# RedDwarfData: a simplified dataset of StarCraft matches

Juan J. Merelo-Guervós, Antonio Fernández-Ares
University of Granada, Spain
Antonio Álvarez Caballero
University of Oviedo, Spain
Pablo García-Sánchez
University of Cádiz, Spain
Victor Rivas
University of Jaen, Spain

(December 29th, 2017)

###### Abstract

The game Starcraft is one of the most interesting arenas to test new machine
learning and computational intelligence techniques; however, StarCraft matches
take a long time and creating a good dataset for training can be hard.
Besides, analyzing match logs to extract the main characteristics can also be
done in many different ways to the point that extracting and processing data
itself can take an inordinate amount of time and of course, depending on what
you choose, can bias learning algorithms. In this paper we present a
simplified dataset extracted from the set of matches published by Robinson and
Watson, which we have called RedDwarfData, containing several thousand matches
processed to frames, so that temporal studies can also be undertaken. This
dataset is available from GitHub under a free license. An initial analysis and
appraisal of these matches is also made.

## Introduction

_StarCraft_ is a well-known Real Time Strategy (_RTS_) game whose first
version was released in 1998 (Yoon 2001). The game consists of two opponents
trying to beat each other over a square-shaped arena. Every player has to
spawn workers and create buildings using the available resources, which are
located over the terrain. Players can use one of three different kinds of
species, called Zergs, Protoss or Terrans, with different kinds of
individuals, which differ in their skills, so that players have a wide range
of possibilities in order to plan their strategies. The need to dynamically
take decisions for both the short and the long terms make RTS adequate
scenarios to test AI algorithms (Ontanón et al. 2013) that could be used later
in real problems including financial, military and logistics scenarios.

_StarCraft_ has become popular among researchers thanks in part to _BWAPI_ ,
an API developed by the community that allows programming artificial agents
and that was developed for StarCraft competitions (Buro and Churchill 2012).
These agents can act just like regular players, according to a previously
established algorithm; but, more importantly, they can collect data as matches
are running. The data collected this way can be later analysed, so that
machine learning techniques can be applied for both supervised and
unsupervised learning.

Developing and releasing datasets for developing and applying these techniques
is thus a way of expanding research in this area and allowing researchers to
develop their own bots or prediction algorithms without having to scrape them
from websites or actually play the games, a time-consuming task, to extract
data from them. Several researchers have done so and we will refer to them in
the state of the art; in this paper we start from Robertson and Watson’s
dataset (Robertson and Watson 2014) and create a simpler one that can be used
without needing many resources; even so, the data set is balanced with respect
to duration, races and number of resources used by the players.

The rest of the paper is organized as follows: next we will present the
Starcraft datasets that have been published so far. In the next section we
will show how Robertson and Watson’s dataset was processed and the rationale
for doing it that way. Then we will present a macro picture of the data set,
to eventually draw some conclusions from it and possible lines of work using
this dataset.

### State of the art

The main motivation for writing this paper was the proliferation of new
StarCraft datasets in the last few years. Players have been uploading their
matches command logs to several websites, such as TeamLiquid
111http://www.teamliquid.net or BWReplays 222http://www.bwreplays.com for over
10 years. These logs are usually employed by the player community to learn new
playing techniques from other players. From the point of view of using them
for AI training, however, these datasets are in raw form and require some kind
of command execution, parsing, data selection and filtering in order to serve
as a good base for learning models (Lin et al. 2017). Moreover, a lot of
uploaded replays may be corrupt or incomplete. Different authors have been
processing these replays to create datasets that can be used to predict the
outcome of a match or a battle or simply gather insights on gaming strategies.
The first paper that describes the processing of replays was the one presented
by Weber and Mateas (Weber and Mateas 2009), where they processed replays to
create a dataset to compare several classification algorithms. 5393 replays
were used and they store specific events during the games, not the full game
state, validating the possibility of predicting the winner of a game to a
certain degree. Later, other datasets that store the full game state to apply
generic machine learning techniques have appeared (Synnaeve and Bessière
2012).

Instead providing the game logs directly, Robertson and Watson (Robertson and
Watson 2014) distributed the logs in a database, created by obtaining the
replays from several replays webpages, executing the game and storing a large
number of game features data every certain number of frames. This dataset has
been used by other researchers, for example, to create models differentiating
by race (Ravari, Bakkes, and Spronck 2016).

Recently, and built upon the Synnaeve and Bessiere work (Synnaeve and Bessière
2012), Lin et al. (Lin et al. 2017) have released the largest dataset to date.
It includes 65646 replays, storing the full state of the game every 3 frames,
including up to 30 features by unit and battle detections, storing all the
data in TorchCraft format to be accessed by specific software clients. Even
using compression mechanisms, this dataset requires 365GB of space. The aim of
using this dataset is to apply Deep Learning algorithms that require a large
amount of data.

However, there is not a small and limited dataset ready to use for teaching
purposes, for example, to directly use as input for certain algorithms without
any modification. This is the reason we have used the Robertson and Watson
database (Robertson and Watson 2014) to extract a limited number of features
and parsed it directly to CSV (Comma Separated Values) format. These CSV files
can be easily downloaded and straightforwardly parsed with any programming
language.

### Data extraction and filtering

One of the advantages of the presented dataset is that they are already pre-
processed and ready to use. The origin of the samples are the replays obtained
by (Robertson and Watson 2014). As previously stated, they provided the data
in the form of a huge relational database, so an intermediate step to produce
an usable and manageable CSV file was needed.

For every available match in the database, identified by it _ReplayID_ , a
different number of samples is extracted. The first step is to take the
_Duration_ and the identifiers _PlayerReplayID_ of the two players involved in
the game. The _PlayerReplayID_ with the minimum value is taken as the first
player; this does not actually introduce a bias.

Because of the dynamic nature of the game, it is important to use a time
reference to be able to track the match in every moment. Instead sampling the
matches every certain number of frames as other authors propose, the chosen
reference is the _Frame_ when a change in resources (_Minerals_ , _Gas_ and
_Supply_) occurs. This instant does not have to be the same for the two
players, so all the instants when a change is discovered are saved. This
choice implies a lot of empty values in the database; however, this is easily
fixed taking the last known value to fill each empty one: if there is not a
change for a player, the resources would be the last seen ones.

This decision is also important to track the units, that are valued by their
price in resources. However, the value recorded in the original database is
split by regions, which are pieces of the map. To register the value of some
units, all the region’s values have to be summed up, but the frame when these
changes occur are not the same that the resources’ ones, so the last change
for each region is saved and accumulated. The same process is also performed
for the enemy units. However, this value is an estimation, not a real one,
because due to the rules, a player do not know nothing about the enemy unless
they see the other, and at that moment, these real values are stored in the
logs.

With all the data well-formed, only the _ReplayID_ , _Winner_ and _Duration_
have to be added to the match. The data of a game does not have any missing
values, so they are ready to be saved. As it was said before, this process has
to be repeated for each match.

### The big picture

A few macro analysis of the data in this dataset will allow us to compare it
with other datasets and also appraise its balance and general features. The
data set, which is available from GitHub under a free license, includes 4655
different matches with matches among all different races available in
Starcraft.

The duration of the matches is represented below.

The mode is between 20 and 25 minutes, with average equal to 25.2570211 and
median 25.2570211; they are not too different which indicates that the
distribution is not too skewed.

The combination Terran vs. Protoss includes the bigger number of matches, with
close to two thousands, of which the majority are won by the Terran. But in
fact, the race that wins the most games it is involved it are the Zerg, as is
shown below.

Both macro measures show that the data set is well balanced, and includes
battles in all possible combinations. On the other hand, we can use match data
to find out if the duration of the match is affected by the combination of
races

And the chart shows that, except for the matches that include Zerg against
Zerg and Protoss vs. Protoss, duration is quite similar and is more affected
by the game dynamics that by differences or similarities between races.

### Conclusions

Our intention with the release of RedDwarfData and this report was to
introduce a new lightweight dataset that includes matches by all races and
that can be used for drawing conclusions about Starcraft matches, as well as
use them to actually train bots that play Starcraft. An initial exploratory
analysis draws the conclusion that the Zerg might be the best race to use in a
game bot; however, this macro conclusion will have to be qualified by an
actual analysis of the strategies generally used by Zergs in the particular
games included in the data set. RedDwarfData and the scripts that are used to
process it are hosted in Github: https://github.com/analca3/StarCraft-winner-
prediction. This paper is hosted also in GitHub, and includes the scripts used
to generate aggregated data and the data that has been used to generate this
paper, which is written in R Markdown and includes the scripts to generate
these charts. It has a Apache license and can be downloaded from GitHub at
https://github.com/geneura-papers/2017-StarCraft-Data. This dataset has been
used already in (Álvarez-Caballero et al. 2017) to make early predictions of
the outcome of the match, in order to shorten the time needed to evaluate bots
in an optimization environment. Our intention is to create surrogate models of
matches so that we can use evolutionary algorithms to create game bots that
can be competitive in this game.

### Acknowledgements

This paper has been funded in part by the Spanish national excellence project
TIN2014-56494-C4-3-P (EphemeCH). We acknowledge also the support of
TIN2017-85727-C4-2-P (DeepBio).

### References

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Fernández-Ares. 2017. “Early Prediction of the Winner in Starcraft Matches.”
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[1]Lingyun Yu

[1]Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China

# Visual Harmony: Text-Visual Interplay in Circular Infographics

Shuqi He  Yuqing Chen  Yuxin Xia  Yichun Li  Hai-Ning Liang
<EMAIL_ADDRESS>*

###### Abstract

Infographics are visual representations designed for efficient and effective
communication of data and knowledge. One crucial aspect of infographic design
is the interplay between text and visual elements, particularly in circular
visualizations where the textual descriptions can either be embedded within
the graphics or placed adjacent to the visual representation. While several
studies have examined text layout design in visualizations in general, the
text-visual interplay in infographics and its subsequent perceptual effects
remain underexplored. To address this, our study investigates how varying text
placement and descriptiveness impact pleasantness, comprehension and overall
memorability in the infographics viewing experience. We recruited 30
participants and presented them with a collection of 15 infographics across a
diverse set of topics, including media and public events, health and
nutrition, science and research, and sustainability. The text placement
(embed, side-to-side) and descriptiveness (simplistic, normal, descriptive)
were systematically manipulated, resulting in a total of six experimental
conditions. Our key findings indicate that text placement can significantly
influence the memorability of infographics, whereas descriptiveness can
significantly impact the pleasantness of the viewing experience. Embedding
text placement and simplistic text can potentially contribute to more
effective infographic designs. These results offer valuable insights for
infographic designers, contributing to the creation of more effective and
memorable visual representations.

###### keywords:

Infographics design, Circular visualization, User perception

##  1 Introduction

Infographics, which represent data and information through a combination of
text and schematics, have been increasingly utilized across various domains
such as education [1, 2, 3, 4], business [5], and healthcare communications
[6, 7, 8]. They have been regarded as effective and visually engaging tools
for communicating and simplifying complex ideas into easily digestible, bite-
size representations [9, 10]. Their ability of presenting information in a
compelling and concise way is particularly helpful in storytelling with an
overwhelming amount of data in this era of information overload [11, 12].
Consequently, growing research interests have emerged in the field of
visualization concerning how to create an effective infographic design [13].

A well-designed infographic is much more than just a pretty picture. Rather,
it involves thoughtful and strategic considerations of various design elements
such color, icons and typography. The use of color in infographics, for
example, goes beyond aesthetic appeal. Strategic color choices can effectively
differentiate between information sections, enhancing the readability and
understanding of the visualization [9]. Likewise, icons and other graphical
elements can capture viewer’s attention and significantly improve
communication efficiency when thoughtfully selected and employed [14, 15, 11].
Finally, typography, which concerns the style, appearance, and arrangement of
text, is another essential element in infographic design. A careful selection
of fonts and text styles can enhance the legibility and aesthetics of
visualizations. Moreover, typographic emphases, such as bolding, italicizing,
and underlining, can highlight critical data points or messages in a
visualization [16]. In this context, text does not only serve a structural
purpose, but it can also communicate subtle nuances in the narrative that may
not be expressed through the graphical elements alone.

Individual design elements such as color, icons, and typography undeniably
play a significant role in infographic design, as supported by extensive
research in the field. Studies have indicated the influence of well-selected
color palettes in enhancing information representation and addressing cultural
nuances [17, 18]. The impact of icons and visual embellishments on aspects
like memorability, visual search, and comprehension is also well documented
[19, 20]. Furthermore, research into typography has shed light on how variable
font variations can contribute to encoding data into readable text [21].

However, while individual elements are crucial, the integration and
arrangement of these elements into a holistic infographic is equally, if not
more important. After all, infographics are not a random assortment of visuals
and texts, but crafted through an integrated representation that results from
the deliberate and harmonious interplay of these components. While receiving
research interests in interactive web-based document design [22, 23], the
effect of text-visual interplay remains relatively under-explored within the
specific context of infographics. As far as we know, there are few studies
that have empirically investigated the integration of these elements within
infographics. Therefore, this study aims to examine how the arrangement and
interaction of text and visuals can contribute to effective infographic
design. To simplify the research landscape and offer a more targeted
exploration, we have chosen to narrow our scope to standalone data
visualization infographics, specifically those involving circular
visualizations. This decision was guided by two main factors. Firstly,
circular visualizations are widely used and account for 69% of glyphs in a
comprehensive corpus that combines images from multiple visualization
publications [24]. Secondly, the structure of circular visualizations provides
ample opportunities for text embedding, making them an ideal candidate for
studying the interplay between text and visuals. Consequently, we investigate
how texts of varied levels of descriptiveness can be placed in relation to the
visuals in these circular infographics.

Figure 1: Overview of the study: (A) a selection of diverse infographic
stimuli used as a starting point for our study; (B) Modifications to each
infographic, altering text placement and descriptiveness to form six distinct
experimental conditions; (C) The consequential assessments of these
variations, measured through perceptual metrics such as pleasantness,
comprehension, and memorability.

This focus on the text-visual interplay within circular visualizations leads
us to the broader question of how design choices in infographics impact user
perception. After all, the effectiveness of an infographic heavily relies on
how it is perceived by viewers. In other words, user experience and perception
are key indicators of infographic quality. In light of this, our study employs
three dimensions of perceptual metrics to assess the effectiveness of design.
These metrics, namely pleasantness, comprehension, and memorability, each
measure distinct aspects of infographics performance. Pleasantness serves as a
measure of the viewer’s overall viewing experience. Comprehension, on the
other hand, probes the success of information transfer from the infographic to
the viewer, assessing how effectively the data and knowledge are being
communicated. Lastly, memorability reflects the longevity of information
retention, providing insights into whether the presented information can be
remembered by the viewer. Collectively, these metrics help quantify the
perceptual effects of our text-visual variations.

In this study, we collected a set of 15 circular infographic visual stimuli
sourced from various websites and created six distinct variations for each
infographic with two different text placements (embedded text, side text) and
three levels of text descriptiveness (simplistic, normal, descriptive). We
recruited 30 participants and assessed their visual perception with
questionnaires tailored to the infographic stimuli, probing their
pleasantness, comprehension and memorability. Figure 1 serves as a step-by-
step visual representation of our comprehensive investigation into the impact
of text-visual interplay in infographics.

Our study contributes to the current literature of infographics design in
three aspects. Firstly, it extends our understanding of text-visual interplay
in the context of infographics, an area which, despite its importance, has
been relatively under-explored in the existing literature. By investigating
how the descriptiveness and the placement of text can influence the viewer’s
experience, our study offers a perspective on the intricate dynamics between
text and visuals in infographic design. Secondly, our study provides evidence-
based design implications for creating visually-pleasing and memorable
infographics. The perceptual metrics of pleasantness, comprehension, and
memorability shed light on how strategic manipulation of text placement and
descriptiveness can optimize the viewing experience and enhance information
retention. Finally, our work can serve as an inspiration for future research
in this field. The findings of this study encourage further exploration into
the integration of various design elements in infographics. Our focus on the
interaction between text and visuals is just one facet of this complex design
puzzle. We hope that our efforts will spark more comprehensive explorations of
the interplay among different design components and in different types of
infographics.

##  2 Related Work

###  2.1 Circular Visualization

Geometric abstraction, encompassing shapes such as lines, circles, and
triangles, has a long history of being used as a visual strategy in data
representation. Serving as both tools for data illustration and elements of
visual interests, these shapes enable the design of informative and visually
appealing infographics [25, 26]. Among these geometric forms, the circle holds
particular significance due to its expressive potential and extensive
connections to everyday objects. This circle’s appeal was profoundly captured
by Wassily Kandinsky, one of the pioneers of abstract art. Kandinsky remarked,
the circle “is the most modest form, but asserts itself unconditionally,” and
it is “a precise, but inexhaustible variable [27].” This powerful symbolism of
circles, mirrored in Kandinsky’s own influential abstract works, combined with
its geometric simplicity, makes it an attractive and meaningful element in
infographic design. This potential was recognized early on by pioneers such as
Playfair [28], with the creation of the pie chart, and Nightingale [29], who
developed eccentric circular visualizations for analyzing the causes of death
of the British army during the Crimean war.

Over time, circular visualizations have evolved and diversified to encompass
various visual models such as rings, spirals, wheels, and even flow diagrams
involving grids and maps [30]. Their adaptability in representing complex data
relationships and interconnected dynamics has made them a preferred model in
infographic design. For instance, in Magagnini’s work [26], circular
visualizations are chosen to visualize and organize contemporary art data.
They can also be used to represent categories, proportions, or cyclic time-
dependent data [31, 32]. For example, Buono et al. [33] utilized a circular
stripe design to visually represent collaborative team activities.

Among circular visualizations, a subgroup employs a space-filling design
pattern, where the areas within the circular layout are densely filled [31]. A
unique feature of these space-filling circular visualizations is their
capacity to incorporate and encode textual information, thereby providing
designers with the unique opportunity to creatively blend text and visual
elements. This fusion of text and visuals lays the groundwork for the
exploration of how text placement and descriptiveness might impact the
effectiveness of infographics, a question we delve into in the subsequent
sections of this paper.

###  2.2 Text-Visual Interplay

The interaction between text and visuals has been studied across different
domains from areas like web-based storytelling, document reading, and
narrative visualizations.

The impact of text-visual layout and linking has been investigated in web-
based storytelling. For instance, Zhi et al. [34] revealed that in stories
containing both text and visualizations, the choice of layout, specifically
the comparison between vertical and slideshow formats, significantly impacted
the reader’s comprehension and overall experience. Interactive linking, the
explicit connection between narrative text and its corresponding
visualization, was found to improve user engagement, enhance comprehension,
and mitigate layout-related challenges.

Another area of focus has been the integration of text and visuals in adaptive
documents. For instance, Badam et al. [35] worked on enhancing comprehension
of data-rich documents by dynamically generating on-demand visualizations
based on the reader’s current focus. Their approach highlighted the potential
of tight coupling between text and visuals in enhancing understanding. On a
related note, Barral et al. [12] designed an adaptive guidance system for
processing narrative visualizations embedded within textual documents. They
demonstrated that guiding users’ attention to salient components in the
visualization, especially through transitions, could significantly improve
comprehension.

In the context of infographics, previous studies have predominantly focused on
the role of text and visuals in visualizations involving information flows and
timelines. For instance, Lu et al. [36] developed the concept of Visual
Information Flow (VIF), where visual elements are linked together to convey
information and narrative. However, their research did not delve into the
specific interplay between textual and visual components. Majooni et al. [37]
compared the effectiveness of different arrangements such as the zigzag form
and other alternatives in infographic structures, providing evidence that
layout significantly influences viewer comprehension and cognitive load.
Despite these advancements, the specific text-visual interplay and placement
in flat infographics, especially those featuring circular visualizations,
remain underexplored.

###  2.3 The Role of Perceptual Metrics

The role of text and visuals extend beyond mere strategies of integration; it
also significantly impacts the viewer’s perception of the presented
information. Consequently, perceptual metrics serve as important performance
indicators for different infographic design.

In this study, we focus on three key dimensions of perceptual metrics:
Pleasantness, a measure of the viewer’s overall viewing experience.
Comprehension, a metric that probes the efficiency of information transfer
from the infographic to the viewer. Memorability, a measure of the viewer’s
ability to retain the presented information over time. These three dimensions
together holistically quantify the perceptual effects of varying text-visual
interplays in infographics, offering valuable insights into the creation of
effective designs.

Pleasantness is an example of affective response that can intertwine with the
engagement experience of data visualizations. In many instances, feelings such
as pleasantness can mirror the depth of user engagement and the overall
quality of the viewing experience [38]. The interplay between affective
responses and infographics has been examined in previous research. For
instance, Lan et al.’s research [39] investigated this intersection and
revealed that viewers can form consistent judgments on the affective responses
elicited by infographics. However, our study’s perspective on pleasantness is
not geared towards the exploration of emotions provoked by the infographics.
Instead, we concentrate on the direct viewing experience of the infographics
themselves. This approach aligns with the efforts of other researchers,
including McGurgan et al. [40], who assessed aesthetic pleasantness through
the lens of expert users, examining charts with varying levels of data ink.

Comprehension plays a central role in infographic design as it pertains to the
viewer’s mental processing of information. It therefore serves as the
cognitive bridge between the data presented in the infographic and the
viewer’s knowledge, facilitating an enhanced understanding of the material
[41]. Various studies have highlighted the use of comprehension as a
perceptual metric in infographic related research. Gallagher et al. [42] used
comprehension as a perceptual metric for investigating the usefulness of
infographics in summarizing information in online learning. They measured
comprehension by asking learners to rate on a five-point Likert scale whether
the infographics were helpful in summarizing and elucidating learning
materials. Meanwhile, Young et al. [5] compared infographics with their text
equivalents. Their evaluation of comprehension involved the use of multiple-
choice questions related to the content of the article, with correct answers
earning points. Similarly, Alsaadoun’s [43] research delved into the
differential comprehension between infographic-aided instructions and
traditional text instructions. They also gauged comprehension using a
multiple-choice test, the results of which suggested that infographics can
notably enhance comprehension. Furthermore, Obie et al. [44] found that the
use of author-driven narratives in visualization could boost both memorability
and comprehension. Their study used Hans Rosling’s charts and found that
participants who interacted with the author-driven videos had better recall
and comprehension than those who interacted with the standalone interactive
visualizations.

Memorability is another significant factor in evaluating the effectiveness of
data visualizations. The ability of a viewer to recall the information
presented in an infographic or visualization is a key determinant of its
success. The study conducted by Borkin et al. [9] highlighted the use of
memorability as a broad metric for the utility of information, underlining
that viewers consistently identified certain visualizations as memorable. In
their findings, unique visualization types outperformed common graphs in
memorability.

Meanwhile, the exploration of data storytelling and its effects on
memorability has led to intriguing findings. Wang et al. [45] and Zdanovic et
al. [46] conducted studies focusing on the role of narrative and storytelling
in data visualization. They found that data comics and storytelling
visualizations, respectively, improved understanding and the ability to recall
information. Contrastingly, Gareau et al.’s research [47] did not find a
significant memory-related advantage for infographics over traditional text
documents when visualizing Census data. However, they noted an interaction
between the type of stimulus and the nature of the task (searching versus
recall), with infographics improving performance in the searching task rather
than recall tasks. Drawing upon these various studies, our investigation aims
to assess memorability as a perceptual metric in our evaluation of text-visual
interplay in infographics.

##  3 Method

Figure 2: An example infographic stimuli edited to six versions with varied
text placement and descriptiveness levels. This particular infographic
captures information about the life cycle of trees [48]. Six distinct versions
of the same infographic have been created. Each version systematically varies
in terms of where the text is placed (embedded text vs side text) and the
level of descriptiveness provided (ranging from minimalistic to descriptive).

###  3.1 Participants

We recruited a total of 30 participants (16 female, 14 male) through direct
contact. The age of the participants ranged from 21 to 27, with an average age
of 24 for the study. The participant pool consisted of 5 undergraduate
students, 23 master’s students, and 2 PhD students and have diverse
backgrounds in fields such as business and finance (8), linguistics (1),
education (1), mathematics (1), design (1), and computer science (18). The
participants’ level of experience interacting with circular visualizations was
screened and recorded. It is noteworthy that 90% of the participants had
previous experience with circular visualizations. Among these individuals, 3
interacted with circular visualizations annually, 10 on a weekly basis, 4 on a
daily basis, and 2 multiple times a day. This sample size was selected to
ensure a diverse range of perspectives while keeping the data manageable. The
participants were grouped into 6 groups, corresponding to the 6 experimental
conditions. Before initiating the experiments, participants were provided with
background information about the project and voluntarily gave their consent to
participate in the data collection process.

###  3.2 Infographic Stimuli Selection

To provide the participants with realistic examples of infographics, we
carefully curated a set of 15 infographics from various websites [49, 50, 51,
52, 53, 54, 55, 56, 57, 48, 58, 59, 60, 61, 62]. Three factors were considered
when selecting appropriate infographics. First, the infographic must feature a
circular visualization. Second, the infographic should present an acceptable
level of clarity and coherence in its data and information. Third, we sought
to exclude infographics containing obscure, domain-specific concepts that
might be beyond the understanding of our target participant pool. Apart from
these, we avoided overly simplified infographics, which offered no scope for
text modification into more descriptive statements.

The curated collection of infographics all featured circular data
visualizations and covered broad spectrums of themes, including media and
public events, health and nutrition, science and research, and sustainability.
This selection was intentionally diverse to represent different domains of
information, hence offering a well-rounded sample of infographic stimuli for
our study.

###  3.3 Experimental Conditions

In this section, we detail the process of modifying each of the selected
infographic stimulus to produce varied versions that served as our study’s
experimental conditions.

These strategic alterations allowed us to systematically examine the effects
of text-visual interplay.

First and foremost, we define the distinctions between the six experimental
conditions in terms of text placement and text descriptiveness. For text
placement, we explored two commonly used methods in circular visualizations:
embedding the text within the graphics and placing the text side-by-side with
the visual representation. For descriptiveness, we categorized the text into
three types: simplistic, normal, and descriptive. These categories were
defined based on both the length and complexity in the text: the simplistic
condition contains fewer than 5 words, the normal condition contains around 10
words, and the descriptive condition contains around 20 words. These
conditions provided a comprehensive overview of the possible combinations of
text placement and descriptiveness in circular visualizations.

Then, we modified each selected infographic to create a set of six varied
versions, corresponding to our experimental conditions. First, we extracted
the text from the original infographics while keeping the visual elements
intact. We then altered the descriptiveness of the initial textual information
through processes such as rewording, rephrasing, shortening, expanding, and
reorganizing. This process yielded three distinct versions of text, varying in
length but maintaining the same core message. The quality of the texts, the
consistency with our pre-defined descriptiveness levels, and the preservation
of the core information were validated by an external evaluator. Lastly, we
reassembled the varied infographics by reintegrating the corresponding text
versions into the visual elements. Depending on the version, we either
embedded the text within the graphic or placed it alongside the visual
representation. Figure 2 illustrates an example set of the six variations
produced from a single original infographic.

###  3.4 Presentation of Infographic Stimuli

The infographic stimuli were presented in a digital format on the screen of a
13-inch personal computer.

Since text descriptiveness is a within-subject factor, each participant was
presented with an equal proportion of simplistic (5), normal (5) and
descriptive (5) infographics. To control for order effects and counteract the
differences in distinct infographics, we adopted a Latin square design,
ensuring that the conditions were counterbalanced.

###  3.5 Experimental Procedure and Evaluation Methods

In this section, we provide details on the empirical experiments conducted
with the participants and the collection of accuracy and perceptual metrics.

Our experiment consisted of four distinct sessions: pre-study baseline,
comprehension, short-term information recall, and long-term information
recall. This approach draws inspiration from Zdanovic et al.’s [46] method of
assessing memorability at three different stages, which we have enhanced by
incorporating an additional testing phase three days after the presentation of
stimuli to differentiate between short- and long-term memorability. Similar
multi-phase evaluation methods have been employed in previous studies, such as
Obie et al.’s [44], which used multiple choice questions to evaluate
information recall across different intervals. The duration of our experiment
sessions varied: the pre-study, short-term, and long-term recall sessions each
lasted approximately 10 minutes, while the comprehension assessment ranged
from 20 to 50 minutes, depending on the participant’s pace. The overall
workflow of the experiment sessions is illustrated in Figure 3.

Figure 3: Illustration of the experimental workflow. Figure 4: Pre-study
baseline assessment Interface. Figure 5: Comprehension assessment interface
showcasing four sequential frames: (1) infographic viewing and summarizing,
(2) answering a multiple choice question, (3) reporting the confidence score,
(4) reporting the pleasantness score. Figure 6: Information recall assessments
interface.

Pre-Study Baseline Assessment. The first session aims to obtain the
participants’ initial level of knowledge prior to viewing the infographic
materials. This session serves as a reference point for measuring the
acquisition and retention of information throughout the subsequent experiment
sessions.

During this phase, the participants completed a fact-based multiple choice
questionnaire related to the concepts of the selected infographics. An example
of the questionnaire is included in Figure 4 for reference. There are a total
of 15 questions, corresponding to the 15 selected infographics. Each question
had four choices, including one correct option, two incorrect options, and an
“I don’t know” option. Upon completion of the questionnaire, we measured the
correctness in the responses, which was subsequently converted into an
accuracy score.

Comprehension Assessment. In the second session, the comprehension assessment
phase, we aimed to assess the participants’ level of understanding after
exposure to infographic stimuli. Each infographic stimulus was individually
displayed on the screen. While viewing the infographic, participants were
asked to write a brief summary of its main idea or theme. This step was
implemented to ensure that participants viewed and mentally processed the
information in the infographic, rather than merely skimming and skipping it
casually. Subsequently on the next screen, participants were faced with a
fact-based multiple-choice question tied to the information from the preceding
infographic. It’s important to note that each infographic stimulus was only
shown once, and participants were not allowed to revisit it.

Following the question, participants were asked to rate their confidence in
their answer using a 9-point Likert scale. Additionally, they were asked to
assess their emotional response to the infographic using a 9-point
pleasantness scale drawn from the Self-Assessment Manikin (SAM) valence scale
[63]. This comprehensive approach allowed us to evaluate not only the accuracy
of participants’ responses but also their perceived understanding of the
information and their emotional response to the different infographics. The
interfaces of the comprehension assessment session is illustrated in Figure 5.

Short-term Information Recall. The short-term information recall was evaluated
three days post the presentation of the infographic stimuli, serving as a
follow-up to the initial assessment. In this session, participants answered a
reworded version of the same set of questions asked previously. Each question
screen showed a blurred version of the corresponding infographic, providing no
visual details or textual content. The use of blurred images served as a brief
reminder of the infographic and the topic of the questions without providing
specific details. This approach facilitated the assessment of information
retention over a short period. The resulting scores, reflecting the
participants’ retained knowledge, could then be compared to their initial
answers, providing an insight into memory decay over time.

Long-term Information Recall. The long-term information recall was evaluated
seven days after the initial presentation of the infographic stimuli. In this
session, participants answered a rephrased version of the original set of
questions. This test mirrored the short-term recall assessment but focused on
gauging the retention of information over a longer period. The interfaces of
the short-term and long-term information recall sessions are illustrated in
Figure 6.

##  4 Results

In our infographic experiments, we gathered two distinct categories of data:
objective data derived from fact-based multiple-choice responses and
subjective data originating from self-assessed scales of pleasantness and
confidence. To process these findings, we first numerically coded the
objective data. Correct answers received a code of 1, while incorrect
responses and those marked as “I don’t know” were assigned a code of 0.
Following this initial coding, we performed data transformation by aggregating
the responses and organizing them according to the specific experimental
conditions and individual participants. Accuracy scores are calculated as the
number of correct responses divided by the total number of responses. We then
proceeded to evaluate the impact of infographic exposure on comprehension and
recall over time.

###  4.1 Validating the Effectiveness of the Infographic Stimuli

Before delving into condition-specific findings, we first validated the
effects of infographic presentations. To accomplish this, we analyzed changes
in accuracy scores at four distinct time points: baseline (before infographic
presentation), comprehension (immediately after), short-term recall (three
days after), and long-term recall (seven days after).

The changes in the accuracy scores are illustrated in Figure 7. The pre-study
baseline score was 0.384 (SD=$0.185$), representing participants’ intrinsic
knowledge before being exposed to the infographics. Following the infographic
presentation, the mean accuracy score increased to 0.684 (SD=$0.208$),
suggesting that the visual stimuli were effective in conveying information and
that participants successfully grasped the main ideas. Subsequent assessments
of short-term and long-term recall aimed to evaluate information retention
over time. In line with Ebbinghaus’s psychological theory of the human
forgetting curve [64], accuracy scores diminished with time, resulting in
short-term recall accuracy of 0.591 (SD=$0.205$) and long-term recall accuracy
of 0.513 (SD=$0.236$).

We employed a repeated-measures ANOVA to compare the statistical differences
between these time-separated measurements. The analysis revealed a significant
difference between the four groups ($p\textless 0.01$ with Greenhouse-Geisser
correction). Furthermore, post-hoc pairwise comparisons using Bonferroni
correction indicated significant differences among all four groups
($p\textless 0.01$ for all comparisons). Overall, these results affirm the
effectiveness of our infographic design in conveying information and validate
the user assessment procedure.

Figure 7: Results of the average accuracy score of the pre-study baseline
assessment before exposure to the infographic stimuli, the comprehension
assessment immediately after the infographic presentation, the short-term
recall assessment three days after the infographic stimuli onset, and the
long-term recall assessment seven days after the infographic stimuli onset.

###  4.2 Investigating Perceptual Effects Using Mean Scores and Change Scores

To examine the intricacies of text placement and descriptiveness and their
effects on various perceptual metrics, we utilized and visualized two distinct
score types: mean scores and change scores.

Mean scores, defined as the arithmetic average of scores within a given
metric, serve to indicate the central location of sample data. In the context
of our study, these scores provide a measure of participants’ knowledge level
or perceptual experiences. We present data regarding participants’
pleasantness, confidence, and comprehension in the form of these mean scores.
Pleasantness scores are standalone metrics representing the degree of
enjoyment participants experienced while viewing the infographics. Similarly,
Confidence scores provide a separate measure, reflecting the level of
confidence participants felt in the accuracy of their responses. The
Comprehension score, meanwhile, reflects the knowledge participants retained
immediately after the infographic presentation.

In contrast, change scores are determined by the difference in an individual’s
scores across two or more distinct time points, providing a valuable tool for
assessing the impact of experimental interventions [65]. In our case, we
utilized change scores to assess two key aspects: information gain and memory
loss. Information gain was measured by calculating the differences between the
comprehension score and the pre-study baseline score. This metric quantified
the extent of knowledge gained from the initial infographic presentation,
providing an indicator of the magnitude of knowledge gained. In tandem with
this, we evaluated memory loss by examining both short-term and long-term
recall. Short-term memory loss, defined as the difference between the short-
term recall accuracy (recorded on day 3) and the comprehension score, aimed to
quantify the degree of information forgotten over a relatively brief period.
Similarly, long-term memory loss was determined by the difference between the
long-term recall accuracy (recorded on day 7) and the comprehension score,
providing an indication of the extent of information loss over a longer time
frame.

Table 1: Outcomes of the split-plot ANOVA, bolding indicate significance. |  Pleasantness |  Confidence |  Comprehension |  Information Gain |  Short-Term Memory Loss |  Long-Term Memory Loss
---|---|---|---|---|---|---
Placement | 0.455 | 0.259 | 1.00 | 0.485 | 0.045 | 0.018
Descriptiveness | 0.018 | 0.397 | 0.703 | 0.440 | 0.512 | 0.435
Figure 8: Pairwise comparison results of the descriptiveness levels on
pleasantness Figure 9: Results of the mean scores and change scores
represented in a grouped bar chart. The unshaded bars represent the embedded
text condition whereas the shaded bars represent the side text condition. The
bars are grouped according to the three levels of text descriptiveness, namely
simplistic, normal and descriptive. A: the pleasantness score, B: the
confidence score, C: the comprehension score, D: the information gain after
viewing the inforgraphic stimuli, E: the short-term memory loss, calculated as
the difference between short-term recall accuracy and comprehension score, F:
the long-term memory loss, calculated as the difference between long-term
recall accuracy and comprehension score. We identified statistical
significance in A, E and F. * indicates $p<0.05$.

To evaluate the statistical impact of each experimental condition, we utilized
a split-plot mixed ANOVA. Text placement served as the between-subject factor,
while text descriptiveness functioned as the within-subject factor. Table 1
encapsulates the outcomes of this analysis. Our results identified a
significant effect between text placement and both short-term ($p=0.045$) and
long-term memory loss ($p=0.018$). In terms of the level of descriptiveness, a
significant relationship was found between text descriptiveness and
pleasantness ($p=0.018$). However, no interaction effects were detected. We
did not identify any statistically significant difference in confidence,
comprehension or information gain between the experimental conditions.

Given that text descriptiveness encompasses three levels, we conducted a post-
hoc test with Bonferroni correction specifically for the pleasantness aspect.
This resulted in a pairwise comparison chart, depicted in Figure 8. We
identified a significant difference in pleasantness between the simplistic and
descriptive conditions ($p=0.041$).

Collectively, the results derived from the six experimental conditions ($2$
Text Placement $\times$ $3$ Text Descriptiveness) are depicted in Figure 9,
summarizing the three mean scores and three change scores.

##  5 Discussion

###  5.1 Simplicity and Pleasantness

Our analysis revealed that text descriptiveness significantly influenced the
degree of pleasantness experienced by participants when viewing infographic
stimuli. Interestingly, as displayed in Figure 9A, we observed that the level
of pleasantness tended to diminish as the text became more descriptive. This
trend suggests a linear relationship, as supported by a test of within-
subjects contrasts ($F=6.939,p=0.014$) within the framework of the split-plot
ANOVA. These findings imply that infographics employing simpler texts may
enhance the viewing pleasantness, while those featuring more descriptive and
extensive texts could potentially detract from the overall experience.

Our findings align well with general design principles that advocate “less is
more”, as well as theoretical concepts of simplicity in information
visualization, such as Tufte’s data-ink ratio [66, 67]. This principle
emphasizes maximizing the use of “ink” to represent data and essential
information while minimizing the use of “ink” for textual embellishments and
other non-essential elements. In the context of our study, simpler texts
contribute to minimizing the overall use of “ink”, thereby improving the data-
ink ratio. This, in turn, potentially enhances the pleasantness and enjoyment
derived from viewing infographics. While the concept of data-ink ratio can be
a subject of debate, it serves as a basic guideline for evaluating simplicity
in design.

The cognitive load theory [68] also offers a potential explanation for the
influence of text descriptiveness on perceived pleasantness. As the
descriptiveness and length of the text increase, the cognitive load required
to process the information contained in an infographic could also increase.
This increasing demand on cognitive resources could subsequently diminish the
viewer’s sense of enjoyment or pleasantness. Therefore, this theory aligns
with our observation that simpler text can lead to a more pleasant experience,
as it potentially reduces the cognitive load, making the infographic easier to
process.

The term “simplicity” encompasses a variety of factors. In our study, we
manipulated the level of descriptiveness primarily by modifying the length of
textual statements, while keeping the fundamental message intact. As such, we
employ the term “simplistic” to describe text that is shorter in length but
still carries essential information. However, it is crucial to acknowledge
that simplicity or complexity cannot be solely determined by length. Various
semantic and lexical factors, such as specific wording, vocabulary rarity,
writing style, sentence structure, and so more, can contribute to the overall
perception. Consequently, our interpretation of the impact of simplicity on
pleasantness is a relatively narrow focus. The multifaceted concept of
simplicity presents ample opportunities for further exploration in future
research endeavors.

Lastly, it should be noted that a significant difference in pleasantness was
only observed between the simplistic and the descriptive conditions
($p=0.041$). This observation suggests that as a perceptual metric shaped by
human judgment, pleasantness exhibits a certain degree of tolerance with
respect to levels of text descriptiveness, only responding to more substantial
variations in these conditions.

###  5.2 Text Embedding and Memory Retention

Upon examining the impact of text placement on memory retention, we observed a
significant difference between embedded text and side text, as demonstrated in
Figure 9E and F. Specifically, conditions involving embedded text resulted in
significantly less short-term ($p=0.045$) and long-term memory loss
($p=0.018$). These findings suggest that the embedding of text within
infographics potentially enhances memory retention when compared to placing
text alongside the visuals.

These results are consistent with theoretical expectations and can be
explained by the split-attention effect as part of the cognitive load theory.
The role of split-attention effect has been extensively investigated in
learning [68, 69] and situated analytics [70]. In our case, the side text
condition may have split the participant’s attention to focus on the text and
visuals separately, requiring additional mental power for integrating the
information together once again. On the other hand, the embedded text
condition creates a unified source of information by integrating text elements
within the visual components, thus mitigate the split-attention effect and
promotes memory retention and learning.

###  5.3 Consistent Comprehension In Varying Text Conditions

Interestingly, our study did not find any statistically significant
differences in comprehension between the varying levels of text conditions.
The comprehension and information gain scores remained within a consistent
range with only small statistical variations (Figure 9C, D).

This observation could be attributed to several factors. First, the
experimental conditions we selected might not have been sufficiently diverse
to induce perceptible differences in comprehension. Secondly, text placement
and descriptiveness might not be the principal factors dictating immediate
comprehension and information transfer.

However, it’s crucial to note that this finding does not diminish the
significance of text-visual interplay in effective communication. In fact, it
opens up avenues for future research to investigate the influence of other
text-visual aspects, such as the role of font size and typographical emphasis
on comprehension.

###  5.4 Infographic Design Implications

Our observations on text descriptiveness and text placement’s impact on
pleasantness and memory retention offer several important implications for
infographic design.

####  5.4.1 Simplicity in text can improve pleasantness experience.

As indicated by our experimental results, simplistic text design contributes
to statistically higher pleasantness scores, indicating an overall improved
user experience. When designing for pleasantness, designers and researchers
should consider incorporating more simplistic textual elements and avoiding
excessively lengthy and verbose explanations. While our study primarily
focused on the length of individual text statements as a measure of
descriptiveness, it’s worth noting that simplicity extends far beyond this
dimension, encompassing a wide array of semantic and lexical factors.

In designing infographics, striking a balance between providing sufficient
context and minimizing the use of text is crucial. The criteria of
“simplistic” text and whether enough context is provided can vary, depending
on the infographic’s purpose and its target audience. For example, more
complex or domain-specific infographics may require more extensive text to
provide necessary context information. Hence, designers should consider the
context and audience for an infographic and adjust their simplicity criteria
accordingly.

It’s also important to note that infographics can encompass different types of
textual information, including but not limited to titles, captions, and data
explanations. Each of these text types may underscore different considerations
in terms of descriptiveness. A title, for example, may need to be succinct and
direct, while background descriptions might require more elaborate
explanations.

In summary, designers and researchers can employ simpler texts in infographics
design for an optimal pleasantness experience. This strategy, however, should
be employed with a clear understanding of the specific purpose and audience of
the infographic, and with an awareness of the different requirements of
various text components.

####  5.4.2 Embedding text within visual elements can improve memory
retention.

Our results illustrate that text placement can have a significant effect on
both short-term and long-term memory retention. In particular, embedding text
within visual elements appears to reduce information loss over time compared
to placing text adjacent to these components. This advantageous outcome may be
attributed to the mitigation of the split-attention effect and the reduction
of cognitive load. As such, when designing for more memorable infographics
that promote long-lasting impressions, designers may consider text embedding
as an effective layout strategy.

While embedded text has shown promising results in our study, certain
practical considerations call for attention when implementing this approach.
The effectiveness of embedded text placement is contingent on the design of
the graphic elements and the density of the text. Optimal use of the limited
space offered by a circular visualization requires a delicate balance between
graphics and text. Overcrowding a small area with dense embedded text may
compromise the clarity and readability of the information, thereby
counteracting the benefits of this placement strategy. Furthermore, intricate
graphical elements risk being covered and obscured by the embedded text,
imparing the visual processing of the infographic. Thus, when considering the
use of embedded text in practice, additional factors that may moderate its
effectiveness should be taken into account.

Furthermore, the design of the textual elements themselves can have an impact
on the memorability of the infographics. While our study did not investigate
this aspect, it’s reasonable to hypothesize that visual attributes of the
text, such as color, density, alignment, and font size, can impact the quality
of the text embedding and, by extension, the overall effectiveness of the
infographic. Therefore, these design features demand careful consideration and
further exploration when embedding text in practice.

Lastly, while our study found promising results for text embedding, it’s
important to acknowledge the limited generalizability of these findings to
other types of visualizations. Our focus was predominantly on circular
infographics, such as pie charts and donut charts, which provide ample open
space for text embedding within the visual elements. However, the
applicability of text embedding may not extend as readily to other
visualization types. For instance, scatter plots or line charts may lack
sufficient space within their visual elements to accommodate text embedding.
Even for visualizations where area is the main visual encoding, such as mosaic
charts and bubble charts, the effectiveness of text embedding in enhancing
memorability remains an open question. Consequently, we urge designers and
researchers to consider these limitations and pursue further exploration. It’s
crucial to understand that the success of text embedding in different
visualization contexts may vary and require different design approaches.
Ultimately, the goal is to tailor the design strategy to the specific
requirements and constraints of each visualization type.

In summary, the findings of our study provide some guiding principles for
circular infographic design.

However, the design space of text-visual interplay in infographics design has
vast potential for further exploration. For instance, the strategies for text
embedding and descriptiveness can vary significantly across languages.
Alphabetic languages and pictographic languages exhibit distinct visual
layouts and spatial coverage. Chinese text, for instance, is often more
compact compared to English when conveying the same sentence. This is because
a single Chinese character represents a complete morpheme, while a letter in
English only denotes part of a morpheme. Therefore, the criteria for
descriptiveness might need adjustment when designing for different languages.
Furthermore, text position and orientation could also affect user perception.
Building upon the split-attention effect, we hypothesize that text labels
placed distant from their associated visual elements might lead to suboptimal
memorability performance. We encourage designers and researchers alike to
delve into the open-ended questions surrounding these methods and extend the
investigation to other types of visualizations. It is through this collective
curiosity and innovation that we can enhance our understanding and
continuously refine the art and science of infographic design.

##  6 Conclusion

In this study, we investigated the effects of text placement and
descriptiveness on the pleasantness, comprehension, and memorability of
circular infographics. We created six unique experimental conditions by
systematically manipulating the placement (embed, side) and descriptiveness
(simplistic, normal, descriptive) of text within fifteen infographic
materials. We recruited thirty participants and evaluated their pleasantness,
comprehension and memory retention using both mean scores and change scores on
accuracy scores and self-reported perceptual experience.

Our empirical results suggested statistically significant findings that
simpler and less descriptive text generally led to a more pleasant viewing
experience. Moreover, we found that the strategy of embedding text within the
infographic positively influenced memory retention when compared with placing
the text alongside the visual elements. These findings underscore the
importance of design decisions in text layout in infographic design. We
provided guiding design implications for circular infographic design that
advocate for simpler, embedded text for enhanced pleasantness and
memorability. However, the design of infographic is a complex procedure that
involves various perceptual intricacies. Careful considerations of the purpose
and audience of the target infographic, as well as more detailed text design
attributes are needed when employing these strategies.

Despite these insights, our study has some limitations. First, the robustness
of the results could be improved by including more levels of experimental
conditions. For example, more variations of the descriptiveness levels could
be included to encapsulate a wider range of text complexities. Second,
additional user experience metrics, such as engagement and accessibility, can
be explored for a holistic understanding of how different design decisions
impact the perceptual effectiveness of infographics. Third, implicit measures,
such as response time, can be included as evaluation measures to provide
additional insights into the learnability of infographics.

Our present study involves manual edits of infographic materials into
different text layouts. However, future work could benefit from adopting
automatic infographic generation strategies[71, 32]. This would enable the
creation of more controlled and consistent infographic stimuli in large
quantities, suitable for robust, large-scale studies. Moreover, this method
could be beneficial when testing generalizability across different types of
visualizations. By developing an informative database encompassing a variety
of topics and distinct visualization charts, we can systematically evaluate
the perceptual effects across different domains and visualization types.

As the field of infographic design continues to evolve, it is our hope that
our findings will inform and inspire further research and innovation in this
area.

## Acknowledgement

This work was supported by the National Natural Science Foundation of China
62272396.

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