Google Understanding Visualization by Understanding Individual Users - Manual

Understanding Visualization by Understanding Individual Users
Caroline Ziemkiewicz
∗
Brown University
Alvitta Ottley †
Tufts University
R. Jordan Crouser ‡
Tufts University
Krysta Chauncey §
Tufts University
Sara L. Su ¶
Google Inc.
Remco Chang
Tufts University
1 T HE C HALLENGE OF I NDIVIDUAL D IFFERENCES
Visualizations are tools to support thinking. They can be used to ex-
ternalize knowledge about a complex analytical task or domain, and
through interaction, they can embody a reasoning process. As such,
visualization cannot be fully understood without also understanding
how the user of a visualization thinks. This understanding is non-
trivial, and has been complicated by mounting evidence that there is
no single type of visualization user. Ultimately, making sense of visu-
alization requires understanding how users vary and why.
Past research in visualization theory has focused primarily on how
data can be mapped to visual forms and how people perceive them.
These research endeavors have led to the identification of fundamental
principles regarding how humans perceive colors and visual patterns,
and have led to the establishment of general design guidelines for
developing useful visualizations. Perceptual visualization theory at-
tempts to understand and model how users perform fundamental low-
level tasks.
However, as visualization gains widespread importance, the tasks
that researchers must study are becoming more complex. In recent
years, visualizations are being used as cognitive aids in problem solv-
ing, as users come to rely on visualizations to help them solve increas-
ingly difficult problems. While color and perceptual theories remain
necessary to make good design decisions, by themselves they are not
sufficient to guide the design of a visualization for a cognitively com-
plex task. These theories, though fundamental, do not address how
users think or how visualizations can be applied as an extension to an
individual’s cognitive ability.
Clearly, we all think differently. There are aspects about you that
differentiate you from everyone else. Your experiences, personality
and cognitive abilities influence your approach to solving a task and
your understanding of a problem domain. In cognitive psychology, re-
searchers have shown that such individual differences can significantly
impact a user’s dexterity using an interface or a tool to solve problems.
Visualization users differ greatly in experiences, backgrounds, per-
sonalities and cognitive abilities, yet visualizations, like much other
software, continue to be designed for a single ideal user. It would be
clearly impractical to design each visualization for an individual user.
However, knowledge of broad differences between user groups could
be used to guide design for specific domains and to suggest multiple
analysis modes or customization options in a single system. There has
recently emerged a new and promising area of research that takes an
opposing approach to the traditional method of “one size fits all” de-
sign. This research suggests that it is the individual users’ cognitive
style as much as the visual design that determines the value of a vi-
∗
e-mail: [email protected]
† e-mail: [email protected]
‡ e-mail: [email protected]
§ e-mail: [email protected]
¶ e-mail:[email protected]
e-mail: [email protected]
sualization. Moreover, these individual differences appear to be more
pronounced in more complex tasks.
Although still at an early stage, these findings suggest that visual-
ization should not be studied in a vacuum, but in the context of differ-
ences among its users. In this viewpoint, we discuss existing work on
how cognitive abilities and personality factors affect visualization use,
and what still needs to be done in order to advance this field of study.
Based on our own experiences in studying individual differences, we
argue that current visualization theory lacks the necessary tools to an-
alyze which factors of a design lead to differences in user behavior.
Developing this understanding would make it possible to study visu-
alization from the perspective of how an analysis process arises from
the interaction between a user and a system. This in turn could lead to
a shift in how we evaluate and design visualizations for different user
groups, tasks, and domains. In order for this to happen, we must first
understand what individual factors affect the use of visualizations.
2 C OGNITIVE F ACTORS IN V ISUALIZATION
Cognitive factors such as spatial ability, verbal ability, and working
memory capacity vary substantially between individuals, and can af-
fect reasoning in many different ways. Spatial and perceptual abilities
in particular have been shown to affect how well users can perform
several different tasks in a visualization system. Velez et al. [17] first
showed that a number of these abilities, including spatial orientation,
spatial visualizaiton, visual memory, and perceptual speed, affect ac-
curacy and response time on a task involving the comprehension of
3D views similar those found in scientific visualization applications.
While this is perhaps unsurprising, subsequent work has shown that
these abilities can affect more abstract 2D visualization tasks as well.
Perceptual abilities include basic visual proficiencies such as scan-
ning speed and visual memory capacity. For example, Conati and
McLaren [5] found that perceptual speed, which measures the speed at
which a person can compare two figures, correlates with a user’s accu-
racy at information retrieval tasks in one of two visualization systems:
a star graph and a heatmap-like view. Users with high perceptual speed
performed better with the heatmap-like view than the star graph on a
comparison task, and vice versa. The authors found that this was only
true for one of the tasks they studied, one in which participants were
asked to compare differences in change over time between two scenar-
ios at a global level. This was perhaps the most complex question they
asked, as most of the others ask the participant to retrieve or compare a
specific variable value. The fact that a more complex inferential task is
the most susceptible to individual differences is notable, as we found
similar effects in our own research.
Spatial ability can be measured by a variety of different tests, which
may express different aspects of this factor. In general, however, it
refers to the ability to accurately reproduce and manipulate spatial
configurations in working memory. In one study of the role of spatial
ability in visualization use, Cohen and Hegarty [4] found that a user’s
spatial ability affects the degree to which interacting with an animated
visualization helps him or her perform a mental rotation task. Partici-
pants were asked to draw cross-sections of a complex 3D object. They
were able to control two animated rotations of the object in order to
complete the task. Participants with high spatial ability produced more
accurate cross-sections and used the visualizations more, while those