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International Journal of Library and Information Services (IJLIS)

  • Open access
  • Published: 19 July 2015

The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works

  • Maria Evagorou 1 ,
  • Sibel Erduran 2 &
  • Terhi Mäntylä 3  

International Journal of STEM Education volume  2 , Article number:  11 ( 2015 ) Cite this article

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The use of visual representations (i.e., photographs, diagrams, models) has been part of science, and their use makes it possible for scientists to interact with and represent complex phenomena, not observable in other ways. Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using visual representations and less on visual representations as epistemic objects. In this paper, we argue that by positioning visual representations as epistemic objects of scientific practices, science education can bring a renewed focus on how visualization contributes to knowledge formation in science from the learners’ perspective.

This is a theoretical paper, and in order to argue about the role of visualization, we first present a case study, that of the discovery of the structure of DNA that highlights the epistemic components of visual information in science. The second case study focuses on Faraday’s use of the lines of magnetic force. Faraday is known of his exploratory, creative, and yet systemic way of experimenting, and the visual reasoning leading to theoretical development was an inherent part of the experimentation. Third, we trace a contemporary account from science focusing on the experimental practices and how reproducibility of experimental procedures can be reinforced through video data.

Conclusions

Our conclusions suggest that in teaching science, the emphasis in visualization should shift from cognitive understanding—using the products of science to understand the content—to engaging in the processes of visualization. Furthermore, we suggest that is it essential to design curriculum materials and learning environments that create a social and epistemic context and invite students to engage in the practice of visualization as evidence, reasoning, experimental procedure, or a means of communication and reflect on these practices. Implications for teacher education include the need for teacher professional development programs to problematize the use of visual representations as epistemic objects that are part of scientific practices.

During the last decades, research and reform documents in science education across the world have been calling for an emphasis not only on the content but also on the processes of science (Bybee 2014 ; Eurydice 2012 ; Duschl and Bybee 2014 ; Osborne 2014 ; Schwartz et al. 2012 ), in order to make science accessible to the students and enable them to understand the epistemic foundation of science. Scientific practices, part of the process of science, are the cognitive and discursive activities that are targeted in science education to develop epistemic understanding and appreciation of the nature of science (Duschl et al. 2008 ) and have been the emphasis of recent reform documents in science education across the world (Achieve 2013 ; Eurydice 2012 ). With the term scientific practices, we refer to the processes that take place during scientific discoveries and include among others: asking questions, developing and using models, engaging in arguments, and constructing and communicating explanations (National Research Council 2012 ). The emphasis on scientific practices aims to move the teaching of science from knowledge to the understanding of the processes and the epistemic aspects of science. Additionally, by placing an emphasis on engaging students in scientific practices, we aim to help students acquire scientific knowledge in meaningful contexts that resemble the reality of scientific discoveries.

Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using visual representations and less on visual representations as epistemic objects. In this paper, we argue that by positioning visual representations as epistemic objects, science education can bring a renewed focus on how visualization contributes to knowledge formation in science from the learners’ perspective. Specifically, the use of visual representations (i.e., photographs, diagrams, tables, charts) has been part of science and over the years has evolved with the new technologies (i.e., from drawings to advanced digital images and three dimensional models). Visualization makes it possible for scientists to interact with complex phenomena (Richards 2003 ), and they might convey important evidence not observable in other ways. Visual representations as a tool to support cognitive understanding in science have been studied extensively (i.e., Gilbert 2010 ; Wu and Shah 2004 ). Studies in science education have explored the use of images in science textbooks (i.e., Dimopoulos et al. 2003 ; Bungum 2008 ), students’ representations or models when doing science (i.e., Gilbert et al. 2008 ; Dori et al. 2003 ; Lehrer and Schauble 2012 ; Schwarz et al. 2009 ), and students’ images of science and scientists (i.e., Chambers 1983 ). Therefore, studies in the field of science education have been using the term visualization as “the formation of an internal representation from an external representation” (Gilbert et al. 2008 , p. 4) or as a tool for conceptual understanding for students.

In this paper, we do not refer to visualization as mental image, model, or presentation only (Gilbert et al. 2008 ; Philips et al. 2010 ) but instead focus on visual representations or visualization as epistemic objects. Specifically, we refer to visualization as a process for knowledge production and growth in science. In this respect, modeling is an aspect of visualization, but what we are focusing on with visualization is not on the use of model as a tool for cognitive understanding (Gilbert 2010 ; Wu and Shah 2004 ) but the on the process of modeling as a scientific practice which includes the construction and use of models, the use of other representations, the communication in the groups with the use of the visual representation, and the appreciation of the difficulties that the science phase in this process. Therefore, the purpose of this paper is to present through the history of science how visualization can be considered not only as a cognitive tool in science education but also as an epistemic object that can potentially support students to understand aspects of the nature of science.

Scientific practices and science education

According to the New Generation Science Standards (Achieve 2013 ), scientific practices refer to: asking questions and defining problems; developing and using models; planning and carrying out investigations; analyzing and interpreting data; using mathematical and computational thinking; constructing explanations and designing solutions; engaging in argument from evidence; and obtaining, evaluating, and communicating information. A significant aspect of scientific practices is that science learning is more than just about learning facts, concepts, theories, and laws. A fuller appreciation of science necessitates the understanding of the science relative to its epistemological grounding and the process that are involved in the production of knowledge (Hogan and Maglienti 2001 ; Wickman 2004 ).

The New Generation Science Standards is, among other changes, shifting away from science inquiry and towards the inclusion of scientific practices (Duschl and Bybee 2014 ; Osborne 2014 ). By comparing the abilities to do scientific inquiry (National Research Council 2000 ) with the set of scientific practices, it is evident that the latter is about engaging in the processes of doing science and experiencing in that way science in a more authentic way. Engaging in scientific practices according to Osborne ( 2014 ) “presents a more authentic picture of the endeavor that is science” (p.183) and also helps the students to develop a deeper understanding of the epistemic aspects of science. Furthermore, as Bybee ( 2014 ) argues, by engaging students in scientific practices, we involve them in an understanding of the nature of science and an understanding on the nature of scientific knowledge.

Science as a practice and scientific practices as a term emerged by the philosopher of science, Kuhn (Osborne 2014 ), refers to the processes in which the scientists engage during knowledge production and communication. The work that is followed by historians, philosophers, and sociologists of science (Latour 2011 ; Longino 2002 ; Nersessian 2008 ) revealed the scientific practices in which the scientists engage in and include among others theory development and specific ways of talking, modeling, and communicating the outcomes of science.

Visualization as an epistemic object

Schematic, pictorial symbols in the design of scientific instruments and analysis of the perceptual and functional information that is being stored in those images have been areas of investigation in philosophy of scientific experimentation (Gooding et al. 1993 ). The nature of visual perception, the relationship between thought and vision, and the role of reproducibility as a norm for experimental research form a central aspect of this domain of research in philosophy of science. For instance, Rothbart ( 1997 ) has argued that visualizations are commonplace in the theoretical sciences even if every scientific theory may not be defined by visualized models.

Visual representations (i.e., photographs, diagrams, tables, charts, models) have been used in science over the years to enable scientists to interact with complex phenomena (Richards 2003 ) and might convey important evidence not observable in other ways (Barber et al. 2006 ). Some authors (e.g., Ruivenkamp and Rip 2010 ) have argued that visualization is as a core activity of some scientific communities of practice (e.g., nanotechnology) while others (e.g., Lynch and Edgerton 1988 ) have differentiated the role of particular visualization techniques (e.g., of digital image processing in astronomy). Visualization in science includes the complex process through which scientists develop or produce imagery, schemes, and graphical representation, and therefore, what is of importance in this process is not only the result but also the methodology employed by the scientists, namely, how this result was produced. Visual representations in science may refer to objects that are believed to have some kind of material or physical existence but equally might refer to purely mental, conceptual, and abstract constructs (Pauwels 2006 ). More specifically, visual representations can be found for: (a) phenomena that are not observable with the eye (i.e., microscopic or macroscopic); (b) phenomena that do not exist as visual representations but can be translated as such (i.e., sound); and (c) in experimental settings to provide visual data representations (i.e., graphs presenting velocity of moving objects). Additionally, since science is not only about replicating reality but also about making it more understandable to people (either to the public or other scientists), visual representations are not only about reproducing the nature but also about: (a) functioning in helping solving a problem, (b) filling gaps in our knowledge, and (c) facilitating knowledge building or transfer (Lynch 2006 ).

Using or developing visual representations in the scientific practice can range from a straightforward to a complicated situation. More specifically, scientists can observe a phenomenon (i.e., mitosis) and represent it visually using a picture or diagram, which is quite straightforward. But they can also use a variety of complicated techniques (i.e., crystallography in the case of DNA studies) that are either available or need to be developed or refined in order to acquire the visual information that can be used in the process of theory development (i.e., Latour and Woolgar 1979 ). Furthermore, some visual representations need decoding, and the scientists need to learn how to read these images (i.e., radiologists); therefore, using visual representations in the process of science requires learning a new language that is specific to the medium/methods that is used (i.e., understanding an X-ray picture is different from understanding an MRI scan) and then communicating that language to other scientists and the public.

There are much intent and purposes of visual representations in scientific practices, as for example to make a diagnosis, compare, describe, and preserve for future study, verify and explore new territory, generate new data (Pauwels 2006 ), or present new methodologies. According to Latour and Woolgar ( 1979 ) and Knorr Cetina ( 1999 ), visual representations can be used either as primary data (i.e., image from a microscope). or can be used to help in concept development (i.e., models of DNA used by Watson and Crick), to uncover relationships and to make the abstract more concrete (graphs of sound waves). Therefore, visual representations and visual practices, in all forms, are an important aspect of the scientific practices in developing, clarifying, and transmitting scientific knowledge (Pauwels 2006 ).

Methods and Results: Merging Visualization and scientific practices in science

In this paper, we present three case studies that embody the working practices of scientists in an effort to present visualization as a scientific practice and present our argument about how visualization is a complex process that could include among others modeling and use of representation but is not only limited to that. The first case study explores the role of visualization in the construction of knowledge about the structure of DNA, using visuals as evidence. The second case study focuses on Faraday’s use of the lines of magnetic force and the visual reasoning leading to the theoretical development that was an inherent part of the experimentation. The third case study focuses on the current practices of scientists in the context of a peer-reviewed journal called the Journal of Visualized Experiments where the methodology is communicated through videotaped procedures. The three case studies represent the research interests of the three authors of this paper and were chosen to present how visualization as a practice can be involved in all stages of doing science, from hypothesizing and evaluating evidence (case study 1) to experimenting and reasoning (case study 2) to communicating the findings and methodology with the research community (case study 3), and represent in this way the three functions of visualization as presented by Lynch ( 2006 ). Furthermore, the last case study showcases how the development of visualization technologies has contributed to the communication of findings and methodologies in science and present in that way an aspect of current scientific practices. In all three cases, our approach is guided by the observation that the visual information is an integral part of scientific practices at the least and furthermore that they are particularly central in the scientific practices of science.

Case study 1: use visual representations as evidence in the discovery of DNA

The focus of the first case study is the discovery of the structure of DNA. The DNA was first isolated in 1869 by Friedrich Miescher, and by the late 1940s, it was known that it contained phosphate, sugar, and four nitrogen-containing chemical bases. However, no one had figured the structure of the DNA until Watson and Crick presented their model of DNA in 1953. Other than the social aspects of the discovery of the DNA, another important aspect was the role of visual evidence that led to knowledge development in the area. More specifically, by studying the personal accounts of Watson ( 1968 ) and Crick ( 1988 ) about the discovery of the structure of the DNA, the following main ideas regarding the role of visual representations in the production of knowledge can be identified: (a) The use of visual representations was an important part of knowledge growth and was often dependent upon the discovery of new technologies (i.e., better microscopes or better techniques in crystallography that would provide better visual representations as evidence of the helical structure of the DNA); and (b) Models (three-dimensional) were used as a way to represent the visual images (X-ray images) and connect them to the evidence provided by other sources to see whether the theory can be supported. Therefore, the model of DNA was built based on the combination of visual evidence and experimental data.

An example showcasing the importance of visual representations in the process of knowledge production in this case is provided by Watson, in his book The Double Helix (1968):

…since the middle of the summer Rosy [Rosalind Franklin] had had evidence for a new three-dimensional form of DNA. It occurred when the DNA 2molecules were surrounded by a large amount of water. When I asked what the pattern was like, Maurice went into the adjacent room to pick up a print of the new form they called the “B” structure. The instant I saw the picture, my mouth fell open and my pulse began to race. The pattern was unbelievably simpler than those previously obtained (A form). Moreover, the black cross of reflections which dominated the picture could arise only from a helical structure. With the A form the argument for the helix was never straightforward, and considerable ambiguity existed as to exactly which type of helical symmetry was present. With the B form however, mere inspection of its X-ray picture gave several of the vital helical parameters. (p. 167-169)

As suggested by Watson’s personal account of the discovery of the DNA, the photo taken by Rosalind Franklin (Fig.  1 ) convinced him that the DNA molecule must consist of two chains arranged in a paired helix, which resembles a spiral staircase or ladder, and on March 7, 1953, Watson and Crick finished and presented their model of the structure of DNA (Watson and Berry 2004 ; Watson 1968 ) which was based on the visual information provided by the X-ray image and their knowledge of chemistry.

X-ray chrystallography of DNA

In analyzing the visualization practice in this case study, we observe the following instances that highlight how the visual information played a role:

Asking questions and defining problems: The real world in the model of science can at some points only be observed through visual representations or representations, i.e., if we are using DNA as an example, the structure of DNA was only observable through the crystallography images produced by Rosalind Franklin in the laboratory. There was no other way to observe the structure of DNA, therefore the real world.

Analyzing and interpreting data: The images that resulted from crystallography as well as their interpretations served as the data for the scientists studying the structure of DNA.

Experimenting: The data in the form of visual information were used to predict the possible structure of the DNA.

Modeling: Based on the prediction, an actual three-dimensional model was prepared by Watson and Crick. The first model did not fit with the real world (refuted by Rosalind Franklin and her research group from King’s College) and Watson and Crick had to go through the same process again to find better visual evidence (better crystallography images) and create an improved visual model.

Example excerpts from Watson’s biography provide further evidence for how visualization practices were applied in the context of the discovery of DNA (Table  1 ).

In summary, by examining the history of the discovery of DNA, we showcased how visual data is used as scientific evidence in science, identifying in that way an aspect of the nature of science that is still unexplored in the history of science and an aspect that has been ignored in the teaching of science. Visual representations are used in many ways: as images, as models, as evidence to support or rebut a model, and as interpretations of reality.

Case study 2: applying visual reasoning in knowledge production, the example of the lines of magnetic force

The focus of this case study is on Faraday’s use of the lines of magnetic force. Faraday is known of his exploratory, creative, and yet systemic way of experimenting, and the visual reasoning leading to theoretical development was an inherent part of this experimentation (Gooding 2006 ). Faraday’s articles or notebooks do not include mathematical formulations; instead, they include images and illustrations from experimental devices and setups to the recapping of his theoretical ideas (Nersessian 2008 ). According to Gooding ( 2006 ), “Faraday’s visual method was designed not to copy apparent features of the world, but to analyse and replicate them” (2006, p. 46).

The lines of force played a central role in Faraday’s research on electricity and magnetism and in the development of his “field theory” (Faraday 1852a ; Nersessian 1984 ). Before Faraday, the experiments with iron filings around magnets were known and the term “magnetic curves” was used for the iron filing patterns and also for the geometrical constructs derived from the mathematical theory of magnetism (Gooding et al. 1993 ). However, Faraday used the lines of force for explaining his experimental observations and in constructing the theory of forces in magnetism and electricity. Examples of Faraday’s different illustrations of lines of magnetic force are given in Fig.  2 . Faraday gave the following experiment-based definition for the lines of magnetic forces:

a Iron filing pattern in case of bar magnet drawn by Faraday (Faraday 1852b , Plate IX, p. 158, Fig. 1), b Faraday’s drawing of lines of magnetic force in case of cylinder magnet, where the experimental procedure, knife blade showing the direction of lines, is combined into drawing (Faraday, 1855, vol. 1, plate 1)

A line of magnetic force may be defined as that line which is described by a very small magnetic needle, when it is so moved in either direction correspondent to its length, that the needle is constantly a tangent to the line of motion; or it is that line along which, if a transverse wire be moved in either direction, there is no tendency to the formation of any current in the wire, whilst if moved in any other direction there is such a tendency; or it is that line which coincides with the direction of the magnecrystallic axis of a crystal of bismuth, which is carried in either direction along it. The direction of these lines about and amongst magnets and electric currents, is easily represented and understood, in a general manner, by the ordinary use of iron filings. (Faraday 1852a , p. 25 (3071))

The definition describes the connection between the experiments and the visual representation of the results. Initially, the lines of force were just geometric representations, but later, Faraday treated them as physical objects (Nersessian 1984 ; Pocovi and Finlay 2002 ):

I have sometimes used the term lines of force so vaguely, as to leave the reader doubtful whether I intended it as a merely representative idea of the forces, or as the description of the path along which the power was continuously exerted. … wherever the expression line of force is taken simply to represent the disposition of forces, it shall have the fullness of that meaning; but that wherever it may seem to represent the idea of the physical mode of transmission of the force, it expresses in that respect the opinion to which I incline at present. The opinion may be erroneous, and yet all that relates or refers to the disposition of the force will remain the same. (Faraday, 1852a , p. 55-56 (3075))

He also felt that the lines of force had greater explanatory power than the dominant theory of action-at-a-distance:

Now it appears to me that these lines may be employed with great advantage to represent nature, condition, direction and comparative amount of the magnetic forces; and that in many cases they have, to the physical reasoned at least, a superiority over that method which represents the forces as concentrated in centres of action… (Faraday, 1852a , p. 26 (3074))

For giving some insight to Faraday’s visual reasoning as an epistemic practice, the following examples of Faraday’s studies of the lines of magnetic force (Faraday 1852a , 1852b ) are presented:

(a) Asking questions and defining problems: The iron filing patterns formed the empirical basis for the visual model: 2D visualization of lines of magnetic force as presented in Fig.  2 . According to Faraday, these iron filing patterns were suitable for illustrating the direction and form of the magnetic lines of force (emphasis added):

It must be well understood that these forms give no indication by their appearance of the relative strength of the magnetic force at different places, inasmuch as the appearance of the lines depends greatly upon the quantity of filings and the amount of tapping; but the direction and forms of these lines are well given, and these indicate, in a considerable degree, the direction in which the forces increase and diminish . (Faraday 1852b , p.158 (3237))

Despite being static and two dimensional on paper, the lines of magnetic force were dynamical (Nersessian 1992 , 2008 ) and three dimensional for Faraday (see Fig.  2 b). For instance, Faraday described the lines of force “expanding”, “bending,” and “being cut” (Nersessian 1992 ). In Fig.  2 b, Faraday has summarized his experiment (bar magnet and knife blade) and its results (lines of force) in one picture.

(b) Analyzing and interpreting data: The model was so powerful for Faraday that he ended up thinking them as physical objects (e.g., Nersessian 1984 ), i.e., making interpretations of the way forces act. Of course, he made a lot of experiments for showing the physical existence of the lines of force, but he did not succeed in it (Nersessian 1984 ). The following quote illuminates Faraday’s use of the lines of force in different situations:

The study of these lines has, at different times, been greatly influential in leading me to various results, which I think prove their utility as well as fertility. Thus, the law of magneto-electric induction; the earth’s inductive action; the relation of magnetism and light; diamagnetic action and its law, and magnetocrystallic action, are the cases of this kind… (Faraday 1852a , p. 55 (3174))

(c) Experimenting: In Faraday's case, he used a lot of exploratory experiments; in case of lines of magnetic force, he used, e.g., iron filings, magnetic needles, or current carrying wires (see the quote above). The magnetic field is not directly observable and the representation of lines of force was a visual model, which includes the direction, form, and magnitude of field.

(d) Modeling: There is no denying that the lines of magnetic force are visual by nature. Faraday’s views of lines of force developed gradually during the years, and he applied and developed them in different contexts such as electromagnetic, electrostatic, and magnetic induction (Nersessian 1984 ). An example of Faraday’s explanation of the effect of the wire b’s position to experiment is given in Fig.  3 . In Fig.  3 , few magnetic lines of force are drawn, and in the quote below, Faraday is explaining the effect using these magnetic lines of force (emphasis added):

Picture of an experiment with different arrangements of wires ( a , b’ , b” ), magnet, and galvanometer. Note the lines of force drawn around the magnet. (Faraday 1852a , p. 34)

It will be evident by inspection of Fig. 3 , that, however the wires are carried away, the general result will, according to the assumed principles of action, be the same; for if a be the axial wire, and b’, b”, b”’ the equatorial wire, represented in three different positions, whatever magnetic lines of force pass across the latter wire in one position, will also pass it in the other, or in any other position which can be given to it. The distance of the wire at the place of intersection with the lines of force, has been shown, by the experiments (3093.), to be unimportant. (Faraday 1852a , p. 34 (3099))

In summary, by examining the history of Faraday’s use of lines of force, we showed how visual imagery and reasoning played an important part in Faraday’s construction and representation of his “field theory”. As Gooding has stated, “many of Faraday’s sketches are far more that depictions of observation, they are tools for reasoning with and about phenomena” (2006, p. 59).

Case study 3: visualizing scientific methods, the case of a journal

The focus of the third case study is the Journal of Visualized Experiments (JoVE) , a peer-reviewed publication indexed in PubMed. The journal devoted to the publication of biological, medical, chemical, and physical research in a video format. The journal describes its history as follows:

JoVE was established as a new tool in life science publication and communication, with participation of scientists from leading research institutions. JoVE takes advantage of video technology to capture and transmit the multiple facets and intricacies of life science research. Visualization greatly facilitates the understanding and efficient reproduction of both basic and complex experimental techniques, thereby addressing two of the biggest challenges faced by today's life science research community: i) low transparency and poor reproducibility of biological experiments and ii) time and labor-intensive nature of learning new experimental techniques. ( http://www.jove.com/ )

By examining the journal content, we generate a set of categories that can be considered as indicators of relevance and significance in terms of epistemic practices of science that have relevance for science education. For example, the quote above illustrates how scientists view some norms of scientific practice including the norms of “transparency” and “reproducibility” of experimental methods and results, and how the visual format of the journal facilitates the implementation of these norms. “Reproducibility” can be considered as an epistemic criterion that sits at the heart of what counts as an experimental procedure in science:

Investigating what should be reproducible and by whom leads to different types of experimental reproducibility, which can be observed to play different roles in experimental practice. A successful application of the strategy of reproducing an experiment is an achievement that may depend on certain isiosyncratic aspects of a local situation. Yet a purely local experiment that cannot be carried out by other experimenters and in other experimental contexts will, in the end be unproductive in science. (Sarkar and Pfeifer 2006 , p.270)

We now turn to an article on “Elevated Plus Maze for Mice” that is available for free on the journal website ( http://www.jove.com/video/1088/elevated-plus-maze-for-mice ). The purpose of this experiment was to investigate anxiety levels in mice through behavioral analysis. The journal article consists of a 9-min video accompanied by text. The video illustrates the handling of the mice in soundproof location with less light, worksheets with characteristics of mice, computer software, apparatus, resources, setting up the computer software, and the video recording of mouse behavior on the computer. The authors describe the apparatus that is used in the experiment and state how procedural differences exist between research groups that lead to difficulties in the interpretation of results:

The apparatus consists of open arms and closed arms, crossed in the middle perpendicularly to each other, and a center area. Mice are given access to all of the arms and are allowed to move freely between them. The number of entries into the open arms and the time spent in the open arms are used as indices of open space-induced anxiety in mice. Unfortunately, the procedural differences that exist between laboratories make it difficult to duplicate and compare results among laboratories.

The authors’ emphasis on the particularity of procedural context echoes in the observations of some philosophers of science:

It is not just the knowledge of experimental objects and phenomena but also their actual existence and occurrence that prove to be dependent on specific, productive interventions by the experimenters” (Sarkar and Pfeifer 2006 , pp. 270-271)

The inclusion of a video of the experimental procedure specifies what the apparatus looks like (Fig.  4 ) and how the behavior of the mice is captured through video recording that feeds into a computer (Fig.  5 ). Subsequently, a computer software which captures different variables such as the distance traveled, the number of entries, and the time spent on each arm of the apparatus. Here, there is visual information at different levels of representation ranging from reconfiguration of raw video data to representations that analyze the data around the variables in question (Fig.  6 ). The practice of levels of visual representations is not particular to the biological sciences. For instance, they are commonplace in nanotechnological practices:

Visual illustration of apparatus

Video processing of experimental set-up

Computer software for video input and variable recording

In the visualization processes, instruments are needed that can register the nanoscale and provide raw data, which needs to be transformed into images. Some Imaging Techniques have software incorporated already where this transformation automatically takes place, providing raw images. Raw data must be translated through the use of Graphic Software and software is also used for the further manipulation of images to highlight what is of interest to capture the (inferred) phenomena -- and to capture the reader. There are two levels of choice: Scientists have to choose which imaging technique and embedded software to use for the job at hand, and they will then have to follow the structure of the software. Within such software, there are explicit choices for the scientists, e.g. about colour coding, and ways of sharpening images. (Ruivenkamp and Rip 2010 , pp.14–15)

On the text that accompanies the video, the authors highlight the role of visualization in their experiment:

Visualization of the protocol will promote better understanding of the details of the entire experimental procedure, allowing for standardization of the protocols used in different laboratories and comparisons of the behavioral phenotypes of various strains of mutant mice assessed using this test.

The software that takes the video data and transforms it into various representations allows the researchers to collect data on mouse behavior more reliably. For instance, the distance traveled across the arms of the apparatus or the time spent on each arm would have been difficult to observe and record precisely. A further aspect to note is how the visualization of the experiment facilitates control of bias. The authors illustrate how the olfactory bias between experimental procedures carried on mice in sequence is avoided by cleaning the equipment.

Our discussion highlights the role of visualization in science, particularly with respect to presenting visualization as part of the scientific practices. We have used case studies from the history of science highlighting a scientist’s account of how visualization played a role in the discovery of DNA and the magnetic field and from a contemporary illustration of a science journal’s practices in incorporating visualization as a way to communicate new findings and methodologies. Our implicit aim in drawing from these case studies was the need to align science education with scientific practices, particularly in terms of how visual representations, stable or dynamic, can engage students in the processes of science and not only to be used as tools for cognitive development in science. Our approach was guided by the notion of “knowledge-as-practice” as advanced by Knorr Cetina ( 1999 ) who studied scientists and characterized their knowledge as practice, a characterization which shifts focus away from ideas inside scientists’ minds to practices that are cultural and deeply contextualized within fields of science. She suggests that people working together can be examined as epistemic cultures whose collective knowledge exists as practice.

It is important to stress, however, that visual representations are not used in isolation, but are supported by other types of evidence as well, or other theories (i.e., in order to understand the helical form of DNA, or the structure, chemistry knowledge was needed). More importantly, this finding can also have implications when teaching science as argument (e.g., Erduran and Jimenez-Aleixandre 2008 ), since the verbal evidence used in the science classroom to maintain an argument could be supported by visual evidence (either a model, representation, image, graph, etc.). For example, in a group of students discussing the outcomes of an introduced species in an ecosystem, pictures of the species and the ecosystem over time, and videos showing the changes in the ecosystem, and the special characteristics of the different species could serve as visual evidence to help the students support their arguments (Evagorou et al. 2012 ). Therefore, an important implication for the teaching of science is the use of visual representations as evidence in the science curriculum as part of knowledge production. Even though studies in the area of science education have focused on the use of models and modeling as a way to support students in the learning of science (Dori et al. 2003 ; Lehrer and Schauble 2012 ; Mendonça and Justi 2013 ; Papaevripidou et al. 2007 ) or on the use of images (i.e., Korfiatis et al. 2003 ), with the term using visuals as evidence, we refer to the collection of all forms of visuals and the processes involved.

Another aspect that was identified through the case studies is that of the visual reasoning (an integral part of Faraday’s investigations). Both the verbalization and visualization were part of the process of generating new knowledge (Gooding 2006 ). Even today, most of the textbooks use the lines of force (or just field lines) as a geometrical representation of field, and the number of field lines is connected to the quantity of flux. Often, the textbooks use the same kind of visual imagery than in what is used by scientists. However, when using images, only certain aspects or features of the phenomena or data are captured or highlighted, and often in tacit ways. Especially in textbooks, the process of producing the image is not presented and instead only the product—image—is left. This could easily lead to an idea of images (i.e., photos, graphs, visual model) being just representations of knowledge and, in the worse case, misinterpreted representations of knowledge as the results of Pocovi and Finlay ( 2002 ) in case of electric field lines show. In order to avoid this, the teachers should be able to explain how the images are produced (what features of phenomena or data the images captures, on what ground the features are chosen to that image, and what features are omitted); in this way, the role of visualization in knowledge production can be made “visible” to students by engaging them in the process of visualization.

The implication of these norms for science teaching and learning is numerous. The classroom contexts can model the generation, sharing and evaluation of evidence, and experimental procedures carried out by students, thereby promoting not only some contemporary cultural norms in scientific practice but also enabling the learning of criteria, standards, and heuristics that scientists use in making decisions on scientific methods. As we have demonstrated with the three case studies, visual representations are part of the process of knowledge growth and communication in science, as demonstrated with two examples from the history of science and an example from current scientific practices. Additionally, visual information, especially with the use of technology is a part of students’ everyday lives. Therefore, we suggest making use of students’ knowledge and technological skills (i.e., how to produce their own videos showing their experimental method or how to identify or provide appropriate visual evidence for a given topic), in order to teach them the aspects of the nature of science that are often neglected both in the history of science and the design of curriculum. Specifically, what we suggest in this paper is that students should actively engage in visualization processes in order to appreciate the diverse nature of doing science and engage in authentic scientific practices.

However, as a word of caution, we need to distinguish the products and processes involved in visualization practices in science:

If one considers scientific representations and the ways in which they can foster or thwart our understanding, it is clear that a mere object approach, which would devote all attention to the representation as a free-standing product of scientific labor, is inadequate. What is needed is a process approach: each visual representation should be linked with its context of production (Pauwels 2006 , p.21).

The aforementioned suggests that the emphasis in visualization should shift from cognitive understanding—using the products of science to understand the content—to engaging in the processes of visualization. Therefore, an implication for the teaching of science includes designing curriculum materials and learning environments that create a social and epistemic context and invite students to engage in the practice of visualization as evidence, reasoning, experimental procedure, or a means of communication (as presented in the three case studies) and reflect on these practices (Ryu et al. 2015 ).

Finally, a question that arises from including visualization in science education, as well as from including scientific practices in science education is whether teachers themselves are prepared to include them as part of their teaching (Bybee 2014 ). Teacher preparation programs and teacher education have been critiqued, studied, and rethought since the time they emerged (Cochran-Smith 2004 ). Despite the years of history in teacher training and teacher education, the debate about initial teacher training and its content still pertains in our community and in policy circles (Cochran-Smith 2004 ; Conway et al. 2009 ). In the last decades, the debate has shifted from a behavioral view of learning and teaching to a learning problem—focusing on that way not only on teachers’ knowledge, skills, and beliefs but also on making the connection of the aforementioned with how and if pupils learn (Cochran-Smith 2004 ). The Science Education in Europe report recommended that “Good quality teachers, with up-to-date knowledge and skills, are the foundation of any system of formal science education” (Osborne and Dillon 2008 , p.9).

However, questions such as what should be the emphasis on pre-service and in-service science teacher training, especially with the new emphasis on scientific practices, still remain unanswered. As Bybee ( 2014 ) argues, starting from the new emphasis on scientific practices in the NGSS, we should consider teacher preparation programs “that would provide undergraduates opportunities to learn the science content and practices in contexts that would be aligned with their future work as teachers” (p.218). Therefore, engaging pre- and in-service teachers in visualization as a scientific practice should be one of the purposes of teacher preparation programs.

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Evagorou, M., Erduran, S. & Mäntylä, T. The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works. IJ STEM Ed 2 , 11 (2015). https://doi.org/10.1186/s40594-015-0024-x

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5. Visual Representation

How can you design computer displays that are as meaningful as possible to human viewers? Answering this question requires understanding of visual representation - the principles by which markings on a surface are made and interpreted. The analysis in this article addresses the most important principles of visual representation for screen design, introduced with examples from the early history of graphical user interfaces . In most cases, these principles have been developed and elaborated within whole fields of study and professional skill - typography , cartography, engineering and architectural draughting, art criticism and semiotics . Improving on the current conventions requires serious skill and understanding. Nevertheless, interaction designers should be able, when necessary, to invent new visual representations.

Introduction to Visual Representation by Alan Blackwell

Alan Blackwell on applying theories of Visual Representation

  • 5.1 Typography and text

For many years, computer displays resembled paper documents. This does not mean that they were simplistic or unreasonably constrained. On the contrary, most aspects of modern industrial society have been successfully achieved using the representational conventions of paper, so those conventions seem to be powerful ones. Information on paper can be structured using tabulated columns, alignment, indentation and emphasis , borders and shading. All of those were incorporated into computer text displays. Interaction conventions, however, were restricted to operations of the typewriter rather than the pencil. Each character typed would appear at a specific location. Locations could be constrained, like filling boxes on a paper form. And shortcut command keys could be defined using onscreen labels or paper overlays. It is not text itself, but keyboard interaction with text that is limited and frustrating compared to what we can do with paper (Sellen and Harper 2001).

But despite the constraints on keyboard interaction, most information on computer screens is still represented as text. Conventions of typography and graphic design help us to interpret that text as if it were on a page, and human readers benefit from many centuries of refinement in text document design. Text itself, including many writing systems as well as specialised notations such as algebra, is a visual representation that has its own research and educational literature. Documents that contain a mix of bordered or coloured regions containing pictures, text and diagrammatic elements can be interpreted according to the conventions of magazine design, poster advertising, form design, textbooks and encyclopaedias. Designers of screen representations should take care to properly apply the specialist knowledge of those graphic and typographic professions. Position on the page, use of typographic grids, and genre-specific illustrative conventions should all be taken into account.

Contemporary example from the grid system website

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Figure 5.1 : Contemporary example from the grid system website

Example of a symbolic algebra expression (the single particle solution to Schrodinger's equation)

Figure 5.2 : Example of a symbolic algebra expression (the single particle solution to Schrodinger's equation)

Table layout of funerals from the plague in London in 1665

Figure 5.3 : Table layout of funerals from the plague in London in 1665

Tabular layout of the first page of the Gutenberg Bible: Volume 1, Old Testament, Epistle of St. Jerome. The Gutenberg Bible was printed by Johannes Gutenberg, in Mainz, Germany in the 1450s

Figure 5.4 : Tabular layout of the first page of the Gutenberg Bible: Volume 1, Old Testament, Epistle of St. Jerome. The Gutenberg Bible was printed by Johannes Gutenberg, in Mainz, Germany in the 1450s

  • 5.1.1 Summary

Most screen-based information is interpreted according to textual and typographic conventions, in which graphical elements are arranged within a visual grid, occasionally divided or contained with ruled and coloured borders. Where to learn more:

thegridsystem.org

Resnick , Elizabeth (2003): Design for Communication: Conceptual Graphic Design Basics. Wiley

  • 5.2 Maps and graphs

The computer has, however, also acquired a specialised visual vocabulary and conventions. Before the text-based computer terminal (or 'glass teletype') became ubiquitous, cathode ray tube displays were already used to display oscilloscope waves and radar echoes. Both could be easily interpreted because of their correspondence to existing paper conventions. An oscilloscope uses a horizontal time axis to trace variation of a quantity over time, as pioneered by William Playfair in his 1786 charts of the British economy. A radar screen shows direction and distance of objects from a central reference point, just as the Hereford Mappa Mundi of 1300 organised places according to their approximate direction and distance from Jerusalem. Many visual displays on computers continue to use these ancient but powerful inventions - the map and the graph. In particular, the first truly large software project, the SAGE air defense system, set out to present data in the form of an augmented radar screen - an abstract map, on which symbols and text could be overlaid. The first graphics computer, the Lincoln Laboratory Whirlwind, was created to show maps, not text.

The technique invented by William Playfair, for visual representation of time series data.

Figure 5.5 : The technique invented by William Playfair, for visual representation of time series data.

Time series data as shown on an oscilloscope screen

Author/Copyright holder: Courtesy of Premek. V. Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.6 : Time series data as shown on an oscilloscope screen

Early radar screen from HMS Belfast built in 1936

Author/Copyright holder: Courtesy of Remi Kaupp. Copyright terms and licence: CC-Att-SA (Creative Commons Attribution-ShareAlike 3.0 Unported)

Figure 5.7 : Early radar screen from HMS Belfast built in 1936

Early weather radar - Hurricane Abby approaching the coast of British Honduras in 1960

Author/Copyright holder: Courtesy of NOAA's National Weather Service. Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.8 : Early weather radar - Hurricane Abby approaching the coast of British Honduras in 1960

The Hereford Mappa Mundi of 1300 organised places according to their approximate direction and distance from Jerusalem

Figure 5.9 : The Hereford Mappa Mundi of 1300 organised places according to their approximate direction and distance from Jerusalem

The SAGE system in use. The SAGE system used light guns as interaction devices.

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Figure 5.10 : The SAGE system in use. The SAGE system used light guns as interaction devices.

The Whirlwind computer at the MIT Lincoln Laboratory

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Figure 5.11 : The Whirlwind computer at the MIT Lincoln Laboratory

  • 5.2.1 Summary

Basic diagrammatic conventions rely on quantitative correspondence between a direction on the surface and a continuous quantity such as time or distance. These should follow established conventions of maps and graphs.

Where to learn more:

MacEachren , Alan M. (2004): How Maps Work: Representation, Visualization, and Design. The Guilford Press

  • 5.3 Schematic drawings

Ivan Sutherland's groundbreaking PhD research with Whirlwind's successor TX-2 introduced several more sophisticated alternatives (Sutherland 1963). The use of a light pen allowed users to draw arbitrary lines, rather than relying on control keys to select predefined options. An obvious application, in the engineering context of Massachusetts Institute of Technology (MIT) where Sutherland worked, was to make engineering drawings such as the girder bridge in Figure 13. Lines on the screen are scaled versions of the actual girders, and text information can be overlaid to give details of force calculations. Plans of this kind, as a visual representation, are closely related to maps. However, where the plane of a map corresponds to a continuous surface, engineering drawings need not be continuous. Each set of connected components must share the same scale, but white space indicates an interpretive break, so that independent representations can potentially share the same divided surface - a convention introduced in Diderot's encyclopedia of 1772, which showed pictures of multiple objects on a page, but cut them loose from any shared pictorial context.

The TX-2 graphics computer, running Ivan Sutherland's Sketchpad software

Author/Copyright holder: Courtesy of Ivan Sutherland. Copyright terms and licence: CC-Att-SA-3 (Creative Commons Attribution-ShareAlike 3.0).

Figure 5.12 : The TX-2 graphics computer, running Ivan Sutherland's Sketchpad software

An example of a force diagram created using Sutherland's Sketchpad

Figure 5.13 : An example of a force diagram created using Sutherland's Sketchpad

A page from the Encyclopédie of Diderot and d'Alembert, combining pictorial elements with diagrammatic lines and categorical use of white space.

Figure 5.14 : A page from the Encyclopédie of Diderot and d'Alembert, combining pictorial elements with diagrammatic lines and categorical use of white space.

  • 5.3.1 Summary

Engineering drawing conventions allow schematic views of connected components to be shown in relative scale, and with text annotations labelling the parts. White space in the representation plane can be used to help the reader distinguish elements from each other rather than directly representing physical space. Where to learn more:

Engineering draughting textbooks

Ferguson , Eugene S. (1994): Engineering and the Mind's Eye. MIT Press

  • 5.4 Pictures

The examples so far may seem rather abstract. Isn't the most 'natural' visual representation simply a picture of the thing you are trying to represent? In that case, what is so hard about design? Just point a camera, and take the picture. It seems like pictures are natural and intuitive, and anyone should be able to understand what they mean. Of course, you might want the picture to be more or less artistic, but that isn't a technical concern, is it? Well, Ivan Sutherland also suggested the potential value that computer screens might offer as artistic tools. His Sketchpad system was used to create a simple animated cartoon of a winking girl. We can use this example to ask whether pictures are necessarily 'natural', and what design factors are relevant to the selection or creation of pictures in an interaction design context.

We would not describe Sutherland's girl as 'realistic', but it is an effective representation of a girl. In fact, it is an unusually good representation of a winking girl, because all the other elements of the picture are completely abstract and generic. It uses a conventional graphic vocabulary of lines and shapes that are understood in our culture to represent eyes, mouths and so on - these elements do not draw attention to themselves, and therefore highlight the winking eye. If a realistic picture of an actual person was used instead, other aspects of the image (the particular person) might distract the viewer from this message.

Sutherland's 'Winking Girl' drawing, created with the Sketchpad system

Figure 5.15 : Sutherland's 'Winking Girl' drawing, created with the Sketchpad system

It is important, when considering the design options for pictures, to avoid the 'resemblance fallacy', i.e. that drawings are able to depict real object or scenes because the viewer's perception of the flat image simulates the visual perception of a real scene. In practice, all pictures rely on conventions of visual representation, and are relatively poor simulations of natural engagement with physical objects, scenes and people. We are in the habit of speaking approvingly of some pictures as more 'realistic' than others (photographs, photorealistic ray-traced renderings, 'old master' oil paintings), but this simply means that they follow more rigorously a particular set of conventions. The informed designer is aware of a wide range of pictorial conventions and options.

As an example of different pictorial conventions, consider the ways that scenes can be rendered using different forms of artistic perspective. The invention of linear perspective introduced a particular convention in which the viewer is encouraged to think of the scene as perceived through a lens or frame while holding his head still, so that nearby objects occupy a disproportionate amount of the visual field. Previously, pictorial representations more often varied the relative size of objects according to their importance - a kind of 'semantic' perspective. Modern viewers tend to think of the perspective of a camera lens as being most natural, due to the ubiquity of photography, but we still understand and respect alternative perspectives, such as the isometric perspective of the pixel art group eBoy, which has been highly influential on video game style.

Example of an early work by Masaccio, demonstrating a 'perspective' in which relative size shows symbolic importance

Author/Copyright holder: Courtesy of Masaccio (1401-1428). Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship))

Figure 5.16 : Example of an early work by Masaccio, demonstrating a 'perspective' in which relative size shows symbolic importance

Example of the strict isometric perspective used by the eBoy group

Author/Copyright holder: eBoy.com. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.17 : Example of the strict isometric perspective used by the eBoy group

Masaccio's mature work The Tribute Money, demonstrating linear perspective

Author/Copyright holder: Courtesy of Masaccio (1401-1428). Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.18 : Masaccio's mature work The Tribute Money, demonstrating linear perspective

As with most conventions of pictorial representation, new perspective rendering conventions are invented and esteemed for their accuracy by critical consensus, and only more slowly adopted by untrained readers. The consensus on preferred perspective shifts across cultures and historical periods. It would be naïve to assume that the conventions of today are the final and perfect product of technical evolution. As with text, we become so accustomed to interpreting these representations that we are blind to the artifice. But professional artists are fully aware of the conventions they use, even where they might have mechanical elements - the way that a photograph is framed changes its meaning, and a skilled pencil drawing is completely unlike visual edge-detection thresholds. A good pictorial representation need not simulate visual experience any more than a good painting of a unicorn need resemble an actual unicorn. When designing user interfaces, all of these techniques are available for use, and new styles of pictorial rendering are constantly being introduced.

  • 5.4.1 Summary

Pictorial representations, including line drawings, paintings, perspective renderings and photographs rely on shared interpretive conventions for their meaning. It is naïve to treat screen representations as though they were simulations of experience in the physical world. Where to learn more:

Micklewright , Keith (2005): Drawing: Mastering the Language of Visual Expression. Harry N. Abrams

Stroebel , Leslie, Todd , Hollis and Zakia , Richard (1979): Visual Concepts for Photographers. Focal Press

  • 5.5 Node-and-link diagrams

The first impulse of a computer scientist, when given a pencil, seems to be to draw boxes and connect them with lines. These node and link diagrams can be analysed in terms of the graph structures that are fundamental to the study of algorithms (but unrelated to the visual representations known as graphs or charts). A predecessor of these connectivity diagrams can be found in electrical circuit schematics, where the exact location of components, and the lengths of the wires, can be arranged anywhere, because they are irrelevant to the circuit function. Another early program created for the TX-2, this time by Ivan Sutherland's brother Bert, allowed users to create circuit diagrams of this kind. The distinctive feature of a node-and-link connectivity diagram is that, since the position of each node is irrelevant to the operation of the circuit, it can be used to carry other information. Marian Petre's research into the work of electronics engineers (Petre 1995) catalogued the ways in which they positioned components in ways that were meaningful to human readers, but not to the computer - like the blank space between Diderot's objects this is a form of 'secondary notation' - use of the plane to assist the reader in ways not related to the technical content.

Circuit connectivity diagrams have been most widely popularised through the London Underground diagram, an invention of electrical engineer Henry Beck. The diagram clarified earlier maps by exploiting the fact that most underground travellers are only interested in order and connectivity, not location, of the stations on the line. (Sadly, the widespread belief that a 'diagram' will be technical and hard to understand means that most people describe this as the London Undergound 'map', despite Beck's insistence on his original term).

Henry Beck's London Underground Diagram (1933)

Author/Copyright holder: Courtesy of Harry C. Beck and possibly F. H. Stingemore, born 1890, died 1954. Stingmore designed posters for the Underground Group and London Transport 1914-1942. Copyright terms and licence: Unknown (pending investigation). See section "Exceptions" in the copyright terms below.

Figure 5.19 : Henry Beck's London Underground Diagram (1933)

Node and link diagram of the kind often drawn by computing professionals

Author/Copyright holder: Computer History Museum, Mountain View, CA, USA. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.20 : Node and link diagram of the kind often drawn by computing professionals

Map of the London Underground network, as it was printed before the design of Beck's diagram (1932)

Figure 5.21 : Map of the London Underground network, as it was printed before the design of Beck's diagram (1932)

  • 5.5.1 Summary

Node and link diagrams are still widely perceived as being too technical for broad acceptance. Nevertheless, they can present information about ordering and relationships clearly, especially if consideration is given to the value of allowing human users to specify positions. Where to learn more:

Diagrammatic representation books

Lowe , Ric (1992): Successful Instructional Diagram.

  • 5.6 Icons and symbols

Maps frequently use symbols to indicate specific kinds of landmark. Sometimes these are recognisably pictorial (the standard symbols for tree and church), but others are fairly arbitrary conventions (the symbol for a railway station). As the resolution of computer displays increased in the 1970s, a greater variety of symbols could be differentiated, by making them more detailed, as in the MIT SDMS (Spatial Data Management System) that mapped a naval battle scenario with symbols for different kinds of ship. However, the dividing line between pictures and symbols is ambiguous. Children's drawings of houses often use conventional symbols (door, four windows, triangle roof and chimney) whether or not their own house has two storeys, or a fireplace. Letters of the Latin alphabet are shapes with completely arbitrary relationship to their phonetic meaning, but the Korean phonetic alphabet is easier to learn because the forms mimic the shape of the mouth when pronouncing those sounds. The field of semiotics offers sophisticated ways of analysing the basis on which marks correspond to meanings. In most cases, the best approach for an interaction designer is simply to adopt familiar conventions. When these do not exist, the design task is more challenging.

It is unclear which of the designers working on the Xerox Star coined the term 'icon' for the small pictures symbolising different kinds of system object. David Canfield Smith winningly described them as being like religious icons, which he said were pictures standing for (abstract) spiritual concepts. But 'icon' is also used as a technical term in semiotics. Unfortunately, few of the Xerox team had a sophisticated understanding of semiotics. It was fine art PhD Susan Kare's design work on the Apple Macintosh that established a visual vocabulary which has informed the genre ever since. Some general advice principles are offered by authors such as Horton (1994), but the successful design of icons is still sporadic. Many software publishers simply opt for a memorable brand logo, while others seriously misjudge the kinds of correspondence that are appropriate (my favourite blooper was a software engineering tool in which a pile of coins was used to access the 'change' command).

It has been suggested that icons, being pictorial, are easier to understand than text, and that pre-literate children, or speakers of different languages, might thereby be able to use computers without being able to read. In practice, most icons simply add decoration to text labels, and those that are intended to be self-explanatory must be supported with textual tooltips. The early Macintosh icons, despite their elegance, were surprisingly open to misinterpretation. One PhD graduate of my acquaintance believed that the Macintosh folder symbol was a briefcase (the folder tag looked like a handle), which allowed her to carry her files from place to place when placed inside it. Although mistaken, this belief never caused her any trouble - any correspondence can work, so long as it is applied consistently.

In art, the term Icon (from Greek, eikon,

Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.22 : In art, the term Icon (from Greek, eikon, "image") commonly refers to religious paintings in Eastern Orthodox, Oriental Orthodox, and Eastern-rite Catholic jurisdictions. Here a 6th-century encaustic icon from Saint Catherine's Monastery, Mount Sinai

In computing, David Canfield Smith described computer icons as being like religious icons, which he said were pictures standing for (abstract) spiritual concepts.

Author/Copyright holder: Apple Computer, Inc. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.23 : In computing, David Canfield Smith described computer icons as being like religious icons, which he said were pictures standing for (abstract) spiritual concepts.

  • 5.6.1 Summary

The design of simple and memorable visual symbols is a sophisticated graphic design skill. Following established conventions is the easiest option, but new symbols must be designed with an awareness of what sort of correspondence is intended - pictorial, symbolic, metonymic (e.g. a key to represent locking), bizarrely mnemonic, but probably not monolingual puns. Where to learn more:

Napoles , Veronica (1987): Corporate Identity Design.

  • 5.7 Visual metaphor

The ambitious graphic designs of the Xerox Star/Alto and Apple Lisa/Macintosh were the first mass-market visual interfaces. They were marketed to office professionals, making the 'cover story' that they resembled an office desktop a convenient explanatory device. Of course, as was frequently noted at the time, these interfaces behaved nothing like a real desktop. The mnemonic symbol for file deletion (a wastebasket) was ridiculous if interpreted as an object placed on a desk. And nobody could explain why the desk had windows in it (the name was derived from the 'clipping window' of the graphics architecture used to implement them - it was at some later point that they began to be explained as resembling sheets of paper on a desk). There were immediate complaints from luminaries such as Alan Kay and Ted Nelson that strict analogical correspondence to physical objects would become obstructive rather than instructive. Nevertheless, for many years the marketing story behind the desktop metaphor was taken seriously, despite the fact that all attempts to improve the Macintosh design with more elaborate visual analogies , as in General Magic and Microsoft Bob, subsequently failed.

The 'desktop' can be far more profitably analysed (and extended) by understanding the representational conventions that it uses. The size and position of icons and windows on the desktop has no meaning, they are not connected, and there is no visual perspective, so it is neither a map, graph nor picture. The real value is the extent to which it allows secondary notation, with the user creating her own meaning by arranging items as she wishes. Window borders separate areas of the screen into different pictorial, text or symbolic contexts as in the typographic page design of a textbook or magazine. Icons use a large variety of conventions to indicate symbolic correspondence to software operations and/or company brands, but they are only occasionally or incidentally organised into more complex semiotic structures.

Apple marketed the visual metaphor in 1983 as a key benefit of the Lisa computer. This advertisement said 'You can work with Lisa the same familiar way you work at your desk'. However a cont

Author/Copyright holder:Apple Computer, Inc and Computer History Museum, Mountain View, CA. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.24 : Apple marketed the visual metaphor in 1983 as a key benefit of the Lisa computer. This advertisement said 'You can work with Lisa the same familiar way you work at your desk'. However a controlled study by Carroll and Mazur (1986) found that the claim for immediately familiar operation may have been exaggerated.

The Xerox Alto and Apple Lisa, early products in which bitmapped displays allowed pictorial icons to be used as mnemonic cues within the 'desktop metaphor'

Figure 5.25 : The Xerox Alto and Apple Lisa, early products in which bitmapped displays allowed pictorial icons to be used as mnemonic cues within the 'desktop metaphor'

Apple Lisa

Author/Copyright holder: Courtesy of Mschlindwein. Copyright terms and licence: CC-Att-SA (Creative Commons Attribution-ShareAlike 3.0 Unported).

Figure 5.26 : Apple Lisa

  • 5.7.1 Summary

Theories of visual representation, rather than theories of visual metaphor, are the best approach to explaining the conventional Macintosh/Windows 'desktop'. There is huge room for improvement. Where to learn more:

Blackwell , Alan (2006): The reification of metaphor as a design tool . In ACM Transactions on Computer-Human Interaction , 13 (4) pp. 490-530

  • 5.8 Unified theories of visual representation

The analysis in this article has addressed the most important principles of visual representation for screen design, introduced with examples from the early history of graphical user interfaces. In most cases, these principles have been developed and elaborated within whole fields of study and professional skill - typography, cartography, engineering and architectural draughting, art criticism and semiotics. Improving on the current conventions requires serious skill and understanding. Nevertheless, interaction designers should be able, when necessary, to invent new visual representations.

One approach is to take a holistic perspective on visual language, information design, notations, or diagrams. Specialist research communities in these fields address many relevant factors from low-level visual perception to critique of visual culture. Across all of them, it can be necessary to ignore (or not be distracted by) technical and marketing claims, and to remember that all visual representations simply comprise marks on a surface that are intended to correspond to things understood by the reader. The two dimensions of the surface can be made to correspond to physical space (in a map), to dimensions of an object, to a pictorial perspective, or to continuous abstract scales (time or quantity). The surface can also be partitioned into regions that should be interpreted differently. Within any region, elements can be aligned, grouped, connected or contained in order to express their relationships. In each case, the correspondence between that arrangement, and the intended interpretation, must be understood by convention, explained, or derived from the structural and perceptual properties of marks on the plane. Finally, any individual element might be assigned meaning according to many different semiotic principles of correspondence.

The following table summarises holistic views, as introduced above, drawing principally on the work of Bertin, Richards, MacEachren, Blackwell & Engelhardt and Engelhardt. Where to learn more:

Engelhardt , Yuri (2002). The Language of Graphics. A framework for the analysis of syntax and meaning in maps, charts and diagrams (PhD Thesis) . University of Amsterdam

Table 5.1 : Summary of the ways in which graphical representations can be applied in design, via different systems of correspondence

Table 5.2 : Screenshot from the site gapminder.org, illustrating a variety of correspondence conventions used in different parts of the page

As an example of how one might analyse (or working backwards, design) a complex visual representation, consider the case of musical scores. These consist of marks on a paper surface, bound into a multi-page book, that is placed on a stand at arms length in front of a performer. Each page is vertically divided into a number of regions, visually separated by white space and grid alignment cues. The regions are ordered, with that at the top of the page coming first. Each region contains two quantitative axes, with the horizontal axis representing time duration, and the vertical axis pitch. The vertical axis is segmented by lines to categorise pitch class. Symbols placed at a given x-y location indicate a specific pitched sound to be initiated at a specific time. A conventional symbol set indicates the duration of the sound. None of the elements use any variation in colour, saturation or texture. A wide variety of text labels and annotation symbols are used to elaborate these basic elements. Music can be, and is, also expressed using many other visual representations (see e.g. Duignan for a survey of representations used in digital music processing).

  • 5.9 Where to learn more

The historical examples of early computer representations used in this article are mainly drawn from Sutherland (Ed. Blackwell and Rodden 2003), Garland (1994), and Blackwell (2006). Historical reviews of visual representation in other fields include Ferguson (1992), Pérez-Gómez and Pelletier (1997), McCloud (1993), Tufte (1983). Reviews of human perceptual principles can be found in Gregory (1970), Ittelson (1996), Ware (2004), Blackwell (2002). Advice on principles of interaction with visual representation is distributed throughout the HCI literature, but classics include Norman (1988), Horton (1994), Shneiderman ( Shneiderman and Plaisant 2009, Card et al 1999, Bederson and Shneiderman 2003) and Spence (2001). Green's Cognitive Dimensions of Notations framework has for many years provided a systematic classification of the design parameters in interactive visual representations. A brief introduction is provided in Blackwell and Green (2003).

Research on visual representation topics is regularly presented at the Diagrams conference series (which has a particular emphasis on cognitive science ), the InfoDesign and Vision Plus conferences (which emphasise graphic and typographic information design), the Visual Languages and Human-Centric Computing symposia (emphasising software tools and development), and the InfoVis and Information Visualisation conferences (emphasising quantitative and scientific data visualisation).

  • 5.9.0.1 IV - International Conference on Information Visualization

2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998

  • 5.9.0.2 DIAGRAMS - International Conference on the Theory and Application of Diagrams

2008 2006 2004 2002 2000

  • 5.9.0.3 VL-HCC - Symposium on Visual Languages and Human Centric Computing

2008 2007 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990

  • 5.9.0.4 InfoVis - IEEE Symposium on Information Visualization

2005 2004 2003 2002 2001 2000 1999 1998 1997 1995

  • 5.10 References

Anderson , Michael, Meyer , Bernd and Olivier , Patrick (2002): Diagrammatic Representation and Reasoning. London, UK,

Bederson , Benjamin B. and Shneiderman , Ben (2003): The Craft of Information Visualization : Readings and Reflections. Morgan Kaufman Publishers

Bertin , Jacques (1967): Semiology of Graphics: Diagrams, Networks, Maps (Sémiologie graphique: Les diagrammes - Les réseaux - Les cartes). English translation by W. J. Berg. Madison, WI, USA, University of Wisconsin Press

Blackwell , Alan (2002): Psychological perspectives on diagrams and their users. In: Anderson , Michael, Meyer , Bernd and Olivier , Patrick (eds.). "Diagrammatic Representation and Reasoning". London, UK: pp. 109-123

Blackwell , Alan and Engelhardt , Yuri (2002): A Meta-Taxonomy for Diagram Research. In: Anderson , Michael, Meyer , Bernd and Olivier , Patrick (eds.). "Diagrammatic Representation and Reasoning". London, UK: pp. 47-64

Blackwell , Alan and Green , T. R. G. (2003): Notational Systems - The Cognitive Dimensions of Notations Framework. In: Carroll , John M. (ed.). "HCI Models, Theories, and Frameworks". San Francisco: Morgan Kaufman Publisherspp. 103-133

Carroll , John M. and Mazur , Sandra A. (1986): LisaLearning . In Computer , 19 (11) pp. 35-49

Garland , Ken (1994): Mr . Beck's Underground Map. Capital Transport Publishing

Goodman , Nelson (1976): Languages of Art. Hackett Publishing Company

Gregory , Richard L. (1970): The Intelligent Eye. London, Weidenfeld and Nicolson

Horton , William (1994): The Icon Book: Visual Symbols for Computer Systems and Documentation. John Wiley and Sons

Ittelson , W. H. (1996): Visual perception of markings . In Psychonomic Bulletin & Review , 3 (2) pp. 171-187

Mccloud , Scott (1994): Understanding Comics: The Invisible Art. Harper Paperbacks

Norman , Donald A. (1988): The Design of Everyday Things. New York, Doubleday

Petre , Marian (1995): Why Looking Isn't Always Seeing: Readership Skills and Graphical Programming . In Communications of the ACM , 38 (6) pp. 33-44

Pérez-Gómez , Alberto and Pelletier , Louise (1997): Architectural Representation and the Perspective Hinge. MIT Press

Richards , Clive (1984). Diagrammatics: an investigation aimed at providing a theoretical framework for studying diagrams and for establishing a taxonomy of their fundamental modes of graphic organization. Unpublished Phd Thesis . Royal College of Art, London, UK

Sellen , Abigail and Harper , Richard H. R. (2001): The Myth of the Paperless Office. MIT Press

Shneiderman , Ben and Plaisant , Catherine (2009): Designing the User Interface : Strategies for Effective Human-Computer Interaction (5th ed.). Addison-Wesley

Spence , Robert (2001): Information Visualization. Addison Wesley

Sutherland , Ivan E. (1963). Sketchpad, A Man-Machine Graphical Communication System. PhD Thesis at Massachusetts Institute of Technology, online version and editors' introduction by Alan Blackwell & K. Rodden. Technical Report 574 . Cambridge University Computer Laboratory

Tufte , Edward R. (1983): The Visual Display of Quantitative Information. Cheshire, CT , Graphics Press

Ware , Colin (2004): Information Visualization: Perception for Design, 2nd Ed. San Francisco, Morgan Kaufman

  • 5 Visual Representation

Human-Computer Interaction: The Foundations of UX Design

visual representation of definition

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5.10 commentary by ben shneiderman.

Since computer displays are such powerful visual appliances, careful designers devote extensive effort to getting the visual representation right. They have to balance the demands of many tasks, diverse users, and challenging requirements, such as short learning time, rapid performance, low error rates, and good retention over time. Designing esthetic interfaces that please and even delight users is a further expectation that designers must meet to be successful. For playful and discretionary tasks esthetic concerns may dominate, but for life critical tasks, rapid performance with low error rates are essential. Alan Blackwell's competent description of many visual representation issues is a great start for newcomers with helpful reminders even for experienced designers. The videos make for a pleasant personal accompaniment that bridges visual representation for interface design with thoughtful analyses of representational art. Blackwell's approach might be enriched by more discussion of visual representations in functional product design tied to meaningful tasks. Learning from paintings of Paris is fine, but aren't there other lessons to learn from visual representations in airport kiosks, automobile dashboards, or intensive care units? These devices as well as most graphical user interfaces and mobile devices raise additional questions of changing state visualization and interaction dynamics. Modern designers need to do more than show the right phone icon, they need to show ringing, busy, inactive, no network, conference mode, etc., which may include color changes (highlighted, grayed out), animations, and accompanying sounds. These designers also need to deal with interactive visual representations that happen with a click, double-click, right-click, drag, drag-and-drop, hover, multi-select, region-select, brushing-linking, and more. The world of mobile devices such as phones, cameras, music players, or medical sensors is the new frontier for design, where visual representations are dynamic and tightly integrated with sound, haptics, and novel actions such as shaking, twisting, or body movements. Even more challenging is the expectation that goes beyond the solitary viewer to the collaboration in which multiple users embedded in a changing physical environment produce new visual representations. These changing and interactive demands on designers invite creative expressions that are very different from designs for static signs, printed diagrams, or interpretive art. The adventure for visual representation designers is to create a new language of interaction that engages users, accelerates learning, provides comprehensible feedback, and offers appropriate warnings when dangers emerge. Blackwell touches on some of these issues in the closing Gapminder example, but I was thirsty for more.

5.11 Commentary by Clive Richards

If I may be permitted a graphically inspired metaphor Alan Blackwell provides us with a neat pen sketch of that extensive scene called 'visual representation' (Blackwell 2011).

"Visualisation has a lot more to offer than most people are aware of today" we are told by Robert Kosara at the end of his commentary (Kosara 2010) on Stephen Few's related article on ' Data visualisation for human perception ' (Few 2010). Korsara is right, and Blackwell maps out the broad territory in which many of these visualisation offerings may be located. In this commentary I offer a few observations on some prominent features in that landscape: dynamics, picturing, semiotics and metaphor.

Ben Shneiderman's critique of Blackwell's piece points to a lack of attention to "... additional questions of changing state visualisations and interaction dynamics" (Shneiderman 2010). Indeed the possibilities offered by these additional questions present some exciting challenges for interaction designers - opportunities to create novel and effective combinations of visual with other sensory and motor experiences in dynamic operational contexts. Shneiderman suggests that: "These changing and interactive demands on designers invite creative expressions that are very different from design for static signs, printed diagrams, or interpretive art". This may be so up to a point, but here Shneinderman and I part company a little. The focus of Blackwell's essay is properly on the visual representation side of facilities available to interaction designers, and in that context he is quite right to give prominence to highly successful but static visual representation precedents, and also to point out the various specialist fields of endeavour in which they have been developed. Some of these representational approaches have histories reaching back thousands of years and are deeply embedded within our culture. It would be foolhardy to disregard conventions established in, say, the print domain, and to try to re-invent everything afresh for the screen, even if this were a practical proposition. Others have made arguments to support looking to historical precedents. For example Michael Twyman has pointed out that when considering typographic cueing and "... the problems of the electronic age ... we have much to learn from the manuscript age" (Twyman 1987, p5). He proposes that studying the early scribes' use of colour, spacing and other graphical devices can usefully inform the design of today's screen-based texts. And as Blackwell points out in his opening section on 'Typography and text' "most information on computer screen is still presented as text".

It is also sometimes assumed that the pictorial representation of a dynamic process is best presented dynamically. However it can be argued that the comic book convention of using a sequence of static frames is sometimes superior for focusing the viewer's attention on the critical events in a process, rather than using an animated sequence in which key moments may be missed. This is of course not to deny the immense value of the moving and interactive visual image in the right context. The Gapminder charts are a case in point (http://www.gapminder.org). Blackwell usefully includes one of these, but as a static presentation. These diagrams come to life and really tell their story through the clustering of balloons that inflate or deflate as they move about the screen when driven through simulated periods of time.

While designing a tool for engineers to learn about the operation and maintenance of an oil system for an aircraft jet engine, Detlev Fischer devised a series of interactive animations, called 'Cinegrams' to display in diagrammatic form various operating procedures (Fischer and Richards 1995). He used the cinematic techniques of time compression and expansion in one animated sequence to show how the slow accumulation of debris in an oil filter, over an extended period of time, would eventually create a blockage to the oil flow and trigger the opening of a by-pass device in split seconds. Notwithstanding my earlier comment about the potential superiority of the comic strip genre for displaying some time dependant processes this particular Cinegram proved very instructive for the targeted users. There are many other examples one could cite where dynamic picturing of this sort has been deployed to similarly good effect in interactive environments.

Shneinderman also comments that: "Blackwell's approach might be enriched by more discussion of visual representation in functional product design tied to meaningful tasks". An area I have worked in is the pictorial representation of engineering assemblies to show that which is normally hidden from view. Techniques to do this on the printed page include 'ghosting' (making occluding parts appear as if transparent), 'exploding' (showing components separately, set out in dis-assembly order along an axis) and cutting away (taking a slice out of an outer shell to reveal mechanisms beneath). All these three-dimensional picturing techniques were used by, if not actually invented by, Leonardo Da Vinci (Richards 2006). All could be enhanced by interactive viewer control - an area of further fruitful exploration for picturing purposes in technical documentation contexts.

Blackwell's section on 'Pictures' warns us that when considering picturing options to avoid the "resemblance fallacy" pointing out the role that convention plays, even in so called photo-realistic images. He also points out that viewers can be distracted from the message by incidental information in 'realistic' pictures. From my own work in the field I know that technical illustrators' synoptic black and white outline depictions are regarded as best for drawing the viewer's attention to the key features of a pictorial representation. Research in this area has shown that when using linear perspective type drawings the appropriate deployment of lines of varying 'weight', rather than of a single thickness, can have a significant effect on viewers' levels of understanding about what is depicted (Richards, Bussard and Newman 2007). This work was done specifically to determine an 'easy to read' visual representational style when manipulating on the screen images of CAD objects. The most effective convention was shown to be: thin lines for edges where both planes forming the edge are visible and thicker lines for edges where only one plane is visible - that is where an outline edge forms a kind of horizon to the object.

These line thickness conventions appear on the face of it to have little to do with how we normally perceive the world, and Blackwell tells us that: "A good pictorial representation need not simulate visual experience any more than a good painting of a unicorn need resemble an actual unicorn". And some particular representations of unicorns can aid our understanding of how to use semiotic theory to figure out how pictures may be interpreted and, importantly, sometimes misunderstood - as I shall describe in the following.

Blackwell mentions semiotics, almost in passing, however it can help unravel some of the complexities of visual representation. Evelyn Goldsmith uses a Charles Addams cartoon to explain the relevance of the 'syntactic', 'semantic' and 'pragmatic' levels of semiotic analysis when applied to pictures (Goldsmith 1978). The cartoon in question, like many of those by Charles Addams, has no caption. It shows two unicorns standing on a small island in the pouring rain forlornly watching the Ark sailing away into the distance. Goldsmith suggests that most viewers will have little trouble in interpreting the overlapping elements in the scene, for example that one unicorn is standing behind the other, nor any difficulty understanding that the texture gradient of the sea stands for a receding horizontal plane. These represent the syntactic level of interpretation. Most adults will correctly identify the various components of the picture at the semantic level, however Goldsmith proposes that a young child might mistake the unicorns for horses and be happy with 'boat' for the Ark. But at the pragmatic level of interpretation, unless a viewer of the picture is aware of the story of Noah's Ark, the joke will be lost  - the connection will not be made between the scene depicted in the drawing and the scarcity of unicorns. This reinforces the point that one should not assume that the understanding of pictures is straightforward. There is much more to it than a simple matter or recognition. This is especially the case when metaphor is involved in visual representation.

Blackwell's section on 'Visual metaphor' is essentially a critique of the use of "theories of visual metaphor" as an "approach to explaining the conventional Mackintosh/Windows 'desktop' ". His is a convincing argument but there is much more which may be said about the use of visual metaphor - especially to show that which otherwise cannot be pictured. In fact most diagrams employ a kind of spatial metaphor when not depicting physical arrangements, for example when using the branches of a tree to represent relations within a family (Richards 2002). The capability to represent the invisible is the great strength of the visual metaphor, but there are dangers, and here I refer back to semiotics and particularly the pragmatic level of analysis. One needs to know the story to get the picture.

In our parental home, one of the many books much loved by my two brothers and me, was The Practical Encyclopaedia for Children (Odhams circa 1948). In it a double page spread illustration shows the possible evolutionary phases of the elephant. These are depicted as a procession of animals in a primordial swamp cum jungle setting. Starting with a tiny fish and passing to a small aquatic creature climbing out of the water onto the bank the procession progresses on through eight phases of transformation, including the Moeritherium and the Paleomatodon, finishing up with the land-based giant of today's African Elephant. Recently one of my brothers confessed to me that through studying this graphical diorama he had believed as a child that the elephant had a life cycle akin to that of a frog. He had understood that the procession was a metaphor for time. He had just got the duration wrong - by several orders of magnitude. He also hadn't understood that each separate depiction was of a different animal. He had used the arguably more sophisticated concept that it was the same animal at different times and stages in its individual development.

Please forgive the cliché if I say that this anecdote clearly illustrates that there can be more to looking at a picture than meets the eye? Blackwell's essay provides some useful pointers for exploring the possibilities of this fascinating territory of picturing and visual representation in general.   

  • Blackwell A 2011 'Visual representation' Interaction-Design.org
  • Few S 2010 ' Data visualisation for human perception ' Interaction-Design.org
  • Fischer D and Richards CJ 1995 'The presentation of time in interactive animated systems diagrams' In: Earnshaw RA and Vince JA (eds) Multimedia Systems and Applications London: Academic Press Ltd (pp141 - 159). ISBN 0-12-227740-6
  • Goldsmith E 1978 An analysis of the elements affecting comprehensibility of illustrations intended as supportive of text PhD thesis (CNAA) Brighton Polytechnic
  • Korsa R 2010 ' Commentary on Stephen Few's article : Data visualisation for human perception' Interaction-Design.org Odhams c. 1949 The practical encyclopaedia for children (pp 194 - 195)
  • Richards CJ 2002 'The fundamental design variables of diagramming' In: Oliver P, Anderson M and Meyer B (eds) Diagrammatic representation and reasoning London: Springer Verlag (pp 85 - 102) ISBN 1-85233-242-5
  • Richards CJ 2006 'Drawing out information - lines of communication in technical illustration' Information Design Journal 14 (2) 93 - 107
  • Richards CJ, Bussard N, Newman R 2007 'Weighing up line weights: the value of differing line thicknesses in technical illustrations' Information Design Journal 15 (2) 171 - 181
  • Shneiderman B 2011 'Commentary on Alan Blackwell's article: Visual representation' Interaction-Design.org
  • Twyman M 1982 'The graphic representation of language' Information Design Journal 3 (1) 2 - 22

5.12 Commentary by Peter C-H. Cheng

Alan Blackwell has provided us with a fine introduction to the design of visual representations. The article does a great job in motivating the novice designer of visual representations to explore some of the fundamental issues that lurk just beneath the surface of creating effective representations.  Furthermore, he gives us all quite a challenge:

Alan, quite rightly, claims that we must consider the fundamental principles of symbolic correspondence, if we are to design new genres of visual representations beyond the common forms of displays and interfaces.  The report begins to equip the novice visual representation designer with an understanding of the nature of symbolic correspondence between the components of visual representations and the things they represent, whether objects, actions or ideas.  In particular, it gives a useful survey of how correspondence works in a range of representations and provides a systematic framework of how systems of correspondence can be applied to design. The interactive screen shot is an exemplary visual representation that vividly reveals the correspondence techniques used in each part of the example diagram.

However, suppose you really wished to rise to the challenge of creating novel visual representations, how far will a knowledge of the fundamentals of symbolic correspondence take you? Drawing on my studies of the role of diagrams in the history of science, experience of inventing novel visual representations and research on problem solving and learning with diagrams, from the perspective of Cognitive Science, my view is that such knowledge will be necessary but not sufficient for your endeavours.  So, what else should the budding visual representation designer consider? From the perspective of cognitive science there are at least three aspects that we may profitably target.

First, there is the knowledge of how human process information; specifically the nature of the human cognitive architecture. By this, I mean more than visual perception, but an understanding of how we mentally receive, store, retrieve, transform and transmit information. The way the mind deals with each of these basic types of information processing provides relevant constrains for the design of visual representations. For instance, humans often, perhaps even typically, encode concepts in the form of hierarchies of schemas, which are information structures that coordinate attributes that describe and differentiate classes of concepts. These hierarchies of schemas underpin our ability to efficiently generalize or specialize concepts. Hence, we can use this knowledge to consider whether particular forms of symbolic correspondence will assist or hinder the forms of inference that we hope the user of the representation may make. For example, are the main symbolic correspondences in a visual representation consistent with the key attributes of the schemas for the concepts being considered?

Second, it may be useful for the designer to consider the broader nature of the tasks that the user may wish to do with the designed representation.  Resource allocation, optimization, calculating quantities, inferences about of possible outcomes, classification, reasoning about extreme or special cases, and debugging: these are just a few of the many possibilities. These tasks are more generic than the information-oriented options considered in the 'design uses' column of Figure 27 in the article. They are worth addressing, because they provide constraints for the initial stages of representation design, by narrowing the search for what are likely to be effective correspondences to adopt. For example, if taxonomic classification is important, then separation and layering will be important correspondences; whereas magnitude calculations may demand scale mapping, Euclidian and metrical correspondences.

The third aspect concerns situations in which the visual representation must support not just a single task, but many diverse tasks. For example, a visual representation to help students learn about electricity will be used to explain the topology of circuits, make computations with electrical quantities, provide explanations of circuit behaviour (in terms of formal algebraic models and as qualitative causal models), facilitate fault finding or trouble shooting, among other activities. The creation of novel representations in such circumstances is perhaps one of the most challenging for designers. So, what knowledge can help? In this case, I advocate attempting to design representations on the basis of an analysis of the underlying conceptual structure of the knowledge of the target domain. Why? Because the nature of the knowledge is invariant across different classes of task. For example, for problem solving and learning of electricity, all the tasks depend upon the common fundamental conceptual structures of the domain that knit together the laws governing the physical properties of electricity and circuit topology. Hence, a representation that makes these concepts readily available through effective representation designed will probably be effective for a wide range of tasks.

In summary, it is desirable for the aspiring visual representation designer to consider symbolic correspondence, but I recommend they cast their net more widely for inspiration by learning about the human cognitive architecture, focusing on the nature of the task for which they are designing, and most critically thinking about the underlying conceptual structure of the knowledge of the target domain.

5.13 Commentary by Brad A. Myers

I have been teaching human-computer interaction to students with a wide range of backgrounds for many years. One of the most difficult areas for them to learn seems to be visual design. Students seem to quickly pick up rules like Nielsen's Heuristics for interaction (Nielsen & Molich, 1990), whereas the guidelines for visual design are much more subtle. Alan Blackwell's article presents many useful points, but a designer needs to know so much more! Whereas students can achieve competence at achieving Nielsen's "consistency and standards," for example, they struggle with selecting an appropriate representation for their information. And only a trained graphic designer is likely to be able to create an attractive and effective icon. Some people have a much better aesthetic sense, and can create much more beautiful and appropriate representations. A key goal of my introductory course, therefore, is to try to impart to the students how difficult it is to do visual design, and how wide the set of choices is. Studying the examples that Blackwell provides will give the reader a small start towards effective visual representations, but the path requires talent, study, and then iterative design and testing to evaluate and improve a design's success.

  • Nielsen, J., & Molich, R. (1990). Heuristic evaluation of user interfaces. Paper presented at the Proc. ACM CHI'90 Conf, Seattle, WA, 249-256.
  • See also: http://www.useit.com/papers/heuristic/heuristic_list.html

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Initial Thoughts

Perspectives & resources, what is high-quality mathematics instruction and why is it important.

  • Page 1: The Importance of High-Quality Mathematics Instruction
  • Page 2: A Standards-Based Mathematics Curriculum
  • Page 3: Evidence-Based Mathematics Practices

What evidence-based mathematics practices can teachers employ?

  • Page 4: Explicit, Systematic Instruction

Page 5: Visual Representations

  • Page 6: Schema Instruction
  • Page 7: Metacognitive Strategies
  • Page 8: Effective Classroom Practices
  • Page 9: References & Additional Resources
  • Page 10: Credits

Teacher at board with student

Research Shows

  • Students who use accurate visual representations are six times more likely to correctly solve mathematics problems than are students who do not use them. However, students who use inaccurate visual representations are less likely to correctly solve mathematics problems than those who do not use visual representations at all. (Boonen, van Wesel, Jolles, & van der Schoot, 2014)
  • Students with a learning disability (LD) often do not create accurate visual representations or use them strategically to solve problems. Teaching students to systematically use a visual representation to solve word problems has led to substantial improvements in math achievement for students with learning disabilities. (van Garderen, Scheuermann, & Jackson, 2012; van Garderen, Scheuermann, & Poch, 2014)
  • Students who use visual representations to solve word problems are more likely to solve the problems accurately. This was equally true for students who had LD, were low-achieving, or were average-achieving. (Krawec, 2014)

Visual representations are flexible; they can be used across grade levels and types of math problems. They can be used by teachers to teach mathematics facts and by students to learn mathematics content. Visual representations can take a number of forms. Click on the links below to view some of the visual representations most commonly used by teachers and students.

How does this practice align?

High-leverage practice (hlp).

  • HLP15 : Provide scaffolded supports

CCSSM: Standards for Mathematical Practice

  • MP1 : Make sense of problems and persevere in solving them.

Number Lines

Definition : A straight line that shows the order of and the relation between numbers.

Common Uses : addition, subtraction, counting

number lines

Strip Diagrams

Definition : A bar divided into rectangles that accurately represent quantities noted in the problem.

Common Uses : addition, fractions, proportions, ratios

strip diagram

Definition : Simple drawings of concrete or real items (e.g., marbles, trucks).

Common Uses : counting, addition, subtraction, multiplication, division

pictures

Graphs/Charts

Definition : Drawings that depict information using lines, shapes, and colors.

Common Uses : comparing numbers, statistics, ratios, algebra

graphs and charts

Graphic Organizers

Definition : Visual that assists students in remembering and organizing information, as well as depicting the relationships between ideas (e.g., word webs, tables, Venn diagrams).

Common Uses : algebra, geometry

Before they can solve problems, however, students must first know what type of visual representation to create and use for a given mathematics problem. Some students—specifically, high-achieving students, gifted students—do this automatically, whereas others need to be explicitly taught how. This is especially the case for students who struggle with mathematics and those with mathematics learning disabilities. Without explicit, systematic instruction on how to create and use visual representations, these students often create visual representations that are disorganized or contain incorrect or partial information. Consider the examples below.

Elementary Example

Mrs. Aldridge ask her first-grade students to add 2 + 4 by drawing dots.

talias drawing of two plus four

Notice that Talia gets the correct answer. However, because Colby draws his dots in haphazard fashion, he fails to count all of them and consequently arrives at the wrong solution.

High School Example

Mr. Huang asks his students to solve the following word problem:

The flagpole needs to be replaced. The school would like to replace it with the same size pole. When Juan stands 11 feet from the base of the pole, the angle of elevation from Juan’s feet to the top of the pole is 70 degrees. How tall is the pole?

Compare the drawings below created by Brody and Zoe to represent this problem. Notice that Brody drew an accurate representation and applied the correct strategy. In contrast, Zoe drew a picture with partially correct information. The 11 is in the correct place, but the 70° is not. As a result of her inaccurate representation, Zoe is unable to move forward and solve the problem. However, given an accurate representation developed by someone else, Zoe is more likely to solve the problem correctly.

brodys drawing

Manipulatives

Some students will not be able to grasp mathematics skills and concepts using only the types of visual representations noted in the table above. Very young children and students who struggle with mathematics often require different types of visual representations known as manipulatives. These concrete, hands-on materials and objects—for example, an abacus or coins—help students to represent the mathematical idea they are trying to learn or the problem they are attempting to solve. Manipulatives can help students develop a conceptual understanding of mathematical topics. (For the purpose of this module, the term concrete objects refers to manipulatives and the term visual representations refers to schematic diagrams.)

It is important that the teacher make explicit the connection between the concrete object and the abstract concept being taught. The goal is for the student to eventually understand the concepts and procedures without the use of manipulatives. For secondary students who struggle with mathematics, teachers should show the abstract along with the concrete or visual representation and explicitly make the connection between them.

A move from concrete objects or visual representations to using abstract equations can be difficult for some students. One strategy teachers can use to help students systematically transition among concrete objects, visual representations, and abstract equations is the Concrete-Representational-Abstract (CRA) framework.

If you would like to learn more about this framework, click here.

Concrete-Representational-Abstract Framework

boy with manipulative number board

  • Concrete —Students interact and manipulate three-dimensional objects, for example algebra tiles or other algebra manipulatives with representations of variables and units.
  • Representational — Students use two-dimensional drawings to represent problems. These pictures may be presented to them by the teacher, or through the curriculum used in the class, or students may draw their own representation of the problem.
  • Abstract — Students solve problems with numbers, symbols, and words without any concrete or representational assistance.

CRA is effective across all age levels and can assist students in learning concepts, procedures, and applications. When implementing each component, teachers should use explicit, systematic instruction and continually monitor student work to assess their understanding, asking them questions about their thinking and providing clarification as needed. Concrete and representational activities must reflect the actual process of solving the problem so that students are able to generalize the process to solve an abstract equation. The illustration below highlights each of these components.

concrete pencils, representational count by marks, abstract numerals

For Your Information

One promising practice for moving secondary students with mathematics difficulties or disabilities from the use of manipulatives and visual representations to the abstract equation quickly is the CRA-I strategy . In this modified version of CRA, the teacher simultaneously presents the content using concrete objects, visual representations of the concrete objects, and the abstract equation. Studies have shown that this framework is effective for teaching algebra to this population of students (Strickland & Maccini, 2012; Strickland & Maccini, 2013; Strickland, 2017).

Kim Paulsen discusses the benefits of manipulatives and a number of things to keep in mind when using them (time: 2:35).

Kim Paulsen, EdD Associate Professor, Special Education Vanderbilt University

View Transcript

kim paulsen

Transcript: Kim Paulsen, EdD

Manipulatives are a great way of helping kids understand conceptually. The use of manipulatives really helps students see that conceptually, and it clicks a little more with them. Some of the things, though, that we need to remember when we’re using manipulatives is that it is important to give students a little bit of free time when you’re using a new manipulative so that they can just explore with them. We need to have specific rules for how to use manipulatives, that they aren’t toys, that they really are learning materials, and how students pick them up, how they put them away, the right time to use them, and making sure that they’re not distracters while we’re actually doing the presentation part of the lesson. One of the important things is that we don’t want students to memorize the algorithm or the procedures while they’re using the manipulatives. It really is just to help them understand conceptually. That doesn’t mean that kids are automatically going to understand conceptually or be able to make that bridge between using the concrete manipulatives into them being able to solve the problems. For some kids, it is difficult to use the manipulatives. That’s not how they learn, and so we don’t want to force kids to have to use manipulatives if it’s not something that is helpful for them. So we have to remember that manipulatives are one way to think about teaching math.

I think part of the reason that some teachers don’t use them is because it takes a lot of time, it takes a lot of organization, and they also feel that students get too reliant on using manipulatives. One way to think about using manipulatives is that you do it a couple of lessons when you’re teaching a new concept, and then take those away so that students are able to do just the computation part of it. It is true we can’t walk around life with manipulatives in our hands. And I think one of the other reasons that a lot of schools or teachers don’t use manipulatives is because they’re very expensive. And so it’s very helpful if all of the teachers in the school can pool resources and have a manipulative room where teachers can go check out manipulatives so that it’s not so expensive. Teachers have to know how to use them, and that takes a lot of practice.

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Encyclopedia of the Sciences of Learning pp 2926–2929 Cite as

Scaffolding Learning by the Use of Visual Representations

  • Ilonca Hardy * 2 &
  • Susanne Koerber 3  
  • Reference work entry

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Graphical representation ; Model ; Scientific inscription ; Visual display

Visual representations are depictions that use space to represent nonspatial concepts, offering a wide range of possibilities to display qualitative and quantitative information in scientific contexts. Due to their facilitation of information processing, their support of knowledge construction, and their modeling of expert reasoning, they are employed as means of scaffolding in instructional contexts.

Theoretical Background

In this entry, we will focus on instructional uses of visual representations within the fields of mathematics and science learning that are motivated by socio-constructivist views of learning. There is a broad literature on the use of visual representations within multimedia learning, which will not be considered in detail.

Distinguishing characteristics of visual representations. Within the broad category of external representations we can distinguish between descriptions...

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diSessa, A. (2004). Metarepresentation: Native competence and target for instruction. Cognition and Instruction, 22 (3), 291–331.

Google Scholar  

Gravemeijer, K., Lehrer, R., van Oers, B., & Verschaffel, L. (2002). Symbolization, modeling and tool use in mathematics education . Dorchedt: Kluwer.

Höffler, T., & Leutner, D. (2007). Instructional animation versus static pictures: A meta-analysis. Learning and Instruction, 17 , 722–738.

Lehrer, R., & Schauble, L. (2003). Origins and evolution of model-based reasoning in mathematics and science. In R. Lesh & H. M. Doerr (Eds.), Beyond constructivism: A models and modeling perspective on mathematics problem-solving, learning, and teaching (pp. 59–70). Mahwah: Lawrence Erlbaum.

Pea, R. (2004). The social and technological dimensions of scaffolding and related theoretical concepts for learning, education, and human activity. Journal of the Learning Sciences, 13 (3), 423–451.

Schnotz, W. (2002). Towards an integrated view of learning from text and visual displays. Educational Psychology Review, 14 (2), 101–120.

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Department of Education, Goethe-University Frankfurt, Senckenberganlage 15, Frankfurt, 60054, Germany

Prof. Dr. Ilonca Hardy *

Department of Psychology, University of Education Freiburg, Freiburg, Germany

Susanne Koerber

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Norbert M. Seel

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Hardy, I., Koerber, S. (2012). Scaffolding Learning by the Use of Visual Representations. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_691

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The visual representation of complexity: Definitions, examples and learning points

Resource link.

  • The visual representation of complexity: Definitions, examples and learning poi…

This visual overview was developed through a research process that identified, defined and illustrated 16 key features of complex systems.

The research addresses the need for images that are widely understood across different fields and sectors for researchers, policymakers, and design practitioners and contributes to an evolving visual language of complexity.

This resource and the following information was contributed by Patricia Rogers (BetterEvaluation).

Authors and their affiliation

Research and design by: Joanna Boehnert (CECAN)

Project mentors: Alex Penn, Peter Barbrook-Johnson, Martha Bicket, and Dione Hills (CECAN)

CECAN: Centre for the Evaluation of Complexity Across the Nexus.

Key features

This poster identifies, defines and illustrates 16 features of complex systems:

  • self-organisation
  • levers and hubs
  • non-linearity
  • domains of stability
  • path dependency
  • tipping points
  • change over time
  • open system
  • unpredictability
  • distributed control
  • nested systems
  • multiple scales and levels

For each item, it provides a brief definition, some examples and learning points.

The poster can be easily printed onto an A3 sheet of paper.

How have you used or intend on using this resource?

I have printed this out to display in my office to keep these ideas handy when writing, talking or thinking about complexity.  I plan to use this in my teaching and in discussions when planning evaluations that address complex aspects.

Why would you recommend it to other people?

There is increasing recognition that complexity has potential value for improving how we plan, manage and evaluate interventions.  However, many discussions of complexity in evaluation only address a few of the issues involved – particularly multiple scales and levels, and non-linearity.  

This document presents an informed summary of 16 different concepts that might need to be considered, enriching the discussions about complexity.  The visualisations and concise definitions have the potential to introduce new concepts to people who will find them useful and to improve communication between people who are using different definitions of complexity.

Boehnert, J. (2018).  The visual representation of complexity: Definitions, examples and learning points . CECAN. Retrieved from:  https://www.cecan.ac.uk/sites/default/files/2018-06/The%20Visual%20Commu...

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Example sentences visual representation

Fittingly, on a periphery wall, 82 dots of light reflect into the darkness of the room, a visual representation of the missing.
Yet our creative minds, in search of a visual representation of aliens in the stars, unwittingly used images of aliens from the deep.
Number lines provide a visual representation of a range of numbers along a horizontal line.
And why he decided that such a bold visual idea needed an equally bold visual representation .
Given an image that represents their pain-related brain activity, they use this visual representation to learn how to decrease their pain.

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  • Review article
  • Open access
  • Published: 11 July 2018

Decision making with visualizations: a cognitive framework across disciplines

  • Lace M. Padilla   ORCID: orcid.org/0000-0001-9251-5279 1 , 2 ,
  • Sarah H. Creem-Regehr 2 ,
  • Mary Hegarty 3 &
  • Jeanine K. Stefanucci 2  

Cognitive Research: Principles and Implications volume  3 , Article number:  29 ( 2018 ) Cite this article

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This article has been updated

Visualizations—visual representations of information, depicted in graphics—are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns). However, findings from different domains are rarely shared across domains though there may be domain-general principles underlying visualizations and their use. The limited cross-domain communication may be due to a lack of a unifying cognitive framework. This review aims to address this gap by proposing an integrative model that is grounded in models of visualization comprehension and a dual-process account of decision making. We review empirical studies of decision making with static two-dimensional visualizations motivated by a wide range of research goals and find significant direct and indirect support for a dual-process account of decision making with visualizations. Consistent with a dual-process model, the first type of visualization decision mechanism produces fast, easy, and computationally light decisions with visualizations. The second facilitates slower, more contemplative, and effortful decisions with visualizations. We illustrate the utility of a dual-process account of decision making with visualizations using four cross-domain findings that may constitute universal visualization principles. Further, we offer guidance for future research, including novel areas of exploration and practical recommendations for visualization designers based on cognitive theory and empirical findings.

Significance

People use visualizations to make large-scale decisions, such as whether to evacuate a town before a hurricane strike, and more personal decisions, such as which medical treatment to undergo. Given their widespread use and social impact, researchers in many domains, including cognitive psychology, information visualization, and medical decision making, study how we make decisions with visualizations. Even though researchers continue to develop a wealth of knowledge on decision making with visualizations, there are obstacles for scientists interested in integrating findings from other domains—including the lack of a cognitive model that accurately describes decision making with visualizations. Research that does not capitalize on all relevant findings progresses slower, lacks generalizability, and may miss novel solutions and insights. Considering the importance and impact of decisions made with visualizations, it is critical that researchers have the resources to utilize cross-domain findings on this topic. This review provides a cognitive model of decision making with visualizations that can be used to synthesize multiple approaches to visualization research. Further, it offers practical recommendations for visualization designers based on the reviewed studies while deepening our understanding of the cognitive processes involved when making decisions with visualizations.

Introduction

Every day we make numerous decisions with the aid of visualizations , including selecting a driving route, deciding whether to undergo a medical treatment, and comparing figures in a research paper. Visualizations are external visual representations that are systematically related to the information that they represent (Bertin, 1983 ; Stenning & Oberlander, 1995 ). The information represented might be about objects, events, or more abstract information (Hegarty, 2011 ). The scope of the previously mentioned examples illustrates the diversity of disciplines that have a vested interest in the influence of visualizations on decision making. While the term decision has a range of meanings in everyday language, here decision making is defined as a choice between two or more competing courses of action (Balleine, 2007 ).

We argue that for visualizations to be most effective, researchers need to integrate decision-making frameworks into visualization cognition research. Reviews of decision making with visual-spatial uncertainty also agree there has been a general lack of emphasis on mental processes within the visualization decision-making literature (Kinkeldey, MacEachren, Riveiro, & Schiewe, 2017 ; Kinkeldey, MacEachren, & Schiewe, 2014 ). The framework that has dominated applied decision-making research for the last 30 years is a dual-process account of decision making. Dual-process theories propose that we have two types of decision processes: one for automatic, easy decisions (Type 1); and another for more contemplative decisions (Type 2) (Kahneman & Frederick, 2002 ; Stanovich, 1999 ). Footnote 1 Even though many research areas involving higher-level cognition have made significant efforts to incorporate dual-process theories (Evans, 2008 ), visualization research has yet to directly test the application of current decision-making frameworks or develop an effective cognitive model for decision making with visualizations. The goal of this work is to integrate a dual-process account of decision making with established cognitive frameworks of visualization comprehension.

In this paper, we present an overview of current decision-making theories and existing visualization cognition frameworks, followed by a proposal for an integrated model of decision making with visualizations, and a selective review of visualization decision-making studies to determine if there is cross-domain support for a dual-process account of decision making with visualizations. As a preview, we will illustrate Type 1 and 2 processing in decision making with visualizations using four cross-domain findings that we observed in the literature review. Our focus here is on demonstrating how dual-processing can be a useful framework for examining visualization decision-making research. We selected the cross-domain findings as relevant demonstrations of Type 1 and 2 processing that were shared across the studies reviewed, but they do not represent all possible examples of dual-processing in visualization decision-making research. The review documents each of the cross-domain findings, in turn, using examples from studies in multiple domains. These cross-domain findings differ in their reliance on Type 1 and Type 2 processing. We conclude with recommendations for future work and implications for visualization designers.

Decision-making frameworks

Decision-making researchers have pursued two dominant research paths to study how humans make decisions under risk. The first assumes that humans make rational decisions, which are based on weighted and ordered probability functions and can be mathematically modeled (e.g. Kunz, 2004 ; Von Neumann, 1953 ). The second proposes that people often make intuitive decisions using heuristics (Gigerenzer, Todd, & ABC Research Group, 2000 ; Kahneman & Tversky, 1982 ). While there is fervent disagreement on the efficacy of heuristics and whether human behavior is rational (Vranas, 2000 ), there is more consensus that we can make both intuitive and strategic decisions (Epstein, Pacini, Denes-Raj, & Heier, 1996 ; Evans, 2008 ; Evans & Stanovich, 2013 ; cf. Keren & Schul, 2009 ). The capacity to make intuitive and strategic decisions is described by a dual-process account of decision making, which suggests that humans make fast, easy, and computationally light decisions (known as Type 1 processing) by default, but can also make slow, contemplative, and effortful decisions by employing Type 2 processing (Kahneman, 2011 ). Various versions of dual-processing theory exist, with the key distinctions being in the attributes associated with each type of process (for a more detailed review of dual-process theories, see Evans & Stanovich, 2013 ). For example, older dual-systems accounts of decision making suggest that each process is associated with specific cognitive or neurological systems. In contrast, dual-process (sometimes termed dual-type) theories propose that the processes are distinct but do not necessarily occur in separate cognitive or neurological systems (hence the use of process over system) (Evans & Stanovich, 2013 ).

Many applied domains have adapted a dual-processing model to explain task- and domain-specific decisions, with varying degrees of success (Evans, 2008 ). For example, when a physician is deciding if a patient should be assigned to a coronary care unit or a regular nursing bed, the doctor can use a heuristic or utilize heart disease predictive instruments to make the decision (Marewski & Gigerenzer, 2012 ). In the case of the heuristic, the doctor would employ a few simple rules (diagrammed in Fig.  1 ) that would guide her decision, such as considering the patient’s chief complaint being chest pain. Another approach is to apply deliberate mental effort to make a more time-consuming and effortful decision, which could include using heart disease predictive instruments (Marewski & Gigerenzer, 2012 ). In a review of how applied domains in higher-level cognition have implemented a dual-processing model for domain-specific decisions, Evans ( 2008 ) argues that prior work has conflicting accounts of Type 1 and 2 processing. Some studies suggest that the two types work in parallel while others reveal conflicts between the Types (Sloman, 2002 ). In the physician example proposed by Marewski and Gigerenzer ( 2012 ), the two types are not mutually exclusive, as doctors can utilize Type 2 to make a more thoughtful decision that is also influenced by some rules of thumb or Type 1. In sum, Evans ( 2008 ) argues that due to the inconsistency of classifying Type 1 and 2, the distinction between only two types is likely an oversimplification. Evans ( 2008 ) suggests that the literature only consistently supports the identification of processes that require a capacity-limited, working memory resource versus those that do not. Evans and Stanovich ( 2013 ) updated their definition based on new behavioral and neuroscience evidence stating, “the defining characteristic of Type 1 processes is their autonomy. They do not require ‘controlled attention,’ which is another way of saying that they make minimal demands on working memory resources” (p. 236). There is also debate on how to define the term working memory (Cowan, 2017 ). In line with prior work on decision making with visualizations (Patterson et al., 2014 ), we adopt the definition that working memory consists of multiple components that maintain a limited amount of information (their capacity) for a finite period (Cowan, 2017 ). Contemporary theories of working memory also stress the ability to engage attention in a controlled manner to suppress automatic responses and maintain the most task-relevant information with limited capacity (Engle, Kane, & Tuholski, 1999 ; Kane, Bleckley, Conway, & Engle, 2001 ; Shipstead, Harrison, & Engle, 2015 ).

figure 1

Coronary care unit decision tree, which illustrates a sequence of rules that a doctor could use to guide treatment decisions. Redrawn from “Heuristic decision making in medicine” by J. Marewski, and G. Gigerenzer 2012, Dialogues in clinical neuroscience, 14(1) , 77. ST-segment change refers to if certain anomaly appears in the patient’s electrocardiogram. NTG nitroglycerin, MI myocardial infarction, T T-waves with peaking or inversion

Identifying processes that require significant working memory provides a definition of Type 2 processing with observable neural correlates. Therefore, in line with Evans and Stanovich ( 2013 ), in the remainder of this manuscript, we will use significant working memory capacity demands and significant need for cognitive control, as defined above, as the criterion for Type 2 processing. In the context of visualization decision making, processes that require significant working memory are those that depend on the deliberate application of working memory to function. Type 1 processing occurs outside of users’ conscious awareness and may utilize small amounts of working memory but does not rely on conscious processing in working memory to drive the process. It should be noted that Type 1 and 2 processing are not mutually exclusive and many real-world decisions likely incorporate all processes. This review will attempt to identify tasks in visualization decision making that require significant working memory and capacity (Type 2 processing) and those that rely more heavily on Type 1 processing, as a first step to combining decision theory with visualization cognition.

Visualization cognition

Visualization cognition is a subset of visuospatial reasoning, which involves deriving meaning from external representations of visual information that maintain consistent spatial relations (Tversky, 2005 ). Broadly, two distinct approaches delineate visualization cognition models (Shah, Freedman, & Vekiri, 2005 ). The first approach refers to perceptually focused frameworks which attempt to specify the processes involved in perceiving visual information in displays and make predictions about the speed and efficiency of acquiring information from a visualization (e.g. Hollands & Spence, 1992 ; Lohse, 1993 ; Meyer, 2000 ; Simkin & Hastie, 1987 ). The second approach considers the influence of prior knowledge as well as perception. For example, Cognitive Fit Theory (Vessey, 1991), suggests that the user compares a learned graphic convention (mental schema) to the visual depiction. Visualizations that do not match the mental schema require cognitive transformations to make the visualization and mental representation align. For example, Fig.  2 illustrates a fictional relationship between the population growth of Species X and a predator species. At first glance, it may appear that when the predator species was introduced that the population of Species X dropped. However, after careful observation, you may notice that the higher population values are located lower on the Y-axis, which does not match our mental schema for graphs. With some effort, you can mentally reorder the values on the Y-axis to match your mental schema and then you may notice that the introduction of the predator species actually correlates with growth in the population of Species X. When the viewer is forced to mentally transform the visualization to match their mental schema, processing steps are increased, which may increase errors, time to complete a task, and demand on working memory (Vessey, 1991).

figure 2

Fictional relationship between the population growth of Species X and a predator species, where the Y-axis ordering does not match standard graphic conventions. Notice that the y-axis is reverse ordered. This figure was inspired by a controversial graphic produced by Christine Chan of Reuters, which showed the relationship between Florida’s “Stand Your Ground” law and firearm murders with the Y-axis reversed ordered (Lallanilla, 2014 )

Pinker ( 1990 ) proposed a cognitive model (see Fig.  3 ), which provides an integrative structure that denotes the distinction between top-down and bottom-up encoding mechanisms in understanding data graphs. Researchers have generalized this model to propose theories of comprehension, learning, and memory with visual information (Hegarty, 2011 ; Kriz & Hegarty, 2007 ; Shah & Freedman, 2011 ). The Pinker ( 1990 ) model suggests that from the visual array , defined as the unprocessed neuronal firing in response to visualizations, bottom-up encoding mechanisms are utilized to construct a visual description , which is the mental encoding of the visual stimulus. Following encoding, viewers mentally search long-term memory for knowledge relevant for interpreting the visualization. This knowledge is proposed to be in the form of a graph schema.

figure 3

Adapted figure from the Pinker ( 1990 ) model of visualization comprehension, which illustrates each process

Then viewers use a match process, where the graph schema that is the most similar to the visual array is retrieved. When a matching graph schema is found, the schema becomes instantiated . The visualization conventions associated with the graph schema can then help the viewer interpret the visualization ( message assembly process). For example, Fig. 3 illustrates comprehension of a bar chart using the Pinker ( 1990 ) model. In this example, the matched graph schema for a bar graph specifies that the dependent variable is on the Y-axis and the independent variable is on the X-axis; the instantiated graph schema incorporates the visual description and this additional information. The conceptual message is the resulting mental representation of the visualization that includes all supplemental information from long-term memory and any mental transformations the viewer may perform on the visualization. Viewers may need to transform their mental representation of the visualization based on their task or conceptual question . In this example, the viewer’s task is to find the average of A and B. To do this, the viewer must interpolate information in the bar chart and update the conceptual message with this additional information. The conceptual question can guide the construction of the mental representation through interrogation , which is the process of seeking out information that is necessary to answer the conceptual question. Top-down encoding mechanisms can influence each of the processes.

The influences of top-down processes are also emphasized in a previous attempt by Patterson et al. ( 2014 ) to extend visualization cognition theories to decision making. The Patterson et al. ( 2014 ) model illustrates how top-down cognitive processing influences encoding, pattern recognition, and working memory, but not decision making or the response. Patterson et al. ( 2014 ) use the multicomponent definition of working memory, proposed by Baddeley and Hitch ( 1974 ) and summarized by Cowan ( 2017 ) as a “multicomponent system that holds information temporarily and mediates its use in ongoing mental activities” (p. 1160). In this conception of working memory, a central executive controls the functions of working memory. The central executive can, among other functions, control attention and hold information in a visuo-spatial temporary store , which is where information can be maintained temporally for decision making without being stored in long-term memory (Baddeley & Hitch, 1974 ).

While incorporating working memory into a visualization decision-making model is valuable, the Patterson et al. ( 2014 ) model leaves some open questions about relationships between components and processes. For example, their model lacks a pathway for working memory to influence decisions based on top-down processing, which is inconsistent with well-established research in decision science (e.g. Gigerenzer & Todd, 1999; Kahneman & Tversky, 1982 ). Additionally, the normal processing pathway, depicted in the Patterson model, is an oversimplification of the interaction between top-down and bottom-up processing that is documented in a large body of literature (e.g. Engel, Fries, & Singer, 2001 ; Mechelli, Price, Friston, & Ishai, 2004 ).

A proposed integrated model of decision making with visualizations

Our proposed model (Fig.  4 ) introduces a dual-process account of decision making (Evans & Stanovich, 2013 ; Gigerenzer & Gaissmaier, 2011 ; Kahneman, 2011 ) into the Pinker ( 1990 ) model of visualization comprehension. A primary addition of our model is the inclusion of working memory, which is utilized to answer the conceptual question and could have a subsequent impact on each stage of the decision-making process, except bottom-up attention. The final stage of our model includes a decision-making process that derives from the conceptual message and informs behavior. In line with a dual-process account (Evans & Stanovich, 2013 ; Gigerenzer & Gaissmaier, 2011 ; Kahneman, 2011 ), the decision step can either be completed with Type 1 processing, which only uses minimal working memory (Evans & Stanovich, 2013 ) or recruit significant working memory, constituting Type 2 processing. Also following Evans and Stanovich ( 2013 ), we argue that people can make a decision with a visualization while using minimal amounts of working memory. We classify this as Type 1 thinking. Lohse ( 1997 ) found that when participants made judgments about budget allocation using profit charts, individuals with less working memory capacity performed equally well compared to those with more working memory capacity, when they only made decisions about three regions (easier task). However, when participants made judgments about nine regions (harder task), individuals with more working memory capacity outperformed those with less working memory capacity. The results of the study reveal that individual differences in working memory capacity only influence performance on complex decision-making tasks (Lohse, 1997 ). Figure  5 (top) illustrates one way that a viewer could make a Type 1 decision about whether the average value of bars A and B is closer to 2 or 2.2. Figure 5 (top) illustrates how a viewer might make a fast and computationally light decision in which she decides that the middle point between the two bars is closer to the salient tick mark of 2 on the Y-axis and answers 2 (which is incorrect). In contrast, Fig.  5 (bottom) shows a second possible method of solving the same problem by utilizing significant working memory (Type 2 processing). In this example, the viewer has recently learned a strategy to address similar problems, uses working memory to guide a top-down attentional search of the visual array, and identifies the values of A and B. Next, she instantiates a different graph schema than in the prior example by utilizing working memory and completes an effortful mental computation of 2.4 + 1.9/2. Ultimately, the application of working memory leads to a different and more effortful decision than in Fig. 5 (top). This example illustrates how significant amounts of working memory can be used at early stages of the decision-making process and produce downstream effects and more considered responses. In the following sections, we provide a selective review of work on decision making with visualizations that demonstrates direct and indirect evidence for our proposed model.

figure 4

Model of visualization decision making, which emphasizes the influence of working memory. Long-term memory can influence all components and processes in the model either via pre-attentive processes or by conscious application of knowledge

figure 5

Examples of a fast Type 1 (top) and slow Type 2 (bottom) decision outlined in our proposed model of decision making with visualizations. In these examples, the viewer’s task is to decide if the average value of bars A and B are closer to 2 or 2.2. The thick dotted line denotes significant working memory and the thin dotted line negligible working memory

Empirical studies of visualization decision making

Review method.

To determine if there is cross-domain empirical support for a dual-process account of decision making with visualizations, we selectively reviewed studies of complex decision making with computer-generated two-dimensional (2D) static visualizations. To illustrate the application of a dual-process account of decision making to visualization research, this review highlights representative studies from diverse application areas. Interdisciplinary groups conducted many of these studies and, as such, it is not accurate to classify the studies in a single discipline. However, to help the reader evaluate the cross-domain nature of these findings, Table  1 includes the application area for the specific tasks used in each study.

In reviewing this work, we observed four key cross-domain findings that support a dual-process account of decision making (see Table  2 ). The first two support the inclusion of Type 1 processing, which is illustrated by the direct path for bottom-up attention to guide decision making with the minimal application of working memory (see Fig. 5 top). The first finding is that visualizations direct viewers’ bottom-up attention , which can both help and hinder decision making (see “ Bottom-up attention ”). The second finding is that visual-spatial biases comprise a unique category of bias that is a direct result of the visual encoding technique (see “ Visual-Spatial Biases ”). The third finding supports the inclusion of Type 2 processing in our proposed model and suggests that visualizations vary in cognitive fit between the visual description, graph schema, and conceptual question. If the fit is poor (i.e. there is a mismatch between the visualization and a decision-making component), working memory is used to perform corrective mental transformations (see “ Cognitive fit ”). The final cross-domain finding proposes that knowledge-driven processes may interact with the effects of the visual encoding technique (see “ Knowledge-driven processing ”) and could be a function of either Type 1 or 2 processes. Each of these findings will be detailed at length in the relevant sections. The four cross-domain findings do not represent an exhaustive list of all cross-domain findings that pertain to visualization cognition. However, these were selected as illustrative examples of Type 1 and 2 processing that include significant contributions from multiple domains. Further, some of the studies could fit into multiple sections and were included in a particular section as illustrative examples.

Bottom-up attention

The first cross-domain finding that characterizes Type 1 processing in visualization decision making is that visualizations direct participants’ bottom-up attention to specific visual features, which can be either beneficial or detrimental to decision making. Bottom-up attention consists of involuntary shifts in focus to salient features of a visualization and does not utilize working memory (Connor, Egeth, & Yantis, 2004 ), therefore it is a Type 1 process. The research reviewed in this section illustrates that bottom-up attention has a profound influence on decision making with visualizations. A summary of visual features that studies have used to attract bottom-up attention can be found in Table  3 .

Numerous studies show that salient information in a visualization draws viewers’ attention (Fabrikant, Hespanha, & Hegarty, 2010 ; Hegarty, Canham, & Fabrikant, 2010 ; Hegarty, Friedman, Boone, & Barrett, 2016 ; Padilla, Ruginski, & Creem-Regehr, 2017 ; Schirillo & Stone, 2005 ; Stone et al., 2003 ; Stone, Yates, & Parker, 1997 ). The most common methods for demonstrating that visualizations focus viewers’ attention is by showing that viewers miss non-salient but task-relevant information (Schirillo & Stone, 2005 ; Stone et al., 1997 ; Stone et al., 2003 ), viewers are biased by salient information (Hegarty et al., 2016 ; Padilla, Ruginski et al., 2017 ) or viewers spend more time looking at salient information in a visualization (Fabrikant et al., 2010 ; Hegarty et al., 2010 ). For example, Stone et al. ( 1997 ) demonstrated that when viewers are asked how much they would pay for an improved product using the visualizations in Fig.  6 , they focus on the number of icons while missing the base rate of 5,000,000. If a viewer simply totals the icons, the standard product appears to be twice as dangerous as the improved product, but because the base rate is large, the actual difference between the two products is insignificantly small (0.0000003; Stone et al., 1997 ). In one experiment, participants were willing to pay $125 more for improved tires when viewing the visualizations in Fig. 6 compared to a purely textual representation of the information. The authors also demonstrated the same effect for improved toothpaste, with participants paying $0.95 more when viewing a visual depiction compared to text. The authors’ term this heuristic of focusing on salient information and ignoring other data the foreground effect (Stone et al., 1997 ) (see also Schirillo & Stone, 2005 ; Stone et al., 2003 ).

figure 6

Icon arrays used to illustrate the risk of standard or improved tires. Participants were tasked with deciding how much they would pay for the improved tires. Note the base rate of 5 M drivers was represented in text. Redrawn from “Effects of numerical and graphical displays on professed risk-taking behavior” by E. R. Stone, J. F. Yates, & A. M. Parker. 1997, Journal of Experimental Psychology: Applied , 3 (4), 243

A more direct test of visualizations guiding bottom-up attention is to examine if salient information biases viewers’ judgments. One method involves identifying salient features using a behaviorally validated saliency model, which predicts the locations that will attract viewers’ bottom-up attention (Harel, 2015 ; Itti, Koch, & Niebur, 1998 ; Rosenholtz & Jin, 2005 ). In one study, researchers compared participants’ judgments with different hurricane forecast visualizations and then, using the Itti et al. ( 1998 ) saliency algorithm, found that the differences in what was salient in the two visualizations correlated with participants’ performance (Padilla, Ruginski et al., 2017 ). Specifically, they suggested that the salient borders of the Cone of Uncertainty (see Fig.  7 , left), which is used by the National Hurricane Center to display hurricane track forecasts, leads some people to incorrectly believe that the hurricane is growing in physical size, which is a misunderstanding of the probability distribution of hurricane paths that the cone is intended to represent (Padilla, Ruginski et al., 2017 ; see also Ruginski et al., 2016 ). Further, they found that when the same data were represented as individual hurricane paths, such that there was no salient boundary (see Fig. 7 , right), viewers intuited the probability of hurricane paths more effectively than the Cone of Uncertainty. However, an individual hurricane path biased viewers’ judgments if it intersected a point of interest. For example, in Fig. 7 (right), participants accurately judged that locations closer to the densely populated lines (highest likelihood of storm path) would receive more damage. This correct judgment changed when a location farther from the center of the storm was intersected by a path, but the closer location was not (see locations a and b in Fig. 7 right). With both visualizations, the researchers found that viewers were negatively biased by the salient features for some tasks (Padilla, Ruginski et al., 2017 ; Ruginski et al., 2016 ).

figure 7

An example of the Cone of Uncertainty ( left ) and the same data represented as hurricane paths ( right ). Participants were tasked with evaluating the level of damage that would incur to offshore oil rigs at specific locations, based on the hurricane forecast visualization. Redrawn from “Effects of ensemble and summary displays on interpretations of geospatial uncertainty data” by L. M. Padilla, I. Ruginski, and S. H. Creem-Regehr. 2017, Cognitive Research: Principles and Implications , 2 (1), 40

That is not to say that saliency only negatively impacts decisions. When incorporated into visualization design, saliency can guide bottom-up attention to task-relevant information, thereby improving performance (e.g. Fabrikant et al., 2010 ; Fagerlin, Wang, & Ubel, 2005 ; Hegarty et al., 2010 ; Schirillo & Stone, 2005 ; Stone et al., 2003 ; Waters, Weinstein, Colditz, & Emmons, 2007 ). One compelling example using both eye-tracking measures and a saliency algorithm demonstrated that salient features of weather maps directed viewers’ attention to different variables that were visualized on the maps (Hegarty et al., 2010 ) (see also Fabrikant et al., 2010 ). Interestingly, when the researchers manipulated the relative salience of temperature versus pressure (see Fig.  8 ), the salient features captured viewers’ overt attention (as measured by eye fixations) but did not influence performance, until participants were trained on how to effectively interpret the features. Once viewers were trained, their judgments were facilitated when the relevant features were more salient (Hegarty et al., 2010 ). This is an instructive example of how saliency may direct viewers’ bottom-up attention but may not influence their performance until viewers have the relevant top-down knowledge to capitalize on the affordances of the visualization.

figure 8

Eye-tracking data from Hegarty et al. ( 2010 ). Participants viewed an arrow located in Utah (obscured by eye-tracking data in the figure) and made judgments about whether the arrow correctly identified the wind direction. The black isobars were the task-relevant information. Notice that after instructions, viewers with the pressure-salient visualizations focused on the isobars surrounding Utah, rather than on the legend or in other regions. The panels correspond to the conditions in the original study

In sum, the reviewed studies suggest that bottom-up attention has a profound influence on decision making with visualizations. This is noteworthy because bottom-up attention is a Type 1 process. At a minimum, the work suggests that Type 1 processing influences the first stages of decision making with visualizations. Further, the studies cited in this section provide support for the inclusion of bottom-up attention in our proposed model.

  • Visual-spatial biases

A second cross-domain finding that relates to Type 1 processing is that visualizations can give rise to visual-spatial biases that can be either beneficial or detrimental to decision making. We are proposing the new concept of visual-spatial biases and defining this term as a bias that elicits heuristics, which are a direct result of the visual encoding technique. Visual-spatial biases likely originate as a Type 1 process as we suspect they are connected to bottom-up attention, and if detrimental to decision making, have to be actively suppressed by top-down knowledge and cognitive control mechanisms (see Table  4 for summary of biases documented in this section). Visual-spatial biases can also improve decision-making performance. As Card, Mackinlay, and Shneiderman ( 1999 ) point out, we can use vision to think , meaning that visualizations can capitalize on visual perception to interpret a visualization without effort when the visual biases elucidated by the visualization are consistent with the correct interpretation.

Tversky ( 2011 ) presents a taxonomy of visual-spatial communications that are intrinsically related to thought, which are likely the bases for visual-spatial biases (see also Fabrikant & Skupin, 2005 ). One of the most commonly documented visual-spatial biases that we observed across domains is a containment conceptualization of boundary representations in visualizations. Tversky ( 2011 ) makes the analogy, “Framing a picture is a way of saying that what is inside the picture has a different status from what is outside the picture” (p. 522). Similarly, Fabrikant and Skupin ( 2005 ) describe how, “They [boundaries] help partition an information space into zones of relative semantic homogeneity” (p. 673). However, in visualization design, it is common to take continuous data and visually represent them with boundaries (i.e. summary statistics, error bars, isocontours, or regions of interest; Padilla et al., 2015 ; Padilla, Quinan, Meyer, & Creem-Regehr, 2017 ). Binning continuous data is a reasonable approach, particularly when intended to make the data simpler for viewers to understand (Padilla, Quinan, et al., 2017 ). However, it may have the unintended consequence of creating artificial boundaries that can bias users—leading them to respond as if data within a containment is more similar than data across boundaries. For example, McKenzie, Hegarty, Barrett, and Goodchild ( 2016 ) showed that participants were more likely to use a containment heuristic to make decisions about Google Map’s blue dot visualization when the positional uncertainty data were visualized as a bounded circle (Fig.  9 right) compared to a Gaussian fade (Fig. 9 left) (see also Newman & Scholl, 2012 ; Ruginski et al., 2016 ). Recent work by Grounds, Joslyn, and Otsuka ( 2017 ) found that viewers demonstrate a “deterministic construal error” or the belief that visualizations of temperature uncertainty represent a deterministic forecast. However, the deterministic construal error was not observed with textual representations of the same data (see also Joslyn & LeClerc, 2013 ).

figure 9

Example stimuli from McKenzie et al. ( 2016 ) showing circular semi-transparent overlays used by Google Maps to indicate the uncertainty of the users’ location. Participants compared two versions of these visualizations and determined which represented the most accurate positional location. Redrawn from “Assessing the effectiveness of different visualizations for judgments of positional uncertainty” by G. McKenzie, M. Hegarty, T. Barrett, and M. Goodchild. 2016, International Journal of Geographical Information Science , 30 (2), 221–239

Additionally, some visual-spatial biases follow the same principles as more well-known decision-making biases revealed by researchers in behavioral economics and decision science. In fact, some decision-making biases, such as anchoring , the tendency to use the first data point to make relative judgments, seem to have visual correlates (Belia, Fidler, Williams, & Cumming, 2005 ). For example, Belia et al. ( 2005 ) asked experts with experience in statistics to align two means (representing “Group 1” and “Group 2”) with error bars so that they represented data ranges that were just significantly different (see Fig.  10 for example of stimuli). They found that when the starting position of Group 2 was around 800 ms, participants placed Group 2 higher than when the starting position for Group 2 was at around 300 ms. This work demonstrates that participants used the starting mean of Group 2 as an anchor or starting point of reference, even though the starting position was arbitrary. Other work finds that visualizations can be used to reduce some decision-making biases including anecdotal evidence bias (Fagerlin et al., 2005 ), side effect aversion (Waters et al., 2007 ; Waters, Weinstein, Colditz, & Emmons, 2006 ), and risk aversion (Schirillo & Stone, 2005 ).

figure 10

Example display and instructions from Belia et al. ( 2005 ). Redrawn from “Researchers misunderstand confidence intervals and standard error bars” by S. Belia, F. Fidler, J. Williams, and G. Cumming. 2005, Psychological Methods, 10 (4), 390. Copyright 2005 by “American Psychological Association”

Additionally, the mere presence of a visualization may inherently bias viewers. For example, viewers find scientific articles with high-quality neuroimaging figures to have greater scientific reasoning than the same article with a bar chart or without a figure (McCabe & Castel, 2008 ). People tend to unconsciously believe that high-quality scientific images reflect high-quality science—as illustrated by work from Keehner, Mayberry, and Fischer ( 2011 ) showing that viewers rate articles with three-dimensional brain images as more scientific than those with 2D images, schematic drawings, or diagrams (See Fig.  11 ). Unintuitively, however, high-quality complex images can be detrimental to performance compared to simpler visualizations (Hegarty, Smallman, & Stull, 2012 ; St. John, Cowen, Smallman, & Oonk, 2001 ; Wilkening & Fabrikant, 2011 ). Hegarty et al. ( 2012 ) demonstrated that novice users prefer realistically depicted maps (see Fig.  12 ), even though these maps increased the time taken to complete the task and focused participants’ attention on irrelevant information (Ancker, Senathirajah, Kukafka, & Starren, 2006 ; Brügger, Fabrikant, & Çöltekin, 2017 ; St. John et al., 2001 ; Wainer, Hambleton, & Meara, 1999 ; Wilkening & Fabrikant, 2011 ). Interestingly, professional meteorologists also demonstrated the same biases as novice viewers (Hegarty et al., 2012 ) (see also Nadav-Greenberg, Joslyn, & Taing, 2008 ).

figure 11

Image showing participants’ ratings of three-dimensionality and scientific credibility for a given neuroimaging visualization, originally published in grayscale (Keehner et al., 2011 )

figure 12

Example stimuli from Hegarty et al. ( 2012 ) showing maps with varying levels of realism. Both novice viewers and meteorologists were tasked with selecting a visualization to use and performing a geospatial task. The panels correspond to the conditions in the original study

We argue that visual-spatial biases reflect a Type 1 process, occurring automatically with minimal working memory. Work by Sanchez and Wiley ( 2006 ) provides direct evidence for this assertion using eye-tracking data to demonstrate that individuals with less working memory capacity attend to irrelevant images in a scientific article more than those with greater working memory capacity. The authors argue that we are naturally drawn to images (particularly high-quality depictions) and that significant working memory capacity is required to shift focus away from images that are task-irrelevant. The ease by which visualizations captivate our focus and direct our bottom-up attention to specific features likely increases the impact of these biases, which may be why some visual-spatial biases are notoriously difficult to override using working memory capacity (see Belia et al., 2005 ; Boone, Gunalp, & Hegarty, in press ; Joslyn & LeClerc, 2013 ; Newman & Scholl, 2012 ). We speculate that some visual-spatial biases are intertwined with bottom-up attention—occurring early in the decision-making process and influencing the down-stream processes (see our model in Fig. 4 for reference), making them particularly unremitting.

Cognitive fit

We also observe a cross-domain finding involving Type 2 processing, which suggests that if there is a mismatch between the visualization and a decision-making component, working memory is used to perform corrective mental transformations. Cognitive fit is a term used to describe the correspondence between the visualization and conceptual question or task (see our model for reference; for an overview of cognitive fit, see Vessey, Zhang, & Galletta, 2006 ). Those interested in examining cognitive fit generally attempt to identify and reduce mismatches between the visualization and one of the decision-making components (see Table  5 for a breakdown of the decision-making components that the reviewed studies evaluated). When there is a mismatch produced by the default Type 1 processing, it is argued that significant working memory (Type 2 processing) is required to resolve the discrepancy via mental transformations (Vessey et al., 2006 ). As working memory is capacity limited, the magnitude of mental transformation or amount of working memory required is one predictor of reaction times and errors.

Direct evidence for this claim comes from work demonstrating that cognitive fit differentially influenced the performance of individuals with more and less working memory capacity (Zhu & Watts, 2010 ). The task was to identify which two nodes in a social media network diagram should be removed to disconnect the maximal number of nodes. As predicted by cognitive fit theory, when the visualization did not facilitate the task (Fig.  13 left), participants with less working memory capacity were slower than those with more working memory capacity. However, when the visualization aligned with the task (Fig.  13 right), there was no difference in performance. This work suggests that when there is misalignment between the visualization and a decision-making process, people with more working memory capacity have the resources to resolve the conflict, while those with less resources show performance degradations. Footnote 2 Other work only found a modest relationship between working memory capacity and correct interpretations of high and low temperature forecast visualizations (Grounds et al., 2017 ), which suggests that, for some visualizations, viewers utilize little working memory.

figure 13

Examples of social media network diagrams from Zhu and Watts ( 2010 ). The authors argue that the figure on the right is more aligned with the task of identifying the most interconnected nodes than the figure on the left

As illustrated in our model, working memory can be recruited to aid all stages of the decision-making process except bottom-up attention. Work that examines cognitive fit theory provides indirect evidence that working memory is required to resolve conflicts in the schema matching and a decision-making component. For example, one way that a mismatch between a viewer’s mental schema and visualization can arise is when the viewer uses a schema that is not optimal for the task. Tversky, Corter, Yu, Mason, and Nickerson ( 2012 ) primed participants to use different schemas by describing the connections in Fig.  14 in terms of either transfer speed or security levels. Participants then decided on the most efficient or secure route for information to travel between computer nodes with either a visualization that encoded data using the thickness of connections, containment, or physical distance (see Fig.  14 ). Tversky et al. ( 2012 ) found that when the links were described based on their information transfer speed, thickness and distance visualizations were the most effective—suggesting that the speed mental schema was most closely matched to the thickness and distance visualizations, whereas the speed schema required mental transformations to align with the containment visualization. Similarly, the thickness and containment visualizations outperformed the distance visualization when the nodes were described as belonging to specific systems with different security levels. This work and others (Feeney, Hola, Liversedge, Findlay, & Metcalf, 2000 ; Gattis & Holyoak, 1996 ; Joslyn & LeClerc, 2013 ; Smelcer & Carmel, 1997 ) provides indirect evidence that gratuitous realignment between mental schema and the visualization can be error-prone and visualization designers should work to reduce the number of transformations required in the decision-making process.

figure 14

Example of stimuli from Tversky et al. ( 2012 ) showing three types of encoding techniques for connections between nodes (thickness, containment, and distance). Participants were asked to select routes between nodes with different descriptions of the visualizations. Redrawn from “Representing category and continuum: Visualizing thought” by B. Tversky, J. Corter, L. Yu, D. Mason, and J. Nickerson. In Diagrams 2012 (p. 27), P. Cox, P. Rodgers, and B. Plimmer (Eds.), 2012, Berlin Heidelberg: Springer-Verlag

Researchers from multiple domains have also documented cases of misalignment between the task, or conceptual question, and the visualization. For example, Vessey and Galletta ( 1991 ) found that participants completed a financial-based task faster when the visualization they chose (graph or table, see Fig.  15 ) matched the task (spatial or textual). For the spatial task, participants decided which month had the greatest difference between deposits and withdrawals. The textual or symbolic tasks involved reporting specific deposit and withdrawal amounts for various months. The authors argued that when there is a mismatch between the task and visualization, the additional transformation accounts for the increased time taken to complete the task (Vessey & Galletta, 1991 ) (see also Dennis & Carte, 1998 ; Huang et al., 2006 ), which likely takes place in the inference process of our proposed model.

figure 15

Examples of stimuli from Vessey and Galletta ( 1991 ) depicting deposits and withdraw amounts over the course of a year with a graph ( a ) and table ( b ). Participants completed either a spatial or textual task with the chart or table. Redrawn from “Cognitive fit: An empirical study of information acquisition” by I. Vessey, and D. Galletta. 1991, Information systems research, 2 (1), 72–73. Copyright 1991 by “INFORMS”

The aforementioned studies provide direct (Zhu & Watts, 2010 ) and indirect (Dennis & Carte, 1998 ; Feeney et al., 2000 ; Gattis & Holyoak, 1996 ; Huang et al., 2006 ; Joslyn & LeClerc, 2013 ; Smelcer & Carmel, 1997 ; Tversky et al., 2012 ; Vessey & Galletta, 1991 ) evidence that Type 2 processing recruits working memory to resolve misalignment between decision-making processes and the visualization that arise from default Type 1 processing. These examples of Type 2 processing using working memory to perform effortful mental computations are consistent with the assertions of Evans and Stanovich ( 2013 ) that Type 2 processes enact goal directed complex processing. However, it is not clear from the reviewed work how exactly the visualization and decision-making components are matched. Newman and Scholl ( 2012 ) propose that we match the schema and visualization based on the similarities between the salient visual features, although this proposal has not been tested. Further, work that assesses cognitive fit in terms of the visualization and task only examines the alignment of broad categories (i.e., spatial or semantic). Beyond these broad classifications, it is not clear how to predict if a task and visualization are aligned. In sum, there is not a sufficient cross-disciplinary theory for how mental schemas and tasks are matched to visualizations. However, it is apparent from the reviewed work that Type 2 processes (requiring working memory) can be recruited during the schema matching and inference processes.

Either type 1 and/or 2

Knowledge-driven processing.

In a review of map-reading cognition, Lobben ( 2004 ) states, “…research should focus not only on the needs of the map reader but also on their map-reading skills and abilities” (p. 271). In line with this statement, the final cross-domain finding is that the effects of knowledge can interact with the affordances or biases inherent in the visualization method. Knowledge may be held temporally in working memory (Type 2), held in long-term knowledge but effortfully used (Type 2), or held in long-term knowledge but automatically applied (Type 1). As a result, knowledge-driven processing can involve either Type 1 or Type 2 processes.

Both short- and long-term knowledge can influence visualization affordances and biases. However, it is difficult to distinguish whether Type 2 processing is using significant working memory capacity to temporarily hold knowledge or if participants have stored the relevant knowledge in long-term memory and processing is more automatic. Complicating the issue, knowledge stored in long-term memory can influence decision making with visualizations using both Type 1 and 2 processing. For example, if you try to remember Pythagorean’s Theorem, which you may have learned in high school or middle school, you may recall that a 2  + b 2  = c 2 , where c represents the length of the hypotenuse and a and b represent the lengths of the other two sides of a triangle. Unless you use geometry regularly, you likely had to strenuously search in long-term memory for the equation, which is a Type 2 process and requires significant working memory capacity. In contrast, if you are asked to recall your childhood phone number, the number might automatically come to mind with minimal working memory required (Type 1 processing).

In this section, we highlight cases where knowledge either influenced decision making with visualizations or was present but did not influence decisions (see Table  6 for the type of knowledge examined in each study). These studies are organized based on how much time the viewers had to incorporate the knowledge (i.e. short-term instructions and long-term individual differences in abilities and expertise), which may be indicative of where the knowledge is stored. However, many factors other than time influence the process of transferring knowledge by working memory capacity to long-term knowledge. Therefore, each of the studies cited in this section could be either Type 1, Type 2, or both types of processing.

One example of participants using short-term knowledge to override a familiarity bias comes from work by Bailey, Carswell, Grant, and Basham ( 2007 ) (see also Shen, Carswell, Santhanam, & Bailey, 2012 ). In a complex geospatial task for which participants made judgments about terrorism threats, participants were more likely to select familiar map-like visualizations rather than ones that would be optimal for the task (see Fig.  16 ) (Bailey et al., 2007 ). Using the same task and visualizations, Shen et al. ( 2012 ) showed that users were more likely to choose an efficacious visualization when given training concerning the importance of cognitive fit and effective visualization techniques. In this case, viewers were able to use knowledge-driven processing to improve their performance. However, Joslyn and LeClerc ( 2013 ) found that when participants viewed temperature uncertainty, visualized as error bars around a mean temperature prediction, they incorrectly believed that the error bars represented high and low temperatures. Surprisingly, participants maintained this belief despite a key, which detailed the correct way to interpret each temperature forecast (see also Boone et al., in press ). The authors speculated that the error bars might have matched viewers’ mental schema for high- and low-temperature forecasts (stored in long-term memory) and they incorrectly utilized the high-/low-temperature schema rather than incorporating new information from the key. Additionally, the authors propose that because the error bars were visually represented as discrete values, that viewers may have had difficulty reimagining the error bars as points on a distribution, which they term a deterministic construal error (Joslyn & LeClerc, 2013 ). Deterministic construal visual-spatial biases may also be one of the sources of misunderstanding of the Cone of Uncertainty (Padilla, Ruginski et al., 2017 ; Ruginski et al., 2016 ). A notable difference between these studies and the work of Shen et al. ( 2012 ) is that Shen et al. ( 2012 ) used instructions to correct a familiarity bias, which is a cognitive bias originally documented in the decision-making literature that is not based on the visual elements in the display. In contrast, the biases in Joslyn and LeClerc ( 2013 ) were visual-spatial biases. This provides further evidence that visual-spatial biases may be a unique category of biases that warrant dedicated exploration, as they are harder to influence with knowledge-driven processing.

figure 16

Example of different types of view orientations used by examined by Bailey et al. ( 2007 ). Participants selected one of these visualizations and then used their selection to make judgments including identifying safe passageways, determining appropriate locations for firefighters, and identifying suspicious locations based on the height of buildings. The panels correspond to the conditions in the original study

Regarding longer-term knowledge, there is substantial evidence that individual differences in knowledge impact decision making with visualizations. For example, numerous studies document the benefit of visualizations for individuals with less health literacy, graph literacy, and numeracy (Galesic & Garcia-Retamero, 2011 ; Galesic, Garcia-Retamero, & Gigerenzer, 2009 ; Keller, Siegrist, & Visschers, 2009 ; Okan, Galesic, & Garcia-Retamero, 2015 ; Okan, Garcia-Retamero, Cokely, & Maldonado, 2012 ; Okan, Garcia-Retamero, Galesic, & Cokely, 2012 ; Reyna, Nelson, Han, & Dieckmann, 2009 ; Rodríguez et al., 2013 ). Visual depictions of health data are particularly useful because health data often take the form of probabilities, which are unintuitive. Visualizations inherently illustrate probabilities (i.e. 10%) as natural frequencies (i.e. 10 out of 100), which are more intuitive (Hoffrage & Gigerenzer, 1998 ). Further, by depicting natural frequencies visually (see example in Fig.  17 ), viewers can make perceptual comparisons rather than mathematical calculations. This dual benefit is likely the reason visualizations produce facilitation for individuals with less health literacy, graph literacy, and numeracy.

figure 17

Example of stimuli used by Galesic et al. ( 2009 ) in a study demonstrating that natural frequency visualizations can help individuals overcome less numeracy. Participants completed three medical scenario tasks using similar visualizations as depicted here, in which they were asked about the effects of aspirin on risk of stroke or heart attack and about a hypothetical new drug. Redrawn from “Using icon arrays to communicate medical risks: overcoming less numeracy” by M. Galesic, R. Garcia-Retamero, and G. Gigerenzer. 2009, Health Psychology, 28 (2), 210

These studies are good examples of how designers can create visualizations that capitalize on Type 1 processing to help viewers accurately make decisions with complex data even when they lack relevant knowledge. Based on the reviewed work, we speculate that well-designed visualizations that utilize Type 1 processing to intuitively illustrate task-relevant relationships in the data may be particularly beneficial for individuals with less numeracy and graph literacy, even for simple tasks. However, poorly designed visualizations that require superfluous mental transformations may be detrimental to the same individuals. Further, individual differences in expertise, such as graph literacy, which have received more attention in healthcare communication (Galesic & Garcia-Retamero, 2011 ; Nayak et al., 2016 ; Okan et al., 2015 ; Okan, Garcia-Retamero, Cokely, & Maldonado, 2012 ; Okan, Garcia-Retamero, Galesic, & Cokely, 2012 ; Rodríguez et al., 2013 ), may play a large role in how viewers complete even simple tasks in other domains such as map-reading (Kinkeldey et al., 2017 ).

Less consistent are findings on how more experienced users incorporate knowledge acquired over longer periods of time to make decisions with visualizations. Some research finds that students’ decision-making and spatial abilities improved during a semester-long course on Geographic Information Science (GIS) (Lee & Bednarz, 2009 ). Other work finds that experts perform the same as novices (Riveiro, 2016 ), experts can exhibit visual-spatial biases (St. John et al., 2001 ) and experts perform more poorly than expected in their domain of visual expertise (Belia et al., 2005 ). This inconsistency may be due in part to the difficulty in identifying when and if more experienced viewers are automatically applying their knowledge or employing working memory. For example, it is unclear if the students in the GIS course documented by Lee and Bednarz ( 2009 ) developed automatic responses (Type 1) or if they learned the information and used working memory capacity to apply their training (Type 2).

Cheong et al. ( 2016 ) offer one way to gauge how performance may change when one is forced to use Type 1 processing, but then allowed to use Type 2 processing. In a wildfire task using multiple depictions of uncertainty (see Fig.  18 ), Cheong et al. ( 2016 ) found that the type of uncertainty visualization mattered when participants had to make fast Type 1 decisions (5 s) about evacuating from a wildfire. But when given sufficient time to make Type 2 decisions (30 s), participants were not influenced by the visualization technique (see also Wilkening & Fabrikant, 2011 ).

figure 18

Example of multiple uncertainty visualization techniques for wildfire risk by Cheong et al. ( 2016 ). Participants were presented with a house location (indicated by an X), and asked if they would stay or leave based on one of the wildfire hazard communication techniques shown here. The panels correspond to the conditions in the original study

Interesting future work could limit experts’ time to complete a task (forcing Type 1 processing) and then determine if their judgments change when given more time to complete the task (allowing for Type 2 processing). To test this possibility further, a dual-task paradigm could be used such that experts’ working memory capacity is depleted by a difficult secondary task that also required working memory capacity. Some examples of secondary tasks in a dual-task paradigm include span tasks that require participants to remember or follow patterns of information, while completing the primary task, then report the remembered or relevant information from the pattern (for a full description of theoretical bases for a dual-task paradigm see Pashler, 1994 ). To our knowledge, only one study has used a dual-task paradigm to evaluate cognitive load of a visualization decision-making task (Bandlow et al., 2011 ). However, a growing body of research on other domains, such as wayfinding and spatial cognition, demonstrates the utility of using dual-task paradigms to understand the types of working memory that users employ for a task (Caffò, Picucci, Di Masi, & Bosco, 2011 ; Meilinger, Knauff, & Bülthoff, 2008 ; Ratliff & Newcombe, 2005 ; Trueswell & Papafragou, 2010 ).

Span tasks are examples of spatial or verbal secondary tasks, which include remembering the orientations of an arrow (taxes visual-spatial memory, (Shah & Miyake, 1996 ) or counting backward by 3 s (taxes verbal processing and short-term memory) (Castro, Strayer, Matzke, & Heathcote, 2018 ). One should expect more interference if the primary and secondary tasks recruit the same processes (i.e. visual-spatial primary task paired with a visual-spatial memory span task). An example of such an experimental design is illustrated in Fig.  19 . In the dual-task trial illustrated in Fig.  19 , if participants responses are as fast and accurate as the baseline trial then participants are likely not using significant amounts of working memory capacity for that task. If the task does require significant working memory capacity, then the inclusion of the secondary task should increase the time taken to complete the primary task and potentially produce errors in both the secondary and primary tasks. In visualization decision-making research, this is an open area of exploration for researchers and designers that are interested in understanding how working memory capacity and a dual-process account of decision making applies to their visualizations and application domains.

figure 19

A diagram of a dual-tasking experiment is shown using the same task as in Fig. 5 . Responses resulting from Type 1 and 2 processing are illustrated. The dual-task trial illustrates how to place additional load on working memory capacity by having the participant perform a demanding secondary task. The impact of the secondary task is illustrated for both time and accuracy. Long-term memory can influence all components and processes in the model either via pre-attentive processes or by conscious application of knowledge

In sum, this section documents cases where knowledge-driven processing does and does not influence decision making with visualizations. Notably, we describe numerous studies where well-designed visualizations (capitalizing on Type 1 processing) focus viewers’ attention on task-relevant relationships in the data, which improves decision accuracy for individuals with less developed health literacy, graph literacy, and numeracy. However, the current work does not test how knowledge-driven processing maps on to the dual-process model of decision making. Knowledge may be held temporally by working memory capacity (Type 2), held in long-term knowledge but strenuously utilized (Type 2), or held in long-term knowledge but automatically applied (Type 1). More work is needed to understand if a dual-process account of decision making accurately describes the influence of knowledge-driven processing on decision making with visualizations. Finally, we detailed an example of a dual-task paradigm as one way to evaluate if viewers are employing Type 1 processing.

Review summary

Throughout this review, we have provided significant direct and indirect evidence that a dual-process account of decision making effectively describes prior findings from numerous domains interested in visualization decision making. The reviewed work provides support for specific processes in our proposed model including the influences of working memory, bottom-up attention, schema matching, inference processes, and decision making. Further, we identified key commonalities in the reviewed work relating to Type 1 and Type 2 processing, which we added to our proposed visualization decision-making model. The first is that utilizing Type 1 processing, visualizations serve to direct participants’ bottom-up attention to specific information, which can be either beneficial or detrimental for decision making (Fabrikant et al., 2010 ; Fagerlin et al., 2005 ; Hegarty et al., 2010 ; Hegarty et al., 2016 ; Padilla, Ruginski et al., 2017 ; Ruginski et al., 2016 ; Schirillo & Stone, 2005 ; Stone et al., 1997 ; Stone et al., 2003 ; Waters et al., 2007 ). Consistent with assertions from cognitive science and scientific visualization (Munzner, 2014 ), we propose that visualization designers should identify the critical information needed for a task and use a visual encoding technique that directs participants’ attention to this information. We encourage visualization designers who are interested in determining which elements in their visualizations will likely attract viewers’ bottom-up attention, to see the Itti et al. ( 1998 ) saliency model, which has been validated with eye-tracking measures (for implementation of this model along with Matlab code see Padilla, Ruginski et al., 2017 ). If deliberate effort is not made to capitalize on Type 1 processing by focusing the viewer’s attention on task-relevant information, then the viewer will likely focus on distractors via Type 1 processing, resulting in poor decision outcomes.

A second cross-domain finding is the introduction of a new concept, visual-spatial biases , which can also be both beneficial and detrimental to decision making. We define this term as a bias that elicits heuristics, which is a direct result of the visual encoding technique. We provide numerous examples of visual-spatial biases across domains (for implementation of this model along with Matlab code, see Padilla, Ruginski et al., 2017 ). The novel utility of identifying visual-spatial biases is that they potentially arise early in the decision-making process during bottom-up attention, thus influencing the entire downstream process, whereas standard heuristics do not exclusively occur at the first stage of decision making. This possibly accounts for the fact that visual-spatial biases have proven difficult to overcome (Belia et al., 2005 ; Grounds et al., 2017 ; Joslyn & LeClerc, 2013 ; Liu et al., 2016 ; McKenzie et al., 2016 ; Newman & Scholl, 2012 ; Padilla, Ruginski et al., 2017 ; Ruginski et al., 2016 ). Work by Tversky ( 2011 ) presents a taxonomy of visual-spatial communications that are intrinsically related to thought, which are likely the bases for visual-spatial biases.

We have also revealed cross-domain findings involving Type 2 processing, which suggest that if there is a mismatch between the visualization and a decision-making component, working memory is used to perform corrective mental transformations. In scenarios where the visualization is aligned with the mental schema and task, performance is fast and accurate (Joslyn & LeClerc, 2013 ). The types of mismatches observed in the reviewed literature are likely both domain-specific and domain-general. For example, situations where viewers employ the correct graph schema for the visualization, but the graph schema does not align with the task, are likely domain-specific (Dennis & Carte, 1998 ; Frownfelter-Lohrke, 1998 ; Gattis & Holyoak, 1996 ; Huang et al., 2006 ; Joslyn & LeClerc, 2013 ; Smelcer & Carmel, 1997 ; Tversky et al., 2012 ). However, other work demonstrates cases where viewers employ a graph schema that does not match the visualization, which is likely domain-general (e.g. Feeney et al., 2000 ; Gattis & Holyoak, 1996 ; Tversky et al., 2012 ). In these cases, viewers could accidentally use the wrong graph schema because it appears to match the visualization or they might not have learned a relevant schema. The likelihood of viewers making attribution errors because they do not know the corresponding schema increases when the visualization is less common, such as with uncertainty visualizations. When there is a mismatch, additional working memory is required resulting in increased time taken to complete the task and in some cases errors (e.g. Joslyn & LeClerc, 2013 ; McKenzie et al., 2016 ; Padilla, Ruginski et al., 2017 ). Based on these findings, we recommend that visualization designers should aim to create visualizations that most closely align with a viewer’s mental schema and task. However, additional empirical research is required to understand the nature of the alignment processes, including the exact method we use to mentally select a schema and the classifications of tasks that match visualizations.

The final cross-domain finding is that knowledge-driven processes can interact or override effects of visualization methods. We find that short-term (Dennis & Carte, 1998 ; Feeney et al., 2000 ; Gattis & Holyoak, 1996 ; Joslyn & LeClerc, 2013 ; Smelcer & Carmel, 1997 ; Tversky et al., 2012 ) and long-term knowledge acquisition (Shen et al., 2012 ) can influence decision making with visualizations. However, there are also examples of knowledge having little influence on decisions, even when prior knowledge could be used to improve performance (Galesic et al., 2009 ; Galesic & Garcia-Retamero, 2011 ; Keller et al., 2009 ; Lee & Bednarz, 2009 ; Okan et al., 2015 ; Okan, Garcia-Retamero, Cokely, & Maldonado, 2012 ; Okan, Garcia-Retamero, Galesic, & Cokely, 2012 ; Reyna et al., 2009 ; Rodríguez et al., 2013 ). We point out that prior knowledge seems to have more of an effect on non-visual-spatial biases, such as a familiarity bias (Belia et al., 2005 ; Joslyn & LeClerc, 2013 ; Riveiro, 2016 ; St. John et al., 2001 ), which suggests that visual-spatial biases may be closely related to bottom-up attention. Further, it is unclear from the reviewed work when knowledge switches from relying on working memory capacity for application to automatic application. We argue that Type 1 and 2 processing have unique advantages and disadvantages for visualization decision making. Therefore, it is valuable to understand which process users are applying for specific tasks in order to make visualizations that elicit optimal performance. In the case of experts and long-term knowledge, we propose that one interesting way to test if users are utilizing significant working memory capacity is to employ a dual-task paradigm (illustrated in Fig.  19 ). A dual-task paradigm can be used to evaluate the amount of working memory required and compare the relative working memory required between competing visualization techniques.

We have also proposed a variety of practical recommendations for visualization designers based on the empirical findings and our cognitive framework. Below is a summary list of our recommendations along with relevant section numbers for reference:

Identify the critical information needed for a task and use a visual encoding technique that directs participants’ attention to this information (“ Bottom-up attention ” section);

To determine which elements in a visualization will likely attract viewers’ bottom-up attention try employing a saliency algorithm (see Padilla, Quinan, et al., 2017 ) (see “ Bottom-up attention ”);

Aim to create visualizations that most closely align with a viewer’s mental schema and task demands (see “ Visual-Spatial Biases ”);

Work to reduce the number of transformations required in the decision-making process (see " Cognitive fit ");

To understand if a viewer is using Type 1 or 2 processing employ a dual-task paradigm (see Fig.  19 );

Consider evaluating the impact of individual differences such as graphic literacy and numeracy on visualization decision making.

Conclusions

We use visual information to inform many important decisions. To develop visualizations that account for real-life decision making, we must understand how and why we come to conclusions with visual information. We propose a dual-process cognitive framework expanding on visualization comprehension theory that is supported by empirical studies to describe the process of decision making with visualizations. We offer practical recommendations for visualization designers that take into account human decision-making processes. Finally, we propose a new avenue of research focused on the influence of visual-spatial biases on decision making.

Change history

02 september 2018.

The original article (Padilla et al., 2018) contained a formatting error in Table 2; this has now been corrected with the appropriate boxes marked clearly.

Dual-process theory will be described in greater detail in next section.

It should be noted that in some cases the activation of Type 2 processing should improve decision accuracy. More research is needed that examines cases where Type 2 could improve decision performance with visualizations.

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This research is based upon work supported by the National Science Foundation under Grants 1212806, 1810498, and 1212577.

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LMP is the primary author of this study; she was central to the development, writing, and conclusions of this work. SHC, MH, and JS contributed to the theoretical development and manuscript preparation. All authors read and approved the final manuscript.

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LMP is a Ph.D. student at the University of Utah in the Cognitive Neural Science department. LMP is a member of the Visual Perception and Spatial Cognition Research Group directed by Sarah Creem-Regehr, Ph.D., Jeanine Stefanucci, Ph.D., and William Thompson, Ph.D. Her work focuses on graphical cognition, decision making with visualizations, and visual perception. She works on large interdisciplinary projects with visualization scientists and anthropologists.

SHC is a Professor in the Psychology Department of the University of Utah. She received her MA and Ph.D. in Psychology from the University of Virginia. Her research serves joint goals of developing theories of perception-action processing mechanisms and applying these theories to relevant real-world problems in order to facilitate observers’ understanding of their spatial environments. In particular, her interests are in space perception, spatial cognition, embodied cognition, and virtual environments. She co-authored the book Visual Perception from a Computer Graphics Perspective ; previously, she was Associate Editor of Psychonomic Bulletin & Review and Experimental Psychology: Human Perception and Performance .

MH is a Professor in the Department of Psychological & Brain Sciences at the University of California, Santa Barbara. She received her Ph.D. in Psychology from Carnegie Mellon University. Her research is concerned with spatial cognition, broadly defined, and includes research on small-scale spatial abilities (e.g. mental rotation and perspective taking), large-scale spatial abilities involved in navigation, comprehension of graphics, and the role of spatial cognition in STEM learning. She served as chair of the governing board of the Cognitive Science Society and is associate editor of Topics in Cognitive Science and past Associate Editor of Journal of Experimental Psychology: Applied .

JS is an Associate Professor in the Psychology Department at the University of Utah. She received her M.A. and Ph.D. in Psychology from the University of Virginia. Her research focuses on better understanding if a person’s bodily states, whether emotional, physiological, or physical, affects their spatial perception and cognition. She conducts this research in natural settings (outdoor or indoor) and in virtual environments. This work is inherently interdisciplinary given it spans research on emotion, health, spatial perception and cognition, and virtual environments. She is on the editorial boards for the Journal of Experimental Psychology: General and Virtual Environments: Frontiers in Robotics and AI . She also co-authored the book Visual Perception from a Computer Graphics Perspective .

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Padilla, L.M., Creem-Regehr, S.H., Hegarty, M. et al. Decision making with visualizations: a cognitive framework across disciplines. Cogn. Research 3 , 29 (2018). https://doi.org/10.1186/s41235-018-0120-9

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Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.

Data visualization can be utilized for a variety of purposes, and it’s important to note that is not only reserved for use by data teams. Management also leverages it to convey organizational structure and hierarchy while data analysts and data scientists use it to discover and explain patterns and trends.  Harvard Business Review  (link resides outside ibm.com) categorizes data visualization into four key purposes: idea generation, idea illustration, visual discovery, and everyday dataviz. We’ll delve deeper into these below:

Idea generation

Data visualization is commonly used to spur idea generation across teams. They are frequently leveraged during brainstorming or  Design Thinking  sessions at the start of a project by supporting the collection of different perspectives and highlighting the common concerns of the collective. While these visualizations are usually unpolished and unrefined, they help set the foundation within the project to ensure that the team is aligned on the problem that they’re looking to address for key stakeholders.

Idea illustration

Data visualization for idea illustration assists in conveying an idea, such as a tactic or process. It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence, but it can also be used to represent organization structures or processes, facilitating communication between the right individuals for specific tasks. Project managers frequently use Gantt charts and waterfall charts to illustrate  workflows .  Data modeling  also uses abstraction to represent and better understand data flow within an enterprise’s information system, making it easier for developers, business analysts, data architects, and others to understand the relationships in a database or data warehouse.

Visual discovery

Visual discovery and every day data viz are more closely aligned with data teams. While visual discovery helps data analysts, data scientists, and other data professionals identify patterns and trends within a dataset, every day data viz supports the subsequent storytelling after a new insight has been found.

Data visualization

Data visualization is a critical step in the data science process, helping teams and individuals convey data more effectively to colleagues and decision makers. Teams that manage reporting systems typically leverage defined template views to monitor performance. However, data visualization isn’t limited to performance dashboards. For example, while  text mining  an analyst may use a word cloud to to capture key concepts, trends, and hidden relationships within this unstructured data. Alternatively, they may utilize a graph structure to illustrate relationships between entities in a knowledge graph. There are a number of ways to represent different types of data, and it’s important to remember that it is a skillset that should extend beyond your core analytics team.

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The earliest form of data visualization can be traced back the Egyptians in the pre-17th century, largely used to assist in navigation. As time progressed, people leveraged data visualizations for broader applications, such as in economic, social, health disciplines. Perhaps most notably, Edward Tufte published  The Visual Display of Quantitative Information  (link resides outside ibm.com), which illustrated that individuals could utilize data visualization to present data in a more effective manner. His book continues to stand the test of time, especially as companies turn to dashboards to report their performance metrics in real-time. Dashboards are effective data visualization tools for tracking and visualizing data from multiple data sources, providing visibility into the effects of specific behaviors by a team or an adjacent one on performance. Dashboards include common visualization techniques, such as:

  • Tables: This consists of rows and columns used to compare variables. Tables can show a great deal of information in a structured way, but they can also overwhelm users that are simply looking for high-level trends.
  • Pie charts and stacked bar charts:  These graphs are divided into sections that represent parts of a whole. They provide a simple way to organize data and compare the size of each component to one other.
  • Line charts and area charts:  These visuals show change in one or more quantities by plotting a series of data points over time and are frequently used within predictive analytics. Line graphs utilize lines to demonstrate these changes while area charts connect data points with line segments, stacking variables on top of one another and using color to distinguish between variables.
  • Histograms: This graph plots a distribution of numbers using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. This visual makes it easy for an end user to identify outliers within a given dataset.
  • Scatter plots: These visuals are beneficial in reveling the relationship between two variables, and they are commonly used within regression data analysis. However, these can sometimes be confused with bubble charts, which are used to visualize three variables via the x-axis, the y-axis, and the size of the bubble.
  • Heat maps:  These graphical representation displays are helpful in visualizing behavioral data by location. This can be a location on a map, or even a webpage.
  • Tree maps, which display hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area size.

Access to data visualization tools has never been easier. Open source libraries, such as D3.js, provide a way for analysts to present data in an interactive way, allowing them to engage a broader audience with new data. Some of the most popular open source visualization libraries include:

  • D3.js: It is a front-end JavaScript library for producing dynamic, interactive data visualizations in web browsers.  D3.js  (link resides outside ibm.com) uses HTML, CSS, and SVG to create visual representations of data that can be viewed on any browser. It also provides features for interactions and animations.
  • ECharts:  A powerful charting and visualization library that offers an easy way to add intuitive, interactive, and highly customizable charts to products, research papers, presentations, etc.  Echarts  (link resides outside ibm.com) is based in JavaScript and ZRender, a lightweight canvas library.
  • Vega:   Vega  (link resides outside ibm.com) defines itself as “visualization grammar,” providing support to customize visualizations across large datasets which are accessible from the web.
  • deck.gl: It is part of Uber's open source visualization framework suite.  deck.gl  (link resides outside ibm.com) is a framework, which is used for  exploratory data analysis  on big data. It helps build high-performance GPU-powered visualization on the web.

With so many data visualization tools readily available, there has also been a rise in ineffective information visualization. Visual communication should be simple and deliberate to ensure that your data visualization helps your target audience arrive at your intended insight or conclusion. The following best practices can help ensure your data visualization is useful and clear:

Set the context: It’s important to provide general background information to ground the audience around why this particular data point is important. For example, if e-mail open rates were underperforming, we may want to illustrate how a company’s open rate compares to the overall industry, demonstrating that the company has a problem within this marketing channel. To drive an action, the audience needs to understand how current performance compares to something tangible, like a goal, benchmark, or other key performance indicators (KPIs).

Know your audience(s): Think about who your visualization is designed for and then make sure your data visualization fits their needs. What is that person trying to accomplish? What kind of questions do they care about? Does your visualization address their concerns? You’ll want the data that you provide to motivate people to act within their scope of their role. If you’re unsure if the visualization is clear, present it to one or two people within your target audience to get feedback, allowing you to make additional edits prior to a large presentation.

Choose an effective visual:  Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.

Keep it simple:  Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.

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Painting Pictures with Data: The Power of Visual Representations

visual representation

Picture this. A chaotic world of abstract concepts and complex data, like a thousand-piece jigsaw puzzle. Each piece, a different variable, a unique detail.

Alone, they’re baffling, nearly indecipherable.

But together? They’re a masterpiece of visual information, a detailed illustration.

American data pioneer Edward Tufte , a notable figure in the graphics press, believed that the art of seeing is not limited to the physical objects around us. He stated, “The commonality between science and art is in trying to see profoundly – to develop strategies of seeing and showing.”

It’s in this context that we delve into the world of data visualization. This is a process where you create visual representations that foster understanding and enhance decision making.

It’s the transformation of data into visual formats. The information could be anything from theoretical frameworks and research findings to word problems. Or anything in-between. And it has the power to change the way you learn, work, and more.

And with the help of modern technology, you can take advantage of data visualization easier than ever today.

What are Visual Representations?

Think of visuals, a smorgasbord of graphical representation, images, pictures, and drawings. Now blend these with ideas, abstract concepts, and data.

You get visual representations . A powerful, potent blend of communication and learning.

As a more formal definition, visual representation is the use of images to represent different types of data and ideas.

They’re more than simply a picture. Visual representations organize information visually , creating a deeper understanding and fostering conceptual understanding. These can be concrete objects or abstract symbols or forms, each telling a unique story. And they can be used to improve understanding everywhere, from a job site to an online article. University professors can even use them to improve their teaching.

But this only scratches the surface of what can be created via visual representation.

Types of Visual Representation for Improving Conceptual Understanding

Graphs, spider diagrams, cluster diagrams – the list is endless!

Each type of visual representation has its specific uses. A mind map template can help you create a detailed illustration of your thought process. It illustrates your ideas or data in an engaging way and reveals how they connect.

Here are a handful of different types of data visualization tools that you can begin using right now.

1. Spider Diagrams

spider diagram - visual representation example

Spider diagrams , or mind maps, are the master web-weavers of visual representation.

They originate from a central concept and extend outwards like a spider’s web. Different ideas or concepts branch out from the center area, providing a holistic view of the topic.

This form of representation is brilliant for showcasing relationships between concepts, fostering a deeper understanding of the subject at hand.

2. Cluster Diagrams

cluster diagram - visual representation example

As champions of grouping and classifying information, cluster diagrams are your go-to tools for usability testing or decision making. They help you group similar ideas together, making it easier to digest and understand information.

They’re great for exploring product features, brainstorming solutions, or sorting out ideas.

3. Pie Charts

Pie chart- visual representation example

Pie charts are the quintessential representatives of quantitative information.

They are a type of visual diagrams that transform complex data and word problems into simple symbols. Each slice of the pie is a story, a visual display of the part-to-whole relationship.

Whether you’re presenting survey results, market share data, or budget allocation, a pie chart offers a straightforward, easily digestible visual representation.

4. Bar Charts

Bar chart- visual representation example

If you’re dealing with comparative data or need a visual for data analysis, bar charts or graphs come to the rescue.

Bar graphs represent different variables or categories against a quantity, making them perfect for representing quantitative information. The vertical or horizontal bars bring the data to life, translating numbers into visual elements that provide context and insights at a glance.

Visual Representations Benefits

1. deeper understanding via visual perception.

Visual representations aren’t just a feast for the eyes; they’re food for thought. They offer a quick way to dig down into more detail when examining an issue.

They mold abstract concepts into concrete objects, breathing life into the raw, quantitative information. As you glimpse into the world of data through these visualization techniques , your perception deepens.

You no longer just see the data; you comprehend it, you understand its story. Complex data sheds its mystifying cloak, revealing itself in a visual format that your mind grasps instantly. It’s like going from a two dimensional to a three dimensional picture of the world.

2. Enhanced Decision Making

Navigating through different variables and relationships can feel like walking through a labyrinth. But visualize these with a spider diagram or cluster diagram, and the path becomes clear. Visual representation is one of the most efficient decision making techniques .

Visual representations illuminate the links and connections, presenting a fuller picture. It’s like having a compass in your decision-making journey, guiding you toward the correct answer.

3. Professional Development

Whether you’re presenting research findings, sharing theoretical frameworks, or revealing historical examples, visual representations are your ace. They equip you with a new language, empowering you to convey your message compellingly.

From the conference room to the university lecture hall, they enhance your communication and teaching skills, propelling your professional development. Try to create a research mind map and compare it to a plain text document full of research documentation and see the difference.

4. Bridging the Gap in Data Analysis

What is data visualization if not the mediator between data analysis and understanding? It’s more than an actual process; it’s a bridge.

It takes you from the shores of raw, complex data to the lands of comprehension and insights. With visualization techniques, such as the use of simple symbols or detailed illustrations, you can navigate through this bridge effortlessly.

5. Enriching Learning Environments

Imagine a teaching setting where concepts are not just told but shown. Where students don’t just listen to word problems but see them represented in charts and graphs. This is what visual representations bring to learning environments.

They transform traditional methods into interactive learning experiences, enabling students to grasp complex ideas and understand relationships more clearly. The result? An enriched learning experience that fosters conceptual understanding.

6. Making Abstract Concepts Understandable

In a world brimming with abstract concepts, visual representations are our saving grace. They serve as translators, decoding these concepts into a language we can understand.

Let’s say you’re trying to grasp a theoretical framework. Reading about it might leave you puzzled. But see it laid out in a spider diagram or a concept map, and the fog lifts. With its different variables clearly represented, the concept becomes tangible.

Visual representations simplify the complex, convert the abstract into concrete, making the inscrutable suddenly crystal clear. It’s the power of transforming word problems into visual displays, a method that doesn’t just provide the correct answer. It also offers a deeper understanding.

How to Make a Cluster Diagram?

Ready to get creative? Let’s make a cluster diagram.

First, choose your central idea or problem. This goes in the center area of your diagram. Next, think about related topics or subtopics. Draw lines from the central idea to these topics. Each line represents a relationship.

how to create a visual representation

While you can create a picture like this by drawing, there’s a better way.

Mindomo is a mind mapping tool that will enable you to create visuals that represent data quickly and easily. It provides a wide range of templates to kick-start your diagramming process. And since it’s an online site, you can access it from anywhere.

With a mind map template, creating a cluster diagram becomes an effortless process. This is especially the case since you can edit its style, colors, and more to your heart’s content. And when you’re done, sharing is as simple as clicking a button.

A Few Final Words About Information Visualization

To wrap it up, visual representations are not just about presenting data or information. They are about creating a shared understanding, facilitating learning, and promoting effective communication. Whether it’s about defining a complex process or representing an abstract concept, visual representations have it all covered. And with tools like Mindomo , creating these visuals is as easy as pie.

In the end, visual representation isn’t just about viewing data, it’s about seeing, understanding, and interacting with it. It’s about immersing yourself in the world of abstract concepts, transforming them into tangible visual elements. It’s about seeing relationships between ideas in full color. It’s a whole new language that opens doors to a world of possibilities.

The correct answer to ‘what is data visualization?’ is simple. It’s the future of learning, teaching, and decision-making.

Keep it smart, simple, and creative! The Mindomo Team

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Ways of Seeing: The scope and limits of visual cognition

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Introduction: What is human visual cognition?

  • Published: October 2003
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Humans can see a great variety of things. They can see tables, trees, flowers, stars, planets, mountains, rivers, substances, tigers, people, vapors, rain, snow, gases, flames, clouds, smoke, shadows, flashes, holes, pictures, signs, movies, events, actions (including people seeing any of the preceding). They can see properties of things such as the color, texture, orientation, shape, contour, location, motion of objects. They can see facts, such as the fact that a given object exemplifies a set of visual attributes and/or stands in some visual relation to other objects. Sight, visual experience or visual perception, is both a particular kind of human experience and a fundamental source of human knowledge of the world. Furthermore, it interacts in multiple ways with human thought, human memory and the rest of human cognition.

Many of the things humans can see they can also think about. Many of the things they can think about, however, they cannot see. For example, they can think about, but they cannot see at all, prime numbers. Nor can they see atoms, molecules and cells without the aid of powerful instruments. Arguably, while atoms, molecules and cells are not visible to the naked eye, unlike numbers, they are not invisible altogether: with powerful microscopes, they become visible. Unlike numerals, however, numbers—whether prime or not—are simply not to be seen at all. Similarly, humans can entertain the thought, but they cannot see, that many of the things they can think about they cannot see.

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The Daily Sound

Everything You Need to Know About Visual Representation

School reports, proposal presentations, office meetings, or teaching students —  whatever it is you’re doing that involves numbers and big amounts of data, visual representation can make your job easier. For many years, we have been using it to make our data perceivable in the eyes of our audience. It helps us give a clear idea of what information means by visual context.

The creativity and effort it takes to come up with a general and effective way to display informative data is tiring. Fortunately, there are graph makers like Venngage that can help you make your presentations vividly and communicatively.

VISUAL-REPRESENTATION

    Campaign creators/Unsplash

What is a visual representation and why is it important?

Visual presentation is an effective and fast-paced method to convey figures and statistical data in a comprehensible manner. It makes information easily accessible by feeding into your audience’s natural tendency to learn by seeing and interacting.

It uses visual data to aid companies in identifying which areas need improvement or in comparing the company’s sales from the last six months. This practice represents numbers in a study, provides shape, pattern, and a brief comparison of data which is crucial in analysis and decision making.

Without a visual representation of data and knowledge, identifying the correlations between the relationship of an independent variable will be challenging.

Who uses visual representation?

With today’s technology, people are inclined to use an easier way to complete tasks. With the help of desktops, laptops, and smartphones, we can easily gather data and digitalize reports.

That’s why almost everyone uses visual representation. Students, employees, and big companies all benefit from it. It’s universal, fast, and efficient in conveying data and knowledge.

Types of Visual Representations

WHY-IS-IT-IMPORTANT

Ruthson Zimmerman/ Unsplash

Visual representation comes in different forms and shapes that help us organize and understand data in a much simpler way. Venngage also offers a wide range of ready-to-use templates for such projects. It lets you choose from a simple line graph to much more complex and detailed presentations.

To give you an idea, here are some examples:

Graphs and Charts

Graphs

Isaac Smith/ Unsplash

Graphs and charts compress a large quantity of information into a comprehensible format that clearly and effectively communicates important elements.

To present your data effectively , you need to know the purpose of your graph/chart and what you want to present. Check and filter what you want to include and whether they should be expressed as frequencies or categories.

Graphs and charts have many different types, familiarizing them makes it easier for a business to choose the one that fits their need most.

maps

Clay Banks/ Unsplash

You can use a map to clearly illustrate gathered demographic illustrations like age, ethnicity, race, gender, and marital status. This type of representation is best used in plotting population data and surveys .

Data relatable illustrations

Using relatable icons or symbols for your data report could also help convey your message to your audience. It also adds life and color to your presentations making your report comprehensible and at the same time pleasing to the eye.

Tree diagram

A tree diagram represents variables that are in sequence or an independent event or condition relating to possible actions. It is also best used in organizing a hierarchy of systems.

How to use visual representation effectively?

Behind every effective visual representation is a hardworking person who puts his ideas and plans into action. Here are some quick tips to help you create a successful visual presentation:

Gather all the needed data

Gathering all the needed data for your report is a crucial step in making a successful presentation. Incomplete data would highly reflect on your visual presentation’s outcome. Always remember that every variable is important in statistical reports.

Organize all data

After gathering all the important data for your presentation, review them one by one and match them according to their relatability. You can pair up each data or group them to make the next step easier.

After organizing your data, analyze what type of data visualization would best suit your report. Choosing the right format would increase your visual representation’s success.

Be creative and fun

Do not forget to add colors to your presentation. Reports can sometimes become too dragging and one way to spice it up is to make your presentation visually enticing.

It sets a light and fun mood that can make presentations and/or learning exciting and easy.

Using visual representation is easy and is a sure way to present your variables effectively. All you have to do is consider your resources and make sure that you have all the essential tools in crafting your visuals. Graph maker platforms can also be of big help if you’re confused about which template would best fit your data.

Now that you’ve had all the information you need in making data presentations, you can now start and make one today!

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  1. Visual Representation Definition And Meaning In English

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  2. Visual Representation in Mathematics

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  3. Visual Representation of Data: Definition & Best Examples in 2023

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  5. Visual Encoding: 10 Examples and Definition (2024)

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  6. Perception Psychology and How We Understand Our World

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  1. Visual representation of school

COMMENTS

  1. What is Visual Representation?

    Visual Representation refers to the principles by which markings on a surface are made and interpreted. Designers use representations like typography and illustrations to communicate information, emotions and concepts. Color, imagery, typography and layout are crucial in this communication.

  2. VISUAL REPRESENTATION definition and meaning

    VISUAL REPRESENTATION definition | Meaning, pronunciation, translations and examples

  3. VISUAL REPRESENTATION collocation

    Examples of VISUAL REPRESENTATION in a sentence, how to use it. 19 examples: The photo provides a visual representation of equality of status, in which no one performer serves…

  4. What is Visual Representation

    A spatial depiction in 2D, 3D, or 4D. Visual representation is mainly the direct or symbolic reflection of something in the format of photos, the images, memes, graphics to represent people, things, a place, or a situation. Published in Chapter: Orientalist Representations of Antakya (Antioch-on-the-Orontes) in Digital Media Narrations ; From ...

  5. Visual Representation

    Definition. The concept of "representation" captures the signs that stand in for and take the place of something else [ 5 ]. Visual representation, in particular, refers to the special case when these signs are visual (as opposed to textual, mathematical, etc.). On the other hand, there is no limit on what may be (visually) represented ...

  6. The role of visual representations in scientific practices: from

    The use of visual representations (i.e., photographs, diagrams, models) has been part of science, and their use makes it possible for scientists to interact with and represent complex phenomena, not observable in other ways. Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using ...

  7. What Is Data Visualization? Definition & Examples

    Data visualization is the graphical representation of information and data. By using v isual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Additionally, it provides an excellent way for employees or business owners to present data to non ...

  8. Visual Representation

    Visual rhetoric Definition of regions. Regions. Alignment grids Borders and frames Area fills White space Gestalt integration. Containment ... a visual representation to help students learn about electricity will be used to explain the topology of circuits, make computations with electrical quantities, provide explanations of circuit behaviour ...

  9. Step 2: Understanding Visual Representation(s)

    Consequently, a visual representation is an event, process, state, or object that carries meaning and that is perceived through the visual sensory channel. Of course, this is a broad definition. It includes writing, too, because writing is perceived visually and refers to a given meaning.

  10. Visualization (graphics)

    As a subject in computer science, scientific visualization is the use of interactive, sensory representations, typically visual, of abstract data to reinforce cognition, hypothesis building, and reasoning. Scientific visualization is the transformation, selection, or representation of data from simulations or experiments, with an implicit or explicit geometric structure, to allow the ...

  11. IRIS

    Page 5: Visual Representations. Yet another evidence-based strategy to help students learn abstract mathematics concepts and solve problems is the use of visual representations. More than simply a picture or detailed illustration, a visual representation—often referred to as a schematic representation or schematic diagram— is an accurate ...

  12. Scaffolding Learning by the Use of Visual Representations

    To this end, visual representations in instruction should ideally (1) scaffold the process of reasoning, (2) support student-initiated conceptual understanding, and (3) allow the transfer between multiple forms of representation. Mechanisms of scaffolding. For meaningful learning to take place, students need to actively access relevant prior ...

  13. The visual representation of complexity: Definitions, examples and

    This visual overview was developed through a research process that identified, defined and illustrated 16 key features of complex systems. The research addresses the need for images that are widely understood across different fields and sectors for researchers, policymakers, and design practitioners and contributes to an evolving visual language of complexity.

  14. VISUAL REPRESENTATION definition in American English

    consume energy. muster the energy. Features such as electric windows . Your score: Feb 12, 2024. extremely romantic. VISUAL REPRESENTATION meaning | Definition, pronunciation, translations and examples in American English.

  15. Decision making with visualizations: a cognitive framework across

    Visualizations—visual representations of information, depicted in graphics—are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns).

  16. What Is Data Visualization?

    Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand. Data visualization can be utilized for a variety of purposes, and it ...

  17. On the Function of Visual Representation

    Specifically, it is posited that there is evidence of selective visual representation, dependent on the importance of the visual stimuli to the particular individual. ... One of its major successes was the definition of the 2½ dimensional sketch, a representation that is used by what has been termed "early vision." The kinds of explicit ...

  18. Visual Representations: Unleashing the Power of Data Visualization

    As a more formal definition, visual representation is the use of images to represent different types of data and ideas. They're more than simply a picture. Visual representations organize information visually, creating a deeper understanding and fostering conceptual understanding. These can be concrete objects or abstract symbols or forms ...

  19. Data Visualization: Definition, Benefits, and Examples

    Data visualization is the representation of information and data using charts, graphs, maps, and other visual tools. These visualizations allow us to easily understand any patterns, trends, or outliers in a data set. Data visualization also presents data to the general public or specific audiences without technical knowledge in an accessible ...

  20. Introduction: What is human visual cognition?

    Visuomotor representations, which are visual representations of those visual aspects of a target that are relevant to the action to be performed, result from what we shall call the 'pragmatic' processing of objects. In normal human beings, visual perception and the visual control of actions work in tandem.

  21. The Pitfalls of Visual Representations: A Review and Classification of

    Despite the notable number of publications on the benefits of using visual representations in a variety of fields (Meyer, Höllerer, Jancsary, & Van Leeuwen, 2013), few studies have systematically investigated the possible pitfalls that exist when creating or interpreting visual representations.Some information visualization researchers, however, have raised the issue and called to action ...

  22. Visual Representation

    Visual representation comes in different forms and shapes that help us organize and understand data in a much simpler way. Venngage also offers a wide range of ready-to-use templates for such projects. It lets you choose from a simple line graph to much more complex and detailed presentations.

  23. visual representation Definition

    visual representation. A visual representation (computer generated or hand-drawn rendering) of the artwork showing detailed and overall views; and a maquette, model, photograph and/or map showing the artwork in context. • Budget: Detailed budget listing all costs associated with the gift and identification of sources for funding.