Pie menus offer several features which are advantageous especially for gaze control. Although the optimal number of slices per pie and of depth layers has already been established for manual control, these values may differ in gaze control due to differences in spatial accuracy and congitive processing. Therefore, we investigated the layout limits for hierarchical pie menu in gaze control. Our user study indicates that providing six slices in multiple depth layers guarantees fast and accurate selections. Moreover, we compared two different methods of selecting a slice. Novices performed well with both, but selecting via selection borders produced better performance for experts than the standard dwell time selection.

1. Pies with EYEs:
The Limits of Hierarchical Pie Menus in Gaze Control
Mario H. Urbina ∗ Maike Lorenz† Anke Huckauf‡
Bauhaus-University Weimar Bauhaus-University Weimar Ulm University
Germany Germany Germany
Abstract control cannot be directly transferred to gaze input, especially due
to technical restrictions in eye tracking systems and because of mo-
Pie menus offer several features which are advantageous especially tor constraints like tremor and drifts, the spatial accuracy is reduced.
for gaze control. Although the optimal number of slices per pie Moreover, voluntary control of eye movements requires conscious
and of depth layers has already been established for manual con- effort. This may alter the complexity of the menus. Hence we
trol, these values may differ in gaze control due to differences in aimed at investigating the limits of the menu width (i.e., the max-
spatial accuracy and congitive processing. Therefore, we investi- imum number of slices (items) per pie) and menu depth (i.e, the
gated the layout limits for hierarchical pie menu in gaze control. maximum number of layers) in gaze control replicating the study
Our user study indicates that providing six slices in multiple depth of Kurtenbach and Buxton [1993] with gaze input.
layers guarantees fast and accurate selections. Moreover, we com-
pared two different methods of selecting a slice. Novices performed In addition, we examined the learnability of gaze controlled pie
well with both, but selecting via selection borders produced better menus. One advantage for experts of using pies is the strategy of
performance for experts than the standard dwell time selection. marking ahead. That is, users know beforehand about the spatial
alignment and can thus dispense with the visual feedback. There-
CR Categories: H5.2 [Information interfaces and presentation]: fore, we also examined performances and trajectories in condi-
User Interfaces—Graphical user interfaces; tions without visual cue. A further question concerned the optimal
method of selecting a slice. Usually, dwelling (i.e. fixating for cer-
tain duration) over a target is applied [Huckauf and Urbina 2008b].
Keywords: gaze control, user interfaces, evaluation methodology, As alternative Urbina and Huckauf [2007] proposed “selection bor-
input devices, pie menus, marking menus ders” (i.e. the outer border of the slice, see Figure 1). This ap-
proach doesn’t require any temporal threshold. Hence, novices can
1 Introduction inspect the slices as long as they need to, and experienced users can
adopt a strategy of marking ahead. Thus, a comparison between
In circular pie menus, items are all equidistant from the cursor (i.e., these selection methods is a further aim of this study.
the centre of the pie). Although pie menus outperform pull down
menus using mouse or stylus [Callahan et al. 1988], they have not
been adopted as a standard for user interaction; Probably due to the
3 Method
well established pull down menu and the barriers that unfamiliar
interfaces pose (e.g., [Zhai 2008]). 3.1 Stimuli
One field in which there are only few standards established is gaze Each pie menu had a radius of 180 px, corresponding to a visual
based interaction. Here, pie menus can be expected to work well; angle of about 7.8◦ . Depending on the number of slices (4, 6, 8,
Especially because small spatial resolution is still a matter in gaze or 12), the slices expanded at their outer border to 314 px, 209 px,
input which might be compensated by pie menus. Indeed, pie 157 px or 105 px. Slices were coloured alternating with white and
menus have already been demonstrated to work well in gaze control light grey. Any gaze into a slice let to highlighting it using light
(e.g., [Istance et al. 2008]), in tasks requiring orientation as well as blue (e.g. Fig. 1). Menus with four slices were labelled as “N” -
in tasks requiring frequent selections [Huckauf and Urbina 2008a]. North, “O” - East [in german], “S” - South and “W” - West. Menus
with eight slices were labelled “N”, “NO”, “O”, “SO”, “S”, “SW”,
2 Research Questions “W” and “NW”. Menus with twelve and six slices were labelled as
a clock (from “1” to “12”, or only even numbers from “2”, to “12”).
When designing a pie menu the crucial factors to be considered are
the number of slices and the number of depth layers in which the
information is presented. For mouse and stylus input, Kurtenbach
and Buxton [1993] found that presenting two to three layers in
combination with eight slices per menu results in fluent behaviour
and good task performance. However, the data provided for manual
∗ e-mail: mario.urbina@uni-weimar.de
† e-mail: lorenzmaike@yahoo.com
‡ e-mail: anke.huckauf@uni-ulm.de
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Request permissions from Permissions Dept, ACM Inc., fax +1 (212) 869-0481 or e-mail Figure 1: Details of the pie menu.
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ETRA 2010, Austin, TX, March 22 – 24, 2010.
© 2010 ACM 978-1-60558-994-7/10/0003 $10.00
93

2. 3.2 Participants
Twelve volunteers participated in the study, aged between 23 and
30 (26 in mean). All reported normal or corrected-to-normal vision
and were familiar with computer usage. Two of them had prior
experience with eye tracking and pie menus.
3.3 Apparatus
The study took place in a room without windows under indirect arti-
ficial lightning. The pie menus were presented on a 21 Sony GDM-
F520 CRT display with a resolution of 1280x960 at a frame rate
of 75Hz. The eye tracking device used was a head-mounted Eye-
link2. The spatial resolution of this set-up, considering the nominal
tracking resolution of 0.5◦ , was about 12 pixels.
Figure 2: Example trial and selection procedure. After selecting
the last slice (d), the next trial starts (e).
3.4 Design
Independent variables throughout the study were the number of
slices per pie (width), the number of hierarchical layers per pie 4.1 Width
(depth), and the method of selection. These factors were varied
blockwise. In total, 13 blocks by 32 trials were to be performed Selection time: For investigating the effects of menu width, blocks
with the configuration and selection method described in Table 1. with menus of four, six, eight, and twelve slices were compared. All
these menus consisted of two depth layers. For four slices, IST took
Table 1: Menu layout, selection method and visualization condition 667.141 ms (standard error se=31.18). For six slices, IST was in
for all 13 blocks. mean 786.35 ms (se=38.60), for eight slices 907.01 ms (se=54.37),
and for twelve slices 933.11 ms (se=40.31) (see Fig. 3). These
Block # Width Depth Sel. Method Visualization differences were of significance (F(3,33)=27.52, p < .001). Post
1 4 2 sel. borders yes hoc comparisons revealed that all numbers of slices differed signif-
2 8 2 sel. borders yes icantly from each other except eight and twelve slices.
3 6 2 sel. borders yes
4 12 2 sel. borders yes
1000
5 4 2 sel. borders yes
Mean Item Selection Time (ms)
950
6 4 3 sel. borders yes 900
7 4 4 sel. borders yes 850
800
8 4 2 sel. borders yes 750
9 4 2 sel. borders no 700
10 4 2 sel. borders yes 650
600
11 4 2 dwell time yes 550
12 8 3 sel. borders yes 500
4 6 8 12
13 8 3 dwell time yes
Number of items
Errors and item selection times (ISTs, measured from the onset of
the pie until the selection of one slice) served as dependent vari- Figure 3: Effect of the number of slices on item selection times.
ables. ISTs were computed instead of the usual task completion
times in order to compare performance between the different menu
layouts. An error was defined as every single false selection. For
Error rate: For four slices, 5.62% errors were produced (se=1.04).
example, for the task “N - O”, the selection of “N - W” or “O - O”
With six slices, the error rate reached 9.58% (se= 1.40), with
was counted as one, the selection of “W- N” as two errors.
eight slices 21.51% (se=3.67), and with twelve slices 22.62%
(se=3.68). Also for the error rate, menu width had a significant
3.5 Procedure effect F(3,33)=16.77, p <.001. Again, this effect was due to differ-
ences between all numbers of slices except eight and twelve slices.
The task was to select as fast and as accurate as possible objects
through a pie menu, which were depicted above the centre top of These data indicate that six slices seem to be the maximal number
the screen. After fixating the start button the pie menu popped up of slices which can be suggested for using pie menus in gaze control
(see Fig. 2a and 2b). Each selection was accompanied by a click both, in terms of fast and accurate performance.
sound [Majaranta et al. 2006]. With a selection, either the next
pie layer popped up or, the menus were closed and the start button 4.2 Depth layers
appeared again together with a new task until the block was finished
(see Fig. 2).
Selection time: For examining the effects of number of layers,
menus of two, three, and four layers were compared, all based on
4 Results pies of four slices. IST was 667.14 ms (se=31.18) for two layers,
749.85 ms (se=48.02) for three layers, and 746.83 ms (se=31.76)
IST and errors were entered into ANOVAs for repeated measures. for four layers (see Fig. 4). These differences were of significance
Except for the investigation of learning effects, data for the menu of (F(2,22)=9.13, p <.001). Post hoc analysis showed that this effect
four slices presented in two layers were taken from the second run. was due to the faster IST with two layers relative to three and four.
94

3. 1000 performance between the steps of both layers should not differ. If,
Mean Item Selection Time (ms)
950
900
however, users solve this task step by step, in the marking ahead
850 condition the first selection might still succeed whereas the second
800
may be more error-prone and/or slower.
750
700
650 Selection time: Performance between the very first block and
600 the block without visual presentation did only marginally differ
550
500
(F(1,11)=4.04, p =.07). In addition, the IST for the first menu layer
2 3 4 was with 951.09 ms (se= 90.18) slower than for the second layer
Depth level
(824.31 ms, se=79.53; F(1,11)=11.29, p <.01, see Fig. 6). How-
ever, there was no interaction between both variables suggesting
that there were no specific differences between both blocks (F<1).
Figure 4: Effect of the number of layers on item selection time.
1200 Pie Menu
Mean Item Selection Time (ms)
1100 Marking Menu
Error rate: Errors were as high as 5.62% (se=1.04) for two, 6.03% 1000
(se=1.04) for three, and 6.06% (se=1.26) for four layers. The effect 900
of menu depth on IST was not significant (F<1). 800
700
These results show that the depth of a pie menu is not as crucial in 600
gaze control as is the width. This is in contrast to the data provided 500
for manual control by Kurtenbach and Buxton [1993]. 1 2
Menu layer
4.3 Learnability
Figure 6: Item selection times for the first and second menu layer
Selection time: Effects of learning were investigated comparing separately for the very first block of the marking menu and the
performance for the menu of four slices arranged in two layers, marking ahead condition.
which was repeated four times throughout the whole experiment.
In the first run, users took 817.03 ms (se=61.81) per item. This
was reduced to 667.14 ms (se= 31.18) in the second, to 633.46 ms Error rate: In errors, performance between the very first run and
(se=30,36) in the third, and to 586.88 ms (se=28.19) in the fourth the marking block did not differ (F<1). As in selection times, the
run (see Fig. 5). The effect of learning was statistically significant menu layers (i.e., first versus second selection) produced a signifi-
(F(3,33)=17.14, p <.001). Each run produced significantly faster cant effect (F1,11)= 14.63, p <.01). This was due to more errors
selection times, except the second and third (p =.15). The decrease in the second (9.5% se=1.31) than in the first menu layer (5.88%,
from the third to the fourth run was marginally significant (p =.06). se=.83). There was no interaction between both variables (F<1).
1000
4.5 Selection Method
Mean Item Selection Time (ms)
950
900
850 Selection time: The investigation of whether selection via selection
800 borders can actually compete with the standard selection procedure
750
700
using dwell times (400 ms) was performed on two menu designs:
650 A small menu of four slices and two depth layers and a larger menu
600
of eight slices and three depth layers. The statistical comparison re-
550
500
vealed a main effect of menu size (F(1,11)=58.04, p <.001) where
1 2 3 4 selection took less time in the small menu (663.37 ms, se=25.29)
Run
relative to the larger one (887.59 ms, se=45.42). However, there
was neither a main effect of selection method (F<1) nor an interac-
tion with it (F<1), indicating that in terms of selection speed, both
Figure 5: Effect of learning on selection times per item. selection methods can be regarded as equally useable.
Error rate: In errors, learning let to a decrease from 16.05% Error rate: In errors, there was also an effect of menu size
(se=2.73) over 5.62% (se=1.04) and 3.30% (se=.82) to 5.72% (F(1,11)=19.56, p <.001. Here were, with 10.55% (se=2.02), less
(se=1.24). These differences were also of significance (F(3, errors per selection for the small pie menu as for the large (21.43%,
33)=18.63, p <.001). Post hoc comparisons revealed that perfor- se=3.49) (see Fig. 7). Selection via selection borders was with
mance in the first session was worse than in all further sessions. 11.72% (se=1.67) more effective than selection via dwell times
(20.27%, se=3.91; F(1,11)=7.55, p <.02). Again, there was no
interaction between both variables (F<1).
4.4 Marking Ahead Selection
In order to further investigating learning, one block without visual 5 Discussion and Conclusion
feedback was performed. The assumption of the marking ahead
strategy is that users have a complete mental conception of the When designing pie menus for gaze control the number of items
whole series of actions. In order to test this assumption, perfor- per layer in a pie menu seems to be the most crucial factor. As
mance in this marking ahead block was compared to performance our data revealed, up to six slices per pie can be effectively and
on the very first run. Importantly, we included the menu layer (i.e., efficiently selected with eye trackers with about 0.5◦ of spatial ac-
selection in the first versus in the second layer) as a further vari- curacy (i.e. professional eye tracking equipment). Of course the ra-
able: If users have a mental conception of the whole task, then dius (180 px in our study) may affect the optimal number of slices
95

4. 30 Border Sel. tions by dwelling on an item produced more errors than selections
Dwell Time
25 by borders. One might thus improve the accuracy by increasing the
20
dwelling time. However, dwell time was perceived as a “more nat-
Errors in %
15
ural”, “intuitive” but also “slower” selection method among partici-
pants without prior experience in gaze control. Taken together, one
10
might suppose that selection by selection borders provides a bet-
5 ter performance for selecting items in a pie menu than dwell times.
0 The arrangement of the pie menus might also be responsible for
4;2 8;3
Menu size
the superiority of selection by borders: Since all new layers were
centred around the outer border of the current pie, selection by bor-
ders already brings the eye towards the centre of the next pie menu.
Figure 7: Effect of the selection method on error rates. Hence, with other designs like centring the pie around the current
fixation position, dwell time selection might compete with selec-
tion by borders. However, as already discussed above, respective
designs may be of disadvantage for the usability and learnability of
and should thus be investigated in further experiments. Addition- pie menus.
ally, one should take into consideration that the tasks for the vari-
ous numbers of slices varied in difficulty: For four and eight slices, To sum up, pie menus are a suitable and promising interfaces for
tasks were given with cardinal points, and for six and twelve slices, gaze interaction can allocate up to six items in width and multiple
they were given using the clock. We suppose cardinal points to be depth layers, allowing a fast and accurate navigation through hier-
more difficult: Some subjects confused “W” with “O” and vice- archical levels by using or combining multiple selection methods.
versa (like confusing left with right), committing in mean 1.91% These qualities may give pie and marking menus the chance to es-
errors, which made up about 20% of the total errors. For the eight tablish as a standard in gaze control.
slices menu, perceiving and remembering coordinates like “SW-
SW - S - W” can be assumed to be more difficult than the numbers References
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