Gaze-Adaptive Lenses for Exploration of Feature-Rich Information Spaces
The ongoing digitalization of our daily lives leads to an increasing amount of digital data.
A challenge is the visualization of detailed information
without the introduction of additional visual clutter.
One common approach to reduce clutter are Dynamic Levels of Detail (LOD)
that can be controlled by panning and zooming the information space.
While this type of interaction well allows the inspection of local details, it can affect
the users’ ability to develop a global perspective of the presented information.
Interactive lenses that provide multiple views of the data simultaneously can help overcome
this issue. However, these lenses typically require additional
manual interaction (navigation and inspection). This can lead to additional cognitive load.
In this work, we propose a combination of gaze and mouse input for the interaction with lenses.
This allows controlling the lens with gaze while still being able to explore the
map with the mouse.
With a pilot study, we compare this approach to a mouse-only system
in terms of efficiency, effectiveness, usability, and applied search strategies.
Yi et al. (2007) identify seven basic categories of interaction that can be performed with
interactive lenses.
Filter (show me something conditionally)
Abstract/Elaborate (show me more or less details)
Select (mark something as interesting)
Encode (show me a different representation)
Reconfigure (show me a different arrangement)
Connect (show me related items)
Explore (show me something else)
In our implementation we focused on the first five.
The system consist of the overview map, which provides the distribution of all restaurants
in the current map view.
A Lens Indicator and the lens itself.
It shows selected map content with a threefold magnification. It visualizes points for each
restaurant location (instead of the heatmap) and details at the POR.
Specifically, names and star ratings are displayed only for
restaurants located within an area of 2.5° visual angle. This spacing provides enough
clearance to display this information without overlap.
For an automatic label placement, we utilized simulated annealing, which is an efficient
approach to optimizes for reducing overlaps between labels and distances to their respective
data points.
In the mouse-only condition, the name and star rating of the restaurant appeared when
the location(s) was in the center of the interactive lens.
In the gaze-adaptive condition, the name and star rating of the restaurants
appeared on screen once participants focused on the location.
In both conditions, participants could use the mouse to pan and zoom the map in the overview
and interactive lens.
A total of 20 participants took part in the pilot study.
Our dependent variables for task performance included the number of locations found,
the overall time to locate and memorize the five target locations,
the ratio between completion time and the number of targets found,
and the degree of fit between the participants’
drawn map and the real map using bi-dimensional regression.
The usability of the conditions was assessed with two questionnaires.
We used the UEQ and the NASA TLX.
For eye tracking, our dependent measures included the ratio between gazes on the interactive
lens and the overviewmap, gaze distribution in the overview map and interactive lens,
as well as gaze dispersion from the center of mass for the interactive lens in pixels.
Participants were faster to complete the task and found more locations with the gaze-adaptive lens.
Additionally, they were more accurate than those with mouse-only lens in the mapping task.
As expected, both conditions were rated as novel since lens interfaces are not very common yet.
For overall Attractiveness there was a slight advantage for the gaze-adaptive condition.
In addition, participants rated the gaze conditions to be easy to get familiar with (Perspicuity).
In terms of Efficiency and Dependability, both
interactive lenses were rated similarly. Interestingly, participants found the mouse-only lens to
be slightly more exciting and motivating to use (Stimulation).
As expected, the distribution of gazes with the mouse-only lens is narrower compared to
the gaze-adaptive lens given that in this condition our design did not render any labels
within the periphery.
This figure show exemplary movement paths of the lens on the overviewmap. Although,
the mouse-only participant only found 3 of the 5 targets, the amount of lens movement
(i.e. mouse interaction) was higher than for the participant using the gaze adaptive lens
and found all 5 targets.
The main benefit of the gaze-adaptive lens is the ability to determine content that is
important for the user without explicit manual input. This can save space for better label
placement and reduce visual clutter because only focused areas need to be rendered with full detail.
The results of the pilot study indicate that gaze-adaptive lenses can improve the performance
in terms of finding locations on a map and mapping them afterwards.
Furthermore, it seems that the gaze interface is easier to get familiar with and generally more attractive.
We believe that this is the result from less mouse movements required.
Here we can see both conditions side by side.