Download Visual Salience in Climate Change Imagery is in the Eye of the

Visual Salience in Climate Change Imagery is in the Eye of the
Beholder
Julie C. Libarkin
Geocognition Research Lab, Department of Geological Sciences and Center for
Integrative Studies in General Science, Michigan State University, East Lansing MI,
USA
Stephen R. Thomas
Department of Zoology and Center for Integrative Studies in General Science,
Michigan State University, East Lansing MI, USA
Gregory Ruetenik
Geocognition Research Lab, Department of Geological Sciences, Michigan State
University, East Lansing MI, USA
Visual Salience in Climate Change Imagery is in the Eye of the
Beholder
Complex phenomena like climate change are often communicated visually. In
this paper, we investigated the effectiveness of common visuals used to convey
key messages about climate change. We also present a new methodology for
analysis of eye tracking data that takes advantage of functionalities already
available in Geographic Information Systems (GIS) software. Eye tracking
analysis of two sets of images, one disseminated by an international organization
and the other redesigns of these images, indicates that viewers do not always
engage with images as intended. While expert scientists generally understand
where to look within traditional images, novices often ignore graphical scales and
keys, focus on artistic yet unimportant elements of schematic images, and view
image components in random order. We show that careful image redesign can
focus viewer attention on important image elements and encourage directed
viewing to enhance the potential for effective communication.
Keywords: eye tracking; climate change; visual communication; visual salience
Introduction
While recognition that Earth’s temperature is increasing has clearly risen in the
past decade (Akerlof et al., 2010), many groups still hold core alternative conceptions
about climate change on Earth. Limited understanding of climate change processes or
implications can be found in K-12 (Sweeney & Sterman, 2007) and college (Lombardi
& Sinatra, 2010) students, teachers (Papadimitriou, 2004), the general public (Weber &
Stern, 2011; Whitmarsh, 2009), and other groups (Maibach, Witte, & Wilson, 2011).
Early work indicated that even highly educated individuals confused ozone depletion
and the greenhouse effect, as well as weather and climate, and considered global
warming and the greenhouse effect to be the same phenomenon (Bostrom, Morgan,
Fischhoff, & Read, 1994). These same ideas continue to be documented in multiple
populations. For example, Papadimitriou (Papadimitriou, 2004) documented confusion
in pre-service teachers, particularly confounding of different environmental issues, such
as ozone depletion and acid rain, with the greenhouse effect. Climate change has
proven to be a particularly difficult concept to communicate (Leiserowitz, 2006;
Sterman, 2011; Whitmarsh & Lorenzoni, 2010), with very little traction observed in
developing new perceptions, behavior and action among the general public (Fortner et
al., 2000; Ockwell, Whitmarsh, & O’Neill, 2009; Whitmarsh & Lorenzoni, 2010)
A wide array of suggestions for improving climate literacy have been proposed
and implemented across many populations and over many years (Gowda, Fox, &
Magelky, 1997; J.D. Sterman, 2011; Svihla & Linn, 2011). Scholars argue that climate
change can be best conveyed through initial focus on uncovering and utilizing existing
alternative conceptions (McCaffrey & Buhr, 2008; Pruneau et al., 2001; Rebich &
Gautier, 2005), recognition by learners that climate change poses a concrete and
immediate risk (Ungar, 2000), use of disaster narratives to encourage decision-making
towards action (Lowe et al., 2006), and inclusion of elements that appeal to values and
affect important in perceptions of risk related to climate change (Leiserowitz, 2006).
Myriad materials to enhance climate literacy have been designed and implemented for
public safety and emergency management personnel (Arndt & LaDue, 2008), college
students (Rebich & Gautier, 2005) and the general public (Solomon et al., 2007). In
fact, climate change instructional tools and pedagogies exist as literally thousands of
documents and websites (e.g., 6061 hits returned for “climate change” at
http://serc.carleton.edu/serc/). However, despite this significant effort to communicate
key concepts about climate change and encourage action, the public maintains
misconceptions (Weber & Stern, 2011) and is generally slow to act (Ungar, 2000).
Discussion of these decades of effort recognizes the need for more research into the
effectiveness of climate change communication (Moser, 2010), as well as the complex
factors that influence climate change understanding (Weber & Stern, 2011).
Scientific findings are often best conveyed through a combination of words and
visuals (Carney & Levin, 2002; Mayer, 1989; Trumbo, 1999). Environmental
communication relies heavily on the benefits of visuals to convey complex relationships
between Earth’s spheres, to generate affective responses to environmental issues, and to
induce behavioural change. Technological advancements allow rapid production of
visuals, incorporating cutting-edge science into materials designed for general public
consumption. Indeed, images have an increasingly significant presence in textbooks
(Smith & Elifson, 1985), journals (Roth, Bowen, & McGinn, 1999), and public media
(Dimopoulos, Koulaidis, & Sklaveniti, 2003). However, the communication value of
these visuals is highly dependent upon their design (Larkin & Simon, 1987; Mayer,
1989; Trumbo, 1999; Tufte, 1983).
The vast majority of research related to public understanding or attitudes
towards climate change focuses on the use of existing text or images. Imagery is
recognized as an important component of persuasive discourse. For the case of climate
change, imagery is clearly an important aspect of materials produced to persuasively
encourage public understanding and action (Hansen & Machin, 2008). By far, research
into the role of the visual in conveying climate change focuses on persuasive
messaging.
Similarly, visuals also play a significant role in transferring knowledge about
environmental concerns, possible solutions, and the role that knowledge plays in
environmental behaviors (Hines, Hungerford, & Tomera, 1987). Knowledge about
environmental issues indirectly affects pro-environmental behavior, primarily in light of
knowledge’s direct association with variables such as social and moral norms and
internal attribution of environmental problems (Bamberg & Möser, 2007). A person’s
understanding of underlying mechanisms of environmental problems and their
relationship to other variables also affects their ability to properly assess the impacts of
their actions. Accurate transfer of information is an essential first step to environmental
behavior.
Image design can be used to affect low-level image processes such as
preattentive attributes that precede attention, or it can influence high-level image
processes that call upon long-term and working memory. Images designed to
communicate scientific information might take advantage of low-level image processing
(e.g. color, size, shape, or orientation) to draw attention to desired locations; whereas,
high-level strategies might use iconographic images familiar to the viewers, or
incorporate layouts that are typical of a discipline.
The impact of image design on low or high-level image processes depends on
qualities of the viewer. Most low-level strategies are typically effective for most
audiences because they rely on physical/neurological characteristics of human vision
and perception (i.e. a red line will typically get more attention than a black one). Even
these strategies though can be ineffective for some populations, such as the inability of a
colorblind individual to distinguish color added for emphasis. Many recommendations
for figure and graph design focus on the qualities of the visual without addressing
qualities of the viewer. Unfortunately, guiding a viewer to specific line on a graph does
not mean that they can then interpret that line’s meaning. Depending on the situation, a
lack of viewer consideration may affect communication. When experts in a field are
communicating with other experts, less attention has to be paid to the cognitive abilities
that the viewer brings to the external representation because training equalizes the
differences between the producer and the viewer of the image. Despite a general
recognition that novices need more guidance in interpreting a visual, (Roth et al., 1999)
found that textbooks often lacked supplementary information that journal articles
showed: novices were given less assistance in image interpretation that that afforded to
experts. We would argue that when the objective of an image is to convey scientific
information to a lay audience, more attention has to be paid to the cognitive demands
that are implicit in the external representation. In modifying the UNEP images we made
a series of changes to improve both low and high-level image processing of the image
by naïve viewers.
A number of approaches can been used to study scientific visuals and their
effectiveness. Analysis of use of images, particularly photos, by news media is common
(Keith, Schwalbe, & Silcock, 2010), as is comparison of student drawings and common
scientific models (Ainsworth, Prain, & Tytler, 2011). Use of survey, interview, and
think-aloud methods are also normal mechanisms for inquiry into image effectiveness.
Less common approaches utilizing new technologies, such as Tablet PCs, have begun to
generate fundamental insights into human cognition about visuals (Forbus, Usher,
Lovett, Lockwood, & Wetzel, J., 2008; Turner & Libarkin, 2011).
Studies of scientific visualization using eye tracking techniques have only
recently begun to generate evidence for the most effective elements of visual design.
Ozcelik and colleagues (2009) report on the value of color coding in conveying
scientific information in visuals. In this study, an individual’s ability to identify
relationships between text and images, as conveyed through color, related to improved
material retention. In addition, this work suggests that the ability to more quickly
identify correspondence between text and images relates to more time spent on
important, conceptual elements. Eye tracking research has not yet fully ventured into
analysis of interactions with authentic climate data or visuals (Breslow, Ratwani, &
Trafton, 2009).
This work asks fundamental questions about the efficacy of current scientific
visualizations for conveying climate change messages to a general public. In particular,
we utilized eye tracking techniques to consider the visual salience of UNEP images to
illustrate climate change to the global public. Similar analysis of redesigns of these
images allows us to consider the extent to which participant attention, discourse and
comprehension are influenced by visual design. This work hypothesizes that images
designed with attention to information design principles that minimize cognitive load
will improve visual communication of science to the public by scaffolding any
cognitive deficits of the viewer. Therefore, we would predict more variation between
scientists and non-scientists in how they engage with images requiring more
complicated or involved cognitive tasks, as reflected in individual gaze plots and
aggregate heat maps, as well as discourse. We would also predict those differences in
performance to become minimized with images that have been designed with regard to
the cognitive abilities of the viewer.
Methodology
Sampling and Design
This study was conducted with an undergraduate student sample at a large Midwestern
University in the USA. Participants (n=10) were volunteers recruited from students
enrolled in entry-level geoscience or geography courses.
The research design mimicked the viewing behavior of an individual engaging
with climate change images outside of a formal educational context. As such,
participants were encouraged to engage in spontaneous looking (SL) under the simple
directive to identify the main message of an image. The SL condition was chosen to
replicate the type of interaction with images expected during newspaper reading,
internet searching, or television viewing. Although participants were prompted to
identify the main message of each image, identification of a message was not required
for subjects to move on in the experimental sequence.
Procedure
All participants were pre-screened one day to one week before the testing date. During
pre-screening, participants were tested for color normal vision through random
assignment of 10 test plates from the Ishihara test plate kit. Participants also completed:
a) a brief demographic survey; and b) a test of climate change conceptions consisting of
one open-ended and six multiple-choice items (Table 1).
On the day of testing, participants individually engaged with eye-tracking
stimuli. After reading and signing a consent form, participant gaze was calibrated on a
Tobii T-60 eye tracker. After calibration, participants worked through the eye tracking
experiment, viewing either the original UNEP images (“O” in Table 2) or revised
images (“R” in Table 2). In all, four non-science majors and one faculty engaged with
the UNEP images; five non-science majors, one undergraduate science major, and one
graduate student engaged with the revised images. During viewing, participants were
prompted with written instructions that were also read aloud by the interviewer: “Talk
about what you see, and what you are thinking. You may spend as long as you like
looking at the image until you feel you understand it. You may also decide that you do
not understand the image, and choose to move on. When you feel like you understand
the image or are simply ready to move on, click the left mouse button. You will be
prompted to answer questions after each image.” After viewing each image,
participants were prompted with written instructions, “What was the main message of
the image you just viewed? Talk out loud.” Finally, participants responded to one or
more multiple-choice questions about each image. Over the course of the experiment,
participants viewed two control images to provide training in thinking aloud, and then
viewed a set of experimental images.
Instrumentation
This study utilized six climate change concept inventory questions to establish preexperiment conceptual understanding of climate change. These six multiple-choice
items derive from the Geoscience Concept Inventory community assessment and adhere
to highest standards in validity and reliability of concept inventories (Table 1; Libarkin,
Ward, Anderson, Kortemeyer, & Raeburn, 2011). Questions have undergone piloting
with non-science majors, teachers, environmental journalists, and have been reviewed
by expert scientists to establish validity and reliability. As such, these questions are a
general measure of climate change literacy. After image viewing, participants also
answered image-specific multiple-choice questions designed to measure the extent to
which participants were able to extract image messages.
Four common IPCC images were re-designed utilizing information design
approaches strategies to improve low and high-level image processing. These four
images conveyed: 1) the carbon cycle, including both reservoirs and processes (Carbon
Cycle), 2) Earth’s temperature in the past and projected increase into the future (Temp
Increase), 3) Cost, in units of Gross Domestic Product (GDP), for stabilization of
carbon dioxide in Earth’s atmosphere (GDP), and 4) Earth’s temperature and carbon
dioxide levels in the past (Temp+CO2). Images were titled to convey the most
important message central to the image design. This aligns with work suggesting that
text and image in public media often do not align in materials communicating climate
change (Anne DiFrancesco & Young, 2011).
The Tobii T-60 eye tracker (Fig. 2) is built into a 17-inch TFT monitor.
Participants gaze at materials on the monitor without physical restriction. This allows
collection of gaze data from participants in situations similar to real-world computerbased engagement with materials, such as reading of websites or online newspapers.
During calibration, research participants sit comfortably and follow a dot as it moves
across the monitor; occasionally recalibration is required to ensure a high degree of data
accuracy. In general, this system is able to collect data regardless of eyewear or limited
pupil exposure due to drooping eyelids, and allows participants freedom of natural head
movement. The system collects 60 gaze samples per second.
A suite of verbal data was also collected during this experiment. During eye
tracking, participants were encouraged to think aloud and articulate what they were
viewing. After image viewing, participants were prompted to describe the main message
of each image. Finally, participants engaged in a retrospective interview while they
viewed their own gaze behavior. This allowed the interviewer an opportunity to ask for
feedback on specific images. Participants were asked one or more probing questions, as:
What are your general thoughts about this image? Did you like or dislike the image?
What parts of the image stood out in particular? Did you have any specific feelings as
you viewed the image? Do you have any suggestions for improving the image? Think
aloud and retrospective interview data were considered only in relation to areas of
interest uncovered through analysis of eye tracks. These data were used as support for
interpretations of participant SL interactions with images.
Data Analysis
Eye-tracking data were analyzed relative to traditional standards and using innovative
approaches. Gaze plots of eye track data were analyzed to consider common patterns in
individual eye movements during SL. Gaze plot analysis offers evidence of order in
which elements are viewed, including elements viewed first or longest, elements that
attract, or do not attract, attention and general sense of image comprehension. More
specific analysis of element gaze order provides evidence of confusion; switching
repeatedly back to specific explanatory elements such as legends, for example, suggests
poor messaging. Kernel density analysis, with output often termed “heat maps” in eye
tracking research, provides a spatial map of longest viewed elements across a study
sample. This can indicate elements that are particularly attractive or visually salient.
All analyses were performed using GIS technology (ESRI ArcGIS). This allows
more complex analysis of spatial-temporal data sets than standard eye tracking
approaches. ArcGIS is a suite of programs commonly used by geographers and
geologists for analyzing spatial, temporally independent data. The potential of ArcGIS
for analyzing temporally dependent spatial data has only recently been recognized, and
as far as we know ArcGIS has never been applied to conduct routine analysis of eye
tracking data. Although ArcGIS does not contain many standard tools for the analysis of
temporally dependent data, GIS’s interface with Python allows production of clusters
and cluster centroids based on unfiltered data exported from the Tobii Eyetracking
Suite. Circles of variable radii were used to represent fixation time at each centroid, and
lines were drawn between successive circles to illustrate gaze paths.
Gaze was recorded sixty times per second. Each gaze point recorded by the eye
tracker is attached to Cartesian coordinates (in pixels), a unique time stamp, and a
metric relating the degree to which the system was able to constrain corneal position.
Lack of corneal constraint is common with high Hz systems, and typically results from
blinking or excess subject movement. We filtered out all points where both eyes had
weak corneal constraint, indicating that neither eye could be found by the system. Data
were then translated over the y-axis in order to map gaze points in ArcGIS, placing
them in the 4th quadrant. This is the default position for static base maps, the role
played by the images being viewed by participants.
We further filtered data by eliminating points not representative of fixations. In
this work, a fixation is considered as a temporal construct, and was set at a minimum
value of 0.1 seconds. Gaze points were eliminated as saccades if they did not fall within
a search radius of 30 pixels of at least 5 other gaze points in temporal sequence. Gaze
points within each other’s search radius that were not eliminated were considered to be
part of the same cluster. Clusters are thus separated by saccades and numbered in the
order they occurred.
Cluster centroids were calculated by fitting a minimum area circle that encloses
all gaze point in the cluster, with at least two points necessarily defining the boundary.
The unweighted centroid of this circle is defined as the centroid of the cluster. We then
created a buffer around each centroid to create a circle of radius 4√n pixels, where n is
the number of points within the cluster. Thus, the area of the representative circle is
directly correlated with fixation time. Traditional gaze plots were thus generated in
ArcGIS by connecting each generated circle in order.
Heat maps are a common display mode utilized by eye tracking researchers.
Heat maps visually grade the amount of fixation time spent by one or more participants
on different parts of an image. In ArcGIS, heat maps can be generated through use of
the kernel density function. The kernel density assigns a numerical value to each cell
within a grid of a given resolution based on the density of points around the cell. The
cell is assigned the summative value of all Gaussian functions centered at points that
exist within a given search radius from each cell. These functions have a maximum
value of one and decrease with distance, approaching a limit of zero near the edge of the
search radius. We used a cell size of 0.1 pixels and a search radius equal to 1/30th of the
minimum dimension of the image; this was chosen to account for variance in resolution
among images. We then assigned a color gradient to each cell ranging from light to
dark red, with light red representing lower values and dark red representing higher
values.
Results
Socio-Demographic Characteristics
A total of twelve participants engaged in one-hour eye tracking and interview sessions
(Table 2). Participants were Caucasian (n=7), African-American (n=3), or Other (n=2).
Non-scientist participants (n=9) were eight females and one male, had engaged in one to
four undergraduate science or math courses (typical of USA non-science majors) and
were 20-29 years of age (M=21.6; SD=3.0). All but one non-scientist participant had at
least one parent with a B.S. or higher education level; one participant’s parents were
both high school non-graduates. One undergraduate science major (20 years), one
graduate student (29 years) and one professor of science (55 years) were utilized as an
expert control. These experts were two females and one male, respectively, and had
parental education levels ranging from high school to advanced graduate education.
Additional details of participant demographics are provided in Table 2.
Participants were asked to rate their level of comfort reading and interpreting
graphs on a scale of “not at all comfortable” (coded 1), “somewhat comfortable” (coded
2), “comfortable” (coded 3), and “very comfortable” (coded 4). Non-scientist
participants reported moderate comfort reading and interpreting graphs (M=2.6;
SD=0.5). The undergraduate expert reported being comfortable with reading and
interpreting graphs, while both the graduate and professional expert reported being very
comfortable.
Climate Change Literacy
Participants’ responses to the open-ended question “What is climate change?”
indicate a typical range of understanding of climate. It is important to note that climate
change is a natural phenomenon, with some modern increase in global temperature
resulting from human activities. Responses ranged from basic weather-related
explanations to explanations that encompass the spatial and temporal nature of climate.
Two responses were difficult to code, although one of these participants indicated that
climate is “not just a weather thing.” One non-scientist indicated a purely weatherrelated concept, as indicated by the response, “Change in weather which could result in
change in temperature.” Regional variations in weather were observed in two responses,
as indicated by, “How cold or warm the atmosphere changes in different regions,” and
“Shifts in weather patterns over large geographic area.” Two participants indicated the
global nature of climate, as in “The change in temperature and precipitation patterns
around the globe, likely do [sic] to human influence.” One of these participants and a
second participant articulated the importance of time in their responses, as in “The
change in weather over a period of time over the entire earth.” Finally, two participants
related climate change to “greenhouse gases” or “GHGs” and two articulated that
climate change is human caused, as in, “…caused by humans” and “likely do [sic] to
human influence.”
Climate change literacy scores for eleven (of n=12) responding participants
ranged from 2-6. Scientists’ scores (M=4.7; SD=1.5) were one point higher on average
than non-scientists’ scores (M=3.7; SD=2.0). Conceptual understanding of climate
change by experts is not expected to be perfect, since being a scientist does not imply
expertise in all areas of science. The undergraduate science major scored a 3, the
graduate student scored a 5 and the professional scored a 6; a similar range of scores for
non-scientists was observed, from 2-6 (Table 1; Fig. 1). All participants correctly chose
a response indicating that “Global warming over the past 50 years is mostly caused by
human activities” (Table 1). Most participants recognized that scientists generally agree
that global warming is happening, although two non-scientists indicated a belief that
scientists do not agree on whether or not global warming is occurring. Participants also
generally recognized that carbon dioxide concentrations have been increasing
exponentially over the past 500 years. Participants had much more difficulty responding
to questions about the greenhouse effect and greenhouse gases. Overall, these data
suggest a population that holds some overall understanding of climate change without
deeper conceptual understanding of underlying processes.
Eye Tracking Results
Eye tracking data were analyzed to identify differences in the SL condition
between a) expert and novice viewing behavior and b) viewing behavior with original
and revised images. Data were analyzed to reveal features that were visually salient as
well as viewing behavior suggestive of confusion. Time-on-task and verbal data were
considered as support for eye tracking data.
Expert-Novice Viewing Behavior
Comparison of expert and novice gaze plots indicate that experts attend to images
differently than novices (Fig. 3,4). In particular, expert gaze tends to be directed
towards scientifically related content. Novice gaze is often less directed, with limited
fixation on scientific features or movement of gaze between features. For example,
expert gaze of the original Temp+CO2 image indicates that the expert (in this case an
undergraduate scientist) recognizes the importance of graph axes in explaining plotted
data. This participant also spends time moving between related highs and lows on the
temperature and CO2 graphs. In contrast, most novices fixate randomly on data. As
shown in Fig. 4, the gaze pattern does not suggest recognition of connections between
the two data sets; rather, the novice is fixating on graphical elements in seemingly
random order. In both cases, text elements, such as the title, receive significant
attention. In addition, the novice fixates on the x-axis labels of the graph (Fig. 4); this is
unusual for most novice gaze on graphical elements observed in this study.
Original Versus Revised Images
Individual gaze plots provide insight into potential confusion and underlying
cognition potentially driving viewing behavior (Fig. 5, 6, 7, 8). Gaze plots relative to the
original Carbon Cycle image (Fig. 5) indicate that participants generally viewed the
image in a disjointed manner, with multiple undirected and repetitive views of the same
features. This disjointed viewing was in evidence even for experts, as shown in Figure
5. In contrast, participants viewed the revised Carbon Cycle image (Fig. 6) almost
cyclically, moving from specific features in generally uniform patterns. This cyclic
viewing occurred regardless of expertise, as shown with the novice view illustrated in
Fig. 6. More complex gaze patterns for other participants retained their cyclic nature,
with repeated visits to features generally following the specified pathways shown within
the image itself.
Participant viewing of graphs (Fig. 7,8) was much more directed than viewing of
abstract representations (Fig. 5,6), regardless of expertise level. Gaze plots of the
“hockey stick curve” image indicate that participants view titles and data in most cases,
and legends in some cases (Fig. 6). Interestingly, neither horizontal nor vertical scales
attract much attention. As with more abstract representations (Fig. 5), viewing behavior
is undirected and visual paths cross many times during viewing (Fig. 7). Viewing of the
revised image illustrating just temperature change indicates that participants generally
are directed in their viewing, moving from the data to one or both axes and to legends
(Fig. 8). In the particular case shown, the participant gazes at the image title, the legend,
and each point on the axes in turn. Note the similarity between this gaze pattern and the
gaze pattern of an expert (scientist) viewing another graph (Fig. 9). This image
highlights the data-driven gaze behavior of experts viewing graphs. Expert gaze moves
from the data towards the horizontal and vertical axes.
Aggregate analysis of viewing behavior across original and revised images
illustrates differences in overall gaze behavior. Kernel density analysis of novice views
of the original (Fig. 10) and revised (Fig. 11) Carbon Cycle images indicates that text
elements consistently attract viewer gaze and fixation. In the original image, artistic
elements, specifically the drawing of a vegetated hillside and a factory, also attract
significant attention. Of note is the more diffuse nature of the heat map of aggregate
fixations on the original image relative to the revised image. This indicates that the
sample of participants interacted with the revised image more uniformly than the
sample interacting with the original image.
Comparison of kernel densities produced for the original (Fig. 12) and revised
(Fig. 13) images relating temperature change over time and into the future (Temp
Increase) illustrates striking similarities and differences in aggregate fixations across the
two graphs. Fixations in both instances are concentrated on the most important parts of
the temperature curve, the increase in temperature in the 20th century and projected
increase into the future. In the original case (Fig. 12), this viewing is coupled by weak
viewing of the two axes that contain the meaning for these data. In the revised case (Fig.
13), participants independently view each axis label as well as the legend. Viewing
patterns in the revised context are similar cross all participants, as represented in Fig. 8.
As noted in nearly all of our studies, text elements attract significant attention. In
Figure 12, these text elements relate to many different messages, including the sole
message conveyed in Figure 13, “Temperature increases in modern times”. The
representation of multiple messages in a single image results in a diffuse gaze pattern
(Fig. 12), while focus on a single message (Fig. 13) yields fixations specifically related
to that single message.
Discussion
Eye tracking data can provide unique insight into the communication potential of
images generated to explain climate change concepts. Viewing behavior across the suit
of images studied here was highly variable across participants and images. While
differences in behavior could result from individual differences in the sample studied,
we can draw some general interpretations about the communication potential of the
original and revised images. In general, we find that experts engage with both original
and revised images with more purposeful gaze behavior than novices. Experts attend to
scientifically important aspects of image, while novices may completely ignore
important components, as with graph axes. Interestingly, both experts and novices
attend to text elements in graphics, suggesting that messages conveyed visually should
be supported by verbal cues.
Differences in gaze behavior between the original and revised images were
noted and suggest that an information-based approach to image design can yield novice
gaze behavior that mimics that of experts. The revision of the Temp Increase image
(Fig. 13) is particularly illustrative of this effect. Novice engagement with the original
image (Fig. 12) was undirected, with gaze that moved disjointedly between image
elements. Novices did not actively engage with data to identify meaning, but rather
moved between text and colored elements in ways that suggested limited data-driven
understanding. In contrast, novice engagement with the revised Temp Increase image
was often purposeful (e.g., Fig 13). The observation of gaze behavior that moves
between data and data-explanatory elements is exactly the behavior the image is
intended to induce.
Participant discourse suggests additional differences in interactions with images
that is worth exploring in future research. For example, understanding of the original
carbon cycle image (Fig. 5) may be more focused on carbon reservoirs than on the
processes that move carbon. One participant viewing and describing this original image
indicated, for example, described the carbon cycle in this way [italics and bold added]:
“…I can see plants, I can see buildings, I can see water, I can see oil or gas deposits, so
I would say the plants are taking in the carbon dioxide through the process of
photosynthesis. Then … I would say the carbon dioxide is also being the soil takes part
of it in and then let’s see, I mean I see fossil fuel emissions pretty much if we burn
fossil fuels carbon dioxide is released.” This quote contains six different reservoirs (in
italics) and two processes (in bold). In contrast, a participant viewing the revised carbon
cycle image articulated a more balanced relationship between reservoirs and processes
[italics and bold added], “This is the image of the carbon dioxide ...cycle showing that
plants take it in, animals let off.... Showing the cycle of carbon dioxide and how it goes
through the atmosphere. The main message was that water removes most carbon.”
This difference in discourse across the two images suggests that the revised image (Fig.
6) could be more appropriate for encouraging a level of systems thinking of importance
in understanding the carbon cycle.
A second example of differences in image interactions suggested by
participants’ words is found in the original and revised images conveying temperature
change (Temp Increase; Fig. 12, 13). A participant viewing and describing the original
image relied almost completely on prior learning to explain the image, “… the year
1000 to like the year 1900. But then you see from like the 21st Century from 2000 the
temperature increases rapidly and I’ll say based on my learning in class and when we
talk about global warming it is occurring and that would based off, yeah I would say
based off of global warming you have…temperatures in recent years so if we are in the
21st Century the graph definitely shows more of what I think would be global warming,
…that increase. Because when you see the year 1000 to 1900 just like it’s slightly
changing…from the year 2000 rapid increase.” Very little of this quote relates to the
image itself, except for the discussion of temperature change between 1900 and 2000
and the rate of change after 2000. This participant explicitly calls upon learning in class
to explain the image’s content, and brings non-image related language, such as “global
warming” into their description. In contrast, a participant viewing the revised image
provided a description solely related to content in the image itself, “This is a line graph
showing temperature increases in modern times from 1600 to 2000. Year along the Xaxis, temperature…on the Y-axis… The mean whole temperature stay relatively
constant at about 56 degrees Fahrenheit for about 300 years and begin to climb in the
1900’s to about 56.5, levelled and increased a little bit and then have been increasing at
a linear rate since then of about 2 degrees according to the projection. The main
message of the image is the temperatures remained fairly steady on a global basis until
1600 to 1900 and then they begin to increase.” Certainly, these differences may arise
from individual differences in participant perception resulting from image cueing or
individual difference; regardless, they warrant further study.
Support for some of our suggested revisions can also be found in participant’s
own words. For example, a viewer of the original Temp+CO2 image (Fig. 5) suggested
that “It might be interesting to see the two lines on just one graph so you could see
exactly where they line up and where they don’t…” in response to the prompt, “Do you
think there is any way to improve this image…?” This suggested revision is actually
identical to the information design oriented revision made in this study (Fig. 6).
Finally, participants were given an unlimited amount of time to view images.
Non-scientists engaged with images for both very short and relatively long periods of
time, while experts engaged with images in consistent ways (Table 3). In particular,
some novices engaged with images for as little as seven seconds before moving on,
while other novices engaged with the same images for over 100 seconds. Experts tended
towards intermediate viewing times. The small sample size prohibits analysis of timeon-task to consider the efficiency of images for conveying information, although this is
the focus of ongoing study with larger samples.
Conclusions
Scholars, reporters, and activists have long recognized the value of visuals in
communication. The work presented here illustrates the importance of design in
encouraging viewing behavior as intended by the designer, providing a first glimpse
into the efficiency of climate change images as documented by eye tracking. This work
illustrates the disconnect that exists between how experts and novices view the same
image. Traditional images are excellent tools for conveyance of messages between
experts, specifically between experts who bring a similar body of knowledge to image
viewing. Images are viewed differently in the absence of that body of knowledge, and
novices do not always interact with visual messages as intended.
One powerful tool for assessing novice interaction with visuals is eye tracking.
Current state-of-the-art in analysis of eye tracking data, however, does not live up to the
potential of the data themselves. The application of GIS technologies to analysis of eye
tracking data proved fruitful here, with potential for powerful application of statistical
approaches already inherent to GIS programs. Future efforts to apply existing spatial
analyses to eye tracking data such as those presented here should be explored. For
example, quantitative analysis, rather than simple qualitative comparisons, of
differences between expert and novice gaze paths is possible in GIS.
This study suggests that 1) Images created by scientists are not necessarily
effective mechanisms for knowledge transfer to novice populations; and 2) Re-designed
images may, or may not, be more effective. Certainly, the re-design of the Temp
Increase image (Fig. 13) generated viewing patterns that were overwhelmingly more
directed than with the original image (Fig. 12), as well as discourse which suggested
more expert-like understanding among novices. Other redesigns, such as the Carbon
Cycle image (Fig. 11), generated viewing patterns that are not clearly more amenable to
knowledge transfer than the original image (Fig. 10).
The evaluation and production of images for communication with the general
public is perhaps more complex than we as scientists have assumed. Consideration of
the bodies of literature on learning, cognitive processing, graphic design, information
design, and semiology together provide a useful roadmap for effective design of
scientific images. We propose a process for creating visuals for scientific
communication that includes three elements:
1. Establishing the objectives behind creating a visual. This requires that
communicators recognize the value of information, consider which information
is necessary for novice understanding of a process, and indeed which processes
are necessary for overall novice understanding and action. For example, one
might argue that understanding all of the reservoirs of carbon on Earth is not a
necessary prerequisite for minimizing one’s own carbon footprint.
2. Creating a visual, or evaluating existing visuals, by considering the graphic
elements inherent to the visual. This would include, for example, considering the
appropriateness of the level of abstraction inherent to the image, required
background knowledge of the viewer to correctly interpret the image, or
recognizing the role of graphic components at drawing visual attention.
3. Documenting the interaction between the graphic and the intended viewer. This
might include think-aloud discussions, eye tracking, focus groups, user testing or
other approaches that document the true interaction between a viewer and a
visual. For example, one should consider whether or not viewers are able to
identify points and trends within a graphic and whether or not viewers recognize
the meaning behind those points and trends. Most importantly, image designers
must show that viewers grasp the intended objective of a visual. In the previous
work, we show that in many cases viewers simply do not understand underlying
objectives, or may leave with erroneous understanding.
Ultimately, the message conveyed by a visual depends strongly on viewer
interactions. Viewers cannot learn from an image when they do not fixate on the
appropriate aspects of an image that are required to convey a scientific message.
Similarly, encouragement of specific viewing patterns, such as the cyclic pattern
exhibited by viewers of the revised Carbon Cycle image (Fig. 6) or the data-driven
viewing observed for graph viewing (Fig. 8), is a necessary first step for acquiring an
understanding of key scientific processes. The presence of multiple messages in a single
image, as in Figure 12, can also result in undirected gaze patterns for novices unfamiliar
with the knowledge base that experts bring to image viewing. Use of information design
principles can engender those viewing patterns most likely to coincide with information
transfer. Assessing what viewers know and can do in conjunction with image
development is essential for knowledge transfer. We conclude that information design
should play a key role in generating visuals for climate change communication.
References
Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to Learn in Science. Science,
333(6046), 1096 -1097. doi:10.1126/science.1204153
Akerlof, K., DeBono, R., Berry, P., Leiserowitz, A., Roser-Renouf, C., Clarke, K.,
Rogaeva, A., et al. (2010). Public Perceptions of Climate Change as a Human
Health Risk: Surveys of the United States, Canada and Malta. International
Journal of Environmental Research and Public Health, 7, 2559-2606.
doi:10.3390/ijerph7062559
Anne DiFrancesco, D., & Young, N. (2011). Seeing climate change: the visual
construction of global warming in Canadian national print media. Cultural
Geographies, 18(4), 517 -536. doi:10.1177/1474474010382072
Arndt, D., & LaDue, D. (2008). Applying Concepts of Adult Education to Improve
Weather and Climate Literacy. Physical Geography, 29(6), 487-499.
doi:10.2747/0272-3646.29.6.487
Bamberg, S., & Möser, G. (2007). Twenty years after Hines, Hungerford, and Tomera:
A new meta-analysis of psycho-social determinants of pro-environmental
behaviour. Journal of Environmental Psychology, 27(1), 14-25.
doi:10.1016/j.jenvp.2006.12.002
Bostrom, A., Morgan, M. G., Fischhoff, B., & Read, D. (1994). What Do People Know
About Global Climate Change? 1. Mental Models. Risk Analysis, 14(6), 959970. doi:10.1111/j.1539-6924.1994.tb00065.x
Breslow, L. A., Ratwani, R. M., & Trafton, J. G. (2009). Cognitive models of the
influence of color scale on data visualization tasks. Human Factors, 51(3), 321338.
Carney, R. N., & Levin, J. R. (2002). Pictorial illustrations still improve students’
learning from text. Educational Psychology Review, 14(1), 5-26.
Dimopoulos, K., Koulaidis, V., & Sklaveniti, S. (2003). Towards an Analysis of Visual
Images in School Science Textbooks and Press Articles about Science and
Technology. Research in Science Education, 33(2), 189-216.
Forbus, K., Usher, J., Lovett, A., Lockwood, K., & Wetzel, J. (2008). CogSketch:
Open-domain sketch understanding for cognitive science research and for
education. Eurographics Workshop on Sketch-Based Interfaces and Modeling.
Fortner, R. W., Lee, J.-Y., Corney, J. R., Romanello, S., Bonnell, J., Luthy, B.,
Figuerido, C., et al. (2000). Public Understanding of Climate Change: Certainty
and willingness to act. Environmental Education Research, 6(2), 127-141.
doi:10.1080/713664673
Gowda, Mvr., Fox, J. C., & Magelky, R. D. (1997). Students’ understanding of climate
change: insights for scientists and educators. Bulletin of the American
Meteorological Society Boston MA, 78(10), 2232-2240.
Hansen, A., & Machin, D. (2008). Visually branding the environment: climate change
as a marketing opportunity. Discourse Studies, 10(6), 777–794.
Hines, J. M., Hungerford, H. R., & Tomera, A. N. (1987). Analysis and Synthesis of
Research on Responsible Environmental Behavior: A Meta-Analysis. Journal of
Environmental Education, 18(2), 1-8.
Keith, S., Schwalbe, C. B., & Silcock, B. W. (2010). Comparing war images across
media platforms: methodological challenges for content analysis. Media, War &
Conflict, 3(1), 87 -98. doi:10.1177/1750635210353676
Larkin, J. H., & Simon, H. A. (1987). Why a Diagram is (Sometimes) Worth Ten
Thousand Words. Cognitive Science, 11(1), 65-100. doi:10.1111/j.15516708.1987.tb00863.x
Leiserowitz, A. (2006). Climate Change Risk Perception and Policy Preferences: The
Role of Affect, Imagery, and Values. Climatic Change, 77(1-2), 45-72.
doi:10.1007/s10584-006-9059-9
Libarkin, J. C., Ward, E. M. G., Anderson, S. W., Kortemeyer, G., & Raeburn, S. P.
(2011). Revisiting the Geoscience Concept Inventory: A call to the community.
GSA Today, 21, 26-28. doi:10.1130/G110GW.1
Lombardi, D., & Sinatra, G. M. (2010). College Students’ Perceptions About the
Plausibility of Human-Induced Climate Change. Research in Science Education,
1–17.
Lowe, T., Brown, K., Dessai, S., de França Doria, M., Haynes, K., & Vincent, K.
(2006). Does tomorrow ever come? Disaster narrative and public perceptions of
climate change. Public Understanding of Science, 15(4), 435 -457.
doi:10.1177/0963662506063796
Maibach, E., Witte, J., & Wilson, K. (2011). “Climategate” Undermined Belief in
Global Warming Among Many American TV Meteorologists. Bulletin of the
American Meteorological Society, 92, 31-37. doi:10.1175/2010BAMS3094.1
Mayer, R. E. (1989). Systematic thinking fostered by illustrations in scientific text.
Journal of Educational Psychology, 81(2), 240-246.
McCaffrey, M., & Buhr, S. (2008). Clarifying Climate Confusion: Addressing Systemic
Holes, Cognitive Gaps, and Misconceptions Through Climate Literacy. Physical
Geography, 29(6), 512-528. doi:10.2747/0272-3646.29.6.512
Moser, S. C. (2010). Communicating climate change: history, challenges, process and
future directions. Wiley Interdisciplinary Reviews: Climate Change, 1(1), 31-53.
doi:10.1002/wcc.11
Ockwell, D., Whitmarsh, L., & O’Neill, S. (2009). Reorienting Climate Change
Communication for Effective Mitigation. Science Communication, 30(3), 305 327. doi:10.1177/1075547008328969
Ozcelik, E., Karakus, T., Kursun, E., & Cagiltay, K. (2009). An eye-tracking study of
how color coding affects multimedia learning. Comput. Educ., 53(2), 445–453.
doi:10.1016/j.compedu.2009.03.002
Papadimitriou, V. (2004). Prospective Primary Teachers’ Understanding of Climate
Change, Greenhouse Effect, and Ozone Layer Depletion. Journal of Science
Education and Technology, 13, 299-307.
doi:10.1023/B:JOST.0000031268.72848.6d
Pruneau, D., Liboiron, L., Vrain, E., Biosphère, L., Gravel, H., Bourque, W., & Langis,
J. (2001). People’s Ideas about Climate Change: A Source of Inspiration for the
Creation of Educational Programs. Canadian Journal of Environmental
Education (CJEE), 6(1), pp. 121-138.
Rebich, S., & Gautier, C. (2005). Concept mapping to reveal prior knowledge and
conceptual change in a mock summit course on global climate change. Journal
of Geoscience Education, 53(4), 355-365.
Roth, W., Bowen, G. M., & McGinn, M. K. (1999). Differences in graph‐related
practices between high school biology textbooks and scientific ecology journals.
Journal of Research in Science Teaching, 36(9), 977-1019.
doi:10.1002/(SICI)1098-2736(199911)36:9<977::AID-TEA3>3.0.CO;2-V
Smith, B., & Elifson, J. M. (1985). Do Pictures Make a Difference in College
Textbooks? College Reading and Learning Assistance Technical Report 85-02.
Retrieved from
http://www.eric.ed.gov/ERICWebPortal/detail?accno=ED256246
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M.,
et al. (2007). Climate Change 2007: The Physical Science Basis. Cambridge,
UK: Cambridge Univ Press.
Sterman, J.D. (2011). Communicating climate change risks in a skeptical world.
Climatic Change, 1–16.
Svihla, V., & Linn, M. C. (2011). A Design-based Approach to Fostering
Understanding of Global Climate Change.
Sweeney, L. B., & Sterman, J. D. (2007). Thinking about systems: student and teacher
conceptions of natural and social systems. System Dynamics Review, 23(2‐3),
285-311. doi:10.1002/sdr.366
Trumbo, J. (1999). Visual Literacy and Science Communication. Science
Communication, 20(4), 409 -425. doi:10.1177/1075547099020004004
Tufte, E. (1983). Visual Display of Quantitative Information. Cheshire, CT: Graphics
Press.
Turner, S., & Libarkin, J. C. (2011). Novel applications of Tablet PCs to investigate
expert cognition in the geosciences. Computers & Geosciences. Retrieved from
http://www.sciencedirect.com/science/article/pii/S009830041100286X
Ungar, S. (2000). Knowledge, ignorance and the popular culture: climate change versus
the ozone hole. Public Understanding of Science, 9(3), 297 -312.
doi:10.1088/0963-6625/9/3/306
Weber, E. U., & Stern, P. C. (2011). Public understanding of climate change in the
United States. American Psychologist, 66(4), 315-328.
Whitmarsh, L. (2009). What’s in a name? Commonalities and differences in public
understanding of “climate change” and “global warming.” Public Understanding
of Science, 18(4), 401-420.
Whitmarsh, L., & Lorenzoni, I. (2010). Behaviour, perceptions, and communication of
climate change. Wiley Interdisciplinary Reviews: Climate Change, n/a-n/a.
doi:10.1002/wcc.7
Table 1. Multiple-choice items used to establish baseline levels of climate change
literacy for all participants.
Question (Ideal response in bold)
1. What is climate change?
`2. Which comes closer to your own view?
(A) Most scientists think global warming is happening.
(B) Most scientists think global warming is not happening.
(C) Scientists generally disagree about whether or not global warming is
happening.
(D) I do not know.
3. What are greenhouse gases?
(A) Gases in the atmosphere that absorb infrared energy.
(B) Gases in the atmosphere that absorb ultraviolet energy.
(C) Gases in the atmosphere that cause rain to become acidic.
(D) Gases in the atmosphere that are produced as plants grow.
(E) I do not know.
4. If human civilization had never developed on Earth, would there be a
greenhouse effect?
(A) Yes, the greenhouse effect is caused by naturally occurring gases.
(B) Yes, the greenhouse effect is caused by plants giving off gases.
(C) No, the greenhouse effect is caused by humans burning fossil fuels.
(D) No, the greenhouse effect is caused by humans depleting ozone.
(E) No, there is no conclusive evidence that a greenhouse effect exists.
(F) I do not know.
5. Which of the following statements about global warming over the past 50
years most closely reflects your viewpoint?
(A) Global warming over the past 50 years is mostly caused by
human activities.
(B) Global warming over the past 50 years is mostly caused by natural
processes.
(C) Global warming has not really occurred over the past 50 years.
(D) I do not know.
6. Which description best represents your understanding of how the amount of
carbon dioxide in the atmosphere has changed over the past 500 years?
(A) No change.
(B) Linear increase.
(C) Linear decrease.
(D) Exponential increase.
(E) Exponential decrease.
(F) I do not know.
7. Which of the following best describes the relationship between the
greenhouse effect and global warming?
(A) The greenhouse effect and global warming are likely the same thing.
(B) Without the greenhouse effect, there would be almost no global
warming.
(C) Without global warming, there would be almost no greenhouse
effect.
(D) The greenhouse effect and global warming are likely unrelated.
(E) There is no definite proof that either the greenhouse effect or global
warming exists.
(F) I do not know.
Participant Responses*
Students, n=8
(Scientists, n=3)
See text.
(A)
(B)
(C)
(D)
6 (3)
0
2
0
(A)
(B)
(C)
(D)
(E)
3 (2)
3
1 (1)
1
0
(A)
(B)
(C)
(D)
(E)
(F)
2 (1)
1
3 (1)
1 (1)
1
0
(A)
(B)
(C)
(D)
8 (3)
0
0
0 (A)
(B)
(C)
(D)
(E)
(F)
0
1
0
6 (3)
1
0
(A)
(B)
(C)
(D)
(E)
(F)
2 (1)
5 (2)
1
0
0
0
*One student did not provide responses to the climate change concept inventory.
Table 2. Participant demographics and prior knowledge.
Undergraduate
Male Guardian
Female Guardian Comfort Reading and
Climate Change
Science Courses
Educ. Level
Educ. Level
Interpreting Graphs
Literacy
002
O
F
21
O
4
B.S.
B.S.
2
6
003
O
F
20
C
2
M.S.+
B.S.
2
005
O
F
21
C
1
M.S.+
M.S.+
3
4
009
O
F
A
2
B.S.
B.S.
2
4
001
R
F
20
C
2
Some College
H.S.
2
2
004
R
F
21
C
3
M.S.+
B.S.
3
4
006
R
F
29
C
3
Some H.S.
Some H.S.
3
6
008
R
M
20
A
1
B.S.
Some College
3
5
010
R
F
21
A
2
M.S.+
M.S.+
3
2
4
M.S.+
M.S.+
3
007*
O
F
20
O
3
011**
O
F
29
C
4
M.S.+
B.S.
4
6
012***
R
M
55
C
4
H.S.
H.S.
4
5
Notes: * undergraduate science major **graduate science student ***science faculty. Ethnicity: C=Caucasian, A = African-American/Black; O = Other or Mixed.
Participant
Image Set
Gender
Age
Ethnicity
Table 3. Time-on-task for original and revised images.
Image
Novice Time (sec)
Expert Time (sec)
Min
Max
Avg
Min
Max
Avg
Carbon Cycle – Original
14.3
98.5
50.4±35.8
44.1
47.1
45.6±2.1
Carbon Cycle – Revised
20.3
86.5
53.0±25.7
---
---
72.3
GDP – Original
25.2
95.6
62.1±37.9
75.2
106.2
90.7±22.0
GDP - Revised
19.3
80.5
51.2±20.8
---
---
121.3
Temp+CO2 – Original
34.7
89.8
59.7±22.9
41.0
74.6
57.8±23.7
Temp+CO2 – Revised
7.4
132.0
58.1±45.3
---
---
35.8
Temp Increase – Original
24.2
96.1
53.6±33.2
34.9
75.0
55.0±28.3
Temp Increase - Revised
9.7
92.8
42.9±45.3
---
---
56.3
Figure 1. Participant scores on climate change concept inventory. All participants
exhibited some knowledge of climate change.
Figure 2. The third author engaging with the Tobii T-60 eye tracker in the research lab.
Figure 3. Individual gaze path for expert viewing the original Temp+CO2 image. This
and subsequent gaze paths were generated in ArcGIS.
Figure 4. Individual gaze path for novice viewing the original Temp+CO2 image.
Figure 5. Individual gaze path for expert viewing the original image illustrating the
Carbon Cycle.
Figure 6. Individual gaze path for novice viewing the revised Carbon Cycle image.
Figure 7. Individual gaze paths for novice viewing the original image Temp Increase
image, sometimes called the “hockey stick curve.”
Figure 8. Individual gaze paths for novice viewing the revised Temp Increase image.
Figure 9. Partial gaze path for expert viewing the revised GDP image.
Figure 10. Kernel density analysis across all novice participants (n=4) viewing the
original image illustrating the Carbon Cycle. Kernel density analysis shown here and in
subsequent images were generated in ArcGIS, and are equivalent to the standard heat
map used in eye tracking reports.
Figure 11. Kernel density analysis across all novice participants (n=4) viewing the
revised Carbon Cycle image.
Figure 12. Kernel density analysis across all novice participants (n=4) viewing the
original Temp Increase image.
Figure 13. Kernel density analysis across all novice participants (n=4) viewing the
revised Temp Increase image.