Personality Traits and Visualization Survey by Christy Case

Personality Traits and Visualization: Survey
1
Christy Case
1 INTRODUCTION
Information visualization systems have
customarily shadowed a patterned model,
characteristically overlooking an individual
users’ requisite, capabilities and inclinations.
Nevertheless, current research has shown that
visualization presentations could be enhanced
by adjusting features of the visualization to
each individual user. The objective of this paper
is to offer research intended to support the
design of innovative user-adaptive visualization
systems. In particular, we debate outcomes on
using information that involve user eye gaze
forms while interacting with a specified
visualization to forecast the users’ visualization
tasks, as well as user perceptive aptitudes
containing perceptual speed, visual functioning
memory, and verbal functioning memory. This
information will present to the reader that such
calculations are considerably enhanced more
than a standard classifier even throughout the
early phases of visualization usage. These
conclusions are deliberated in view of planning
visualization systems that can adjust to each
individual user in real-time.
Subsequently visual analytics primarily involves
the close relationship between human and the
computer, facilitating communiqué among the
two is acute for constructing valuable systems.
Though the computer can link large volumes of
data on display via visualization, the human’s
contribution to an analytic computer system is
fundamentally restricted to mouse and the
keyboard. “This human-to-computer
construction delivers limited bandwidth and no
means for the human to express analytical
requests and intentions, other than to explicitly
demand the computer to accomplish specific
operations.” [1]
Researchers have established that even though
the mouse and keyboard seem to be restrictive,
an inordinate amount of a user’s logical resolve
and approach, intellectual methods, and
personal character could be improved from this
interaction information. Machine learning
researchers have convalesced characteristics
for re-validating particular operators in “real
time” utilizing data over “raw” mouse
exchanges and keyboard responses; no user
behaviors or schemes.
Visual analytics have presented that strategies
can be pulled out from interface logs only,
nevertheless at the price of countless hours of
wearisome efforts. Regrettably these labor-
intensive methods are not reasonable for
actual systems to adjust to users. The methods
necessary to learn about users and their
approaches and behaviors in real time do not
occur to our understanding. In this paper, we
determine on a small scale that it is conceivable
to automatically abstract information about
users and their analysis methods. Definitely, by
expending familiar machine learning systems,
we show that we can: (a) calculate a user’s task
performance, and (b) conjecture some user
character traits. Supplementary, it can be
recognized that these results are able to be
attained fast enough that they could possibly
be applied to real-time systems. [3]
Additional explanations for studying integral
differences is that they are examples over
which interface designers have no control and
recent examination shows that personality
plays a key role in appearance when using a
visualization system and that approaches of
some personality mannerisms can serve as

2
forecasters of a user’s inclination to adjust their
conceptual model to numerous visual
representations. What these discoveries
highlight is the significance of seeing individual
differences such as a users’ personality when
planning new visualizations.
2 APPROACH
2.1 In the article, Finding Waldo: Learning
about Users from their Interactions, the key
ideas were to show that participants could be
classified as fast or slow at the visual search
task by applying machine learning to three
encodings of participants’ interaction data:
(a) state-based, (b) event-based, and
(c) sequence-based. Secondly, it was to apply
these same techniques to categorize
participants based on personality traits and
determine attainment for the traits locus of
control, extraversion and neuroticism. Thirdly,
a key idea of this article was to assess the
likelihood of relating this work to real-time
systems by providing results using smaller
durations of data gathering. [1]
Fig. 2. Visualizations of transitions between viewpoints seen by
participants during the study (see Section 5). Subfigures (a) and (b)
show slow and fast users respectively, as determined by the mean
nomed splitting method (see Section 6). Subfigures (c) and (d) are split
with the mean nomed method based on locus of control, a personality
measure of a person’s perceived control over external events on a scale
from externally-controlled to internally-controlled.
2.2 In the article, Towards the Personal
Equation of Interaction: The Impact of
Personality Factors on Visual Analytics Interface
Interaction, two studies were conducted. Each
study employed a within-participants design,
and compared procedural learning behaviors in
an information visualization and a web table.
“Study 1 tested: procedural learning
performance with a series of five questions in
an individual interface. Study 2 tested:
procedural learning performance, with a total

3
of six questions in each interface (3 training and
3 task). The procedural task completion times
in both studies were combined for the purpose
of analysis. Both studies asked participants to
interact with two interfaces built to display
genomic information. One interface is the web-
based National Center for Biotechnology
Information (NCBI) MapViewer for genomic
information. (See Figure 1.) [2]
Figure 1. NCBI MapViewer
The other interface is an interactive data
visualization (GVis) of genomic relationships.
(See Figure 2.)
Locus of Control attested to be an important
character trait no matter what the interface or
task was presented. The faster participants in
both interfaces were individuals who had a
more internal locus of control (LOC), which is
characterized by a credence in personal control
over life experiences. This discovery is in close
agreement with a considerable amount of the
obtainable literature on locus of control (LOC).
Persons with an added internal locus have been
found to have better problem-solving skills [3],
to be more resolved to solve a task when it
turned out to be difficult, and to be more likely
to advance an intrinsic (internal) stimulus to
complete a challenging task. [2] For a summary
of the findings from these studies please see
Figure 5.
2.3
In the article, User See, User Point:
Gaze and Cursor Alignment in Web Search, it
was revealed that user, time, and search task
each add to the distinction in “gaze-cursor”
configuration. The “gaze and cursor” points are
also well aligned when the gaze position is
associated to an imminent cursor position.
Additionally, by differentiating between five
diverse cursor behaviors—inactive, examining,
reading, action, and click—we get a better
awareness of the power of position. As a
matter of fact, capabilities were discovered for

4
improving upon using cursor position alone to
forecast the gaze position by exhausting several
cursor features. “Cursor movements, scrolling,
and other client-side interactions are easy to
collect at scale, which many Web analytics
services offer to do. But claiming that the
cursor approximates the gaze is misguided—as
we have shown, this is often not the case
depending on time and behavior. Instead, it is
important to predict the real location of the
attention when an eye-tracker is unavailable.”
(See Figure 6) [3]
Search Engine Results Pages (SERP)
2.4 In the article, Manipulating and controlling
for personality effects on visualization tasks, it
was demonstrated that by influencing a user’s
locus of control (LOC), significant altered
behavioral configurations could be provoked.
Results also highlighted the sensitivity of
assessment measures such as reaction time to
small situational variations. (See Figure 3) It is
believed that these findings help build toward
understanding that personality factors can
affect the way humans solve problems with
visualizations and contribute to the
development of systems that are vigorous to
the properties of single variances. It is thought
that this research helps form near a
relationship between the system and the user,
where not only do users adapt their systems to
better suit their analytical needs, but systems
can also boost variation by the user to improve
performance.
2.5 Finally, the article, How Visualization Layout
Relates to Locus of Control and Other
Personality Factors, makes a number of
contributions to our understanding of how
personality factors affect how people use
visualizations: First, it was presented that
experimental evidence showed how visual
layout is a key factor in previous findings in
individual differences and these individual
differences must come from collecting
knowledge across studies. The findings
expanded previous work by showing that the
effect of locus of control can still be established

5
when limiting visualization differences to layout
factors. Second, these findings are used to
argue for a model of visualization usage built on
a user’s implementation of external
representations. Findings recommend that
locus of control affects the use of dissimilar
visualization types by affecting user’s
inclination to adapt to an innovative
externalization of data. This outline places
these results in the framework of external
exemplification as a model for visualization use.
As with Locus of Control (LOC), participants
were divided into high, low, and average sets
for both Extraversion and Neuroticism using
one standard deviation from the mean as
dividing arguments. (See Figure 3) This split
participants into three groups constructed on
Extraversion: introverted (less than 2.29),
average extraversion, and extraverted (greater
than 3.86). [5] For Neuroticism, these groups
were low neuroticism (less than 2.02), average
neuroticism, and high neuroticism (greater than
3.53). It was discovered that no overall effects
of either of the two Big Five measures had
correct response times. As with LOC, the
Pearson’s chi-square test was used on accuracy
and assembled personality type to test
differences in accuracy. [5] “Introverts were
more accurate across all four views and both
question types than extraverts.” [5]
Comparably the same effect was found for
participants using high neuroticism scores.
Along with these participants, highly obsessed
participants were significantly more accurate
than the other groups in the high-structure V4
condition. (See Figure 4) [5] Overall, the more
neurotic participants appeared to offer
responses in a higher percentage of questions
correctly as assessments developed more
container-like, although the other groups
presented the opposite trend (See Figure 5).
There was no comparable noteworthy effects
for extraversion. Furthermore, unlike Green
and Fisher [2], it was discovered that there was
no effect of either extraversion or neuroticism
on response time for search tasks.

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In this paper, findings were contributed on how
users with dissimilar personality styles respond
to changing layout styles utilized in a hierarchy
visualization. Evidence was found that offered
systematic differences in layout style and
certainly influenced a user’s response time and
accuracy with various kinds of visualizations
that are informationally equal but different in
layout. These results appear to fit a pattern in
which users with a more external locus of
control are more efficient at using a
visualization which uses a highly explicit visual
representation than users with a more internal
locus of control. It is expected that these
findings can serve as a stage headed for
improved understanding of why understated
variances among users’ personality styles can
have a startling effect on visualization use.
3 COMPARISON AND DISCUSSION
Current and earlier studies emphasize that
assessing tools to help people think is a
complex undertaking. Results propose that
modern efficiency methods of speed and
precision may not capture all of what we hold
as being important in a visualization. Although
accuracy only may not replicate the actual
effort of a task, collaboration stages prove to
be much more complex to minor variations in
user preference to offer generalizable data
about a system. Calculations must
consequently go beyond simple analyzing of
the efficiency of a visual design but should also
include methods that analyze the user’s
cognitive influences. In reading the article,
Finding Waldo: Learning about Users from their
Interactions, the experimental task was a
simple version of a basic visual analytics sub-
task. The results could be reinforced by
increasing the experiment to test Waldo in
diverse locations, or different incentives like
maps with buildings and cars. The scope of
applicability might be calculated by testing
other basic visual analytics tasks such as using
tables to discover data or associating values
through visual methods. [1]
In the article, Towards the Personal Equation of
Interaction: The Impact of Personality
Factors on Visual Analytics Interface
Interaction, it was found that the advantage of
locus of control (LOC) demonstrated to be an
important personality attribute no matter what
the interface or task.

7
In the article, User See, User Point: Gaze and
Cursor Alignment in Web Search, there were
advantages and disadvantages. The study
demonstrated that “gaze-cursor alignment is
situational, as it depends on the time spent on
the page, personal browsing habits, and a
user’s current cursor behavior (inactive,
examining, reading, action).” [3] The
experiment disclosed that a model exhausting
these structures could calculate the subject’s
gaze considerably well instead of using the
cursor position only. These conclusions have
inferences for using comprehensive cursor data
more effectively, which has already been
demonstrated to be proficiently attainable at
scale. Cursor tracking, deemed the “poor man’s
eye tracker”, [3] could estimate gaze tracking
without the eye tracker reliant on the
accurateness necessary. While cursor
structures would permit us to model various
features of user attention in situations as they
browse the Web from home, it cannot totally
substitute for gaze. “For example, eye-gaze
fixation is a positive signal of interest because
the user pays more attention to that position,
but prolonged cursor fixation may not be since
given the findings in the study, the user’s
attention is probably elsewhere.” [3] More
effort is necessary to analyze in part so that the
association amongst the cursor and gaze
fixations can be consistently understood as
consideration.
In the article, Manipulating and controlling for
personality effects on visualization tasks, there
is a disadvantage because of the way people
think and solve problems are often times
situationally dependent. It is totally conceivable
that understated aspects of user study
procedures, task question design, and even a
researchers’ behavior can recruit unintended
cognitive instructing and contribute a
participant feeling more or less in control. [4] If
assignment performance can be exaggerated
by a user’s mental state, this type of
unintentional priming might damage the
legitimacy of assessment outcomes.
There are also advantages and disadvantages in
studies found when reading the article, How
Visualization Layout Relates to Locus of Control
and Other Personality Factors. The studies
mentioned in this article, and others like them,
provided growing confirmation that personality
and design style can have a substantial result
on whether a user agrees to a visualization
design. It is conceivable that a “user’s
personality can serve as shorthand for subtle
cognitive-style alterations that are not
definitely quantifiable otherwise, but that can
increase significance in the investigative
framework of visualization use.” [5] The
important result of locus of control on
performance proposes that it processes
something principally significant to visualization
use, and so in this article there is a focus on
how considering locus of control can be utilized
to expand visualization design. Though an
analysis of the outcomes recommends
opportunities for design, additional research is
required to validate the increasing frame of
perceptions into individual users and
visualization. Impending effort in this area
should contain the expansion of prescribed
design strategies motivated by a
supplementary all-inclusive analysis of
individual differences and their associations to
visual design features.

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4 PROPOSAL OF FUTURE WORK
There are plans to extend work mounting on
three fronts: (1) assessing supplementary
personal behaviors, like cognitive influences
such as working memory, (2) initiating
additional machine-learning algorithms and
indoctrinations to learn from more of the data
being composed, like the periods of the
collaborations and (3) encompassing research
with different tasks containing deeper
examination of visual examination. It is
believed that there are numerous prospects to
cover, both experimentally and logically. [1]
Supplementary, in the original article, Finding
Waldo: Learning about Users from their
Interactions, of the data depictions that were
evaluated, only the lowest-level interactions
encoded information about time passing during
the task. The other representations do not
encode the time between states or button
presses, but that information could be useful
for a future study. In the longer term, it could
be intended to isolate predictive matrices and
validate a battery of measures that will
successfully inform interface design based on
the types of cognitive tasks undertaken. As
seen in the studies supplied from the article,
Towards the Personal Equation of Interaction:
The Impact of Personality Factors on Visual
Analytics Interface Interaction, these measures
will likely involve more than personality factor
matrices; other areas of exploration include
perceptual logics and use of decision-making
heuristics. [2] In addition to informing design,
as laid out in the article, “personal information
could be used to provide real-time interface
variation to benefit user desires and
inclinations, and provide a basis for strong
group profiles of users that share combined
differences.” [3] That said, individual
differences research remains necessary.
Because some users are simply more
susceptible to prompting than others it may be
necessary in identifying these less flexible user
groups and how they respond to varying
visualization designs; it may still be important if
we are to totally comprehend how instructing
methods can be used to regulate personality
properties. Since locus of control was studied
within these articles, comparable priming
methods occur for other cognitive states. [2]
These may also provide opportunities for
controlling individual differences within
evaluation studies. However, in this instance it
is first necessary to determine whether the
cognitive states affect performance on
visualization tasks. In future work, it is hoped
that a mixture of these results could be
pursued. Much more can be accomplished to
discover “commonalities” among the measures
and findings on how users adapt to
visualizations. [2][3][5]
REFERENCES
[1] Eli T Brown, Alvitta Ottley, Helen Zhao, Quan
Lin, Richard Souvenir, Alex Endert, Remco
Chang. Finding Waldo: Learning about Users
from their Interactions. IEEE Transactions on
Visualization and Computer Graphics, Vol. 20,
No. 12, December 2014
[2] T. M. Green and B. Fisher. Towards the
personal equation of interaction:
The impact of personality factors on visual
analytics interface interaction.
In Proceedings of the IEEE Symposium on Visual
Analytics Science and Technology (VAST), pages
203–30. IEEE, 2010.
[3] J. Huang, R. White, and G. Buscher. User
see, user point: gaze and cursor alignment in
web search. In Proceedings of the SIGCHI

9
Conference on Human Factors in Computing
Systems (CHI), pages 1341–1350, New York, NY,
USA, 2012. ACM.
[4] A. Ottley, R. J. Crouser, C. Ziemkiewicz, and
R. Chang. Manipulating and controlling for
personality effects on visualization tasks. SAGE
Publications Information Visualization, 2013.
[5] C. Ziemkiewicz, A. Ottley, R. J. Crouser, A. R.
Yauilla, S. L. Su, W. Ribarsky, and R. Chang. How
visualization layout relates to locus of control
and other personality factors. IEEE Transactions
on Visualization and Computer Graphics (TVCG),
19(7):1109–113, 2013.

Personality Traits and Visualization Survey by Christy Case

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