For a book that attempts to explain how to understand visuals in life sciences, it seems prudent to first explain what we mean by “visual,” even if it may seem quite a common word.
In everyday conversation, “visual” is often used as an adjective and means “relating to seeing or sight,” as in “visual impression” or “visual effect.” In the context of this book, “visual” is used similarly as an adjective, but in addition, and more often, it is used as a noun. As a noun, it refers to the variety of images used in life science communication. For example, photographs are a type of visual commonly used in life science communication, and so are drawings.
I understand that, as a noun, the word “visual” is decidedly more awkward than, say, “image” or “picture.” However, people who study visual representations like the word because it is a more generic concept, an umbrella term. Other words tend to be narrower in scope or have specific implications. For example, “picture” too strongly suggests a photograph and a photograph alone, whereas “image” may remind one of something that is entirely imaginary, as in a “mental image.”
The Importance of Visuals in Life Sciences: A Brief History
Visuals are essential in life sciences – not only for communicating scientific findings and discoveries, but also for carrying out scientific research in the first place. They are part and parcel, you may say, of the science.
Think about it: Scientists rely on imaging technologies to observe and make sense of the research subjects they study. Looking at a virus under the microscope, for example, allows scientists to understand the virus’s structure and, from there, to explore its function. Photographing a plant from flowers to leaves allows scientists to make accurate identification of the species and, from there, to assess its cultivation. Without being able to visualize the life around us, there can be no life sciences.
When scientists or science writers are ready to communicate research findings, visuals take on multiple roles. They appear in print publications, on TV, on the internet, in the classroom. Some of these visuals serve as scientific evidence: If one has observed a certain unique viral structure under the microscope, what’s better evidence than a detailed micrograph to show the said structure? Some visuals also serve as argument: If one has a hypothesis for how a virus spreads, putting that process in a simplified diagram, as opposed to lengthy texts, can help people grasp the gist of the argument and, potentially, be persuaded of its soundness.
The importance of visuals in life sciences is not a recent phenomenon either. The rise of life sciences itself, one might say, is connected with the rising popularity of visuals. A well-known testimony to this connection is Micrographia, a book published in 1665 by English scientist and architect Robert Hooke (1635–1703). The complete title of the book is a bit long and awkward. It goes Micrographia: or Some Physiological Descriptions of Minute Bodies Made by Magnifying Glasses. With Observations and Inquiries Thereupon.
In the book, Hooke presented the minute bodies he saw using the microscopic technologies available to him at the time. With 38 stunning copperplate engravings, Micrographia illustrated the structure of crystals and plants. But what made the book eternally famous are, decidedly, its bugs: ants, fleas, flies, lice, you name it. The fascinating – or repulsive, depending on whom you ask – details of these bugs are lavishly rendered in the book. The depiction of a flea, for example, unfolds to more than twice the size of the book (Figure 1.1).
Figure 1.1 Robert Hooke’s depiction of a flea.
A century earlier than giant fleas, similarly stunning depictions of the human body had been rendered. In 1543, Andreas Vesalius (1514–1564), a Belgian anatomist and physician, presented to the world De Humani Corporis Fabrica Libri Septem (Seven Books on the Fabric of the Human Body). The volume was considered one of the first, if not the first, accurate depictions of the human body.
Anatomic accuracy is only part of the volume’s fame. As Micrographia, De Humani Corporis Fabrica Libri Septem is a spectacular visual feast. Included in it are elaborate woodcut illustrations of human dissections. The human bodies don’t simply lie lifeless on a dissection table. Rather, they are stood in dramatic poses against a landscape, as if erected pieces of classical statues. They are known as the “muscle men.” The men’s bodily tissues are stripped away layer by layer in each passing plate to reveal the anatomy of the human body (Figure 1.2). Meanwhile, the landscapes of the individual plates form a continuing panorama. As the muscle man is stripped clean and eventually collapses, the life behind him goes on.
Figure 1.2 Andreas Vesalius’ depiction of the muscle men.
If you think sixteenth- and seventeenth-century scientists are a bit sensational with their giant insects and theatrical humans, today’s scientists outdo them with pure technological prowess. Modern life science research frequently deals with things that are not only invisible to the naked eye, but also difficult to conceptualize in the first place: DNA, genes, genomes. To visualize such concepts, scientists rely on sophisticated technologies, from X-ray crystallography that reveals DNA’s structure, to heat maps that show levels of gene expression through color-coded boxes (Figure 1.3), to sequencing technologies that turn genomes into endless scrolls of letters.
Figure 1.3 A heat map.
In Figure 1.3, each row represents a different gene, and each column represents a different test sample. The color of each little box stands for the change in gene expression for each gene in each sample. Red shows increased, or upregulated, expression – the brighter the red, the higher the expression. Green shows decreased, or downregulated, expression – the brighter the green, the lower the expression. Based on the gene expression patterns, we can then attempt to group similar genes and samples for study.
The Changing Nature of Life Science Visuals
As the above brief history shows, visuals have long been and will continue to be paramount to life science research. What is not yet apparent is that different visual “styles,” if you will, have come and gone. Yes, the advance of scientific methods and technologies necessarily results in different kinds of visuals. But that’s not all. The nature of the visuals, the value we attach to them, as Galison explained, have also evolved.
Before the nineteenth century, natural sciences saw the function of their visuals as one of revealing “truth to nature.” This “truth,” in case one wonders, is not the contemporary notion of “objectivity” that is often associated with scientific efforts. In fact, in some ways, it is the opposite of the modern concept of objectivity. “Truth to nature” believes that we can’t grasp the true nature by observing and recording individual plants or animals as they are, because individual specimens often contain idiosyncrasies and imperfections. The leaves of a plant may have been chewed on by an insect; the wings of the insect may have been notched by a predator. Recording these realities, it was believed, doesn’t reveal truth.
To get at the truth of nature, we must rise above individual specimens and their idiosyncrasies. How do we do that, you ask. By relying on geniuses – geniuses such as Robert Hooke and Andreas Vesalius. Their artistic and scientific talents allowed them to see past, through, and beyond individual specimens and to create perfect, awe-inspiring fleas and men. This is why their visual creations have that unmistakable touch of artistic invention, even divine intervention. They say, as it were, Look at this specimen, so symmetrical, so wonderful, so ideal. Make no mistake. They are not accidents. They are God’s creations.
Around 1830, the “truth to nature” ideal, though not extinguished, started to give way to the concept of “mechanical objectivity.” The difference between the two concepts was stark. While “truth to nature” relied on artistic correction and invention to render truth, “mechanical objectivity” sees artistic correction and invention as contaminations of scientific data. The role of visual creators, the objectivity approach announced, is not to idealize or editorialize what they see, but to simply record what they see, warts and all. And if that means drawing a plant with gnawed leaves, so be it. Only such work can let nature “speak for itself.”
Under this belief, scientists were expected to practice a strict moral culture. They must not let their imagination run wild, they must not invent details to fit their theories, and there will be absolutely no retouching of any visuals, even if it is to sharpen them to make them more legible. Scientists were to be copy machines through which nature reveals itself. In fact, if possible, using real machines would be even better, because they give us the ultimate mechanical objectivity. Real machines, such as a camera, are not motivated by personal interests, are free from human interventions, and can therefore create the most objective visuals. These beliefs, as we will see later, are flawed. But that’s for the rest of the book.
Although the pursuit of objective visuals is still a relevant goal in today’s life sciences, it is no longer the only or the most important one. The newest kid on the block, starting to make its appearance in the early twentieth century, is interpreted visuals. As their name suggests, these visuals are a result of interpretation, not mechanical copy-making. Whose interpretation, you ask? Experts, trained scientists. The idea is that these experts have accumulated a vast amount of experience and knowledge; they have observed hundreds, if not thousands, of the same specimens. While each individual plant of the same species may look a bit different – a little bigger or smaller, a little crooked or bent – the experts can synthesize the differences and create something that captures the essence of the plant.
Compared to mechanically objective visuals, interpreted visuals have some distinct advantages. Imagine medical students who are being trained to recognize brain lesions using radiographs of real patients. Given a group of inexperienced students, the chances are that the lesions will not jump out at them. In other words, the task of looking at something and learning to recognize it will become a task of looking for something, not knowing what one is looking for. The “objective” radiograph, then, would lose its instructive value. By contrast, an interpreted drawing of the radiograph can tone down irrelevant details and exaggerate the essential features of the lesions. This way, the visual can instill in students the basic knowledge to identify similar, but less obvious, lesions in actual patients.
Challenges with Using Visuals in Popular Science Communication
Before the rise of modern science circa seventeenth century, there was not a clear demarcation between science communication meant for professional scientists and communication meant for the wealthy, the intellectual, and the generally curious. Robert Hooke’s Micrographia, for example, was a bestseller and inspired wide public interest.
One can therefore say that, as the above history shows, visuals have been used in popular science communication for a long time. That tradition has only intensified in contemporary popular life science communication, as we will see throughout this book. The advent of digital technologies and online publishing assisted with this trend: Engraving or printing visuals in a book are costly, but in a blog or ebook the added cost of visuals is minimal.
But does this mean that all these visuals are actually helpful to everyday readers? Is each picture, as we are wont to think, really worth a thousand words?
For specialist audiences such as trained microbiologists or neurobiologist, visuals within their specialties often make automatic sense – because these visuals are an ingrained part of their training and have become, so to speak, common knowledge. The mere appearance of a visual can potentially reveal how it came about, why it was made, what technologies were employed to make it, what the colors denote, what the scales suggest, etc.
For nonspecialist readers, that’s often not the case. As Trumbo wrote about DNA and the seemingly well-known image of the double helix,
Most of us have seen images of the double helix, that wonderful spiral strand of “something,” first proposed by Watson and Crick. What is its scale? Is DNA larger than a drop of water, a molecule, an atom? What is its color? Does it really look like a spiral? This animated, spiral-shaped image fills the television screen during a report about cloning. It spins clockwise, or is it counterclockwise? It appears in advertising for a biotechnology company wrapped around a typographic logo. It appears in a lively cartoon sequence within the movie Jurassic Park. What exactly are we seeing?
As this quote implies, the double-helix visual plays multiple roles in the public sphere: It is used to explain genetic research; it is turned into cinematic entertainment; it is an endorsement of commercialized biotechnology. Can the same visual fulfill these very different roles and purposes? What information does the public gain when they look at the visual? What visceral reactions do they form? If readers cannot determine the scale of DNA from a spinning double helix, is the visual really helping to illustrate what DNA is and does? Should we add more technical details to the spiral to make it more informative? Is that going to help? Or is it going to make things worse? And what do we mean by “help” or “make things worse”?
These are complex questions that don’t have easy answers. But for that reason, they are precisely the kinds of questions that are bound up with the misconceptions and misunderstandings surrounding life science visuals. And they are the kinds of questions this book ponders.
But Hasn’t This Work Been Done?
Surprisingly, although a lot of work has been done on life science visuals, very little of the kind that I proposed above has. Existing studies often focus on the visual practices and needs of professional scientists. For example, they look at how scientists create persuasive visual evidence in their publications. Or, they discuss what visual technologies scientists need in order to better present the increasingly complex and large data sets they obtain.
In addition, existing studies also look at the experiences and needs of science educators and science students. Here, we learn that visuals used in science textbooks leave quite a bit to be desired. They have poor data mapping, contain inadequate captions, and use surface features that may mislead students. To their credit, these studies recognize that reading science visuals is a complex process that must be taught to students for students to be truly integrated into the world of science.
All of these studies are of course important and useful in their own ways, but they have largely overlooked you and me, the everyday readers. The general public audiences are not professional scientists who create visuals for publication, nor do they partake in formal education to learn under the guidance of an instructor. Rather, as a National Science Board survey indicates, most American adults gain information about science through informal channels such as the internet, television, newspapers, and magazines.
Given the importance of visuals to the production and communication of life sciences, it is high time that we scrutinize their use in popular communication. In fact, I would argue that, to obtain wider impact, it is more important that we pay attention to the vast number of public readers out there than to the limited number of professional scientists and scientists in training.
This is true for all disciplines of science, but especially true for life sciences, which are intimately related to people’s everyday lives and personal wellbeing. Take the discipline of genetics as an example. Classical genetics from the early twentieth century offered a way for farmers to better control their crops and boosted the agricultural industry. The infamous eugenics movement in the 1930s, in trying to “regulate” human birth and development, condoned involuntary sterilization, often forced on minority populations. Today’s genetics research holds a lot of promise for human health and welfare, from genetic testing to genetic therapy, though it is also at the center of much controversy, from genetically modified food to designer babies. Beyond genetics, other life science research also has profound social implications, from the preservation of diverse species on Earth to the elimination of fast-spreading, deadly viruses.
With so much at stake, the public needs to be actively involved in understanding and engaging with life sciences, and scientists need an informed and engaged public that supports sound, ethical scientific efforts.
The Deficient Public
Now that I’ve mentioned the importance of public understanding of and engagement with science, I must add that “public,” “understanding,” and “engagement” are all loaded words in the context of popular science communication research.
To start, the word “public” tends to convey the impression of the public as a homogenous, united front. But this is certainly not true. In reality, the public consists of diverse communities, groups, and individuals with varying education, interest in science, trust in science, political stances, life values, etc. It is beyond the scope of this book to account for all these variations, but I do want to acknowledge the fact that the public is a plural concept and invite all readers to do the same.
Next, the words “understanding” and “engagement” are loaded because they carry a lot of historical baggage when we consider the relationship between the public and science. The traditional view of this relationship, to put it bluntly, assumes that science (and by extension, trained scientists) are superior, and that the general public has a lot of catching up to do. Scientists are the ones (and only ones) who have the ability and duty to educate the masses and teach them essential scientific concepts so that the public can become literate enough to comprehend and appreciate the gist of modern science. Once sufficiently literate, the masses stand a chance to be rational and productive citizens in the modern society – and they will trust science and support scientific research.
Studies that are grounded in this approach often use standard surveys to reveal the public’s poor grasp of basic scientific concepts (such as their inability to define what counts as a “scientific experiment”). Based on these survey findings, the studies then recommend proper education. For example, people are advised to take at least some college-level science classes.
This perspective, the so-called “deficit view” – the public being the deficient party – dominated in the 1970s, 1980s, and into the 1990s. In fact, it still holds sway today among some scientists. But we have now come to recognize that this view is problematic. It is based on a very rigid, elitist view of what counts as “expertise” and thus a very rigid, elitist view of who has expertise and who doesn’t. Everyday readers may not be able to technically define what counts as a formal scientific experiment, but that doesn’t mean they can’t recognize it when they see one, or that they all lack the ability to think logically, to weigh evidence, and to make sense of scientific findings.
Brian Wynne’s work on Cumbrian hill sheep farming is one of the most well-known studies that demonstrate these points. In 1986, after the Chernobyl Nuclear Power Plant exploded, thunderstorms washed nuclear fallout onto Britain’s upland Cumbrian area. The fallout contaminated the soil and vegetation and threatened to contaminate the sheep that grazed in the area. In the aftermath, scientists from the Ministry of Agriculture, Fisheries, and Food tried to assess and reduce sheep contamination in the area. With their rigid understanding of what a perfect scientific experiment should look like, these experts ignored farmers’ knowledge of the local environment, how the hill sheep behave, and the reality of hill farming management. As a result, much of the scientists’ formal assessment and experimentation failed.
For example, at one point, the scientists wanted to conduct an experiment on the ability of the mineral bentonite to absorb contaminants from the land and reduce sheep contamination. To do that, they marked out different plots, spread different concentrations of bentonite in each plot, and left some plots untreated as controls. Different sheep were then assigned to graze in different plots and periodically tested to assess the effect of the mineral. The farmers pointed out to the scientists that the experiment as designed wouldn’t work. Hill sheep are used to roaming open lands. If they are fenced in, they would waste away, which would ruin the experiment. The scientists ignored the farmers’ concern, deeming it irrelevant to the rigor of scientific experiment. But soon enough they quietly abandoned the experiment for the precise reason the farmers had identified.
Engaging the Public
Studies such as Wynne’s gave rise to a new approach to map the relationship between the public and science, one that may be called the engagement approach. This new approach emphasizes “dialog” and “engagement” rather than “understanding” – because the word “understanding” always already implies deficiency on the public’s part. The new approach asserts that science is a social enterprise and that everyday people’s life experiences, local knowledge, and values matter. Simply providing the public with more scientific facts isn’t going to turn them into believers or supporters of science. The public is not a problem to be fixed, but a party to be actively involved and engaged in science.
While the engagement approach has come a long way from the deficit approach, it is not without problems of its own. First, there is no specific agreement on what exactly counts as “engagement,” or how it can be made effective. Some suggest trying to stimulate the public’s curiosity and sense of wonder about science; others encourage shared decision-making between citizens and scientists; still others focus on facilitating direct conversations between the public and scientists. Without effective guidelines, engagement efforts risk becoming mere tokens.
Second, talks of public engagement risk creating a romantic view of the public, an ultra-version of the sheep farmers in Wynne’s study, if you will: inherently wise, possessing knowledge uniquely relevant to scientific research, and having pragmatic values that are superior to the narrow agenda of trained scientists. Pushed too far, this romantic view can render scientific training irrelevant. Also, the public is made out to be a homogenous group when, in reality, not all members of the public will have the same amount of interest, knowledge, or experience about science or the same amount of trust in science.
Finally, the engagement approach continues to create, if only inadvertently, a split between science and humanities, scientists and citizens, hard facts and soft feelings. The public remains the public, while the science is, well, the science, and one is not like the other.
Where Do We Go from Here?
While it is relatively easy to critique existing approaches, it is much harder to figure out a perfect alternative. The relationship between science and the public is complex! With that in mind, it is a start to recognize that we ought to consciously bridge rather than separate the two, to learn to think of them as the inherently connected entities that they are, or should be. After all, why develop science if not to serve the public?
The next step is to recognize that neither science nor the public is a singular concept. Scientists come from different disciplines, have varying training experiences, and enjoy diverse cultural backgrounds. They don’t think the same way about the role of the public or the value of popular science. Many of those who communicate science to the public are not trained scientists either. They are science writers and journalists who may or may not have a formal science background. Similarly, the public is an inherently variable group; or, rather, each individual is an inherently multiple identity. We are patients, consumers, taxpayers, parents, etc. Not all of us think or feel the same way about science or specific science topics, and the different hats each of us wears tug us in different directions.
Given all these variations, what is likely to work, as Holliman et al. argue, is not a “one size fits all” approach to popular science communication, but a fluid, context-specific approach. In some communication contexts, we may want to empower the public, acknowledge their local knowledge, and encourage two-way communication or public debate. In other contexts, we may need one-way delivery – via TV, newspaper, the internet – of the latest and newest scientific findings, fashioned in ways that meet the needs of a public audience. And in many contexts, we may need a combination of both, mixed at different ratios.
What this means for science communication research is to forget about lofty theories that try to generalize broadly, but instead examine specific cases in detail to glean nuggets of useful information.
The same, as I attempt to show in this book, applies to studies of popular life science visuals. It is through specific examples that we can appreciate the importance and challenges of using visuals in popular science communication. It is through specific examples that we can hope to dispel misconceptions and stereotypes and gain lessons learned.
More fundamentally, it is through specific visual examples that we can try to bridge science and society, to see that life science visuals are more than some data that came out of a science lab, that they are also products of social and cultural contexts, or they should be. From there, we can start to appreciate the multiple functions and effects these visuals have.
What Kinds of Visuals Will We Examine?
In the rest of the book, I apply all the above to practice. The misconceptions and stereotypes about life science visuals are discussed against the backdrop of the changing nature of science visuals and the different ways to think about the relationship between science and the public. To do so, I have selected the seven most commonly used kinds of visuals in popular life science communication. Each type is described and discussed in one chapter.
The first type of visual is photographs, the topic of Chapter 2. Photographs are commonly used in life science communication. Too often, they are seen as objective records of reality, as machine-captured visual evidence where the pixels are what they are. Chapter 2 confronts this perception. It concedes that photographs can serve as observational records, but they are not free from human manipulation. Machines, after all, are set and operated by humans, and anything from exposure time to shutter speed can affect the outcome. There is also the purposeful selection of what scenes to capture, what scenes to ignore, and post-editing enabled by software such as Photoshop.
Moreover, in today’s popular science communication, photographs are often purposefully used for their ability to evoke strong viewer reactions and emotions. Examples of such photographs are abundant in the media. For example, photographs of Frankenstein food – such as vegetables and fruits taking a shot of colorful fluid straight from a syringe – are designed to incite public outcry against genetically modified food. These and other such photographs have their value in conveying social and political attitudes, but that is precisely it: This intention and effect need to be known so that public viewers do not take these symbolic photographs as scientific evidence.
The second type of visual I discuss is micrographs. Chapter 3 provides a concise history of the use of micrographs in life science communication, from hand-drawn microscopic observations to scanning electron micrography. This history demonstrates our obsession with seeing ever more minutely, more clearly, and more reductively – that is, we seem to believe that when we break nature down into its smallest parts, we will understand it. But, in reality, a reductive focus can pose a challenge to nonspecialist viewers because microorganisms such as the SARS-CoV-2 virus have no counterpart in the everyday visual world we live in. Without contextual cues, then, nonspecialist viewers gain very little by looking at a static, ultra-zoomed-in micrograph. Depending on the microscopic techniques used, a virus can also take on diverse appearances, which may cause more confusion than clarification.
In addition, as with photographs, micrographs are too often considered a realistic portrait of microcosmic life. But, in reality, creators of micrographs enjoy quite a bit of creative license. For example, colors are used exuberantly, not entirely or even necessarily to enhance meaning, but to create emotional reactions. This is particularly ironic when we consider that viruses and most cells do not have color and, in that sense, are free of the emotional baggage we grant them. Yes, artistic micrographs serve to celebrate life as the beautiful and mesmerizing thing that it is. At the same time, they are bound up with science’s need to promote itself, to portray itself as a similarly beautiful and mesmerizing enterprise.
The third type of visual, and the focus of Chapter 4, is illustrations, or drawings, a staple in life science communication. In the year 2020, the single most popular visual in the media is the gray–red, fuzzy-looking SARS-CoV-2 virus developed by the US Centers for Disease Control and Prevention. Despite being commonplace, scientific illustrations are in many ways a blackbox: They mask the creative – and scientific – decisions that go into making the illustrations and present an end product that says, as it were, this is the way life is. The use of precise lines, explicit shapes, and other ultrarealistic details all help to convey this visual certainty.
It is, then, easy to forget that illustrations are interpretive visuals, or someone’s interpretation of life. For example, a colorful, textured, three-dimensional image of a protein is not reality; it is an artistic rendering based on deduced atomic structures. While these artistic choices can create visual appeals and enhance understanding, they also risk becoming “seductive details” that distract readers and create false impressions. A case in point is visual metaphors – illustrations that reduce sophisticated concepts into smart-looking yet ambiguous metaphors, such as drawing a tree and morphing its branches into the DNA double helix. Not only are these images technically inaccurate, but their metaphorical meanings are vague. At worst, they promote false security in viewers that science is simple, familiar, and not worth asking specific questions.
The fourth type of visual discussed in this book is graphs, which are commonly used in life science communication to convey quantitative data. Chapter 5 starts by overviewing the most common types of graphs, such as bars, lines, pies, and histograms. It discusses these graphs’ common usage in life science communication. The chapter also discusses pictographs, including their history, growing popularity, and advantages, as well as the challenges of using them in life science communication.
The main argument of Chapter 5 is that graphs, contrary to what some visual creators may believe, are often difficult for nonspecialist readers to understand. For many viewers, the scientific concepts and quantitative data being graphed are abstract or unfamiliar to begin with. Certain graphic conventions and stylistic choices make the matter worse, such as the use of three dimensions, truncated axes, multiple y-axes, intrusive details, and a lack of integrated verbal explanation. Given these barriers, graphs that are meant to synthesize a large amount of data may end up confusing rather than enlightening viewers. In fact, these graphs can leave public readers with a heightened conviction that science is an inaccessible enterprise. Worse still, graphs that are purposefully designed to encourage a certain reading may mislead viewers.
The fifth type of visual, and the focus of Chapter 6, is interactive visuals, which are web-based visuals that allow direct user interactions such as clicking and dragging. Life sciences are big data sciences, and when it comes to reporting big data, online, interactive visuals have inherent advantages. They can present enormous amounts of data for correlation and comparison. They do not overwhelm viewers by presenting everything at once. Depending on user input, select data can be visualized on demand.
At the same time, interactive computer technology alone does not equate to effective visual communication. Not only do interactive visuals need to be usable – that is, pressing a button is easy and will result in a certain action – they also need to be useful. That is, they need to support the kind of action and output that users desire. If users want to click on one specific city in an interactive map to find local epidemiological data, a map that is chock-full of data but prevents targeted selection is, strictly speaking, useless.
Games offer another type of interactive visuals. Well-known examples in life sciences include Foldit, a protein structure prediction game, and Phylo, a multiple sequence alignment game. These games take advantage of humans’ superb visual recognition ability to solve computationally expensive tasks such as arranging DNA sequences, and they have been hailed as great successes of public engagement in science. However, on closer inspection, these games often do not engage players in anything scientific per se, beyond using their labor. Whether or not that is okay is an interesting question to ponder.
Last, in Chapter 7, we turn our attention to infographics. Infographics have gained tremendous popularity in contemporary popular science communication. They are used to illustrate anything from global population change to the biomechanics of a running cheetah. But infographics are quite a nebulous concept. They are essentially information in graphic formats. As such, infographics can be as simple as a pie graph with some text positioned around it, or as complex as a constellation of graphs, illustrations, photographs, and text.
If there is one overarching difference between infographics and other types of visuals, it is that infographics, with their multiple and combined use of visuals and text, are more likely to enhance understanding yet at the same time just as liable to cause confusion. The various misconceptions concerning other types of visuals have compound relevance in infographics. Because of this, visual creators need to be extra conscious – and conscientious – of their designs, and viewers need to be extra critical of their interpretations.
In short, we have quite a spectrum of visuals to cover – and quite a spectrum of life and science to go along with them. I hope you find the journey ahead an intriguing and informative one.
