0 Research evaluation All the listed authors are affiliated with King’s
College London. E-mail: {sara.tandon $|$ alfie.abdulrahman $|$
<EMAIL_ADDRESS>Tandon et al.: Measuring Effects of Spatial
Visualization and Domain on Visualization Task Performance

# Measuring Effects of Spatial Visualization and Domain on Visualization Task
Performance: A Comparative Study

Sara Tandon  Alfie Abdul-Rahman  and Rita Borgo

###### Abstract

Understanding your audience is foundational to creating high impact
visualization designs. However, individual differences and cognitive abilities
also influence interactions with information visualization. Differing user
needs and abilities suggest that an individual’s background could influence
cognitive performance and interactions with visuals in a systematic way. This
study builds on current research in domain-specific visualization and
cognition to address if domain and spatial visualization ability combine to
affect performance on information visualization tasks. We measure spatial
visualization and visual task performance between those with tertiary
education and professional profile in business, law & political science, and
math & computer science. We conducted an online study with 90 participants
using an established psychometric test to assess spatial visualization
ability, and bar chart layouts rotated along Cartesian and polar coordinates
to assess performance on spatially rotated data. Accuracy and response times
varied with domain across chart types and task difficulty. We found that
accuracy and time correlate with spatial visualization level, and education in
math & computer science can indicate higher spatial visualization.
Additionally, we found distinct motivations can affect performance in that
higher motivation could contribute to increased levels of accuracy. Our
findings indicate discipline not only affects user needs and interactions with
data visualization, but also cognitive traits. Our results can advance
inclusive practices in visualization design and add to knowledge in domain-
specific visual research that can empower designers across disciplines to
create effective visualizations.

###### keywords:

Human-subjects quantitative studies, visualization, perception, bar charts,
education, domain-specific, discipline, empirical evaluation, spatial ability,
cognitive abilities

K.6.1Management of Computing and Information SystemsProject and People
ManagementLife Cycle; K.7.mThe Computing ProfessionMiscellaneousEthics
Stimuli created for this study displaying a bar chart rotated along Cartesian
and radial axes: Vertical, Horizontal, Radial, and Circular Bar Plot.

Introduction

Data shapes modern society and affects every aspect of our lives: political,
social, and physical. Information visualization is just as prevalent – it
lives in magazines, news outlets, scientific papers, blogs, and countless
online platforms to facilitate engagement with data in a way text alone
cannot. The data visualization community has long explored how to design
universally accessible and engaging visualizations that empower communities to
understand and reason with data [69]. Information visualization is not simply
a matter of reading data properties encoded in visual form – there are complex
cognitive activities at work that are influenced by visual structure [96].
Visualization research has followed several paths toward understanding users
and their needs including understanding how cognitive abilities affect
perception and how disciplines differ in terms of data and tasks [67, 88, 96,
53, 78]. Education psychology has explored the connections between education
background, performance, and the cognitive ability of spatial visualization,
noting these are not easily separated in individuals [9, 91]. Recently, work
has been done in visualization toward integrating these tracks of research to
move away from universal guidelines for the monolithic “user” and towards
effective design that considers the differing cognitive traits of target
disciplines [43].

We build on this past research to explore and investigate the cognitive trait
of spatial visualization and its interaction within and across not only
professional domains but also educational backgrounds. We focus our study on
the disciplines of business, law & political science, and math & computer
science; the use of visualization is core to these disciplines, from decision
making to policy development to data analysis. In this paper, we present past
research related to spatial visualization in psychology and visualization
design that led us to create hypotheses around how our target domains would
perform spatial visualization assessments and relevant visual tasks. We pose a
study methodology that links spatial visualization abilities to visual task
paradigms that evoke the same cognitive functions. Finally, we offer a
detailed statistical analysis demonstrating that visualization task
performance (accuracy and response times) varies with spatial visualization
and discipline. Additionally, we present how domain differences in motivations
around use of data visualization might affect performance on visual tasks.

The results we collected add to evidence that spatial visualization and domain
interact to affect visual task performance and should be considered as
combined factors that can influence design choices; visualization design
should consider not only the needs of varying domains, but the abilities of
individuals in those domains [43]. This paper contributes toward cataloguing
visualization performance differences amongst spatial visualization levels and
domains that can lead to empowering individuals across disciplines to make
memorable references and inferences effectively and efficiently using
visualizations designed for them.

##  1 Related Work

Our study builds on research in spatial visualization, education psychology,
and information visualization. It bridges visualization research in user
cognition and domain needs/expertise to inform design as a holistic process.
We draw on methods from psychology and visualization to investigate the
relationships between spatial visualization, domain, and visualization
comprehension.

###  1.1 Spatial Visualization

Spatial ability is a cognitive ability referring to skills involved with
retrieval, retention, and transformation of visual information [59, 52]. It
impacts a viewer’s use of graphics as it influences the processing of spatial
relationships between graphical elements and/or their cognitive resource
allocation [87]. Spatial visualization is a specific aspect of spatial
abilities that involves the ability to remain unconfused by varying
orientations or rotational positions in which a spatial pattern may be
presented, thus it is elicited most in tests that involve manipulation or
transformation of a visual stimulus [59, 52]. Spatial visualization allows an
individual to compare different encodings of visualizations quickly and
accurately [59], and has been connected to spatial scaling, figure
construction, magnitude sense, numerical estimation, subdivisions of charts,
and even textual spatial analogies [94, 61]. In all, high spatial
visualization implies the ability to manipulate and change internal
representations of visualizations, leading to measurable outcomes when
interacting with external visualizations [55, 22, 45].

###  1.2 Education Psychology and Spatial Visualization

Spatial abilities, including spatial visualization, have often been tested in
educational settings as an indicator of increased performance in STEM
subjects, especially in performance in mathematical subjects [75, 61, 48, 9,
79, 94, 35]. Increased spatial visualization ability has been specifically
tied to increased performance in students of geology and architecture as well
[66, 75]. Additionally, substantial spatial visualization deficits have been
identified as the primary component in certain learning disorders wherein
students struggle to use visual and graphic representations of data [14].

Research has also been done into spatial visualization of students and
professional across a range of domains [17, 79, 91]. Burnett and Lane [17]
tested college students in their first semester and again after two years of
study in various subjects. They found that spatial visualization performance
improved with advance in education across all subjects, but by a lower
magnitude for humanities and social science students than math and physical
science students. Shea et al. [79] and Wai et al. [91] conducted comprehensive
studies assessing spatial abilities across domains, levels of education, and
occupation. Both found students of humanities, education, law, business, and
medicine to have lower spatial abilities than math/computer science, physical
science, and engineering students. This shows that specific domains and
duration of experience could influence individual ability to use information
visualization. Evidence that it is difficult to separate spatial visualization
abilities from education and professional choices could empower designers to
create impactful visuals for specific communities.

###  1.3 Spatial Visualization in Information Visualization

A rich body of literature exists investigating spatial abilities in the
context of information visualization [67, 88, 96, 51, 89, 87, 92, 43].
Research has demonstrated that high spatial visualization specifically, can
correlate with higher recall, understanding, and increased task performance
using data visualization [51, 67].

Kim et al. [51] observed that for both parallel coordinate plots and written
descriptions of data, participants with high spatial visualization had higher
accuracy when recalling values. Vicente et al. [89] found low spatial ability
corresponded with inferior performance on retrieval tasks within visual file
structures: low spatial individuals were two times slower and more likely to
get lost in the structure than high spatial individuals. Kellen [50] and
Ottley et al. [67] found that high spatial individuals had significantly
increased accuracy in their performance on solving conditional probability
problems when aided by a visualization. On their study using line-up tests,
VanderPlas and Hofmann [87] found that performance of undergraduate students
correlated with visual ability and whether a student was a STEM major. Studies
also indicated that speed is often related to understanding [82, 88] and was
sometimes the bigger indicator of lower cognitive abilities compared to
accuracy [92, 27]. Wenhong [92] found that high spatial ability led to reduced
response times and higher accuracy with graphics and table visualizations, and
interestingly that those with low spatial ability still chose graphic
visualizations for tasks. Wenhong noted that often individuals are not aware
of their own cognitive style, but still prefer visual representations of data.
These findings indicate space for accessible design in information
visualization for low spatial individuals [93].

While cognitive abilities are thoroughly studied in visualization design,
educational or professional domain is often overlooked as a relevant influence
and not reported on [51, 50, 89, 67, 88, 82] or referenced, but not analyzed
[27, 92]. However, there is also rich research in domain-focused participatory
methodologies that imply domain can also impact design.

###  1.4 Designing Visualization for Domain

Literature on visualization design highlights interaction with, and discovery
of, needs for different domains to inform the design process, but they do not
explicitly consider differing cognitive traits of the domains [77, 78, 42, 81,
53, 62]. The design study methodology proposed in [78] emphasizes the
fundamental need to design with and for the needs of specific domains.
Distinct from discovering practices and requirements of specific domains, it
also advocates for designers to be aware of literature pertaining to relevant
design problems, but not specifically to cognitive and perceptive knowledge,
which are themselves distinct from visual task needs of domain experts. Other
methods like participatory design, design by immersion, and domain liaisons
[42, 81] potentially have benefits due to implicitly integrating design
reflective of cognitive capabilities, without the need to explicitly rely on
any empirical, systematic analysis of cognitive abilities.

Kirby & Meyer [53] and Munzner [62] further emphasize collaboration with
domain experts and employment of applied psychology methodologies to measure
cognition and perception, but do not touch upon capitalizing on the cognitive
features of domains in guiding design. Peck et al.’s multidimensional model of
individual differences in HCI [70] separates individual experiences/bias from
cognitive abilities in visualization design: [43] notes that treating these as
orthogonal traits implies an individual’s background and experiences do not
influence their cognitive traits and vice versa, which could lead to missed
opportunities and ineffective design choices.

Hall et al. [43] made an initial exploration into how spatial visualization
differs between professional domains (Education, Chemistry, and Computer
Science) and relates to visualization processing. They confirmed spatial
visualization differences amongst disciplines and that domain and spatial
visualization together influenced performance and qualitative strategies on
tasks related to 2D to 3D representations of isocontour plots and scatter
plots. Their findings imply differences in terms of task performance and
spatial visualization between domains and that these differences will endure
with design implications for visualization. Research demonstrates there are
ties between spatial visualization, domain, and visualization performance - we
endeavor to further findings into how cognitive differences between domains
relate to visualization performance to affect design decisions and empower
designers to create more relevant and cognitively appropriate visuals.

Figure 1: Activity sequence of the study. In Part 1, the paper is folded and
punched above the line. Participants are given four multiple-choice responses
to choose between, the selected choice is highlighted and participants can
move forward. Scores are the number answered correctly out of 10 in 3 minutes.

##  2 Motivation & Hypothesis

Drawing on the outcomes and identified gaps from previous work, we sought out
to study spatial visualization across domains and their combined influences on
visualization interaction. We ensured alignment between profession and
education background as research demonstrates increased spatial visualization
depending on education domain [17]. If occupation does not align with
educational background, the abilities gained in education should be
considered.

We chose to study those with professional profile and education background in
Law and Political Science (LPS), Business, and Math and Computer Science
(MCS). Differences between MCS and other disciplines have implications on
visualization research findings as they are often “standard participants” for
many studies on task performance and design choices; a considerable amount of
information visualization is also created and studied by those in MCS related
fields [20, 21, 87]. We chose Business as there is a wealth of literature
exploring data visualization for business intelligence and business decisions,
which often cites design and cognition research [36, 95, 84, 57]. However, we
are unaware of any comparative studies between MCS and Business domain
cognitive abilities applied to data visualization task performance. We chose
LPS due to the relative lack of research investigating data visualization and
law & politics, despite the imperative use of data in law & politics [97, 46].
However, given many governments have scientific advisory and consulting
committees, and politicians make health and governance related decisions based
on this advice [25], it may be worth exploring any differences in visual
cognition between those creating and consuming visualizations for political
decisions.

Additionally, these domains display varying levels of spatial visualization
ability with MCS exhibiting higher than average abilities, and Business and
LPS below average [91]. Given that spatial visualization affects interactions
with visualization, there may be measurable differences in how these domains
interact with data visualizations given their discipline. Our study aims to
explore if spatial visualization ability differs across domain, how spatial
visualization level affects interactions with data visualization, and if
domain interacts with spatial visualization to affect performance in a
quantitatively significant way. We explore these questions by investigating
the following hypotheses.

H1:

Spatial visualization will differ across domains. Research has demonstrated
systematic differences in spatial visualization abilities between domains [79,
91]. Drawing from these findings, we anticipate MCS will have the highest
spatial visualization ability and LPS will have the lowest. As spatial
visualization affects visualization interaction, these systematic differences
between domains may impact visual reasoning in a discriminating way.

H2:

Performance (accuracy and response time) will correlate with spatial
visualization level. Prior work in information visualization shows accuracy
and timing of visualization tasks vary with spatial visualization abilities
[67, 88, 96, 51, 89, 87, 92, 43]. High spatial individuals tend to have higher
accuracy and reduced response times. We expect to see comparable results,
emphasizing the role of spatial visualization in performance and use of
visualizations.

H3:

Performance (accuracy and response time) will correlate with domain such that
average performance will vary between disciplines. Hall et al.’s research [43]
partially confirms this hypothesis between Chemistry, CS, and Education. They
found similar performance and spatial visualization between CS and Education,
but observed an overall time/error trade-off between the disciplines
regardless of spatial visualization levels. However, they found correlations
between spatial visualization and task performance across all disciplines,
implying that spatial visualization is an important underlying source of
variations. We anticipate marked differences between MCS, Business, and LPS
given the three domains varying levels of interaction, creation, and
consumption of data visualization [38, 95, 97], with MCS performing the best
and LPS the worst following their spatial visualization levels. Confirming
this would substantiate the work of Hall et al., that effective visualizations
should consider not only the needs of a discipline, but the abilities of the
individuals within domains.

##  3 Methodology

A two-part online study was created to address the research questions and
hypotheses (see Fig. 1). Part 1 of the study consisted of a brief spatial
visualization psychometric test from The Kit of Factor Referenced Cognitive
Tests [32] known as a paper folding or punch test (see Fig. 1). Part 2
consisted of stimuli and questions asking individuals to draw conclusions from
the visualizations. Inspired by current methodology in vision science [33], we
began by evaluating paradigms that extend from psychological assessments of
spatial visualization to visualization evaluation. Principles and studies in
psychology dictate spatial visualization relates directly to ability to
quickly and accurately compare encodings and layouts of a given visualization
along with value estimation, magnitude sense, and subdivisions of charts [59,
94]. Working from these principles, we chose visual search as our base
paradigm [33], utilizing data from two charts rotated along Cartesian and
polar coordinates. We provide a detailed discussion of study structure and
stimuli design (see supplementary material (SM) for comprehensive stimuli
set).

###  3.1 Recruitment

Participants were recruited through Prolific [68]. The participants were 18
years and older and were fluent in English. Due to the limited number of
eligible participants on Prolific when filtered by education and profession,
we were able to recruit a balanced sample of 30 participants in each domain
with valid data, for a total of 90 participants (participant details below).
Participants were paid at £7.71/hour in accordance with Prolific’s fair pay
policy. The average response time was $37$ minutes and $7$ seconds, resulting
in an average pay of £4.78 per participant. We collected data across three
days and different time slots to diversify the pool of potential participants.
We collected data on Jan 28, 2022, Feb 3, 2022 and Feb 17, 2022.

###  3.2 Apparatus

The online study was created using a Flask Web App with D3.js for chart
generation. Participants went through consent and training before each part of
the study and were allowed to leave the study at any time, ending their
participation. A trigger warning was presented to participants before agreeing
to take part in the study as the data related to the COVID-19 pandemic
included case, hospitalization, death, and vaccination statistics. No
identifiable data was collected and all data was stored and maintained on a
private server at the authors’ institution.

(a) Easy.

(b) Medium.

(c) Hard.

Figure 2: (a) Example of an Easy question with the Radial chart on left and
density 14, (b) Medium question with Horizontal chart on right of density 7,
and (c) Hard question with Circular Bar on the right and density 7 (refer to
SM for a more comprehensive set).

###  3.3 Study Structure

After consent and demographics collection, the study process consisted of two
parts with an additional page of 5-point Likert scale questions, assessing
perceived difficulty and personal motivations regarding data visualization
(see Section 3.3.6). Participants were asked to confirm readiness to move to
the next block between each step to allow for breaks as needed. Training for
Part 1 of the study came from the Kit of Factor Referenced Cognitive Tests and
included a sample question that participants had to answer correctly to move
forward. Active training for Part 2 consisted of 4 sample questions displaying
the various charts participants would see throughout the study.

We ran a pilot test to measure the fatigue, length, and complexity of the
study. Results from the pilot suggested changes to the study workflow; it
became clear that the spatial visualization assessment should be placed at the
beginning to ensure accurate scores which could otherwise be hindered by
fatigue from Part 2, being the longer portion of the study. Additionally, we
decided to integrate a break halfway through Part 2, consisting of a catch
question wherein users were encouraged to take a break, answer with a specific
response, and move on when they were ready. The catch question was not
analyzed (other than ensuring all respondents answered correctly for quality
assurance) and was not included in response time data analysis.

####  3.3.1 Screening & Demographics

Following consent, participants completed a set of 7 demographic questions to
collect information about gender, age, education history, profession, and
countries of origin and influence. Those who self-identified on Prolific as
having studied and working in one of our target areas were admitted and had to
reconfirm their primary field of study and work in demographic collection. To
reduce confounding factors we only selected participants where education and
professional profile were aligned if they were working. No participants whose
primary subject and profession failed to align with our targets were included
in our analysis. Respondents could only continue through the study if
participating on a laptop or desktop computer with a JavaScript enabled web
browser to increase consistency of display. Prolific offers a participant
score that is based on the quality of an individual’s past submissions on
their site: our participants had scores of 97% or higher.

We excluded the data of 25 participants from Prolific due to either lack of
consistency in primary field of study, data validation failure, or if they
experienced a server interruption during the study. We gathered data until we
achieved balanced samples with high quality data between our target domains.

####  3.3.2 Spatial Visualization Assessment

Following demographics, participants began the spatial visualization
assessment from the Kit of Factor Referenced Cognitive Tests [32], a well-
established 2D psychometric assessment [1, 59] largely utilized in previous
data visualization studies [51, 67, 58]. This assessment has the added benefit
of a 3-minute time limit for participants to complete the test. This time
limit contributes to limiting fatigue of participants taking an online
crowdsourced test contributing to higher data quality [12]. We gathered both
the selected answer and the response time for each question of the assessment.
The test consists of 10 questions in which an image of a paper is folded and
punched; participants must then choose from 5 options what the paper will look
like when unfolded (see Fig. 1 for a sample question). The spatial
visualization score is calculated as the number of correct answers out of 10.
Congruent to [51, 67, 75, 29] the score was centered by its mean so that
participants above the mean are classified as having high spatial
visualization, while participants below are classified as having low spatial
visualization.

####  3.3.3 Part 2 Stimuli Design

We manipulated three elements to analyze performance over: data density, chart
type, and task difficulty.

##### Data

We initially considered synthetic data distributions, however, opted for real-
world data to favor familiarity with a data set and scenario applications with
participants from a range of domains [12]. The data used in this study was
COVID-19 Pandemic data from a comprehensive world statistics site [72]. The
data reflects actual case, vaccination, hospitalization, and death statistics
from March 2020-January 2022 across 95 countries. This data was chosen as the
scale of the ongoing global pandemic allowed an assumption of a basic level of
familiarity with the data and no need for training or expert knowledge [12].
Additionally, the World Health Organization, governments, universities, and
data visualization tech companies produced many COVID-19 visual dashboards for
public consumption and policy recommendation during the pandemic – many of
these dashboards contained spatially rotated data of varying densities [64,
26, 56, 8, 65].

The data is displayed across two levels of density: 7 and 14 points. A key
manipulation for visual search is the number of objects present in the search
scene (target(s) + distractor(s) = set size) [33]. We chose 7 as our first set
size; 7 is known as a magic number in data visualization, as the limit of the
span of absolute judgement and immediate memory sits at about 7 points [60].
This implies that moving sufficiently beyond 7 data points will increase
cognitive load; thus we chose 14 as our second set size. Across stimuli, the
data was ordered alphabetically by country. Conventionally, visual search
studies vary the number of targets and/or distractors present over sub-
conditions [33] – we varied the two set sizes amongst the four chart types and
three questions detailed below, while maintaining the same density for side-
by-side charts.

##### Chart Types

The stimuli draw on elements from both psychology and previous visualization
studies in spatial visualization [43, 59, 89]. We formed our stimuli keeping
in mind that spatial visualization directly affects ability to compare visual
encodings and layouts quickly and accurately [59], and that our spatial
visualization assessment uses 2D rotated stimuli on a Euclidean plane [32]. We
chose to display two spatially rotated charts side-by-side to assess
performance as participants utilize both charts to respond to a given
question. Using two spatially rotated charts side-by-side simulates the
cognitive processes involved in spatial visualization assessments. The
anchoring stimuli chosen for this study was a vertical bar chart. Bar charts
are often used as stimuli in cognitive ability and visualization research as
they are the most ubiquitous data visualization used over the world [10] and
have been shown to capture performance differences between individuals of
differing abilities [18, 28]. Additionally, spatially rotated bar charts were
heavily utilized in COVID-19 pandemic data dashboards [64, 26, 56, 8, 65]. As
points on a Euclidean plane can be displayed using the Cartesian and polar
coordinate systems, we use both for our stimuli [47]. We rotated the value-
axis of the bar charts in both planes. Anchoring bars on the x-axis in a
Cartesian coordinate system provides a vertical bar chart with the value-axis
increasing along the y-axis; anchoring bars on the y-axis results in a
horizontal bar chart with the value-axis increasing along the x-axis. We then
transformed to a polar coordinate plane with value-axis increasing along both
$(r,\theta)$. Anchoring bars along a vertical axis with the value scale
increasing with $\theta$ results in a radial bar chart. Finally, anchoring the
bars at the pole with the value-axis increasing with $r$ results in a circular
bar plot (see Fig. Measuring Effects of Spatial Visualization and Domain on
Visualization Task Performance: A Comparative Study for chart types). We
followed conventional design principles for circular bar plots, including
increasing radial distance on a linear scale given data density, and a large
inner radius to reduce perception bias [44]. We note that circular bar plots
have some distortion of the bar as the radial distance increases to decrease
visual bias toward underestimating small bar heights [13]. Further, previous
research demonstrates that although accuracy can be inhibited in polar
coordinate charts compared to Cartesian, they are still widely used and are
aesthetically appealing, thus we used them as sub-conditions in this study
[16]. We maintained the vertical bar chart throughout the questions and paired
it with either itself or one of the three spatially rotated charts to allow
for performance comparison between chart types (e.g., is performance between
two Cartesian plots different than a Cartesian & polar chart?). We ensured
that the vertical chart appeared on the left and right of the rotated chart
across densities and question types. We maintained gray-scale charts to avoid
any color interference. As polar coordinate plots require chart lines for
readability, we maintained similar lines on the Cartesian charts as well. We
varied the four chart types amongst the two set sizes and three questions.

####  3.3.4 Part 2 Task Design

The basic task paradigm we chose was a visual search process across two charts
and densities. Visual search plays an important role in the cognitive process
and is a vital element to visualization [11]. As the stimuli consist of two
side-by-side charts, the visual search process we chose was a conjunctive
search: visual search involving identifying a previously requested target
surrounded by distractors possessing no distinct features from the target
itself [80]. Conjunctive search involves search over two channels and
increases with difficulty as density of distractors increase [80]. We paired
conjunctive search with tasks increasing in difficulty (see below) across two
levels of density (as described above) to compare performance across varying
levels of difficulty. We created three 3-alternative forced-choice multiple-
choice questions that required conjunctive use of two chart types for
response, we categorized them as Easy, Medium, and Hard. The three response
options were randomized across all questions. Questions were randomized after
the training section. Fig. 2 shows three examples of different levels of
difficulty and density, SM provides a comprehensive overview of all the
combinations.

Easy Question

The easy question is a search and comparison task. It displayed two charts
with case numbers from the same set of countries across two months. This
question is classified as easy, as it consists of a conjunctive search for the
same singular target variable across both displayed charts. Once located,
participants compare the target variable across the charts i.e., participants
searched for a given country in the first chart and compared case numbers of
that country to the second chart to respond if cases were higher, lower, or
the same as the previous month.

Easy question example: In Gibraltar, are cases higher, lower, or the same in
June compared to the previous month? Easy Responses: Cases are higher, cases
are lower, cases are the same.

Medium Question

The medium question is a search and comparison task across an increased number
of targets. It displayed two charts, the left with death rates, the right with
hospitalization rates of the same set of countries on a given date. This
question is classified as medium as it increases the variables for conjunctive
search and comparison to three targets across both charts; participants
searched for the three countries with the highest number of deaths in the
first chart and responded with which of those countries had the highest
hospitalizations from the second chart.

Medium question example: Of the three countries with the highest number of
deaths, which has the highest hospitalizations? Medium responses consisted of
the correct response, one of the countries with the highest number of deaths,
but not the highest hospitalizations, and one random country (see Fig. 2).

Hard Question

The hard question is a search and comparison task that involves mathematical
estimation. It displayed two charts, the left with the raw number (in
millions) of vaccinated people, the right with the raw population (in
millions) of the same set of countries on a given date. This question is
classified as hard as after a conjunctive search for one target variable
across charts, participants had to perform a mathematical computation to
estimate a derived variable. Computing a derived variable is a common task in
data analysis and often appears as a sub-task in other operations – the more
complex the aggregation the more difficult the interaction [4]. In this
question, participants are required to estimate the vaccinated portion of
people in a target country.

Hard question example: Approximately what portion of people are vaccinated in
Bhutan? Hard responses: less than $\frac{1}{3}$, between
$\frac{1}{3}-\frac{2}{3}$, more than $\frac{2}{3}$.

####  3.3.5 Part 2 Measures

Four possible chart pairings, times two possible layouts (e.g., Vertical or
Radial chart on either right or left), times two levels of data density, times
three question types/difficulty levels made for a total of $42$ multiple-
choice questions. To assess performance, we recorded the selected answer and
the response time (RT) of each question in all parts of the study. Stimuli
were shown randomly to participants to minimize learning effects.

####  3.3.6 Motivations and Difficulty Assessment

While not part of our main hypotheses, we also recorded participants’
perception of difficulty of both parts of the study. This was assessed on a
5-point Likert scale ranging from Easy to Difficult. These questions were
asked to ascertain if perception of difficulty is linked to performance in
both parts, and to aid in explainability of outcomes for anomalies or
unexpected results.

Further, research shows that cognitive skills (and specifically spatial skills
[9, 35]) and personal motivations are closely related and can together explain
achievement in STEM subjects [9, 19, 40, 15]. Motivation is part of an
individual’s goal structures, and their belief about what is important, and
determines whether they will engage in a given activity [5]. Research shows
that motivation and feelings towards mathematics (or math anxiety) are
interrelated [9]. Additionally, performance on spatial tasks, and tasks
involving mathematics, are influenced by self-efficacy, math anxiety, and
intrinsic and extrinsic motivators [40, 7, 35]. To increase explainability and
psychologically grounded methods in visualization research, we adapted the
Science Motivation Questionnaire (SMQ) from [40], which is grounded in
prominent theories of academic motivation and encompasses many of the
motivational factors found to influence performance in STEM [9]. We chose one
question from each of the motivational factors measured by the SMQ: intrinsic,
extrinsic, self-efficacy, self-determination, and math anxiety. Each question
mirrored the SMQ with the word “science” replaced by “data visualization” and
was rated by participants on a 5-point Likert scale as per the questionnaire
(see SM).

Figure 3: H1: Spatial visualization per domain, with the CI of means (left)
and of mean differences (right). Error bars represent 95% Bootstrap confidence
intervals. In the mean differences plot (right), those tighter and farther
away from 0 provide stronger evidence of differences. Stars indicate evidence
of significant differences across charts. Figure 4: H2: Spatial visualization
score with respect to performance: Pearson correlation between spatial
visualization and accuracy (left) at R=.255, p=0.01. Pearson correlation
between spatial visualization and response time (right) at R=-0.408, p=0.0006.
Time in seconds.

###  3.4 Participants

We successfully gathered 30 participants with reliable data in each target
domain, giving us a balanced sample, for 90 total participants. We followed a
strict rule of balanced samples across groups to support our between subject
design. The demographic statistics as a whole and across the three domains
were as follows. Gender participation overall: 51% male, 49% female. Business:
$47\%$ male, $53\%$ female. LPS: $30\%$ male, $70\%$ female. MCS: $77\%$ male,
$23\%$ female. These are consistent with known educational domain gender
differences across Europe [31, 90]. The average age ($\pm$ standard deviation)
of all participants was $24\pm 4$, $23\pm 4$ for Business, $25\pm 5$ for LPS,
and $23\pm 4$ for MCS. In an effort toward trans-cultural research design in
data visualization [2, 3], we opened our study to respondents from all
countries. To this end we deployed our study across several days and different
time slots to cater for time zone differences. Overall, we had $49\%$ from the
Global South and $51\%$ from the Global North. Business: $57\%$ Global South,
$43\%$ Global North. LPS: $57\%$ Global South, $43\%$ Global North. MCS:
$33\%$ Global South, $67\%$ Global North.

##  4 Results

We analyzed differences in performance and spatial visualization using sample
means, hypothesis testing at the $p<0.05$ level of confidence, and 95%
confidence intervals. Confidence intervals (CIs) were constructed in Python
using bias-corrected and accelerated bootstrapping (BCa) with 5000 iterations.
After testing independent groups for normalcy, we decided to utilize BCa to
create confidence intervals along with hypothesis testing using the Mann-
Whitney U test to obtain a test statistic and p-value for further validation,
as both are robust to non-parametric data [63, 49, 71]. We use both to
demonstrate multiple analysis techniques and strength of evidence about the
population means, as recommended in recent reviews [41, 30]. Confidence
intervals allow for both traditional statistically significant interpretation
(if the interval does not overlap 0), as well as subtle differences: the
farther from 0 and the tighter it is, the stronger the evidence [83].
Additionally, we analyzed differences in spatial visualization using a
Pearson’s correlation test, where $p<0.05$ indicates correlation and R ranging
$0.1-0.3$ indicates weak, $0.3-0.5$ moderate, and $0.5-1.0$ strong correlation
[23]. Below we report on our high-level findings; detailed means and stepwise
analysis are reported in SM due to space restrictions.

###  4.1 Demographic Differences

While not part of our main hypotheses, we evaluated spatial visualization and
accuracy levels across demographics.

Between self-identified males and females, we found no significant differences
in spatial visualization level, keeping with previous research [9, 76]. While
we detected no significant difference in RT, there was a significant
difference of $7\%$ in their accuracy (CI$(2,11)$, $p<0.01$). We note that the
uneven distribution of men in MCS is likely a driving factor in this
difference [48, 86].

We found strong evidence ($p<0.001$) that those who were raised in the Global
South score lower on spatial visualization than those in the Global North (by
$2/10$ questions). Additionally, overall mean times per question were 7.2s
longer (CI$(4,9)$, $p<0.01$) and overall accuracy $5\%$ lower for those from
the Global South ($p=0.05$). These differences reflect current research in
psychology and information and communication technology demonstrating
differing levels of cognitive ability and visualization interpretation between
those in the Global South and North [34, 37, 73, 6].

###  4.2 H1: Spatial Visualization by Discipline

We hypothesized that spatial visualization ability would differ across
discipline with MCS having the highest spatial visualization followed by
Business, then LPS.

Those in MCS had a mean spatial visualization score of $5.3$. There is
compelling evidence that MCS had higher spatial visualization levels than
Business ($p<0.01$) and LPS ($p<0.001$) by on average $2/10$ for both.
Business had a mean score of $3.6$ while LPS had a mean score of $3.5$ – there
was no evidence of difference between them. The CI of mean differences between
MCS and Business is $(0.6,2.7)$ while the CI for MCS and LPS is $(0.8,2.7)$,
indicating a slightly stronger difference between MCS and LPS (see Fig.3).
Though Business and LPS have slightly differing spatial abilities according to
[91], our finding is in line with [79] which does not show a significant
difference between spatial abilities of Business and LPS. Similar to both [91,
79], we found greater mean differences between MCS and Business/LPS than
between Business and LPS themselves. See Section 4.5 for further insight.

$\Rightarrow$ We partially confirmed H1, spatial visualization of participants
differed for some domains. The ranking of disciplines in terms of spatial
visualization is LPS $\approx$ Business $<$ MCS. This finding is still
consistent with previous research [79, 91] where LPS and Business have
similarly low levels of spatial ability, however we found no difference where
they find that LPS has slightly lower levels than Business.

Figure 5: H3: Vertical Bars performance means (left) and mean differences
(right), for Accuracy and Response Time. Time in seconds.

Figure 6: H3: Horizontal Bars performance means (left) and mean differences
(right), for Accuracy and Response Time. Time in seconds.

Figure 7: H3: Radial Bar Chart performance means (left) and mean differences
(right), for Accuracy and Response Time. Time in seconds.

Figure 8: H3: Circular Bar Plot performance means (left) and mean differences
(right), for Accuracy and Response Time. Time in seconds.

###  4.3 H2: Performance by Spatial Visualization

We hypothesized that performance (accuracy and response time) would differ
between those with high and low spatial visualization. To determine this, we
look at both the correlation between spatial visualization and performance, as
well as tested sample means and CIs across densities, charts, and questions.
For all 90 participants, the mean spatial visualization score was $4/10$. We
had 51 participants with low spatial visualization (9 MCS, 22 Business, 20
LPS) and 39 with high spatial visualization (21 MCS, 8 Business, 10 LPS). Low
spatial individuals had a mean score of $2.5/10$, while High had $6/10$,
meaning high spatial levels were about $4/10$ higher (CI$(3.3,4.1)$,
$p<0.001$).

We found evidence ($p\leq 0.01$) of moderate correlation as spatial
visualization increases with both accuracy (R=0.3) and RT (R=0.4) (see Fig.
4). We compared performance of low and high spatial visualization participants
across the two density levels of 7 and 14 data points, 4 chart types and 3
density levels. We report results from our analysis below (SM reports detailed
breakdowns of sample means and CIs).

####  4.3.1 By Density

Response Time: Mean times were higher for low spatial individuals compared to
high spatial for both densities: 8.4s (CI$(6,12)$, $p<0.001$) higher for
charts with 7 data points and 9.6s (CI$(7,13)$, $p<0.001$) higher for charts
with 14 data points.

Within low spatial individuals, charts with 14 data points took 4.1s longer
than charts with 7 data points (CI$(2,8)$, $p<0.001$). High spatial
individuals took 2.9s longer on charts with 14 data points (CI$(1,5)$,
$p<0.001$) compared to charts with 7 data points.

Accuracy: We detected a slightly higher accuracy means on density 7 charts for
high spatial individuals than low spatial by 5.2% (CI$(0.7,10)$, $p<0.05$). No
significant accuracy differences were detected between or within low and high
spatial individuals across chart density.

####  4.3.2 By Chart Type

Response Time: For Vertical charts, low spatial individuals took 6.4s
(CI$(3,10)$, $p<0.001$) longer than high spatial; for Horizontal low spatial
took 9.7s (CI$(6,14)$, $p<0.001$) longer; for Radial they took 7.6s
(CI$(4,12)$, $p<0.001$) longer; and for Circular Bar Plots, low spatial took
11.0s (CI$(7,16)$, $p<0.001$) longer.

Within low spatial individuals, polar coordinate charts took 6.0s longer than
Cartesian coordinates (CI$(2,10)$, $p<0.001$). Within high spatial
individuals, polar coordinate charts took 5.2s longer than Cartesian
coordinates (CI$(4,7)$, $p<0.001$).

Accuracy: There is some evidence that on Vertical charts low spatial means
were 5.7% (CI$(0.3,11)$, $p<0.05$) lower than high spatial. For Horizontal
charts, low spatial means were 5.1% (CI$(0.6,10)$, $p<0.05$) lower. On
Circular Bar plots low spatial was 7.8% (CI$(2,14)$, $p<0.05$) lower. There
was no accuracy difference detected for Radial charts.

Within low spatial individuals, polar coordinate chart means were 10.1% lower
than Cartesian coordinate charts (CI$(5,15)$, $p<0.001$). Within high spatial
individuals, polar coordinate chart means were similarly 10.5% lower than
Cartesian charts (CI$(6,15)$, $p<0.001$).

####  4.3.3 By Question Difficulty

Response Time: There is compelling evidence that RT means were higher for low
spatial individuals across all levels of difficulty: by 8.7s for Easy
(CI$(6,12)$, $p<0.001$), by 7.5s for Medium (CI$(4,12)$, $p<0.01$), and by
10.7s for Hard (CI$(7,15)$, $p<0.001$).

Within low spatial individuals, RT means were higher for Medium by 5.7s
(CI$(2,10)$, $p<0.001$) and Hard by 7.0s (CI$(3,12)$, $p<0.001$) when compared
to Easy time means, with no significant difference between Medium and Hard
questions. For high spatial individuals, time means were also higher for
Medium by 7.0s (CI$(5,9)$, $p<0.001$) and Hard by 5.1s (CI$(3,7)$, $p<0.001$)
when compared to Easy times. Interestingly, high spatial time means for Hard
questions were 1.9s lower than for Medium times (CI$(0.5,4.3)$, $p<0.01$).

Accuracy: Accuracy was similar between low and high spatial individuals in
Easy and Medium questions. However, the low spatial means were lower by 10.1%
(CI$(4,17)$, $p=0.01$) for Hard questions.

Within low spatial individuals, accuracy means were 11.9% lower for Medium
(CI$(6,17)$, $p<0.001$) and 17.8% lower for Hard (CI$(11,24)$, $p<0.001$) when
compared to Easy questions. High spatial accuracy means were 16.1% lower for
Medium (CI$(11,22)$, $p<0.001$) and 12.3% lower for Hard (CI$(9,17)$,
$p<0.001$) when compared to Easy questions. Neither had notable accuracy
differences between Medium and Hard questions within group.

$\Rightarrow$ When looking at overall performance, we confirmed H2, spatial
visualization level does affect RTs (lower spatial visualization, longer time
to answer questions) and a moderate amount of accuracy. Low and high spatial
individuals have a similar discrepancy in performance as density increases and
coordinates move between Cartesian and polar.

###  4.4 H3: Performance by Domain

We hypothesized that performance (accuracy and response time) would differ
between disciplines, with MCS performing best and LPS worst (following their
spatial ability). Looking at overall mean differences, we found that MCS was
faster than Business by 7.2s (CI$(5,10)$, $p<0.001$), and LPS was faster than
Business by 7.6s (CI$(5,11)$, $p<0.001$). No difference was detected in
overall RT between MCS and LPS. For overall accuracy however, MCS was 12.8%
more accurate than LPS (CI$(9,19)$, $p<0.001$), and Business was 10% more
accurate than LPS (CI$(5,16)$, $p<0.001$). No difference was detected in
overall accuracy between MCS and Business.

We detail results of our analysis of accuracy and RT across three variables:
density, chart type (Figs. 6-8), and question difficulty. Detailed breakdown
of the analysis and means are provided in the SM.

####  4.4.1 By Density

Response Time: For charts of density 7, there is evidence that mean times were
higher for Business compared to LPS by 7.6s (CI$(4,13)$, $p<0.001$) and MCS by
7.3s (CI$(4,13)$, $p<0.001$). There is also evidence mean times were higher
for Business in charts of density 14: higher than LPS by 7.7s (CI$(4,13)$,
$p<0.001$) and MCS by 7.0s (CI$(3,12)$, $p<0.001$). There is no evidence of
significant differences between MCS and LPS mean times.

Within groups there was similar increased time means for charts of density 14
when compared to density 7: Business by 3.5s, LPS by 3.4s, MCS by 3.8s (all
$p<0.001$).

Accuracy: For charts of density 7, there is strong evidence that MCS had 11.6%
higher accuracy (CI$(7,18)$, $p<0.001$) and Business had 9.8% higher accuracy
means (CI$(5,16)$, $p<0.001$) than LPS. Additionally, for charts of density
14, MCS had 14.0% (CI$(9,20)$, $p<0.001$) higher accuracy and Business had
10.2% (CI$(5,17)$, $p<0.001$) higher accuracy means than LPS. There is no
evidence of differences between MCS and Business in accuracy.

There is no evidence of accuracy differences between densities within groups.

####  4.4.2 By Chart Type

See Figs. 6, 6, 8, and 8 for accuracy and RT means and difference CIs.

Response Time: There is evidence that mean times were 8.0s higher for Business
than LPS ($p<0.001$) and 5.4s higher than MCS ($p<0.05$) for Vertical charts.
For Horizontal charts, Business took 6.6s longer than LPS ($p<0.001$) and 8.2s
longer than MCS ($p<0.001$). Looking at Radial charts, Business took 9.2s
longer than LPS ($p<0.001$) and 6.8s longer than MCS ($p<0.05$). Last, for
Circular Bar Plots, Business took 6.9s longer than LPS ($p<0.001$) and 7.6s
longer than MCS ($p=0.01$).

Within all domains, there was a similar increase in time between Cartesian and
polar coordinate charts: Business by 5.9s (CI$(0.5,11)$, $p<0.001$), LPS by
5.0s (CI$(2,8)$, $p<0.001$), and MCS by 6.0s (CI$(4,8)$, $p<0.001$).

Accuracy: For Vertical charts, there is no evidence of accuracy differences.
For Horizontal charts however, there is strong evidence that mean accuracy of
MCS was 11.4% higher than LPS and Business was 9.4% higher (both $p<0.001$).
For Radial charts, there is evidence MCS and Business had higher accuracy
means than LPS (by 15.6% and 11.7%, respectively, at $p<0.001$). For Circular
Bars MCS means were higher by 15.3% and Business means higher by 11.7% than
LPS, again at $p<0.001$. There is no significant evidence of difference in
means between MCS and Business across chart types.

Within all domains, accuracy means were similarly lower for polar coordinate
plots when compared to Cartesian coordinate charts: Business by 9.8%
(CI$(5,15)$, $p<0.001$), LPS by 13.7% (CI$(6,20)$, $p<0.001$), and MCS by 7.5%
(CI$(3,12)$, $p<0.01$).

####  4.4.3 By Question Difficulty

Response Time: For Easy questions, there is evidence of increased mean times
for Business compared to LPS by 5.4s (CI$(2,10)$, $p=0.001$) and MCS by 7.2s
(CI$(4,12)$, $p<0.01$). Business also has increased times for Medium questions
compared to LPS by 9.3s (CI$(5,17)$, $p<0.001$) and MCS by 7.3s (CI$(3,15)$,
$p=0.01$). For Hard questions, Business had increased times by 8.2s compared
to LPS (CI$(3,14)$, $p<0.001$) and 7.0s to MCS (CI$(3,13)$, $p<0.01$). There
is no evidence of differences in time means for LPS and MCS.

Within Business there were increased times of 7.6s to Medium questions
(CI$(2,15)$, $p<0.001$) and 7.0s to Hard questions (CI$(2,13)$, $p<0.001$)
from Easy questions. For LPS there were increased times of 3.7s to Medium
questions (CI$(0.6,7)$, $p=0.01$) and 4.3s to Hard questions (CI$(0.5,9)$,
$p<0.05$) from Easy questions. For MCS there were increased times of 7.6s to
Medium questions (CI$(5,11)$, $p<0.001$) and 7.2s to Hard questions
(CI$(5,10)$, $p<0.001$) from Easy questions. No differences were detected in
mean times between Medium and Hard questions across domains.

Accuracy: For Easy questions there is some evidence that MCS had higher
accuracy than LPS by 9.8% (CI$(5,18)$, $p<0.01$) and Business by 3.1%
(CI$(0.5,6)$, $p<0.05$). Additionally, there was no detected difference
between Business and LPS accuracy means for Easy nor Medium questions. There
is some evidence that MCS had 11.9% higher scores than LPS in Medium questions
(CI$(4,20)$, $p<0.01$). For Hard questions, Business had higher accuracy means
than LPS by 16.4% (CI$(8,25)$, $p=0.01$) and MCS was higher than LPS by 16.7%
(CI$(8,25)$, $p=0.01$). Other than in Easy questions there was no difference
detected between MCS and Business.

Within Business, accuracy means fell by 14.2% (CI$(10,20)$, $p<0.001$) from
Easy to Medium questions and by 11.2% (CI$(6,17)$, $p<0.001$) from Easy to
Hard. Within LPS accuracy means fell by 14.5% (CI$(5,23)$, $p<0.001$) from
Easy to Medium questions and by 20.1% (CI$(11,30)$, $p<0.001$) from Easy to
Hard. For MCS accuracy means fell by 12.4% (CI$(7,18)$, $p<0.001$) from Easy
to Medium questions and by 14.0% (CI$(10,19)$, $p<0.001$) from Easy to Hard.
There was no evidence of differences between Medium and Hard questions within
domains.

$\Rightarrow$ Our results partially confirm H3. MCS participants tend to have
higher accuracy than LPS and lower RTs than Business participants. MCS has
higher accuracy than LPS across density, chart types (save Vertical charts),
and question difficulty; MCS also has faster times than Business across
density, chart types, and question difficulty. However, although spatial
visualization levels are similar for Business and LPS, Business participants
tend to have higher RTs and higher accuracy than LPS. Business and LPS did not
differ significantly in accuracy only on Easy & Medium questions, and Vertical
charts – although LPS was faster. Overall, we observed a time/error trade-off
between Business and LPS – the more time spent, the higher the accuracy. This
finding is in line with [43] that found time/error trade offs between domains
with similar levels of spatial visualization (CS and Education). However, in
our study no trade off was detected for MCS participants (who had higher
spatial visualization levels) which were generally just as fast as LPS and
just as accurate as Business. The difference in results could potentially be
linked to the fact that we compared across a balanced sample of participants
and included education background to professional profile in case profession
was misaligned, where [43] did not. This indicates cognitive abilities do
interact with domain to affect performance in both accuracy and RT, and that
accuracy can be improved with increased RTs. The latter finding is common in
visualization research on spatial visualization [85, 29, 43].

###  4.5 Difficulty and Motivation

Motivation | Mean Scores | Significant Differences
---|---|---
Overall (out of 20) |  | High Spatial - 11.1
---
Low Spatial - 11.7
None
| MCS - 12
---
Business - 12.3
LPS - 9.9
| MCS>LPS: CI$(0.6,4)$, $p<0.01$
---
Business>LPS: CI$(0.8,4)$, $p=0.01$
Intrinsic (out of 4) Spend my own time learning about data visualization |  | High Spatial - 1.4
---
Low Spatial - 2.0
CI$(0.2,1)$, $p<0.01$
| MCS - 1.8
---
Business - 1.9
LPS - 1.7
None
Extrinsic (out of 4) My career or studies involve data visualization |  | High Spatial - 2.3
---
Low Spatial - 2.1
None
| MCS - 2.6
---
Business - 2.4
LPS - 1.6
| MCS>LPS: CI$(0.6,2)$, $p<0.001$
---
Business>LPS: CI$(0.3,1)$, $p<0.01$
Self-Determination (out of 4) I put effort into learning about data visualization |  | High Spatial - 2.2
---
Low Spatial - 2.9
CI$(0.19,1.1)$, $p<0.01$
| MCS - 2.2
---
Business - 3.0
LPS - 2.5
| Business>MCS: CI$(0.3,1)$, $p<0.01$
---
Business>LPS: CI$(0.03,1)$, $p<0.05$
Self-Efficacy (out of 4) I am confident I will perform well on data visualization tasks |  | High Spatial - 2.7
---
Low Spatial - 2.7
None
| MCS - 2.8
---
Business - 2.9
LPS - 2.5
| Business>LPS: CI$(0.03,0.8)$, $p<0.05$
---
Low Math Anxiety (out of 4) Note: higher score indicates lower anxiety |  | High Spatial - 2.4
---
Low Spatial - 1.9
None
| MCS - 2.6
---
Business - 2.1
LPS - 1.7
MCS>LPS: CI$(0.2,2)$, $p=0.01$

Table 1: Motivation scores and significant differences between spatial
visualization level and education domain. Details in SM.

The final tasks of the study were to rate the difficulty of each section along
with rating personal motivations regarding data visualization. High spatial
individuals rated the spatial visualization assessment (out of 4) $1.7/4$ on
average, while low spatial individuals rated it $2.19/4$ \- this was
significantly different at $p<0.05$ with CI$(0,0.94)$. No differences were
detected between the low and high spatial ratings of Part 2. The only
significant difference found between domains was the average difficulty rating
of the spatial visualization assessment between LPS ($2.5/4$) and MCS
($1.7/4$) at $p<0.05$ with CI$(0,1.1)$. Business rated the spatial
visualization assessment $(1.93/4)$ on average but was not significantly
different from LPS nor MCS. There were no differences detected between the
domain’s average rating of Part 2.

As per the SMQ, each motivation response corresponded to a score from 1-4,
rating agreement with the statement [40]. The statement measuring anxiety was
reverse scored so that higher scores correspond to lower anxiety - thus, we
refer to this construct as low math anxiety. See Table 1 for motivation
ratings and significant differences.

The overall average rating of data visualization motivation was $11.4/20$.
Those above the average motivation score had quicker RTs overall by 7.1s than
those below the average (CI$(5,10)$, $p<0.001$). They were significantly
faster across densities, chart types, and question types (all $p<0.001$).
Additionally, those above the average had higher accuracy in charts of density
14 ($p<0.05$) and polar coordinate charts ($p<0.05$) - see SM for details.

As seen in Table 1, low spatial individuals have higher intrinsic motivation
and self-determination than high spatial individuals. This finding is in line
with research demonstrating that those with low visual processing cognitive
abilities still choose visual data interfaces i.e., preference to use
visualizations is not directly indicative of visual cognitive abilities [54].

Interestingly, both MCS and Business had significantly higher overall
motivation scores compared to LPS and had higher accuracy overall.
Specifically, Business outranked LPS in extrinsic motivation, self-efficacy,
and self-determination (where Business also outranked MCS). MCS outranked LPS
only in extrinsic motivation and low math anxiety. This indicates data
visualization is more prevalent in MCS and Business domains than LPS and thus
there may be a higher value placed on accuracy in visualization tasks.
Additionally, putting effort into learning about data visualization and
confidence in tasks may increase accuracy – this is aligned with [39, 24] that
demonstrate that higher self-determination, self-efficacy, and confidence are
correlated with increased performance in mathematics tasks.

##  5 Discussion

Our results confirm and build upon research in information visualization that
both spatial visualization and domain influence use of data visualization [87,
53]. These individual differences are often treated independently, but we
build on work that brings them together to increase effectiveness and reach of
visualization [43]. We ensured balanced samples between domains, aimed at
gender and global diversity, and included education background in the
professional profile to reduce potential confounding factors. We found that
there are differences between domains in statistical performance and
motivations around data visualization, leading to implications and insights
for design.

##### Spatial visualization performance

We investigated how spatial visualization ability and discipline come together
to affect task performance (accuracy and RT) on common data visualizations. We
confirmed past research that spatial abilities vary with domain [91], with MCS
having significantly higher spatial visualization than Business and LPS (Fig.
3, H1).

Consistent with visualization research into spatial visualization [87, 92,
89], we found increased level of spatial visualization correlates with higher
accuracy and quicker RTs (H2). This finding speaks to space in information
visualization for inclusivity, given learning disorders and populations that
have been connected to low spatial abilities [14, 37]. RTs differed amongst
all sub-conditions (density, chart type, and question difficulty) and accuracy
differed for Hard questions amongst all chart types between spatial
visualization levels. We found a similar discrepancy in performance between
Cartesian and polar coordinate charts within low and high spatial individuals.
Additionally, we found that for Hard questions (involving math computation)
high spatial individuals took less time than with Medium questions, but they
maintained significantly higher accuracy than low spatial. This supports
research in education psychology that high spatial individuals tend to perform
better in mathematics [94, 9].

Difficulty & Motivation: Low spatial individuals found the spatial
visualization assessment more difficult than high spatial, did not perceive
the multiple-choice questions as more difficult, yet did not perform as well.
Additionally, Low spatial individuals had higher levels of intrinsic
motivation and self-determination (see Table 1). Both findings support [92],
that individuals are often unaware of their abilities and still choose to
interact with and enjoy visual data representations.

##### Domain Performance

Between domains, we found behavior consistent with spatial visualization level
and performance (H3) such that MCS had higher accuracy than LPS and quicker
RTs than Business. This difference was consistent across sub-conditions. This
finding is consistent with research into psychology and visualization that
individuals who studied STEM subjects have increased spatial abilities and
performance in visualization tasks [9, 87].

Difficulty & Motivation: Consistent with spatial visualization levels, LPS
found the spatial visualization assessment more difficult than MCS, with
Business rating difficulty between the two. We found an overall time/error
trade-off with Business and LPS (i.e., the more time spent on questions the
higher the accuracy). We conjecture this trade-off, and difficulty perception,
has to do with the motivations and domain differences between Business and
LPS. Literature suggests there is higher value placed on data visualization in
Business [36, 95, 84, 57] when compared to LPS [97, 46]. This is reflected in
the motivations of Business and LPS participants in our study (see Table 1).
Notably, MCS and Business reported that data visualization is involved in
their career or studies to a higher degree than LPS. Business additionally
rated confidence in doing well on visualization tasks as higher than LPS and
reported they put more effort into learning about data visualization than both
MCS and LPS. These motivations have been shown to correlate with increased
performance in mathematics and engagement, performance, and higher quality
learning in education in general [9, 74]. From this, it follows that even with
similar levels of spatial visualization, those in the Business domain would
take time to have higher accuracy on data visualization tasks where those in
LPS might not.

The fact that LPS exhibit different abilities and performance should be
significant to the visualization community. Individuals in LPS are regularly
at the center of government systems and are often advised by scientific bodies
and advisors, especially during the ongoing COVID-19 pandemic [25]. There
could be broad reaching consequences if designers fail to cater to the
abilities of those making high impact governance decisions when sharing
scientific information. Given bodies like the World Health Organization,
governments, universities, and data visualization tech companies created
COVID-19 dashboards with spatially rotated data [64, 26, 56, 8, 65], it is
critical that information visualization is accessible and effective for an
imperative group of individuals with huge influence on society.

##### Outlook & Future Work

The scope of this study did not include the causal origins of the different
spatial visualization abilities amongst disciplines - this is still an open
question. It is important to note that other factors can influence task
performance such as domain knowledge, representational fluency, visual
familiarity, or demographic differences [53, 43, 37]; the interplay between
these factors is complex and none alone can explain differences. Regardless of
the origins of the differences, our study demonstrates how spatial
visualization level and domain affect visualization use, which leads to
important design implications. Further studies can be done on additional
domains to expand knowledge of performance differences in visual tasks and
spatial visualization between groups for higher impact designs. To increase
inclusive design practices, interventions might be studied that allow low
spatial individuals to increase accuracy and/or decrease RT needed for visual
tasks: benefiting those in LPS and Business domains. Our finding that spatial
visualization level is an important cause behind difference in performance is
reflected in intervention research for cognitive abilities [28, 18].
Performance including interventions and guided interactions (e.g., explicit
linking across charts, feature highlighting, information redundancy) could be
tested and balanced with cognitive load drawbacks for effectiveness.

Our study advances initial work in the visualization community toward
cataloguing cognitive differences of domains [43] and is a step toward
increasing impact of visual design across disciplines.

##  6 Conclusion

The aim of our research was to build on work in visualization exploring
spatial visualization differences amongst domains and its effect on
visualization use. Our study expanded research to the domains of Business and
LPS and included real-world data and visual scenarios of spatially rotated
data. We presented additional evidence that the interplay between cognitive
and demographic factors should be considered to increase effectiveness and
inclusivity of visual design. Additionally, our research showed motivational
differences between domains that could affect interaction and design needs. As
the field of visualization evaluates cognitive and domain differences and the
interplay between the two, studies like this can ensure informed and effectual
design for all communities.

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