. (1996) argued that the formationof strong referential links between visual (icon)and verbal (function) was an important predictorof the accuracy and time taken to name pictures.If there were several possible names for a picture,then the time taken to name a picture increasedbecause the strength of the individual referentialconnections was reduced. Once learning of ref-erential connections has reached asymptote, iconfamiliarity is likely to become more important.Object familiarity is a strong predictor of the timetaken to name pictures (e.g., Cuetos, Ellis, & Al-varez, 1999), and its effects do not appear todiminish over time (Jescheniak & Levelt, 1994).This parallels the findings reported here, in whichperformance differences between familiar andunfamiliar items remained even after users hadgained considerable experience with the icons. It

is important to note, however, that although thereappears to be some correspondence between John-son et al.’s (1996) theory and our findings, theassumptions underpinning the theory need to betested specifically with respect to icons.

Anumber of questions arise from our research.Given the importance of the closeness of the asso-ciation between the icon and its function name,future research needs to determine precisely whatdetermines semantic distance and how it mightbe measured objectively. Recently an objectivemeasure of semantic distance between pairs ofwords was obtained by Maki, McKinley, andThompson (2004). They combined informationfrom an electronic dictionary, WordNet (Fell-baum, 1998), with co-occurrence and associativenorms to produce an objective measure that cor-related well with ratings of semantic distance.There are no equivalent dictionaries or electronicdatabases available for icons, so it may be sometime before an objective measure of the semanticdistance between icon and function is produced.Ultimately a better understanding of semantic dis-tance would provide a way of allowing researchersto determine a priori how comprehensible iconsare likely to be, at least for individuals learningicon sets. Further consideration also needs to begiven to the role of the function name itself andhow its characteristics impact on ease of identifi-cation. Factors such as the frequency, length, andphonological and morphological structure of thefunction name may all be important. The criticalquestion is how important these function charac-teristics may be, relative to the characteristics ofthe icon itself. As yet, this question remains unan-swered. More work also needs to be done to ex-amine the effects that the workload associatedwith an interface has on the ease with which iconsare interpreted and how this might interact withicon characteristics.

These findings have relevance for design prac-tice. They suggest that designers should considerthe association between icon and function (seman-tic distance), particularly if they need to be under-stood immediately with little or no learning or arerarely used (e.g., for warnings; see Mayhorn,Wogalter, & Bell, 2004). However, in situationswhere icons are likely to be used on a regular basis,familiarity with what is depicted in the icon ap-pears to be most important. Given that previousresearch suggests that familiarity is an index of thequality and accessibility of long-term memory

476 June 2007 – Human Factors

representations, further research needs to addresshow icons can be designed to facilitate the devel-opment of rich and accessible long-term memoryrepresentations.

ACKNOWLEDGMENTS

This work was supported by an EPSRC/BAESYSTEMS Industrial Case Studentship (Ref.98805245). We gratefully acknowledge NeilCarter’s technical and programming support.

REFERENCES

Arend, U., Muthig, K.-P., & Wandmacher, J. (1987). Evidence for glob-al feature superiority in menu selection by icons. Behavior andInformation Technology, 6, 411–426.

Bates, E., D’Amico, S., Jacobsen, T., Székely, A., Andonova, E.,Devescovi, A., et al. (2003). Timed picture naming in seven lan-guages. Psychonomic Bulletin and Review, 10, 344–380.

Bocker, M. (1993). Amultiple index approach for the evaluation of pic-tograms. In Proceedings of the 14th International Symposium onHuman Factors in Telecommunication (pp. 73–84). Heidelberg: R.v. Decker’s Verlag, G. Schenck GmbH.

Byrne, M. D. (1993). Using icons to find documents: Simplicity is crit-ical. In S. Ashlund, K. Mullet, A. Henderson, E. Hollnagel, & T.While (Eds.), Interchi ’93 – Bridges between worlds: Proceedingsof the Conference on Human Factors in Computing Systems (pp.446–465). Reading, MA: Addison-Wesley.

Caplin, S. (2001). Icon design: Graphic icons in computer interfacedesign. London: Cassell.

Carr, T. H. (1986). Perceiving visual language. In K. R. Boff, L.Kaufman, & J. P. Thomas (Eds.), Handbook of perception andhuman performance: Vol. 2. Cognitive processes and performance(pp. 29–92). New York: Wiley.

Cuetos, F., Ellis, A. W., & Alvarez, B. (1999). Naming times for theSnodgrass and Vanderwart pictures in Spanish. Behavior ResearchMethods, Instruments and Computers, 31, 650–658.

Familant, M. E., & Detweiler, M. C. (1993). Iconic reference: Evolvingperspectives and an organizing framework. International Journalof Man-Machine Studies, 39, 705–728.

Fellbaum, C. (Ed.). (1998). WordNet: An electronic lexical database.Cambridge, MA: MIT Press. Available from http://www.cogsci.princeton.edu

Garcia, M., Badre, A. N., & Stasko, T. (1994). Development and vali-dation of icons varying in their abstractness. Interacting withComputers, 6, 191–211.

Gilhooly, K. J., & Logie, R. H. (1980). Age-of-acquisition, imagery,concreteness, familiarity, and ambiguity measures for 1,944 words.Behavior Research Methods and Instrumentation, 12, 395–427.

Gittens, D. (1986). Icon-based human-computer interaction. Interna-tional Journal of Man-Machine Studies, 24, 519–543.

Goonetilleke, R. S. Shih, H. M., On, H. K., & Fritsch, J. (2001). Effectsof training and representational characteristics in icon design.International Journal of Human-Computer Studies, 55, 741–760.

Green, A. J. K., & Barnard, P. J. (1990). Iconic interfacing: The role oficon distinctiveness and fixed or variable screen locations. In D.Diaper, D. Gilmore, G. Cockton, & B. Shackel (Eds.), Human-computer interaction – Interact ’90 (pp. 457–462). Amsterdam:Elsevier Science Publishers.

Hancock, H. E., Rogers, W. A., Schroeder, D., & Fisk, A. D. (2004).Safety symbol comprehension: Effects of symbol type, familiarity,and age. Human Factors, 46, 183–195.

Hartshorne, C. & Weiss, P. (Eds.). (1932). The collected papers ofCharles Sanders Peirce. Cambridge, MA: Harvard University Press.

Horton, W. (1994). The icon book: Visual symbols for computer systemsand documentation. New York: Wiley.

Jescheniak, J., & Levelt, W. (1994). Word frequency effects in speechproduction: Retrieval of syntactic information and of phonological

form. Journal of Experimental Psychology: Learning, Memory,and Cognition, 20, 824–843.

Johnson, C. J., Paivio, A., & Clark, J. M. (1996). Cognitive componentsof picture naming. Psychological Bulletin, 120, 113–139.

Keller, P., & Stevens, C. (2004). Meaning from environmental sounds:Types of signal-referent relations and their effect on recognizingauditory icons. Journal of Experimental Psychology: Applied, 10,3–12.

Lambon-Ralph, M., Graham, K. S., Ellis, A. W., & Hodges, J. R.(1998). Naming in semantic dementia: What matters? Neuro-psychologia, 36, 775–784.

Maki, W. S., McKinley, L. N., & Thompson, A. G. (2004). Semanticdistance norms computed from an electronic dictionary (WordNet).Behavior Research Methods, Instruments and Computers, 36,421–431.

Mayhorn, C. B., Wogalter, M. W., & Bell, J. L. (2004). Homeland secu-rity safety symbols: Are we ready? Ergonomics in Design, 14(4),6–14.

McDougall, S. J. P., Curry, M. B., & de Bruijn, O. (1999). Measuringsymbol and icon characteristics: Norms for concreteness, com-plexity, meaningfulness, familiarity and semantic distance for 239symbols. Behavior Research Methods, Instruments and Computers,31, 487–519.

McDougall, S. J. P., Curry, M. B., & de Bruijn, O. (2001). The effectsof visual information on users’ mental models: An evaluation ofpathfinder analysis as a measure of icon usability. InternationalJournal of Cognitive Ergonomics, 5, 59–84.

McDougall, S. J. P., de Bruijn, O., & Curry, M. B. (2000). Exploringthe effects of icon characteristics on user performance: The role oficon concreteness, complexity, and distinctiveness. Journal of Ex-perimental Psychology: Applied, 6, 291–306.

Moyes, J., & Jordan, P. W. (1993). Icon design and its effect on guess-ability, learnability and experienced user performance. In J. D. Alty,D. Diaper, & S. Guest (Eds.), People and computers VIII (pp.49–59). Cambridge, UK: Cambridge University Society.

Paivio, A., Yuille, J. C., & Madigan, S. A. (1968). Concreteness,imagery and meaningfulness values for 925 nouns. Journal ofExperimental Psychology, 76, 1–25.

Rogers, Y., & Oborne, D. J. (1987). Pictorial communication of abstractverbs in related to human-computer interaction. British Journal ofPsychology, 78, 99–112.

Scott, D. (1993). Visual search in modern human-computer interfaces.Behaviour and Information Technology, 12, 174–189.

Snodgrass, J. G., & Vanderwart, M. (1980). A standardized set of 260pictures: Norms for name agreement, image agreement, familiari-ty and visual complexity. Journal of Experimental Psychology:Human Learning and Memory, 6, 174–215.

Stammers, R. B., & Hoffman, J. (1991). Transfer between icon sets andratings of icon concreteness and appropriateness. In Proceedingsof the Human Factors Society 35th Annual Meeting (pp. 354–358).Santa Monica, CA: Human Factors and Ergonomics Society.

Stotts, D. B. (1998). The usefulness of icons on the computer interface:Effect of graphical abstraction and functional representation onexperienced and novice users. In Proceedings of the Human Factorsand Ergonomics Society 42nd Annual Meeting (pp. 453–457). SantaMonica, CA: Human Factors and Ergonomics Society.

Sarah J. Isherwood is a lecturer in health care at LeedsUniversity. She received her Ph.D. in psychology fromthe University of Wales Swansea in 2002.

Siné J. P. McDougall is a professor of psychology atUniversity of Wales Swansea. She received her Ph.D. inpsychology from the University of London in 1993.

Martin B. Curry is a senior human factors specialist atNational Air Traffic Services, Bournemouth. Hereceived his Ph.D. in psychology from the University ofLondon in 1987.

Date received: March 3, 2005Date accepted: March 2, 2006