Available online at www.sciencedirect.com

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doi:10.1016/j.ijh

nCorrespondin

E-mail addr

david@isys.uni

gutwin@cs.usa1Tel: þ43 462Tel: þ1 306

Int. J. Human-Computer Studies 70 (2012) 218–233

www.elsevier.com/locate/ijhcs

Understanding performance in touch selections: Tap, drag and radialpointing drag with finger, stylus and mouse

A. Cockburna,n, D. Ahlstromb,1, C. Gutwinc,2

aDepartment of Computer Science and Software Engineering, Private Bag 4800, University of Canterbury, Christchurch, New ZealandbDepartment of Informatics Systems, Klagenfurt University, Universitatsstrasse 65-78, 9020 Klagenfurt, Austria

cDepartment of Computer Science, University of Saskatchewan, 110 Science Place, Saskatoon, Saskatchewan, Canada

Received 9 February 2011; received in revised form 25 October 2011; accepted 22 November 2011

Communicated by S. Wiedenbeck

Available online 30 November 2011

Abstract

Touch-based interaction with computing devices is becoming more and more common. In order to design for this setting, it is critical

to understand the basic human factors of touch interactions such as tapping and dragging; however, there is relatively little empirical

research in this area, particularly for touch-based dragging.

To provide foundational knowledge in this area, and to help designers understand the human factors of touch-based interactions, we

conducted an experiment using three input devices (the finger, a stylus, and a mouse as a performance baseline) and three different

pointing activities. The pointing activities were bidirectional tapping, one-dimensional dragging, and radial dragging (pointing to items

arranged in a circle around the cursor). Tapping activities represent the elemental target selection method and are analysed as a

performance baseline. Dragging is also a basic interaction method and understanding its performance is important for touch-based

interfaces because it involves relatively high contact friction. Radial dragging is also important for touch-based systems as this technique

is claimed to be well suited to direct input yet radial selections normally involve the relatively unstudied dragging action, and there have

been few studies of the interaction mechanics of radial dragging. Performance models of tap, drag, and radial dragging are analysed.

For tapping tasks, we confirm prior results showing finger pointing to be faster than the stylus/mouse but inaccurate, particularly with

small targets. In dragging tasks, we also confirm that finger input is slower than the mouse and stylus, probably due to the relatively high

surface friction. Dragging errors were low in all conditions. As expected, performance conformed to Fitts’ Law.

Our results for radial dragging are new, showing that errors, task time and movement distance are all linearly correlated with number

of items available. We demonstrate that this performance is modelled by the Steering Law (where the tunnel width increases with

movement distance) rather than Fitts’ Law. Other radial dragging results showed that the stylus is fastest, followed by the mouse and

finger, but that the stylus has the highest error rate of the three devices. Finger selections in the North-West direction were particularly

slow and error prone, possibly due to a tendency for the finger to stick–slip when dragging in that direction.

& 2011 Elsevier Ltd. All rights reserved.

Keywords: Touch interaction; Dragging; Radial menus

1. Introduction

Direct-touch interfaces are now becoming common incomputing devices such as tablets, smart phones, handheld

e front matter & 2011 Elsevier Ltd. All rights reserved.

cs.2011.11.002

g author. Tel.: þ64 3 364 2987x7768; fax: þ64 3 364 2569.

esses: andy@cosc.canterbury.ac.nz (A. Cockburn),

-klu.ac.at (D. Ahlstrom),

sk.ca (C. Gutwin).

3 2700 3514; fax: þ43 0 463 2700 3598.

966 8646; fax: þ1 306 966 4884.

game platforms, and digital surfaces. Touch-based inter-action is natural and simple, and can be easily carried outin mobile settings using a stylus or finger. As this style ofinput becomes a standard approach for designing inter-active systems, it is important to understand the underlyinghuman factors of input techniques that involve directcontact with the input surface. However, there are surpris-ingly few empirical results available to help designersunderstand the fundamental performance issues involvedin carrying out basic interface actions with direct touch.

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233 219

To provide an initial foundation for understanding theseissues, this paper describes the results of an experimentinvestigating and comparing three input techniques (finger-based direct touch, direct stylus, and indirect mouse as aperformance baseline) across three different types of low-level pointing activity (traditional target acquisition via‘tapping’, drag-based target acquisition, and radial drag-ging as required by pie menus (Callahan et al., 1988;Kurtenbach et al., 1993)).

We analysed tap and drag interactions because they areelemental and although they have been comprehensivelystudied with indirect pointing devices (e.g., Gillan et al.(1990), Kabbash et al. (1993), and MacKenzie et al. (1991))there has been very little analysis of their performance intouch-based computing. We also analysed radial dragging,which is important for four reasons. First, it has beennoted that radial controls such as pie menus are well suitedto direct input techniques (Kurtenbach and Buxton, 1991),such as touch or stylus. Second, radial selections normallyuse a directional dragging action, and consequently theyprovide a platform for examining how contact with thedisplay surface can influence dragging in different direc-tions. Third, the basic human factors of radial pointingwith any form of input device are not well understood(Ahlstrom et al., 2010). This is surprising given that radial‘pie menus’ (Callahan et al., 1988) and gestural ‘markingmenus’ (Kurtenbach and Buxton, 1993; Kurtenbach et al.,1993) have been evaluated and refined for more than twodecades. Fourth, radial marking menus facilitate a naturaltransition from novice to expert use (Kurtenbach andBuxton, 1993), and they are therefore increasinglydeployed in high end software systems (e.g., AutodeskMaya and computer games).

The two main contributions of this paper are as follows:We demonstrate that the comparative efficiency of

finger, stylus and mouse input depends on the pointingtask. In particular, we show that the finger is slower thanthe stylus or mouse in dragging and radial dragging tasks(probably due to its susceptibility to surface friction).Although slow for dragging, we confirm prior resultsshowing that the finger is the fastest (but least accurate)technique for tapping tasks.

We provide the first comprehensive analysis of humanperformance in radial dragging. Prior studies have exam-ined radial gestures with marking menus, but there hasbeen little analysis of radial selections where the users’actions are guided by visual feedback. Our results showthat radial dragging selection time and movement distanceare linear functions of the number of items, and wedemonstrate that selection time is modelled by an inter-esting case of the Steering Law (where tunnel widthincreases with distance) rather than by Fitts’ Law. Wealso provide insights into the relative performance of thedifferent input devices in radial dragging, including theobservation that finger selections are particularly slow anderror prone when moving to targets in a North-Westdirection, probably due to a tendency for the finger to

stick–slip when moving co-linearly in the direction itpoints.The next section presents related work on direct input

devices and on radial selections. We then present ourexperimental method and results, followed by a discussionand conclusions.

2. Related work

Target acquisition with a pointing device is an elementalactivity in most graphical user interfaces. It is thereforeunsurprising that human performance in target acquisitionhas been closely scrutinised, partly because several keyparameters combine to produce an extremely large spacefor empirical analysis. These parameters include thefollowing: pointing directness—the presence or absence ofa physical separation between the input device and theobject being selected (e.g., Forlines and Balakrishnan,2008; Forlines et al., 2007; Sasangohar et al., 2009);selection metaphor, such as tapping/pressing on the object(Balakrishnan, 2004; MacKenzie, 1991; Ren and Moriya,2000), dragging over it (MacKenzie et al., 1991), crossingits boundary (Accot and Zhai, 2002; Forlines andBalakrishnan, 2008), directional movements (Callahanet al., 1988; Fitzmaurice et al., 2003), and gesture(Kurtenbach and Buxton, 1993; Kurtenbach et al., 1993);input device, such as mouse, trackball, joystick, trackpad,stylus, touch, etc.; control-display gain, which is the natureof the mapping from control stimulus to output effect inindirect pointing (Casiez et al., 2008); and target properties

and feedback effects, such as particularly small targets(Benko et al., 2006) and their feedback (Cockburn andBrewster, 2005; Cockburn and Brock, 2006), and targetaware versus target agnostic pointing metaphors(Grossman and Balakrishnan, 2005; Ramos et al., 2007).This section reviews prior work on target acquisition,

focusing on the key issues addressed in this paper: first,performance comparisons across input devices; second,studies of radial interfaces.

2.1. Comparisons across input devices and input styles

Fitts’ (1954) seminal experiments investigating thehuman motor system involved participants using a hand-held stylus to directly tap on targets of controlledwidth (W) and separation amplitude (A), yielding a stronglinear relationship between target acquisition time andthe logarithm of the ratio of amplitude to width.Many subsequent studies (e.g., MacKenzie (1992) andBalakrishnan (2004)) have also confirmed that the relation-ship holds for indirect pointing where there is a physicalseparation between input device and display. Typically, theShannon formulation (MacKenzie, 1991) of Fitts’ Law isused to express the relationship as T¼aþb� log2(A/Wþ1). While Fitts’ Law models the acquisition ofdiscrete targets, Accot and Zhai’s (1997) Steering Lawmodels movement time through a constrained path of

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233220

length A that is composed of arbitrarily short straightsegments s of width W(s) as follows:

T ¼ b

Z A

0

ds=W ðsÞ

In an early study, Mack and Lang (1989) comparedmouse, direct stylus, finger-touch, and keyboard interac-tion using tasks that involved compound interactions, suchas comparing two documents across windows. They con-cluded that the stylus was significantly faster than thekeyboard, similar to the mouse, and slightly faster thandirect touch, and that there were accuracy problems withfinger pointing. The fast performance of the stylus wasconfirmed by MacKenzie et al. (1991) who scrutinisedpointing tasks across mouse, indirect stylus, and trackballdevices. Their results showed that the stylus and mouseperformed similarly, and that the trackball was muchslower and more error prone. Kabbash et al. (1993)replicated the method, adding a factor for preferred andnon-preferred hand, producing similar results to the pre-vious study, and showing that the preferred hand issuperior for precise targeting activities.

Mack and Lang’s (1989) study used direct stylus inputwhile MacKenzie et al. (1991) used an indirect stylus (aWacom digitising tablet). A recent study by Forlines andBalakrishnan (2008) investigated the impact that pointingdirectness has on performance, comparing indirect versusdirect stylus pointing, finding that the two modes areessentially equivalent for pointing activities, but that directinput outperforms indirect for crossing activities. Crossingselections are distinct from both pointing and draggingactivities because selections are completed by draggingacross the item’s boundary rather than with an explicitaction over the item (e.g. button release or lift off).Forlines and Balakrishnan’s (2008) study builds on earlierwork investigating different selection modalities for touch(Potter et al., 1988) and stylus (Ren and Moriya, 2000)interactions, which examined various terminating states fortouch based selections, including first-contact, slide-over,lift-off, and so on. In general, selection modalities per-formed better when they allowed users to hit the surfacebefore sliding into the target, then lifting off to confirm theselection.

Sasangohar et al. (2009) recently compared finger touchand mouse performance in a Fitts’ Law bidirectionaltapping task, finding that finger touch was generally faster,but less accurate than the mouse, particularly with smalltargets. Their analysis followed up an earlier study byForlines et al. (2007), which suggested that touch outper-forms the mouse for bimanual tabletop interaction, butthat the mouse may be more appropriate than touch forinteractions requiring a selection point. Finger accuracyproblems were also noted by Lee and Zhai (2009) in theircomparison of finger and stylus pointing on mobile devicesusing various feedback cues. They also observed that thefinger was slightly slower than the stylus when audio and

tactile feedback were absent, but that there was noperformance difference when either of these feedbackmodalities were present.Dragging interactions have received relatively little

attention, which is surprising for two reasons: first, drag-ging is an important and elemental interaction, andsecond, friction differences are likely to have a majorimpact on performance with different devices (e.g., mouse,stylus, and finger). MacKenzie et al. (1991) conducted oneof the few comparative analyses of how dragging perfor-mance is influenced by input device, examining mouse,trackball and indirect stylus (a Wacom Tablet). Theirresults showed dragging to be slower than pointing, andthat performance degraded more between pointingand dragging with the mouse than with the stylus ortrackball. The only prior study of finger-touch dragging(that we are aware of) is that of Forlines et al. (2007),which included performance comparisons of the mouseand finger in a composite task that required tappingfrom a circular starting target to a square intermediatetarget, then dragging the square until it ‘docked’ with aterminating third target (also square). The terminatingstate for the dragging activity was automatically detectedwhen the centre of the dragged square was within 5 pixelsof other’s centre. Their results showed that touch wasfaster than the mouse for tapping tasks, but slower fordocking activities.Forlines et al.’s (2007) experiment provides important

insights into touch based dragging. Like all experiments,however, the specific method selected gives rise to certainthreats to validity. First, all dragging actions required ahigh level of precision (within 5 pixels), regardless of thetarget’s size. Second, the completion of dragging actionswas automatically detected (within the 5 pixel offset),which is atypical of real actions where the user explicitlylifts from the surface to complete the drag. These experi-mental decisions lead to concerns that the results may notgeneralise to typical desktop dragging actions where thecursor needs to be enclosed by the target to complete thedragging action and where the user explicitly terminatesthe action. (These concerns are addressed by the design ofour study described below.)Table 1 summarises these studies, showing how various

projects have compared across different aspects of targetacquisition performance using different input devices andselection styles.While touch-based dragging has been only lightly stu-

died, the ‘fat finger’ problem of precise target acquisition infinger interactions have been reported in many studies(Albinsson and Zhai, 2003; Mack and Lang, 1989; Potteret al., 1988). This has led to a variety of design solutionsbased on offsetting the cursor from the finger (e.g., Potteret al. (1988)), zooming (e.g., Roudaut et al. (2008) andOlwal and Feiner (2003)), bimanual interaction (e.g.,Benko et al. (2006)), and on-screen widgets that providelarge controls for precise positioning (e.g., Albinsson andZhai (2003)).

Table 1

Studies comparing performance across different input devices.

Style Study Trackball Mouse Stylus Indirect Stylus Direct FingerDesktop tasksTap et al.

Tap & Cross

Tap & Drag et alet al.et al.

Tap, Drag, Radial-drag

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233 221

2.2. Radial interfaces and studies

Arranging command alternatives like segments of acircular pie that is centred on the cursor’s location hasimportant theoretical performance advantages over tradi-tional linear menu arrangement. First, target items can bespecified with very small directional movements—ratherthan moving over a series of intervening candidate itemsto reach the target, all items are accessible just off the circle’scentre, resulting in a very small Fitts’ Law amplitude ofmovement. Second, the effective width of each item increasesthe further the user moves from the circle centre, allowingusers flexibility in their speed/accuracy considerations—forexample, when travelling on a bumpy road the user canmove further into the segments to create wider targets,something that is not necessary when comfortably seated ata desk. Third, radial menus facilitate a natural progressionfrom novice to expert performance—while novices can relyon visual feedback to guide their directional selections,experts can exploit muscle memory to make rapid gesturalselections by flicking in the appropriate direction.

Callahan et al. (1988) provide the first empirical evalua-tion of performance with radial selections using a piemenu, demonstrating a 15–20% performance advantageover traditional linear menus. Kurtenbach et al. (1993)extended pie menus into ‘marking menus’ by allowingexperts to select items with directional gestures that pre-empt the pie menu’s visual display. Their primary interestwas in understanding upper performance levels for experts,including hierarchical layouts (Kurtenbach and Buxton,1993; Kurtenbach and Buxton, 1994). Note that radialselections with marking menus require a dragging actionregardless of input device—when issued via a touchsensitive surface lifting the finger or device completes theaction, and when executed with a mouse, releasing themouse button completes the action. If marking menuselections were completed with discrete taps (one to initiatethe selection, and another to terminate it) software wouldbe unable to accurately discriminate between two rapid butdifferent item selections and one continuous marking menuselection.

Enticed by the natural transition from novice to expertperformance, and by the potential for high performance

ceilings, many subsequent researchers have extended andrefined radial menu designs in many different contexts.These include supporting eyes-free menu selections with‘earPod’ (Zhao et al., 2007), focusþcontext zoomingdesigns (Huot and Lecolinet, 2006), hierarchical designs(Francone et al., 2009) for long lists on mobile devices,designs that increase the breadth of the command voca-bulary by using directional deviations at the end ofgestural strokes (Bailly et al., 2008), variants specificallydesigned for bimanual use (Benko et al., 2006), and designsthat remain visibly ready-to-hand by tracking the cursor(Fitzmaurice et al., 2003).Although researchers have closely examined expert

gestural selections and have extensively explored the designspace of radial menus, there is a surprising lack of low-level performance analysis into how users point or drag toradial targets. The only study we are aware of is our earlierwork (Ahlstrom et al., 2010), which used radial menus asan exemplar design for testing a performance model. Inthat paper we made a passing observation that radialselections appeared to deviate from Fitts’ Law, but wenoted that our experimental method allowed two explana-tions for the deviation—in our experiment radial menudiameter increased with number of items, so either thediameter or the number of items (or a combination) causedthe effect. The experiment described in this paper removesthis ambiguity.

3. Experiment

The experiment is designed to characterise the relativemerits of different input devices (mouse, direct-stylus, andfinger) for different types of elemental pointing activities(selecting targets by tapping, dragging, and radial drag-ging). Tapping tasks are included as a critical performancebaseline. Dragging is included because it is an elementalinteraction that has received little attention in priorresearch. The study by Forlines et al. (2007) (described inSection 2.1) is an important exception, but their methodrequired high precision (5 pixel accuracy) and used auto-matic detection of completion, both of which are unusualin desktop interaction. Radial dragging is analysed becauseit is a special case of drag-based interaction (in which drag

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233222

direction is a critical factor) and because human perfor-mance in radial acquisition is not well understood.

The experiment used a within-subjects design, with allparticipants using all three input devices and all pointingtasks. Each participant completed the radial dragging tasksin one experimental session, and then returned the follow-ing week to complete a second session analysing tappingand dragging tasks. We used this separation of sessionsbecause candidate participants expressed a strong prefer-ence for two shorter sessions of intense target selectionsrather than a single long one, and as we had no intentionto compare radial performance with tap/drag there waslittle reason to ignore their preference.

Each experimental session lasted approximately 30 min.

Fig. 2. Adjustment to the cursor location, as used with Finger input in the

dragging and radial dragging conditions.

3.1. Apparatus and participants

The mouse and stylus tasks were completed using an HPCompaq 2710p Tablet PC with a 12.1 in. display set to itsnative resolution of 1280� 800 pixels (see Fig. 1a). Mouseinput was received through a cordless laser mouse (Logi-tech MX610) and stylus input was achieved using theactively tracked stylus, which had a hover detection rangeof approximately 15 mm. When used with the mouse, thescreen was folded up (as shown in Fig. 1a), and when usedwith stylus input the screen was folded down into tabletmode, with the device resting on the table to give ahorizontal input surface.

Finger input tasks were performed using a HP TouchS-mart 600 PC with a 23 in. display set to its nativeresolution of 1920� 1080 pixels. It was horizontally placedon a table to provide comfortable finger based interaction(Fig. 1b).

To enable performance comparisons across the differ-ently sized hardware platforms, the physical sizes of alltargets were controlled to match across conditions. Forexample, the radius of all radial menus was 8 cm, regard-less of hardware platform (as shown in Fig. 1a and b).

To minimise the impact of different system feedbackacross input device (mouse, stylus, and finger) the same

Fig. 1. Hardware apparatus. (a) HP Compaq 27

cursor representation was used with all conditions: atraditional white ‘arrow’ cursor, pointing in the 11 o’clockdirection. With the finger, however, the system registeredcontact point is approximately centred in the middle of thefinger, which occludes the cursor. For finger tapping tasks,in which selections were completed by touching the displaysurface, the system registered contact point was used andthe cursor location was not modified. For finger draggingand radial selections, however, the displayed cursor loca-tion was dynamically adjusted 6.6 mm West and 10.6 mmNorth of the system-registered contact point (see Fig. 2).This adjustment was carried out after pilot studies showedcursor occlusion to be a major limiting factor in perfor-mance during finger dragging and finger radial selections.Eighteen volunteers (six female) aged 21–59 years (mean

31.33 years, s.d. 9.54) participated in the experiment.All participants used a computer and mouse on a dailybasis. None reported using a stylus computer or touchsc-reens more than ‘‘a few’’ times. All participants were righthanded.

3.2. Method

The experiment consisted of two sessions for eachparticipant: one for radial dragging and another forbidirectional tapping/dragging tasks completed the follow-ing week. The method for tapping and dragging tasks isdescribed first, followed by the radial dragging method.

10p and (b) Horizonal HP TouchSmart 600.

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233 223

3.2.1. Tapping and dragging

Tapping and dragging performance were analysed using aone-dimensional pointing task, consisting of horizontalmovement between initial and target locations shown onthe screen as vertical bars that extended to the screen height(see Fig. 3). Tapping selections were initiated and terminatedby tapping the surface or clicking the mouse button—

between taps the stylus/finger was off the surface, and withthe mouse, its button was released. Timing began when thefinger/stylus was lifted from the display surface or when themouse button was released, and timing stopped when thefinger/stylus/mouse-button was next released (completing asecond tap). Dragging tasks were initiated by pressing themouse button or touching the surface, and terminated byreleasing the button or lifting off the surface—the stylus/finger remained in contact with the surface during movementbetween initial and target regions, and with the mouse itsbutton was held down. Drag timing began when the cursorexited the initial region, and terminated when the finger/stylus/mouse-button was released. Trials that were termi-nated outside the target bar were logged as errors and werere-cued until successfully completed.

Each trial proceeded as follows. The initial and target barswere both displayed on the screen, with the initial locationblue, the target green, and the area between them pink. Theparticipant then moved the cursor over the initial region andpressed the mouse button or made contact with the displaysurface and remained stationary for a one second timeoutperiod. While stationary over the initial region a green halowas shown around it (Fig. 3), indicating that the within-region state was attained, and that the timeout had notexpired. When the timeout expired, the halo disappeared andthe pink shading between the initial and target region wasreplaced with a white background. This indicated that theparticipant could begin the trial when ready. There was nofeedback to indicate that the cursor was over the targetexcept for the collocation of the cursor and the target. Whencompleting tapping tasks with the finger, there was no cursorwhile the finger was off the surface. Similarly, there was nocursor feedback with the stylus when the stylus tip was morethan 15 mm from the display surface. We decided to not

Fig. 3. A tapping or dragging task. Participants remained stationary over

the initial region until a timeout expired, at which point the colour of the

inter-target region changed from pink to white. (For interpretation of the

references to colour in this figure legend, the reader is referred to the web

version of this article.)

provide target-highlighting feedback of the over-target statefor either tapping or dragging tasks following pilot studies.The preliminary studies suggested that such feedback differ-ently influences tapping and dragging. When tapping, pilotstudy participants did not use the feedback, and insteadtreated the selecting tap as a single discrete action (ratherthan waiting for feedback to confirm the over-target stateprior to release). With dragging, however, pilot studyparticipants appeared to rely on the visual feedback priorto release. As we intended to compare mechanical perfor-mance between tapping and dragging we removed over-target feedback from both conditions. For similar reasons offacilitating comparison between mechanical aspects of tapand drag selections, we chose to not show the dragged itemmoving with the cursor. Participants were trained in thisprocedure using a practice block of eight selections (fourleftward, four rightward). Practice blocks were used beforeeach combination of input device and interaction type (tap ordrag). Half of the participants completed all dragging trialswith the three input devices before proceeding to tappingtasks, and the other half completed tapping trials first. Eachparticipant used the same input device order for tapping anddragging tasks, but this order was balanced across partici-pants using an incomplete Latin Square.The experimental tasks were administered in seven blocks

of eight selections each (four to the left, four to the right). Alltrials within each block used the same width and amplitudeof movement to give seven levels of Fitts’ Law index ofdifficulty, from 2.32 bits (width W¼12.5 mm, amplitudeA¼50 mm) to 5.36 bits (W¼5 mm, A¼200 mm), createdfrom three levels of width (5, 12.5, and 20 mm) and fourlevels of distance (50, 100, 150, and 200 mm), uncrossed.Table 2 shows the combinations of A and W used to give theseven levels of index of difficulty. The order of exposure tothe seven levels of index of difficulty was randomised.In summary, the tapping and dragging data consisted of:

18 participants�3 input devices A {mouse, finger, stylus}�2 interaction types A {tap, drag}�7 indexes of difficulty A {2.32, 2.58, 3.09, 3.46, 4.09,

4.95, and 5.36 bits}�2 target directions A {left, right}�4 repetitions ¼ 6048 correct trials.

3.2.2. Radial dragging

Radial dragging performance was analysed using asimilar method to that of tapping and dragging, describedabove. However, instead of seven levels of index ofdifficulty there were seven different numbers of radial

Table 2

Widths and distances used for the seven levels of index of difficulty.

Width (mm) 12.5 20 20 5 12.5 5 5

Distance (mm) 50 100 150 50 200 150 200

ID (bits) 2.32 2.58 3.09 3.46 4.09 4.95 5.36

Fig. 4. Visual states during radial selections (a) target preview and (b) premature movement. (For interpretation of the references to color in this figure,

the reader is referred to the web version of this article.)

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233224

items (2, 4, 9, 16, 25, 36, and 49 items). Also, instead oftwo directions (left and right), we analysed selectionperformance across four directional quadrants (North toEast, East to South, South to West, and West to North).

Each experimental trial proceeded as follows. Partici-pants initially moved over a small blue square (2� 2 mm2)shown at the centre of the screen, and pressed the mousebutton or made contact with the surface using the finger/stylus. This displayed a 16 cm diameter dark grey radialmenu, with a small (2 mm diameter) blue centre circle, anda green target item (Fig. 4a). After remaining stationaryover the centre circle for 1 s, the dark grey colour wasremoved, indicating that the user could drag to select theitem. The stationary timeout period was intended to assurethat the participants had visually identified their targetbefore beginning movement. Timing began when thecursor left the centre circle, and terminated with the lift-off selection (regardless of correct item selection). Movingoutside the blue centre circle before the timeout expiredresulted in red highlighting around the radial menu,indicating the need to re-enter the centre region(Fig. 4b). Participants were familiarised with this proce-dure by completing 12 selections using a 16 item radialmenu before completing experimental trials with each ofthe three input devices.

Note that the targeting movement was completed with adragging action: the mouse button remained down, and thestylus/finger remained in contact with the display throughoutthe movement. Consequently, these radial selections can beconsidered to be a special case of dragging. Other radialselection modalities are possible, such as tap-to-post followedby tap-to-select, but we selected drag-based movementbecause it is consistent with marking menu designs, wherenovices target radial items with a visually guided draggingaction and experts use a directional ‘flick’ in contact with thedisplay surface (or mouse button depressed). The risks ofgeneralising our results to other radial selection modalitiesare discussed in Section 5.3.1.

All participants completed seven blocks of trials with allthree input devices (mouse, finger and stylus; orderbalanced using a Latin square). All blocks with one inputdevice were completed before moving on to the next. Oneblock was used for each number of items (2, 4, 9, 16, 25,36, and 49 items; random order), and each block consistedof 24 selections—six selections in each of the four direc-tional quadrants. Failed trials (incorrect item selected or aselection outside the radial menu) were logged and re-queued at a random position among the unfinished trialswithin the running block.In summary, the radial selections consisted of:

18 participants�3 input devices A {mouse, finger, stylus}�7 numbers of items A {2, 4, 9, 16, 25, 36, and 49

items}�4 target directions A {NE, SE, SW, NW}�6 repetitions¼9072 correct trials.

4. Results

The results are presented in the order Tapping, Drag-ging, and Radial Dragging. In each case the primaryanalyses concern error rate and error-free target acquisi-tion time. Direction (East versus West) had no significanteffect on results for tapping and dragging, so in these caseswe collapse across levels of the factor for brevity.

4.1. Tapping

Data from one of the participants was discarded due toequipment failure (affecting only the tapping trials). Theremaining data is analysed using a 3� 7 repeated measuresAnalysis of Variance (ANOVA) for factors input device

and index of difficulty.

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233 225

4.1.1. Error rate

The overall error rate was 5.2%, but it was not evenlydistributed across input device. There was a 6.8% errorrate with the finger, 3.1% with the mouse, and 2.2% withthe stylus, giving a significant main effect of input device

(F2,32¼16.3, po .001). There was also a significant effectof index of difficulty (F6,96¼9.8, po .001) due to errorsincreasing with harder targets (from 0.87% with large andclose targets to 8.3% with small distant ones). Finally,there was a significant device� difficulty interaction(F12,192¼5.5, po .001), best explained by the contrastbetween a relatively constant error rate with the mouseacross index of difficulty, versus an abrupt increase withthe finger. These results are summarised in Fig. 5a.

Fig. 5a shows that the finger was particularly inaccurate(error rates of 13–14%) at index of difficulty levels 3.46,4.94, and 5.36. Table 2 explains this finding, revealing thatonly these levels used the smallest sized targets, withW¼5 mm. The ‘fat finger’ problem is clearly evident with5mm targets, yet many touch activated controls on mobiledevices are much smaller.

Fig. 5a also shows an error spike with the stylus (8.4%)with the highest level of index of difficulty. Furtherexperiments are needed to examine the reliability of thiseffect, but the spike suggests that acquiring distant smalltargets with the stylus is error prone, while acquiring nearsmall targets is not. We suspect this is due to problems ofparallax when observing the correspondence betweenstylus tip and cursor location—the parallax displacementis small when the target is near the eye, but larger when thetarget is distant and therefore viewed from a more obliqueangle. Similar effects were observed by Forlines et al.(2007) with distant finger-based selections.

4.1.2. Target acquisition time

The overall mean selection time (errors removed) was730 ms (s.d. 299). The finger (mean 572 ms, s.d. 213) wassubstantially faster than the stylus (mean 751, s.d. 337) andmouse (mean 866, s.d. 207), giving a significant main effectof device: F2,32¼14.9, po .001. As expected, there was asignificant main effect of index of difficulty (F6,96¼98.6,po .001), with the increase in task time across levels of thefactor summarised in Fig. 5b. As suggested by the nearly

0%2%4%6%8%

10%12%14%16%18%20%

2.32 2.58 3.09 3.46 4.09 4.95 5.36

Mea

n er

ror r

ate

Index of Difficulty

StylusMouseFinger

Mea

n se

lect

ion

time

Fig. 5. Tapping task results for the three devices across index of difficulty. Perc

regression models). Error bars show 71 standard error of the mean. (a) Erro

parallel regression models in Fig. 5b, there was no device

� difficulty interaction (F12,192¼0.5, p¼0.92). Asexpected, Fitts’ Law models for the tapping tasks aregood, with R2 values of 0.97, 0.97, and 0.99 with stylus,finger, and mouse, respectively (also shown in Fig. 5b).

4.2. Dragging

Error and timing data for the 18 participants areexamined using a 3� 7 repeated measures ANOVA forfactors input device and index of difficulty.

4.2.1. Error rate

Overall, there was a smaller proportion of draggingerrors (1.6%) than tapping errors (5.2%). This is bestexplained by the availability of continual feedback with thefinger, as in finger-dragging tasks the offset cursor could beused to confirm the over-target state (in tapping tasks thefinger occluded the cursor, so finger selections werecompleted without visual feedback). There was no signifi-cant difference between mean error rates with the differentinput devices (F2,34¼0.8, p¼0.46), at 1.0%, 1.4%, and1.7% with the finger, mouse, and stylus, respectively (seeFig. 6a). The presence of continual guiding feedback toassist accuracy also explains the absence of significantdifferences across levels of index of difficulty (F6,102¼1.27,p¼0.28). Finally, there was no device � difficulty interac-tion (F12,204¼1.34, p¼0.2).

4.2.2. Target acquisition time

Like tapping tasks, there was a significant main effect ofinput device (F2,34¼14.1, po .001), but the relative perfor-mance of the devices is in a different order to tapping, withthe stylus fastest (mean 728 ms, s.d. 283), followed by themouse (mean 838 ms, s.d. 250) and the finger slowest(mean 922 ms, s.d. 360). There was also a significant maineffect of index of difficulty (F6,102¼202.7, po .001), and asignificant device� difficulty interaction (F12,204¼3.28,po .001). As Fig. 6b shows, the interaction is bestexplained by the mouse performing relatively poorly withlow index of difficulty targets (large, near ones), butrelatively well with hard ones (far and small targets). Fitts’Law models with all three devices are good (R2

Z0.98).

MTm = 274+160IDR2 = 0.99

MTs = 219+144IDR2 = 0.97

MTf = 57+140IDR2 = 0.970

200400600800

100012001400

2 6

(mse

c)

Index of Difficulty

Mouse

Stylus

Finger

3 4 5

entage error rates (left) and mean acquisition times (right, including linear

r rate and (b) selection time.

0

200

400

600

800

1000

1200

Tap Drag

Mea

n se

lect

ion

time

(mse

c)

Task type

Mouse

Stylus

Finger

Fig. 7. Mean selection time for the three input devices across tapping and

dragging tasks. Error bars show 71 standard error of the mean.

0%

1%

2%

3%

4%

5%

6%

2.32 2.58 3.09 3.46 4.09 4.95 5.36

Mea

n er

ror r

ate

Index of Difficulty

StylusMouseFinger

MTf = 15+246xIDR2 = 0.98

MTm = 155+185xIDR2 = 0.99

MTs = -46+210xIDR2 = 0.996

0200400600800

1000120014001600

2 6Mea

n se

lect

ion

time

(mse

c)

Index of Difficulty

Finger

Mouse

Stylus

3 4 5

Fig. 6. Dragging results for the three devices across index of difficulty. Percentage error rates (left) and mean acquisition times (right, including linear

regression models). Error bars show 71 standard error of the mean. (a) Error rate and (b) selection time.

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233226

4.3. Summary of tapping and dragging, and comparison

across tasks

In summary, tapping trials were relatively inaccurate,particularly with the finger. Two factors explain thefinger’s inaccuracy with tapping. First, there was no systemfeedback about the location of the finger prior to complet-ing selections by tapping the screen. This is unlike selec-tions with the stylus and mouse, where the cursor isdynamically displayed throughout the pointing activity,so the user can confirm the over-target state beforecompleting the action. Second, the ‘fat finger’ problem(see related work) means that the finger is a large andrelatively crude pointing device for small targets.

There is an interesting finding that the relative efficiency ofthe different devices depends on the task type: for example,the finger was fastest in tapping tasks but slowest withdragging. To scrutinise this relative efficiency difference weconducted an additional 3� 2 repeated measures ANOVAfor the three devices and for the two task types (tapping ordragging). The results are summarised in Fig. 7. It shows thatthe mouse and stylus perform similarly across tasks, whileperformance with the finger degrades substantially betweentapping and dragging (giving a significant device� task

interaction; F2,32¼31.4, po.001).

4.4. Radial selections

Error, timing, and movement data for the 18 participantsare examined using a 3� 4� 6 repeated measures ANOVAfor factors input device (finger, stylus and mouse), quadrant

(target item located in the North East, SE, SW, or NWquadrant of the radial menu), and number of items (4, 9, 16,25, 36, and 49 items). Data for two-item radial menus arealso reported, but are not included in the analysis of varianceas two-item radial menus do not support four quadrants. Thedependent measures were: percentage of trials that includedan error, and selection time.As noted in the Related Work section, there has been

little work on performance models and characterizationsof radial dragging. Therefore, in addition to error ratesand acquisition times, we also analyse radial draggingmovement characteristics and performance models.

4.4.1. Error rate

The overall error rate was a low 2.3% of trials. There wasa significant main effect of input device (F2,34¼5.9, po .01),with the stylus being the most inaccurate device (mean3.4%), followed by the finger (2.0%) and mouse (1.4%). Asexpected, there was a significant effect of number of items

(F5,85¼13.7, po .001), with errors increasing from 0.4%with 2 items to 4.9% with 49 items (Fig. 8a). Moresurprisingly, there was also a significant effect of quadrant

(F3,51¼5.0, po .005), with many more errors when selectingitems in the North Western quadrant. Fig. 8b shows thiseffect, with the stylus and finger showing marked increasesin errors for the NW quadrant. Fig. 8b suggests that themouse is less heavily influenced by errors across quadrantsthan the other two devices, but this is not supported by adevice� quadrant interaction: F6,102¼1.8, p¼ .11. However,Fig. 8a confirms a significant device� number of items

interaction (F10,170¼2.2, po .05), which can be mainlyattributed to errors increasing more slowly across numberof items with the mouse than with the finger or stylus.

4.4.2. Target acquisition time

The overall mean selection time for radial menu selectionswas low, at 554 ms (s.d., 328). ANOVA showed a significant

0%1%2%3%4%5%6%7%8%9%

2 4 9 16 25 36 49

Mea

n er

ror r

ate

(%)

Number of items

Finger

Mouse

Stylus

0%

1%

2%

3%

4%

5%

6%

7%

NE SE SW NW

Mea

n er

ror r

ate

(%)

Direction

Finger

Mouse

Stylus

Fig. 8. Radial selection error rates with the three devices across number of items (left) and across directional quadrant (right). (a) Error rate by number of

items and (b) error rate by direction.

MTf = 262+17.2nR2 = 0.98

MTm = 214+13.2nR2 = 0.94

MTs = 181+12.7nR2 = 0.98

0

200

400

600

800

1000

1200

0 60Mea

n ac

quis

ition

tim

e (m

sec)

Number of items (n)

Finger

Mouse

Stylus

0100200300400500600700800900

NE SE SW NW

Mea

n ac

quis

ition

tim

e (m

sec)

Direction

Finger Mouse Stylus

20 40

Fig. 9. Radial selection times with the three techniques across number of items (left, including linear best fit models) and directional quadrant (right) in

the radial menu. (a) Time by number of items and (b) time by direction.

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233 227

main effect of input device (F2,34¼13.9, po .001), with thestylus fastest (476 ms, s.d. 259), followed by the mouse(525 ms, s.d. 241), and finger slowest (662 ms, s.d. 424). Thisis the same overall performance trend as dragging (stylus,followed by mouse and finger), which is unsurprising giventhat radial selections were completed with a dragging action.As expected, there was a significant effect of number of items

(F5,85¼191.8, po .001), with acquisition time increasinglinearly with the number of items in the radial menu (allR2 valuesZ0.94). This data is summarised in Fig. 9a.

There was also a significant effect of quadrant (F3,51¼13.6,po .001), with mean performance slowest in the NW quad-rant (mean 612, s.d., 410) and fastest in the NE (518, 291).However, this effect is most strongly attributed to theparticularly poor performance of the finger in the NWquadrant, which is also the main cause of a significantdevice� quadrant interaction (F6,102¼5.5, po.001), as shownin Fig. 9b.

4.4.3. Movement distance

Radial target acquisition is unusual in that users havepartial control over the width of their targets—by movingfurther from the menu centre, users can enlarge the target’seffective width. How far do users choose to move with menuscontaining differing numbers of items, and is movement

distance influenced by input device? To answer these ques-tions, we analysed movement distance (from menu centre topoint of selection completion) using the same 3� 4� 6 RM-ANOVA as used for error rate and selection time.The mean movement distance was highest with the finger

(mean 19.6 mm, s.d. 12.2), followed by the mouse (14.6 mm,s.d. 7.4) and stylus (10.8 mm, s.d. 5.5), giving a significantmain effect of device: F2,34¼17.2, po.001. Movement dis-tance increased linearly with the number of items in the menu(see Fig. 10a), with strong linear coefficients of determination(R2

Z0.96) for each of the devices. A significant devi-

ce� number of items interaction (F10,85¼52.8, po.001) isevident in Fig. 10a, with finger movement distance increasingmore rapidly across number of items than mouse or stylus.Finally, there was a significant main effect of quadrant

(F3,51¼11.8, po.001) and a significant device� quadrant

interaction (F6,102¼4.2, po.005), as shown in Fig. 10b. Bothof these effects are best attributed to the relatively shortmovements in the SE quadrant with the finger. Potentialexplanations for these findings are discussed in Section 5.2.

4.4.4. Modelling radial target acquisition

Is radial target acquisition best characterised as apointing activity (modelled by Fitts’ Law), a steeringactivity (modelling by the Steering Law), or neither? To

MDf = 9.4+0.38nR2 = 0.96

MDm = 7.75+0.3nR2 = 0.97

MDs = 5.75+0.2nR2 = 0.99

0

5

10

15

20

25

30

35

0 60Mea

n m

ovem

ent d

ista

nce

(mm

)

Number of items (n)

Finger

Mouse

Stylus

0

5

10

15

20

25

NE SE SW NW

Mea

n m

ovem

ent d

ista

nce

(mm

)

Direction

Finger Mouse Stylus

20 40

Fig. 10. Radial movement distances with the three techniques across number of items (left, including linear best fit models) and directional quadrant

(right) in the radial menu. (a) Distance by number of items and (b) distance by direction.

0

1

2

3

4

5

0 70

Pred

icte

d se

lect

ion

time

(sec

s)

Number of items (n)

Steering Law

Fitts' Law

10 20 30 40 50 60

Fig. 11. Fitts’ Law and Steering Law prediction trends across increasing

numbers of radial menu items. The predicted values are based on

arbitrary intercept and slope values. The Fitts’ Law prediction is

predominantly a log function of n, while the Steering Law prediction is

predominantly a linear function of n.

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233228

answer this question we adapted the equations for Fitts’Law and the Steering Law to radial selections, as follows.

4.4.4.1. Radial Fitts’ Law. Fitts’ Law predicts that targetacquisition movement time is a linear function of thelogarithm of the ratio of movement amplitude to width

MTfitts ¼ aþb log2A

Wþ1

� �

where a and b are empirically derived intercept and slopeconstants. In radial selections, the ratio of amplitude towidth is a trigonometric function of the number of items,n, in the radial menu (since the width of the pie segmentincreases as the user moves further from the centre).Consequently, in radial selections, Fitts’ Law predictionscan be rewritten as

MTfitts ¼ aþb log21

2cot

pn

� �þ1

� �:

As the cotangent of p/n rapidly asymptotes to a linearfunction of n (for n42), the Fitts’ Law prediction forradial selections is essentially a logarithmic function of thenumber of radial menu items. Fig. 11 shows the predictiontrend across increasing values of n, using arbitrary valuesfor a and b (a¼100 ms and b¼500 ms).

4.4.4.2. Radial Steering Law. Using the Steering Law(Accot and Zhai, 1997), radial menu selection time canbe predicted by treating each selection as being comprisedof a sequence of N straight tunnels, each of length siþ1�si,and width W(si)

MTsteer ¼ aþXN

i ¼ 1

bsiþ1�si

W ðsiÞ

where a and b are empirically derived intercept and slopeconstants, respectively. The width of the tunnel for anymovement distance N from the menu centre can becalculated as a function of the tangent of the number of

items (n) in the menu

W ðsiÞ ¼ 2N tanpn

� �:

Substituting this width value into the Steering Lawequation for MT, for any value of N resolves to

MTsteer ¼ aþb

2HN cot

pn

� �

where HN is the Nth harmonic number (a constant). Forany fixed movement distance, this function predicts thatmovement time asymptotes to a linear function of thenumber of menu items. A prediction across n for 50 pixelmovements is shown in Fig. 11, using arbitrary slopeconstants a¼b¼100 ms.As Fig. 9a shows, linear regression models of the actual

mean acquisition times from the experiment against num-ber of items were particularly strong with the finger andstylus (both R2

¼0.98), and slightly less strong with themouse (R2

¼0.94). Logarithmic regression models wereweaker in all three cases (0.88, 0.88, and 0.93 with thefinger, stylus, and mouse, respectively).

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233 229

Consequently, the regression models suggest that radialdragging is better modelled as a Steering activity than asFitts’ Law pointing. Although this may have been unsur-prising with the benefit of empirical hindsight, prior to theempirical analysis the authors had differing opinions aboutwhich model would hold and rational arguments insupport of their differing views.

5. Discussion

In summary, finger input was the fastest method fortapping tasks, but the slowest for dragging and radialselections. Finger selections were also error prone whentapping on the small (5 mm) targets. The comparativelylarge finger size in contrast to the small target size (the ‘fatfinger’ problem) partially explains the high error rate withsmall targets, but data from the dragging condition showsthat users can achieve good finger accuracy rates withsmall targets when they can adjust their selections inresponse to system feedback (in this case, a cursor, asshown in Fig. 2). Finger radial selections were slower fortargets located in the NW quadrant of the radial menu,which we discuss further below.

The stylus was the fastest input device for dragging andradial selections, and the second fastest for tapping selec-tions. Somewhat surprisingly, it had the lowest mean errorrate in tapping selections, but the highest in radial selec-tions. Stylus errors in radial selections also increased morerapidly across number of items than they did with themouse and finger. This rapid increase in stylus error ratesacross number of items is possibly caused by a tendency tocomplete selections by extending and contracting thefingers rather than moving the forearm to reposition thehand. This explanation is supported by Fig. 10a, whichshows that stylus selections were completed with compara-tively short movements away from radial menu centres(roughly half the distance of the finger movements).Consequently, participants were completing their selec-tions at a low level of target width and making associatederrors. Further experimentation is required to confirm thisexplanation. Radial selection errors with the stylus wereparticularly pronounced when moving towards the NWquadrant, which we discuss in Section 5.2.

A final result worth summarising is that radial selectionsare best modelled using the Steering Law (rather thanFitts’ Law), treating the selection movement as a con-strained tunnel where the width of the tunnel increases as afunction of the amplitude of movement.

5.1. Comparison with prior findings

Sasangohar et al. (2009) compared tapping performancebetween direct finger touch input and a mouse. Theyreport that movement times ranged from 403 ms to1051 ms using touch and from 607 ms to 1323 ms usingthe mouse, giving a 33% performance benefit for the fingerwith low index of difficulty tasks and a 26% benefit in high

difficulty tasks. These results support our findings for thefinger and mouse comparison in tapping tasks, where thefinger’s performance benefit over the mouse ranged from39% (low difficulty) to 32% (high difficulty). Sasangoharet al. (2009) also reported that the error rate with the finger(mean 9.8%) was much higher than the mouse (2.1%),which reflects our results (finger mean 6.8%; mousemean 3.1%).Lee and Zhai (2009) compared finger and stylus input

(via tapping) on mobile devices. Their tasks involvedtyping a series of ten characters into a mobile phonecalculator (e.g., ‘1450� 9276¼ ’), giving a dependent mea-sure of characters input per second. Data from their ‘nofeedback’ Experiment 1 condition (most similar to our ownstudy) showed mean performance with the finger was 2.35characters per second (equivalent to 426 ms per key) and2.65 characters per second with the stylus (equivalent to378 ms per key), giving an 11% performance advantage forthe stylus. Our experiment, in contrast, showed a 24%performance advantage for the finger over the stylus. Thedifference in results between these studies is most likely tostem from the divergent experimental methods, with threeparticular points of difference: first, their study involved aseries of button presses while our acquisitions werediscretely cued; second, Lee and Zhai’s (2009) target layoutleft no space between targets (which may have elevated theperceived need for precise finger movement) while ourswere well separated; third, and most importantly, ourtiming analysis removed erroneous trials (separately ana-lysing errors and error-free performance), while Lee andZhai’s analysis included error correction within the overalltime data for the typing task. Both our study and Lee andZhai’s showed much higher error rates with the finger thanstylus for tapping tasks.MacKenzie et al.’s (1991) analysis of tapping and

dragging tasks with the mouse, indirect stylus and track-ball showed that the mouse and stylus performed similarlyin tapping tasks, with a slight performance advantage forthe stylus over the mouse. Our results show the same slightadvantage. One difference between MacKenzie et al.’s(1991) results and our own is that their dragging taskswere completed more slowly than tapping tasks (particu-larly so with the mouse), while ours were not. Thisdifference across studies warrants further investigation,but we suspect it stems from a major difference inexperimental methodology. In MacKenzie et al.’s (1991)study, ten bidirectional trials at the same amplitude andwidth were completed together, with the completion of onetrial automatically initiating the next. Consequently, par-ticipants could adapt across the ten repeated trials to theprecise motor actions required for the acquisitions. In ourexperiment, however, blocks of trials at the same A and W

value were shorter (four to the left and four to the right)and involved a stationary timeout between each trial. Ourmethodology may have provided less opportunity forparticipants’ to calibrate their finger, forearm and wristmovement to the same paired targets.

Table 3

Summary of main comparative performance findings: aob indicates a is faster or less error prone than b.

Activity Comparative performance Notes

Tapping

Time Fingerostylusomouse Treatment of errors and target size are important. Finger errors are high with small targets.

Our analysis removed errors in time analysis while Lee and Zhai (2009) did not, explaining

divergent results. High amplitude acquisitions are also likely to increase errors with direct

pointing methods due to parallax errors (Forlines et al., 2007).

Errors Stylusomouseofinger

Dragging

Time Stylusomouseofinger Note that our method used a displaced cursor from the fingertip (Fig. 2) to reduce the high

incidence of errors observed with small targets in pilot testing.Errors FingerEmouseEstylus

Tap versus drag

Finger Tapodrag Finger difference is possibly due to surface friction effects. The similarity of tap and drag tasks

with stylus and mouse diverges from MacKenzie et al.’s (1991) results, which showed tapping

to be faster than dragging with an indirect stylus and mouse.

Stylus Tap Edrag

Mouse TapEdrag

Radial

Time Stylusomouseofinger Similar to dragging

Errors Mouseofingerostylus Indicating a speed/accuracy trade off with the stylus due to short movement distances

(Fig. 10b).

Direction NW direction particularly slow with

finger and inaccurate with stylus

Possibly due to stick–slip effects with the finger and effects of finger extension with the stylus.

Model Steering law Steering law models radial acquisition times better than Fitts’ Law.

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233230

Our results mostly support those of Forlines et al.(2007), who analysed mouse and touch tapping anddragging, with the finger being faster for tapping and themouse faster for dragging. However, several differences inthe studies and results are worth noting. First, the smallesttargets used in our study were much smaller than those inForlines et al. (2007) (5 mm versus �18 mm). Like us,Forlines et al. (2007) note a significant effect of target size,with errors increasing for harder targets, yet our tappingerror rates were much lower than theirs. Our finger errorrates peak at �14% with 5 mm targets at 200 mmamplitude compared to theirs at �40% for �18 mmtargets at 548 mm amplitude. Forlines et al. (2007)included high amplitude selections (up to �658 mm)because they were specifically interested in tabletop inter-actions, and they observed that distant finger selectionswere error prone due to problems with both parallax andfinger posture. The differences in our studies mean thatwhile some of our results may not generalise to highamplitude touch interactions, they provide accuracy guide-lines for touch interactions with small targets at theamplitudes that can be expected on tablet computers andmobile devices. Furthermore, our dragging results showthat users can maintain high levels of touch draggingaccuracy, even with small targets, because dragging natu-rally allows over-target feedback. (Forlines et al. (2007)also showed good touch-dragging accuracy, but errorswere only made when users lifted off the surface before thesystem automatically detected task completion).

Prior studies of radial selections have focussed onanalysing gestural marking menu selections or on compar-ing variants of marking menus with traditional menus (seeSection 2.2). There is only one prior study of the pointingcomponent of radial item selection that we are aware of

(Ahlstrom et al., 2010), which included a brief mention of alinear relationship between radial selection time and thenumber of radial menu items, stating ‘‘This is an interest-ing result that warrants further investigation’’ (pp. 1376).Our study supports this observation and extends thefindings. This study also eliminates the experimentalconfound reported by Ahlstrom et al. (2010), which variedradial menu diameter with the number of menu items.Table 3 summarises the main results and notes points of

divergence from prior work.

5.2. Effects of direction in radial selections

The direction of movement in the radial selection taskshad a marked impact on performance with the stylus andfinger, but not with the mouse. Figs. 8b and 9b show thatfinger movements in the north-west direction were moreerror prone and slower than other directions. We attributethis effect to the tendency for the finger to ‘judder’ whenmoving collinearly in the direction the finger naturallypoints (NW for right-handed users). When moving in theNW direction, friction induces a tendency for the finger tobend. Any bend reduces the angle of friction and conse-quently increases the force required to overcome thefriction according to the angle of friction equation:tan a¼ F=N, where a is the angle of the finger fromvertical, F is the directional friction, and N is the verticallydownwards force. This changing friction induces a ‘stick–slip’ effect, or finger judder. When dragging in directionsother than NW, there is much less tendency for the fingerto bend as the forces oppose the finger’s skeletal structurerather than the extensor muscles. The judder effect hasbeen noted in other touch interaction research such asMoscovich (2009) who states ‘‘Specifically, participants

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233 231

cited difficulty sliding their finger upwards on the screen,saying that they felt that their finger would often stick orskip when sliding up’’ (pp. 20).

Somewhat surprisingly, the stylus was also much moreinaccurate in the NW direction (and a little more inaccu-rate in the SE direction), as shown in Fig. 8b. We suspectthis is due to two musculoskeletal factors. First, to make aNW selection, the fingers can be extended to reach thestylus tip toward the target, but this is an unnatural pen/stylus posture in which precise control is likely to becompromised. Second, to avoid finger extension, usersmust lift their hand and move their entire forearm toreach the target. This large-scale repositioning is notrequired for NE and SW targets where wrist flexion andextension can be used.

5.3. Study limitations and opportunities for further work

5.3.1. Selection modality

Stylus and touch interactions can be completed in manyways, including first-contact, slide-over, lift-off, and so on.The performance of these different modalities has beencompared in several studies, as outlined in Section 2.1. Inour experiment the selection modalities for tapping anddragging tasks were largely dictated by the tasksthemselves—for dragging tasks, departure from the start-ing location and lift-off are the obvious starting andterminating actions; and for tapping tasks there are fewlogical alternatives to lift off (to initiate and terminate).

Similarly, for radial selections we used the modality thatwe felt would be the most commonly used designalternative—a dragging action terminated with a lift-offselection. However, other radial selection modalities arealso feasible, such as tap-to-post followed by tap-to-select.Further work is needed to determine how a change inradial selection modality would influence the results—forexample, a tap-to-post and tap-to-select selection modalityshould eliminate any stick-slip effect for selections in theNW quadrant.

5.3.2. Impact of feedback in tapping and dragging selections

Current touch input technology does not register thefinger’s location until it makes contact with the surface.Consequently, our finger-tapping trials provided no cursorfeedback during movement through the air. This is unlikethe mouse and stylus condition, where the cursor dynami-cally indicates position throughout the movement (assum-ing the stylus remains within tracking range on the screen).Much of the inaccuracy observed for the finger with smalltargets can be attributed to this lack of feedback, assuggested by the relatively low error rate in finger-dragtasks. Also, our experimental method did not providetarget highlighting to indicate the over-target state, nor didit show an underlying object moving in response todragging actions. Further experiments are required totease out the impact of these feedback effects.

New finger input technologies will remove this limita-tion. For example, proximity- or camera-based technolo-gies are emerging (e.g., SideSight (Butler et al., 2008)) thatwill dynamically track finger location off the displaysurface, and new studies will be required to determinetheir efficiency.As well as visual feedback of the cursor’s location, many

researchers are examining the use of haptic effects to assistin touch-based target acquisition. These include simpleclicks and buzzes (Poupyrev and Maruyama, 2003) as wellas variable friction effects (Levesque et al., 2011), all ofwhich may improve touch-based target acquisition.

5.3.3. Understanding and controlling the impact of display

friction

We have used contact friction with the display surface asan explanation for some of the results, such as the slowperformance of the finger in dragging tasks, and whentargeting radial items located in the NW quadrant. Tovalidate this explanation, further work is needed to eithercontrol or measure the level of friction experienced. Whilenew technologies such as variable friction displays(Levesque et al., 2011) allow relative friction levels to bevaried (so that users feel increased or decreased frictionover particular items) they do not allow control of absolutefriction levels due to differences in skin texture andmoisture. In further work we intend to measure the levelof friction experienced by mounting a touch sensitivedevice on a force metre.

6. Conclusions

The form factor of computing devices is rapidly expand-ing beyond traditional desktop computers to includehandheld mobile devices, tablets, kiosk touchscreens, andtabletop computing. New pointing methods, particularlydirect finger touch, are being deployed to further enrich theuser’s experience with these devices. We have analysed andcompared the performance of three input devices (themouse, the stylus and the finger) across three differenttypes of target acquisition activity (tapping, dragging, andradial dragging). The results show that the relative perfor-mance of these devices is dependent on the task—the fingeris fastest for tapping activities (though inaccurate withsmall targets), but slowest for dragging. The finger wasalso particularly slow and inaccurate for dragging in theNorth-West direction, which we attribute to a frictionstick–slip effect causing finger judder. While the stylus isfast for dragging activities, it can be inaccurate, particu-larly for targets in the North-West direction. Finally, wedemonstrated that radial target acquisition performance isa linear function of the number of items. We also showthat this is predicted by a special case of the Steering Law,where the tunnel width increases with distance, rather thanFitts’ Law.

A. Cockburn et al. / Int. J. Human-Computer Studies 70 (2012) 218–233232

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