Welcome everyone here both in Lodz as well as connected remotely
all over the world.
We have three extremely exciting papers today. One presented from Japan remotely
and two present it here on site, which is also very nice.
We have this session.
My name is Sebastian Feger.
I'm from LMU Munich.
I'm chairing this session today and the session is on pointing and
decals.
So we will learn about how to interact with information and how to
Reorganize and arrange information in different ways.
So, without further ado, I would invite the first Speaker, who is
connected remotely Hiroki Usuba from Meiji University in Japan to
talk about uncertain touchable sizes.
Hi I'm Hiroki Usuba.
I'm a PhD student at Meiji University.
Today I will talk about a study Modeling movement times and success
rates for acquisition of one-dimensional targets with uncertain touchable
sizes.
Graphical user interfaces.
A target has a visual width and a touchable width.
The visual width is the area, in which a target is displayed on screen.
The touchable width is an area in which users can touch a target.
For example, in button a the visual width is smaller than
touchable width, because the button's background is transparent.
Button b the visual width equals the touchable width because the
background is colored.
In button c the visual width is larger than the touchable width, because the order of the stand
anchor text is reversed and only the text is a hyperlink.
In the Artistic Touch interfaces, of course the visual and
touchable widths exist.
For example, in iOS settings.
The visual width equals the touchable width, because the item are
strictly split to the right.
So, you got the end between the fry with rides?
On the other hand, in the Gmail sidebar to visual width is smaller than
the touchable width, because the items are not split.
So users cannot determine the exact size of the touchable width from
the target's appearance.
In this situation two strategies exist.
If the users have used the application several times, they aim for the
touchable width learned through use.
If the users are using the application for the first time,
They may assume the visual width is touchable and aim for it.
In this study, we investigate the effects of the visual and touchable
widths on touch pointing performance, and build models for predicting
this performance.
Existing models are limited to two situations where the visual and touchable
width are equal.
The objective of this study is to address this limitation.
We test two situations. One is where users roughly know the touchable width
And the other is where users do not know the size of the touchable width.
The former is operations in frequently used applications and the latter is
operations in applications being used for the first time.
Experiment
One simulated operations, in frequently used applications.
This experiment consists of two blocks, the exploration block and data
collection block.
In exploration block, the participant tap the blue start bar
And then somewhere around the green target bar. Thereby the
participants memorized are the explorers the size of the touchable size of the
In the data-collection block, the participants tap the start bar and then the
touchable widths of the targets
That the participants
Memorized. In experiments
The participants repeated the two blocks under experimental
conditions.
The Experiment conditions was shown on the screen.
There are one type of the distance to the Target, four types of the
visual width, and four types of touchable widths.
So the visual width was larger than, equal to, or smaller than the
touchable width.
As a result of the experiment.
We found the movement time depended significantly on the visual and
touchable width.
So increasing the visual or touchable width, decreases the movement
time.
These effects of the widths were consistent with that of the width of
the Fitts task.
The basis of the results, were real movement times models.
The model or the results are lower AIC or higher adjusted r squared.
Model three, which is the model of mouse
Pointing showed the lowest AIC. Model 7, which is created by combining
model 3 and Ko et al's FFitts Law showed highest adjusted r squared.
The difference of the AIC and R squared between models 3 and 7
are small and model 3 had fewer parameters than Model
Seven.
So, Model 3 was the best model among the candidates.
The success rate is predicted by predicting that touch Point
distribution.
We find the standard deviation of the touch points depended
significantly on the touchable width.
Increasing the touchable width increased the sigma.
Again, we found increasing the touchable width, increased the sigma
Plus in the task when the touchpoint fall in the touchable width, the trial
was regarded as a success.
So are the bestest of the parks, the previous model can be converted
as shown on the screen.
The cross-validation showed a low average of MAE. The results were
compared to those of the previous studies.
Next experiment simulated operations in applications being used for
the first time.
So unlike experiment one, this experiment consists of
Only that data-collection block.
The visual width is smaller than or equal to the touchable width.
As a result of the experiment we found movement time depended
significantly on only the visual width.
So increasing the visual width decreased the movement time.
On the basis of the results were very similar movement time models.
Model one, which is created by replacing W in Fitts' law
with visual width showed the lowest AIC.
Models 1, 5 and 6
Showed the highest adjusted r squared.
Model five was created by replacing W in Ko et al'
FFitts Law with visual width.
Model 1 had fewer parameters than Models 5 and 6
So, model one was the best model among the candidates.
Next, we'll plant touch Point distribution.
Sigma did not depend on the visual and touchable widths.
So, the sigma was concerned.
Again, in the task, when the touchpoint fall in the touchable width
that trial was regarded as a success.
So on the basis of this pact, the visual the previous model can be
converted as shown on the screen.
The cross-validation showed a low average MAE.
The results were compared to those of the previous studies.
Based on the result, we did a design implication. In operations in
frequently used applications.
We found the movement.
time depended on the visual and touchable widths and success rate depended
on touchable width.
Continuous tapping applications, for example, shopping and gaming
applications, the movement time is important.
So, UI designers should make the touchable size as large as
possible, and make the target's appearance clear.
On the other hand applications where one tap is important.
For example, the Gmail sidebar, the success rate is important.
So UI designers should make the touchable size as large as
possible.
Next the operations in applications being used
For the first time.
We found the movement time depend on the visual width and success rate
depended on the touchable width.
So UI designers should make the target's appearance and touchable size
equal and make the target as large as possible.
For example, the designers must avoid showing the
target as only an icon, and they should fill the background of
the target.
This is the summary of the presentation today.
Thank you for listening.