visual representation for words

Visuwords™

Visual Dictionary Visual Thesaurus Interactive Lexicon

Not your grandad's dictionary

Educational

Explore the lexicon. Whether you are a native English speaker or a second language user—either as a student or a teacher—browse the language in ways you couldn't with traditional printed reference materials.

As a writer or journalist or other word wizard, use the graphs to associate words and expand on concepts. Brainstorm. Move beyond synonyms and find other kinds of relational connections.

Go Word Spelunking!

Still not sure what Visuwords™ is about? Hit that explore button and pull up something random.

What is visual representation?

In the vast landscape of communication, where words alone may fall short, visual representation emerges as a powerful ally. In a world inundated with information, the ability to convey complex ideas, emotions, and data through visual means is becoming increasingly crucial. But what exactly is visual representation, and why does it hold such sway in our understanding?

Defining Visual Representation:

Visual representation is the act of conveying information, ideas, or concepts through visual elements such as images, charts, graphs, maps, and other graphical forms. It’s a means of translating the abstract into the tangible, providing a visual language that transcends the limitations of words alone.

The Power of Images:

The adage “a picture is worth a thousand words” encapsulates the essence of visual representation. Images have an unparalleled ability to evoke emotions, tell stories, and communicate complex ideas in an instant. Whether it’s a photograph capturing a poignant moment or an infographic distilling intricate data, images possess a unique capacity to resonate with and engage the viewer on a visceral level.

Facilitating Understanding:

One of the primary functions of visual representation is to enhance understanding. Humans are inherently visual creatures, and we often process and retain visual information more effectively than text. Complex concepts that might be challenging to grasp through written explanations can be simplified and clarified through visual aids. This is particularly valuable in fields such as science, where intricate processes and structures can be elucidated through diagrams and illustrations.

Visual representation also plays a crucial role in education. In classrooms around the world, teachers leverage visual aids to facilitate learning, making lessons more engaging and accessible. From simple charts that break down historical timelines to interactive simulations that bring scientific principles to life, visual representation is a cornerstone of effective pedagogy.

Data Visualization:

In an era dominated by big data, the importance of data visualization cannot be overstated. Raw numbers and statistics can be overwhelming and abstract, but when presented visually, they transform into meaningful insights. Graphs, charts, and maps are powerful tools for conveying trends, patterns, and correlations, enabling decision-makers to glean actionable intelligence from vast datasets.

Consider the impact of a well-crafted infographic that distills complex research findings into a visually digestible format. Data visualization not only simplifies information but also allows for more informed decision-making in fields ranging from business and healthcare to social sciences and environmental studies.

Cultural and Artistic Expression:

Visual representation extends beyond the realm of information and education; it is also a potent form of cultural and artistic expression. Paintings, sculptures, photographs, and other visual arts serve as mediums through which individuals can convey their emotions, perspectives, and cultural narratives. Artistic visual representation has the power to transcend language barriers, fostering a shared human experience that resonates universally.

Conclusion:

In a world inundated with information, visual representation stands as a beacon of clarity and understanding. Whether it’s simplifying complex concepts, conveying data-driven insights, or expressing the depth of human emotion, visual elements enrich our communication in ways that words alone cannot. As we navigate an increasingly visual society, recognizing and harnessing the power of visual representation is not just a skill but a necessity for effective communication and comprehension. So, let us embrace the visual language that surrounds us, unlocking a deeper, more nuanced understanding of the world.

• Business Essentials
• Leadership & Management
• Credential of Leadership, Impact, and Management in Business (CLIMB)
• Entrepreneurship & Innovation
• Digital Transformation
• Finance & Accounting
• Business in Society
• For Organizations
• Support Portal
• Media Coverage
• Founding Donors
• Leadership Team

• Harvard Business School →
• HBS Online →
• Business Insights →

Business Insights

Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.

• Career Development
• Communication
• Decision-Making
• Earning Your MBA
• Negotiation
• News & Events
• Productivity
• Staff Spotlight
• Student Profiles
• Work-Life Balance
• AI Essentials for Business
• Alternative Investments
• Business Analytics
• Business Strategy
• Business and Climate Change
• Creating Brand Value
• Design Thinking and Innovation
• Digital Marketing Strategy
• Disruptive Strategy
• Economics for Managers
• Entrepreneurship Essentials
• Financial Accounting
• Global Business
• Launching Tech Ventures
• Leadership Principles
• Leadership, Ethics, and Corporate Accountability
• Leading Change and Organizational Renewal
• Leading with Finance
• Management Essentials
• Negotiation Mastery
• Organizational Leadership
• Power and Influence for Positive Impact
• Strategy Execution
• Sustainable Business Strategy
• Sustainable Investing
• Winning with Digital Platforms

17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

Access your free e-book today.

What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

About the Author

How Words Are Represented in the Brain

Summary: A new study sheds light on the neurobiology of reading.

Source: University of Pittsburgh.

Using direct neural recordings from the visual word form area, researchers were able to see words that patients read as the patients read them.

Reading is a relatively modern and uniquely human skill. For this reason, visual word recognition has been a puzzle for neuroscientists because the neural systems responsible for reading could not have evolved for this purpose. “The existence of brain regions dedicated to reading has been fiercely debated for almost 200 years,” said Avniel Ghuman, an assistant professor in the University of Pittsburgh Department of Neurological Surgery. “Wernicke, Dejerine, and Charcot, among the most important and influential neurologists and neuroscientists of the 19th century, debated whether or not there was a visual center for words in the brain.”

In recent years, much of this debate has centered on the left mid-fusiform gyrus, which some call the visual word form area. A recent study by Pitt neuroscience researchers addresses this debate and sheds light on our understanding of the neurobiology of reading.

In a study to be published July 19 in the Proceedings of the National Academy of Sciences , Ghuman, Elizabeth Hirshorn of Pitt’s Learning Research and Development Center (LRDC), and colleagues from the Department of Psychology and Center for the Neural Basis of Cognition used direct neural recordings and brain stimulation to study the role of the visual word form area in reading in four epileptic patients. The patients chose surgical treatment for their drug-resistant epilepsy and volunteered to participate in the research study. As part of the surgical treatment, neurosurgeons implanted electrodes in the patients’ visual word form area, providing an unprecedented opportunity to understand how the brain recognizes printed words.

First, painless electrical brain stimulation was used through the electrodes to disrupt the normal functioning of the visual word form area, which adversely affected the patients’ ability to read words. One patient dramatically misperceived letters, and another felt that there were words and parts of words present that were not in what she was reading. Stimulation to this region did not disrupt their ability to name objects or faces.

In addition to stimulating through these electrodes, the activity from the area was recorded while the patients read words. Using techniques from machine learning to analyze the brain activity that evolved over a few hundred milliseconds from this region, the researchers could tell what word a patient was reading at a particular moment. This suggests that neural activity in the area codes knowledge about learned visual words that can be used to discriminate even words that are only one letter different from one another (for example, “hint” and “lint”).

“This study shows that the visual word form area is exquisitely tuned to the fine details of written words and that this area plays a critical role in refining the brain’s representation of what we are reading. The disrupted word and letter perception seen with stimulation provides direct evidence that the visual word form area plays a dedicated role in skilled reading,” said Hirshorn. “These results also have important implications for understanding and treating reading disorders. The activity in the visual word form area, along with its interactions with other brain areas involved in language processing, could be a marker for proficient reading. Having a better understanding of this neural system could be critical for diagnosing reading disorders and developing targeted therapies.”

“It is exciting that with modern brain-recording techniques and advanced analysis methods, we are finally able to start answering questions about the brain and the mind that people have asked for centuries and contribute to our understanding of reading disorders,” said Ghuman.

Source: Joe Miksch – University of Pittsburgh Image Source: This NeuroscienceNews.com image is in the public domain. Video Source: The video is credited to Laboratory of Cognitive Neurodynamics. Original Research: Full open access research for “Decoding and disrupting left midfusiform gyrus activity during word reading” by Elizabeth A. Hirshorn, Yuanning Li, Michael J. Ward, R. Mark Richardson, Julie A. Fiez, and Avniel Singh Ghuman in PNAS . Published online July 19 2016 doi:10.1073/pnas.1604126113

[cbtabs][cbtab title=”MLA”]University of Pittsburgh. “How Words Are Represented in the Brain.” NeuroscienceNews. NeuroscienceNews, 21 July 2016. <https://neurosciencenews.com/visual-word-form-area-neuroscience-4723/>.[/cbtab][cbtab title=”APA”]University of Pittsburgh. (2016, July 21). How Words Are Represented in the Brain. NeuroscienceNew . Retrieved July 21, 2016 from https://neurosciencenews.com/visual-word-form-area-neuroscience-4723/[/cbtab][cbtab title=”Chicago”]University of Pittsburgh. “How Words Are Represented in the Brain.” https://neurosciencenews.com/visual-word-form-area-neuroscience-4723/ (accessed July 21, 2016).[/cbtab][/cbtabs]

Decoding and disrupting left midfusiform gyrus activity during word reading

The nature of the visual representation for words has been fiercely debated for over 150 y. We used direct brain stimulation, pre- and postsurgical behavioral measures, and intracranial electroencephalography to provide support for, and elaborate upon, the visual word form hypothesis. This hypothesis states that activity in the left midfusiform gyrus (lmFG) reflects visually organized information about words and word parts. In patients with electrodes placed directly in their lmFG, we found that disrupting lmFG activity through stimulation, and later surgical resection in one of the patients, led to impaired perception of whole words and letters. Furthermore, using machine-learning methods to analyze the electrophysiological data from these electrodes, we found that information contained in early lmFG activity was consistent with an orthographic similarity space. Finally, the lmFG contributed to at least two distinguishable stages of word processing, an early stage that reflects gist-level visual representation sensitive to orthographic statistics, and a later stage that reflects more precise representation sufficient for the individuation of orthographic word forms. These results provide strong support for the visual word form hypothesis and demonstrate that across time the lmFG is involved in multiple stages of orthographic representation.

“Decoding and disrupting left midfusiform gyrus activity during word reading” by Elizabeth A. Hirshorn, Yuanning Li, Michael J. Ward, R. Mark Richardson, Julie A. Fiez, and Avniel Singh Ghuman in PNAS . Published online July 19 2016 doi:10.1073/pnas.1604126113

Tau May Protect Brain Cells from Oxidative Damage

Morphine’s Pain Relief Mechanism Unveiled

Dogs Using Soundboard Buttons Understand Words

How We Recognize Words in Real-Time

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 20 September 2018

Words affect visual perception by activating object shape representations

• Samuel Noorman 1 ,
• David A. Neville 1 &
• Irina Simanova 1  

Scientific Reports volume  8 , Article number:  14156 ( 2018 ) Cite this article

7552 Accesses

19 Citations

3 Altmetric

Metrics details

• Object vision

Linguistic labels are known to facilitate object recognition, yet the mechanism of this facilitation is not well understood. Previous psychophysical studies have suggested that words guide visual perception by activating information about visual object shape. Here we aimed to test this hypothesis at the neural level, and to tease apart the visual and semantic contribution of words to visual object recognition. We created a set of object pictures from two semantic categories with varying shapes, and obtained subjective ratings of their shape and category similarity. We then conducted a word-picture matching experiment, while recording participants’ EEG, and tested if the shape or the category similarity between the word’s referent and target picture explained the spatiotemporal pattern of the picture-evoked responses. The results show that hearing a word activates representations of its referent’s shape, which interacts with the visual processing of a subsequent picture within 100 ms from its onset. Furthermore, non-visual categorical information, carried by the word, affects the visual processing at later stages. These findings advance our understanding of the interaction between language and visual perception and provide insights into how the meanings of words are represented in the brain.

Similar content being viewed by others

Object representations in the human brain reflect the co-occurrence statistics of vision and language

Predicting how surface texture and shape combine in the human visual system to direct attention

Shape facilitates number: brain potentials and microstates reveal the interplay between shape and numerosity in human vision

Introduction.

Humans possess the unique ability to label objects. How does this ability transform cognition and perception? This question goes to the core of what it means to be human. Among philosophers 1 , 2 , 3 , 4 , 5 and cognitive scientists 6 , 7 , 8 , 9 , many have commented on the unique way in which verbal labels enable humans to access and manipulate mental representations. However, only recently the interplay between verbal labels, concepts, and percepts at the neural level has become a subject of research 10 . An important empirical question is: what kind of representations are activated by linguistic labels? Here we address this question by studying how labels affect the processing of upcoming visual information. Namely, we test the hypothesis that words guide visual perception by activating information about visual object shape.

Several studies have shown that labels facilitate object recognition 6 , 11 , 12 , 13 , 14 , 15 , 16 , 17 and visual object detection 18 , 19 , 20 , 21 . It has been proposed that cueing an object presentation with a word leads to more efficient visual processing compared to cueing with other types of cues 12 , 14 . Consider a classical experiment, in which one hears an auditory cue and then sees a picture. The cue can be either an object label or an equally familiar and unambiguous nonverbal sound. The task is to respond “yes” if the cue and the picture match (e.g., a picture of a dog follows a barking sound), and “no” otherwise. Using this task, Lupyan and Thompson Schill 13 found that linguistic cues lead to faster and more accurate responses compared to non-linguistic sounds.

A more recent study by Boutonnet and Lupyan 22 investigated the neural correlates of this label advantage effect. Participants performed the same cue-picture matching task, while their electroencephalography (EEG) signal was recorded. Analysis of the event-related potentials (ERPs) in response to the target pictures revealed the label advantage as early as 100 ms after picture presentation, in the time window of the P1 evoked component. In particular, pictures that were cued by labels elicited an earlier and more positive P1, compared with the same pictures cued by nonverbal sounds. Further, the word-picture congruency was predicted from the P1 latency on a trial-by-trial basis, but only in the label-cued trials. These results indicate that verbal cues provide top-down guidance on visual perception, and change how subsequently incoming visual information is processed early on. This suggestion is in line with the recent advance in visual perception research, which shows that object recognition is afforded by bidirectional information flow 23 , 24 , 25 .

Boutonnet and Lupyan 22 conclude that labels generate categorical predictions. However, they do not dissociate between the effects of low-level, purely visual and higher-level semantic information. The distinction is relevant for understanding what type of representation is activated by a verbal label. Previous studies have addressed this question by tracking patterns of eye fixations on objects in response to spoken words. When presented with an array of objects following a target word people typically fixate on objects that are visually related to the target. For example, when hearing the word “belt”, they would fixate on a visually similar picture of a snake. However, people also show a substantial bias in orienting toward semantically related objects, e.g. a picture of socks after hearing the target word “belt”. These observations have led to the cascaded model of visual-linguistic interactions 26 , 27 , 28 , 29 , which suggests that words evoke both visual and semantic representations. However, the dynamics of these activations remain poorly understood. Huettig and McQueen 27 showed that activation of semantic and visual representation occurs largely simultaneously (see also Ferreira et al . 26 ). More recently De Groot et al . 30 found that the bias in orienting towards semantically related objects occurs later than biases towards visually similar objects. Moreover, the temporal dynamics of the semantic bias stayed the same regardless of the presence of visual competitors, suggesting that the semantic information is accessed independently of the visual bias. Eye-tracking, however, can only provide an indirect measure of the temporal dynamics of cognitive processes, and does not reveal the underlying neurocognitive mechanisms. In the present study, we use the advantage of EEG to address this problem.

We specifically dissociate category information from visual object shape. Category distinctions are typically highly correlated with object shape 31 . When children learn to name objects, they pick shape as the most relevant feature: children are most likely to extend a new word to a new object that is similar to the word’s original referent in shape, rather than in colour, texture, etc. (see e.g. 32 , 33 ). The rapid growth of infants’ vocabulary at the age of 18–24 months is strongly correlated with the ability to categorise objects based on shape 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 . A recent study with 3-year old children addressed the interaction between language and visual shape perception using a visual search paradigm 40 . Children first saw a cue picture of an object, and then had to identify this object among an array of distractors, with either similar or dissimilar shapes. On half of the trials the cue picture was accompanied by the object’s name. The reaction times showed the label advantage: children were faster in identifying the target when first hearing its name, compared to the no-name trials. They were also faster in identifying the target among the distractors with dissimilar shapes. Most notably, there was an interaction between shape and language: labels especially enhanced the target detection among objects with dissimilar shape. This indicates that words might guide visual search towards detection of object shape.

In the present study, we aim to disentangle the visual and semantic category contributions in the effect of words on object recognition at the neural level. Based on the study by Boutonnet and Lupyan 22 and the evidence from the literature on language development outlined above 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 we hypothesise that verbal cues activate representations of their referents’ visual object shape, which affects early stages of the upcoming stimulus’ visual processing. Further, based on the evidence from the eye-tracking study by de Groot et al . 30 , we hypothesise that the effects of visual shape are separable from those of category, both in timing and topography of EEG.

To test these hypotheses, we created a set of object pictures from two categories with varying shapes, while carefully controlling other visual variables. We obtained subjective measures of the shape similarity and category similarity between the objects, based on participants’ ratings. We then conducted a word-picture matching experiment, while recording participants’ EEG, and assessed how the shape and semantic category information carried by the cue word affects the processing of the upcoming picture’s shape and semantic category, respectively. Following our hypotheses, we predicted that the behavioural measures (response times) would be explained by both shape and category similarity between the cues and the pictures. To address the shape or the category similarity effects at the neural level, we employed a novel similarity-based analysis combined with non-parametric cluster-based statistics. This approach allows us to evaluate the effects of shape and category similarity without an explicit assumption on their timing or topography. We expect that early event-related responses would be explained by the shape similarity between the cues and the pictures. Moreover, we expect to see later, shape-independent effects of the category similarity. If our hypothesis does not hold, we expect the effects of visual and category similarity to occur simultaneously and have similar topography 26 , 27 .

Participants

20 native Dutch speakers (11 males, aged 23 ± 2.4), recruited via the Radboud Research Participation System, participated in the study. All participants were right-handed, and reported that they did not suffer from any psychological or neurological disorders. The experiments were approved by the local ethics committee (Commissie Mensgebonden Onderzoek Regio Arnhem-Nijmegen), and all the subjects gave written informed consent prior to the experiment. All experiments were performed in accordance with relevant guidelines and regulations. Subjects received either monetary compensation or course credits for their participation.

Four different fruits (apricot, kiwi, pear, banana) and four different vegetables (onion, potato, eggplant and zucchini) were selected as target objects. Within the category, each object had unique shape property, thus comprising four pairs of objects with similar shape: sphere, ovoid, cylinder and cone. We used detailed photographs of these fruits and vegetables, obtained in Google search with the option “free for use, share and modification”. There were five images per object, thus 40 different images in total. The images were greyscaled, placed on the white background to fit the frame of 333 × 333 pixels (7° of visual angle). Luminance and spatial frequency were matched using the Shine toolbox 41 . Figure  1 illustrates the shape overlap of the images. Additionally, for each object an auditory name (spoken Dutch words) was recorded digitally at 16 bits with a sampling rate of 44.100 Hz). The mean auditory word length was 698.7 ms ± 150.8 ms.

The pixelwise overlap computed by summing all stimuli images per object, per category (the rightmost column), and per shape type (the bottom row). There were eight different objects and 40 unique object images. The objects comprised of two categories and four shape types.

Behavioural similarity rating

The object similarity judgements for both the category and shape dimensions were collected from all participants. Participants completed the rating task prior to the EEG task. During the rating task they were presented with a word and an array of objects, and were asked to indicate how similar each object is to the word, on a scale from 1 to 5. Each participant rated all 40 images relative to all eight words. The rating procedure was repeated twice, once for the shape dimension and once for the category dimension (Fig.  2 ).

Similarity matrices and reaction times. Prior to the experiment, participants completed the rating task where they indicated how similar each target object is to each cue word, on a scale from one to five, separately for the shape and for the category dimensions. Panels A and B show the similarity ratings averaged per cue-target pair and across subjects, for the Shape ( A ) and Category ( B ) dimensions (red represents large similarities). During the main experiment, participants replied with the button press if the target object matched or mismatched the cue word. Panel C shows the reaction times (in ms) averaged per cue-target pair and across subjects (red represents slower reaction times). Note that in all reported analyses we used individual, rather than group-averaged similarity ratings and reaction times. The group-averaged data are only shown here for illustration. The similarity data ( A , B ) and the reaction times ( C ) on the diagonals of the matrices, which correspond to the congruent pairs, are not shown, because only incongruent trials were used for the analysis.

EEG experiment

Participants completed 960 trials of the word-picture matching task. On each trial, participants heard a cue word (a fruit or vegetable name), followed by a picture after one second delay. They were instructed to respond via button press whether the picture matched the word (yes or no). The picture remained visible for 1000 ms. In 30% of trials (congruent trials) the picture matched the cue. In the remaining 70% of trials (incongruent trials), the picture was of another fruit or vegetable. Each incongruent combination of a cue word and a target picture was repeated 12 times, and each congruent pair 36 times. The order of trials was randomised across participants. The total experiment duration was approximately one hour, and participants took nine short breaks of 30 seconds.

EEG recording and processing

Continuous EEG was registered using a 64 channel ActiCap system (Brain Products GmbH) filtered at 0.2–200 Hz and sampled at 500 Hz with the BrainVision Recorder Professional software (Brain Products GmbH). An equidistant electrode cap was used to position 60 electrodes on the scalp. EEG data were recorded against the reference at the right mastoid; an additional electrode measured the voltage on the left mastoid, and the data were offline converted to a linked-mastoids reference. Bipolar electrooculogram (EOG) was computed using electrodes that were placed horizontally and vertically around the eyes. For all subsequent processing and analysis, we selected only incongruent trials in which participants correctly identified word-picture mismatch within 1500 ms after the stimulus onset (98 ± 1.6% of all incongruent trials per subject, on average). Data segments of 1200 ms, starting from 200 ms before image onset, were extracted. Segments containing eye-movements, or muscle artifacts were identified based on signal variance. Identified segments were inspected visually and rejected if contamination with artifacts was confirmed. On average, 8.27% of the trials were rejected. In the remaining data, line noise (50 Hz and harmonics) was removed using a discrete Fourier transform filter. The data were subsequently bandpass filtered from 0.5 to 40 Hz and baseline corrected to the 200 ms before image onset. Finally, using independent component analysis, artifacts caused by blinks and other events not related to brain activity were removed from the data. All offline data processing was performed using MATLAB R2015A and FieldTrip 42 .

Data analysis

Step 1: computing correlations.

a)   Reaction time data: For each participant, the mean reaction times were computed for each type of incongruent word-picture combination. This resulted in a vector of 56 mean RT values per participant. We then used a correlation analysis to test if the RTs are explained by the similarity between cues and pictures. To elaborate, for each participant, we computed a Spearman rank correlation between the word-picture similarity and the corresponding RTs. We tested two different similarity models: i) subjective shape similarity per subject and ii) and subjective category similarity per subject.

b)   EEG data : For each participant, ERPs in response to the picture presentation were computed for each type of incongruent word-picture combination. Thus, the averaging resulted in 56 ERP waveforms for each channel. We further used a correlation-based analysis to test if the pattern of the evoked responses across the word-picture pairs could be explained by the similarity between cues and pictures. We tested two similarity models: i) subjective shape similarity per subject and ii) subjective category similarity per subject. We thus applied exactly the same procedure as described above in the RT analysis, but now linear Spearman rank correlations were calculated between the similarity ratings and the ERP’s. Correlations were computed for each channel and time point. This resulted in a channel x time matrix of correlation coefficients for each participant and for each model. A similar analysis approach has been used in a priming experiment before 43 .

Step 2: Inferential statistics

a)   Reaction time data: To statistically quantify the correlation effects at the group level, we performed a one-sample t-test against the hypothesis that the mean group correlation is 0. We performed this test separately for each similarity model.

b)   EEG data: The step of computing correlations between the ERPs and similarity ratings, described above, resulted in a channel x time matrix of correlation coefficients for each participant. At the group level, we aim to test if the correlations in each channel x time sample are different from zero. However, testing each time x channel sample independently leads to massive multiple comparisons. To account for the multiple comparisons problem, we used nonparametric cluster-based permutation statistics approach 44 . In this method, the complete channel x time matrix is tested by computing a single test statistic, and therefore, the multiple comparisons problem is resolved. We elaborate on this procedure in the following paragraphs.

We followed the procedure described previously 45 , 46 . We first computed a paired-sample t-test for each channel x time point, where we compared the correlation coefficients from 20 participants with a vector of 20 zeros. All t values above a threshold corresponding to an uncorrected p value of 0.05 were formed into clusters by grouping together adjacent significant channels (based on a minimum neighbourhood distance between the electrode sites) and time samples. This step was performed separately for samples with positive and negative t values (two-tailed test). The t values within each cluster were then summed to produce a cluster-level t score (cluster statistic).

This statistic was entered in the cluster-based permutation procedure 44 . To obtain a randomization distribution to compare with the observed clusters, we randomly exchanged the condition labels between the true and null conditions (that is, the vector of zero correlations, same as described above). We then computed the paired sample t-test. This procedure is equivalent to randomly multiplying the correlation values by 1 and −1, and computing a one-sample t-test against zero 45 , 46 . This step was repeated across 5000 permutations of the data. For each permutation, we computed the cluster-sums of subthreshold t-values. The most extreme cluster-level t score on each iteration were retained to build a null hypothesis distribution. The position of the original real cluster-level t scores within this null hypothesis distribution indicates how probable such an observation would be if the null hypothesis was true (no systematic difference from 0 in correlations across participants). Hence, if a given negative/positive cluster had a cluster-level t score lower/higher than 97.5% of the respective null distribution t scores, then this was considered a significant effect (5% α level).

For the final analysis, we focused on the time interval from 0 to 600 ms after the target stimulus onset. The sensitivity of the cluster-based ERP statistics depends on the length of the time interval that is analysed. To increase the sensitivity of the statistical test, it is therefore recommended to limit the time interval on the basis of prior information about the time course of the effect. Since we were particularly interested in the early effects, we chose to run separate analyses in the early (0–300 ms) and late (300–600 ms) intervals after the stimulus onset.

Behavioural results

The average shape similarity rating across all cue-target pairs across all subjects was 2.55 ± 0.51; its average range was 2.90 ± 0.67 (Fig.  2 , Panel A). The average category similarity rating across all cue-target pairs across all subjects was 2.72 ± 0.51; and its average range was 2.80 ± 0.93 (Fig.  2 , Panel B). The average reaction time (RT) across all trials across all subjects was 554 ± 107 ms; the average range of RT across all trials and all subjects was 241 ± 73 ms (Fig.  2 , Panel C). We found a robust correlation between reaction times and subjective shape similarity (mean correlation between individual subjective shape similarity and reaction times M = 0.36 ± 0.22, significantly different from zero across subjects t(19) = 6.95, p < 0.001, d = 1.55). The more similar the target object was to the cue word’s referent in terms of shape, the longer it took for participants to identify incongruence. At the same time, the reaction times were not correlated with the category similarity ratings (M = 0.007 ± 0.16, t(19) = 0.18, n.s.).

EEG results

Table  1 presents the statistics and the temporal extent of the clusters obtained in the permutation analysis. The topoplots on the Fig.  3 illustrate their spatial extent and the waveforms in Fig.  4 illustrate their temporal extend and the correspondence to the event-related response peaks. In the following we describe the obtained clusters that are statistically significant at p < 0.025. Additionally, we obtained several marginally significant clusters. For the sake of comprehensiveness, we report all clusters with p < 0.1 in the Table  1 .

Results of the main cluster-based permutation analysis for Shape (Panel A ) and the Category (Panel B ). The colour represents the group mean Spearman rho, averaged within the time interval of 50 ms (time intervals in ms are shown). The black markers over EEG electrode sites indicate that a significant cluster (p < 0.025, see Table  1 ) included this EEG channel within the given time interval. The larger the marker, the longer the channel remained statistically significant within the given interval.

Correlation values plotted against the ERPs. ERPs (black) are averaged over all cue-target combinations over all participants. Correlations with the shape and category similarity in each channel is plotted in red and blue, respectively, with a significant difference from zero, based on the cluster analysis, marked in bold, in red in blue, respectively. A selection of 16 channels (out of original 60) corresponding to the standard 10–20 electrode system is shown.

The similarity in shape between cues and targets affected the entire dynamics of the visual processing. The event-related signals in the posterior channels starting at 86 ms after the picture onset correlates positively with the shape similarity. As shown by the ERP waveforms in Fig.  4 , this cluster in the posterior channels corresponds to the P1 peak or the P1-N1 complex. Next, a large negative cluster spreading over the central regions begins at about 174 ms, followed by a posterior positive cluster after 464 ms.

The results were very different for the category similarity, where the only significant cluster was obtained at a very late time, at 452 ms after the picture onset. Notably, the spatial and temporal extent of this cluster was similar to that of the latest shape similarity cluster. We hypothesised that this effect could be driven by non-independency between the shape and category similarity ratings. Indeed, we found a small but reliable correlation between the similarity and the category ratings: on average, Spearman’s rhos of the participants (M = −0.12 ± 0.11) were significantly smaller than zero (t(19) = −4.95, p < 0.001). We also found that when the eight word-picture pairs most similar in shape were excluded, Spearman’s rhos (M = 0.02 ± 0.11) did not differ significantly from zero (t(19) = 0.8, n.s.). All these pairs (kiwi-potato, banana-zucchini, pear-eggplant, apricot-onion, and the respective reversed pairs) were different in category. Thus, the correlation between the shape and category similarity was driven by these pairs.

To tease apart the effect of shape from that of category, we ran two additional post-hoc analyses. First, we repeated the main EEG analysis, using the partial correlation approach, testing for the correlation between ERP signals and category/shape similarity, while removing the shared variance. Second, we repeated the original correlation analysis while excluding the eight word-picture pairs that drove the correlation between shape and category similarity. The results of the post-hoc analyses are shown in the Table  1 . The results of the partial correlation were similar to the normal correlation, however the magnitude of the late category effect has reduced. The second post-hoc analysis yielded a similar picture: the late ERP responses still correlated with the word-picture category similarity, but the effect became smaller and dropped below the significance threshold. This indicates, that the late category effect could be, at least partly, explained by the association between the shape and category in the designed stimuli.

The results of the partial correlation analysis of the shape similarity did not differ from the results of the main analysis and are not shown in the table.

The effects of shape

Linguistic labels are known to facilitate object recognition, yet the mechanism of this facilitation is not fully understood. A large number of psychophysical studies have suggested that words activate the visual representation of their reference, and particularly its most salient features, such as visual shape 17 , 19 , 40 . At the same time, recent visual search experiments have suggested that higher-level semantic aspects of words also affect identifcation of the visual target 30 . In the present work we aimed to tease apart the visual shape and the semantic category effects of words on object recognition, and study the dynamics of these effects at the neural level. We conducted an EEG word-picture matching experiment, using objects from two categories and with four different shapes. We predicted that participants’ reaction times would be explained by both shape and category similarity between the cues and the pictures, and that the effects of shape and category would be dissociable in the timing and topography of EEG. Contrary to our expectations, we found that only the word-picture shape similarity, but not the category similarity robustly predicts the reaction times. The shape similarity also correlated strongly with the ERPs starting in the posterior channels at about 90 ms after picture onset. The timing and topography of this effect (see Figs  3 and 4 ) are in line with the earlier finding by Boutonnet and Lupyan 22 , who showed, in a similar experiment, that the P1 ERP component was modulated by word-picture congruency. Here we have extended this earlier finding by showing, unambiguously, that this early effect on visual processing can reflect an anticipation of the upcoming visual object shape.

Several recent theoretical and empirical studies have attempted to explain the interaction between language and perception from the predictive coding perspective 10 , 12 , 47 , 48 , 49 , 50 . Verbal cues are inherently predictive: we tend to talk about objects that we see, and the valid word-object combinations are overtrained by years of language use. Moreover, in the present experiment the words were predictive of the shape of the upcoming object: the probability of seeing a round object following the word “onion” was higher than seeing any other shape, because in 30% of the trials the cue word was followed by a congruent object. According to the predictive coding account, the input in sensory cortices is constantly evaluated in comparison with top-down predictions, or expectations 10 , 12 , 47 , 48 , 49 , 50 . A mismatch between the prediction and the input results in a “prediction error” response. Anticipated stimuli evoke a smaller prediction error, i.e. a reduced neural response compared to unpredicted stimuli 48 , 50 , 51 , 52 , 53 , 54 . Our results, however, are not in line with this prediction: event-related responses over the posterior electrode sites at 86 to 216 ms after picture onset showed a positive correlation with the word-picture shape similarity, most prominently during the P1 (and partly the following N1-P2) ERP components (see Fig.  4 ). This means that objects with the anticipated shape elicited responses with larger amplitudes. One possible explanation is that in our experiment prediction (i.e. expectations based on the prior probability) was confounded with attention (i.e. task relevance). Indeed, in the present task, participants had to make a decision on the target based on the information provided by the cue, and were thus likely to attend to shape information. The attentional enhancement of the hemodynamic 55 , 56 , 57 and electrophysiological 58 responses in the visual cortex is well known. Recent studies have attempted to explain this phenomenon from the predictive coding perspective, by manipulating attention and prediction independently. Indeed, attention reverses expectation suppression in the visual cortex: when stimuli are attended, the neural response is larger in amplitude for predicted compared to unpredicted stimuli 51 . This observation is in line with the response patterns we observed in the present experiment.

Interestingly, we observed an even earlier, marginally significant cluster of correlations between shape similarity and the ERPs, starting at 44 ms after picture onset. The fronto-central location of this cluster allows for the intriguing interpretation that the effect is due to the top-down flow of information from prefrontal attentional control brain areas. The neural correlates of visual attention are well studied in primates. Consider a visual search experiment, where monkeys are trained to search for a target object within an array of distractors. It has been established that neurons in prefrontal cortex respond selectively to the targets, relative to distractors, and selectivity in those areas precedes similar selectivity in the extrastriate and temporal cortex 59 , 60 . The input from prefrontal cortex thus modulates the target selectivity in the extrastriate areas, enabling visual target detection. Accordingly, the similarity analysis in the present study points toward prefrontal attentional selection that precedes the extrastriate selection: objects with the anticipated shape elicit responses with larger (more negative, see Fig.  4 ) amplitudes over fronto-central electrodes, resulting in a negative correlation. This suggests a similar mechanism for the language-driven attention control in humans. At the same time, this interpretation is tentative, and the effect should be further investigated.

The effects of category

As mentioned above, contrary to our expectations we did not find any effect of category of the cue words’ referents on participants’ response times to the target stimuli. However, as we had expected, the effect of category did manifest in target-evoked ERPs. We found a very late category effect, starting at 450 ms after visual stimulus onset. The post-hoc analyses revealed that this effect was still significant, but reduced when the shared variance between the shape and category similarity ratings was controlled for. The effect was, therefore, partly dependent on the shared variance among similarity ratings, reflecting the cue-target pairs that were most similar in their shape (e.g. kiwi - potato; see the EEG results). An attractive explanation for this dependency and the effect’s late latency, is that the greater the word-picture pair’s shape similarity, the more participants had to include category in their decision about whether the cue and picture matched, or not.

Interestingly, weaker effects of category similarity were also found in earlier time windows. Notably, the negative correlation cluster between the ERP data and the category similarity at around 200 ms after the stimulus presentation was still present in the post-hoc tests, and the size of this effect hardly changed with the post-hoc manipulations. The timing and the posterior location of this effect is in line with the congruence effect on the amplitude of the P2 ERP component, observed by Boutonnet and Lupyan 22 . In the present study, this effect, however, was only marginally significant, and thus requires further investigation.

Altogether, our results indicate that the category of a word’s referent might influence processing of subsequent visual stimuli at multiple stages, independent of the referent’s visual shape. We find an interesting parallel with recent neuroimaging studies, attempting to disentangle the contribution of visual and conceptual information to the brain’s object representations. Namely, several recent functional magnetic resonance imaging (fMRI) studies addressed the question if the category selectivity in the ventral temporal cortex can be reduced to the selectivity of visual features, particularly, shape. While some findings support this idea 61 , 62 , other studies report convincingly irreducible category selectivity effects. For example, similarly to our design, Bracci and Op de Beeck 63 created a two-factorial stimulus set with images that explicitly dissociate shape from category. Using representational similarity analysis, they identified patterns of fMRI activity associated with the representation of objective visual silhouette, perceived shape and category. Encoding of the perceived shape was closely related to the encoding of category in high-level visual cortex. Nevertheless, the representations of shape and category did not fully overlap; category representation spread more anteriorly in the ventral stream and covered areas of the dorsal stream 63 , 64 . In another recent study, Proklova et al . 65 compared the patterns of fMRI activity evoked by animate and inanimate objects, in pairs matched for shape, such as a rope coil and a snake. Although the shape feature could well explain the evoked fMRI patterns in the ventral temporal cortex, categorical information, orthogonal to the shape, also contributed to the object representation in more anterior areas.

Limitations and future directions

Our findings support the hypothesis that words aid processing of relevant visual properties of denoted objects. The present experiment focuses on object shape. Shape is one of the most prominent features for discriminating common objects. Still, it can be less relevant for some objects than for others. Different visual and non-visual features, such as colour or taste, can be discriminative for objects at a more specific, subcategory level, and thus be activated by labels in a corresponding task or context. In fact, the word-picture relationship that we term “categorical similarity” in the present experiment could be a collection of representations in the visual as well as non-visual modalities, such as colour, taste, or sound. Future studies should address the neural dynamics of activation of these features in the language-perception interaction.

Another limitation of our design is that the shape differences between the objects in our study could be greater than the categorical differences. We chose for the close object categories “fruits” and “vegetables” in order to minimise perceptual differences between the categories, e.g. in size and texture. It remains a question if the categorical effects would be more pronounced when the cues and targets are less related, e.g. like the stimuli used by Proklova et al . 65 .

Conclusions

Previous studies have discovered that visual perception can be affected by the top-down guidance of words. Our results advance this line of research by revealing how different kinds of information carried by a word contribute to the different stages of visual processing. We provide evidence that hearing a word can activate representations of its referent’s shape, which interacts with the shape processing of a subsequent visual stimulus. This interaction is detectable from very early on in the occipital electrodes’ event-related EEG signal. We also found that a word’s non-visual categorical information can affect visual processing at later stages: an interaction between the category of the word and the category of the visual stimulus was detectable in the EEG signal much later after visual stimulus onset. These findings provide insight into the interaction between language and perception and into how the meaning of words might be represented in the brain.

Data Availability

The datasets generated and analysed during the current study are available at data.donders.ru.nl, the online data repository of the Donders Institute.

Lee, P. The Whorf Theory Complex: A Critical Reconstruction . (John Benjamins Publishing, 1996).

Cassirer, E. An Essay on Man: An Introduction to a Philosophy of Human Culture . (Yale University Press, 1972).

Dennett, D. C. The Role Of Language In Intelligence. In Sprache und Denken/Language and Thought (2013).

Dennett, D. C. Learning and Labeling. Mind Lang. 8 , 540–548 (1993).

Article   Google Scholar  

Wolff, P. & Holmes, K. J. Linguistic relativity. Wiley Interdiscip. Rev. Cogn. Sci. 2 , 253–265 (2011).

Article   PubMed   Google Scholar  

Lupyan, G. Linguistically modulated perception and cognition: the label-feedback hypothesis. Front. Psychol. 3 , 54 (2012).

PubMed   PubMed Central   Google Scholar  

Casasanto, D. Who’s Afraid of the Big Bad Whorf? Crosslinguistic Differences in Temporal Language and Thought. Lang. Learn. 58 , 63–79 (2008).

Boroditsky, L. How the Languages We Speak Shape the Ways We Think. In The Cambridge Handbook of Psycholinguistics 615–632 (2012).

Vygotskiĭ, L. S., Hanfmann, E. & Vakar, G. Thought and Language . (MIT Press, 2012).

Simanova, I., Francken, J. C., de Lange, F. P. & Bekkering, H. Linguistic priors shape categorical perception. Language, Cognition and Neuroscience 31 , 159–165 (2015).

Lupyan, G. & Swingley, D. Self-directed speech affects visual search performance. Q. J. Exp. Psychol. 65 , 1068–1085 (2012).

Lupyan, G. Language augmented prediction. Front. Psychol. 3 , 422 (2012).

Lupyan, G. & Thompson-Schill, S. L. The evocative power of words: activation of concepts by verbal and nonverbal means. J. Exp. Psychol. Gen. 141 , 170–186 (2012).

Lupyan, G. What Do Words Do? Toward a Theory of Language-Augmented Thought. In Psychology of Learning and Motivation 255–297 (2012).

Lupyan, G. Beyond communication: Language modulates visual processing. In The Evolution of Language https://doi.org/10.1142/9789814295222_0084 (2010).

Edmiston, P. & Lupyan, G. What makes words special? Words as unmotivated cues. Cognition 143 , 93–100 (2015).

Zwaan, R. A., Stanfield, R. A. & Yaxley, R. H. Language comprehenders mentally represent the shapes of objects. Psychol. Sci. 13 , 168–171 (2002).

Ostarek, M. & Huettig, F. Spoken words can make the invisible visible-Testing the involvement of low-level visual representations in spoken word processing. J. Exp. Psychol. Hum. Percept. Perform. 43 , 499–508 (2017).

Lupyan, G. & Ward, E. J. Language can boost otherwise unseen objects into visual awareness. Proc. Natl. Acad. Sci. USA 110 , 14196–14201 (2013).

Article   ADS   CAS   PubMed Central   PubMed   Google Scholar  

Ward, E. J. & Lupyan, G. Linguistic penetration of suppressed visual representations. J. Vis. 11 , 322–322 (2011).

Pinto, Y., van Gaal, S., de Lange, F. P., Lamme, V. A. F. & Seth, A. K. Expectations accelerate entry of visual stimuli into awareness. J. Vis. 15 , 13 (2015).

Boutonnet, B. & Lupyan, G. Words Jump-Start Vision: A Label Advantage in Object Recognition. J. Neurosci. 35 , 9329–9335 (2015).

Article   CAS   PubMed Central   PubMed   Google Scholar  

Bar, M. et al . Top-down facilitation of visual recognition. Proc. Natl. Acad. Sci. USA 103 , 449–454 (2006).

Summerfield, C. & de Lange, F. P. Expectation in perceptual decision making: neural and computational mechanisms. Nat. Rev. Neurosci. 15 , 745–756 (2014).

Article   CAS   PubMed   Google Scholar  

Lisman, J. The Challenge of Understanding the Brain: Where We Stand in 2015. Neuron 86 , 864–882 (2015).

Ferreira, F., Apel, J. & Henderson, J. M. Taking a new look at looking at nothing. Trends Cogn. Sci. 12 , 405–410 (2008).

Huettig, F. & McQueen, J. M. The tug of war between phonological, semantic and shape information in language-mediated visual search. J. Mem. Lang. 57 , 460–482 (2007).

Huettig, F. & Altmann, G. T. M. Word meaning and the control of eye fixation: semantic competitor effects and the visual world paradigm. Cognition 96 , B23–32 (2005).

Huettig, F., Olivers, C. N. L. & Hartsuiker, R. J. Looking, language, and memory: bridging research from the visual world and visual search paradigms. Acta Psychol. 137 , 138–150 (2011).

de Groot, F., Huettig, F. & Olivers, C. N. L. When meaning matters: The temporal dynamics of semantic influences on visual attention. J. Exp. Psychol. Hum. Percept. Perform. 42 , 180–196 (2016).

Simanova, I., van Gerven, M., Oostenveld, R. & Hagoort, P. Identifying object categories from event-related EEG: toward decoding of conceptual representations. PLoS One 5 , e14465 (2010).

Landau, B., Smith, L. B. & Jones, S. S. The importance of shape in early lexical learning. Cogn. Dev. 3 , 299–321 (1988).

Landau, B., Smith, L. & Jones, S. Object Shape, Object Function, and Object Name. J. Mem. Lang. 38 , 1–27 (1998).

Cantrell, L. & Smith, L. B. Set size, individuation, and attention to shape. Cognition 126 , 258–267 (2013).

Ferguson, B. & Waxman, S. Linking language and categorization in infancy. J. Child Lang. 44 , 527–552 (2017).

Gershkoff-Stowe, L. & Smith, L. B. Shape and the first hundred nouns. Child Dev. 75 , 1098–1114 (2004).

Perry, L. K. & Samuelson, L. K. The shape of the vocabulary predicts the shape of the bias. Front. Psychol. 2 , 345 (2011).

Article   PubMed Central   PubMed   Google Scholar  

Samuelson, L. K. & McMurray, B. What does it take to learn a word? Wiley Interdiscip. Rev. Cogn. Sci . 8 (2017).

Google Scholar  

Yee, M., Jones, S. S. & Smith, L. B. Changes in Visual Object Recognition Precede the Shape Bias in Early Noun Learning. Front. Psychol . 3 (2012).

Vales, C. & Smith, L. B. Words, shape, visual search and visual working memory in 3-year-old children. Dev. Sci. 18 , 65–79 (2015).

Willenbockel, V. et al . Controlling low-level image properties: the SHINE toolbox. Behav. Res. Methods 42 , 671–684 (2010).

Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011 , 156869 (2011).

van Vliet, M., Van Hulle, M. M. & Salmelin, R. Exploring the Organization of Semantic Memory through Unsupervised Analysis of Event-related Potentials. J. Cogn. Neurosci. 30 , 381–392 (2018).

Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164 , 177–190 (2007).

Scheeringa, R., Koopmans, P. J., van Mourik, T., Jensen, O. & Norris, D. G. The relationship between oscillatory EEG activity and the laminar-specific BOLD signal. Proc. Natl. Acad. Sci. USA 113 , 6761–6766 (2016).

Scheeringa, R. et al . Neuronal dynamics underlying high- and low-frequency EEG oscillations contribute independently to the human BOLD signal. Neuron 69 , 572–583 (2011).

Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36 , 181–204 (2013).

Friston, K. A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360 , 815–836 (2005).

Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11 , 127–138 (2010).

Summerfield, C., Trittschuh, E. H., Monti, J. M., Mesulam, M. M. & Egner, T. Neural repetition suppression reflects fulfilled perceptual expectations. Nat. Neurosci. 11 , 1004–1006 (2008).

Kok, P., Rahnev, D., Jehee, J. F. M., Lau, H. C. & de Lange, F. P. Attention Reverses the Effect of Prediction in Silencing Sensory Signals. Cereb. Cortex 22 , 2197–2206 (2011).

Feldman, H. & Friston, K. J. Attention, Uncertainty, and Free-Energy. Front. Hum. Neurosci . 4 (2010).

Rao, R. P. N. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2 , 79–87 (1999).

Friston, K. The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13 , 293–301 (2009).

Doherty, J. R. Synergistic Effect of Combined Temporal and Spatial Expectations on Visual Attention. Journal of Neuroscience 25 , 8259–8266 (2005).

Corbetta, M., Miezin, F., Dobmeyer, S., Shulman, G. & Petersen, S. Attentional modulation of neural processing of shape, color, and velocity in humans. Science 248 , 1556–1559 (1990).

Article   ADS   CAS   PubMed   Google Scholar  

Kastner, S. Mechanisms of Directed Attention in the Human Extrastriate Cortex as Revealed by Functional MRI. Science 282 , 108–111 (1998).

Luck, S. J., Woodman, G. F. & Vogel, E. K. Event-related potential studies of attention. Trends Cogn. Sci. 4 , 432–440 (2000).

Zhou, H. & Desimone, R. Feature-based attention in the frontal eye field and area V4 during visual search. Neuron 70 , 1205–1217 (2011).

Bichot, N. P., Heard, M. T., DeGennaro, E. M. & Desimone, R. A Source for Feature-Based Attention in the Prefrontal Cortex. Neuron 88 , 832–844 (2015).

Coggan, D. D., Liu, W., Baker, D. H. & Andrews, T. J. Category-selective patterns of neural response in the ventral visual pathway in the absence of categorical information. Neuroimage 135 , 107–114 (2016).

Rice, G. E., Watson, D. M., Hartley, T. & Andrews, T. J. Low-level image properties of visual objects predict patterns of neural response across category-selective regions of the ventral visual pathway. J. Neurosci. 34 , 8837–8844 (2014).

Bracci, S. & Op de Beeck, H. Dissociations and Associations between Shape and Category Representations in the Two Visual Pathways. J. Neurosci. 36 , 432–444 (2016).

Bracci, S., Brendan Ritchie, J. & Op de Beeck, H. On the partnership between neural representations of object categories and visual features in the ventral visual pathway. Neuropsychologia 105 , 153–164 (2017).

Proklova, D., Kaiser, D. & Peelen, M. V. Disentangling Representations of Object Shape and Object Category in Human Visual Cortex: The Animate-Inanimate Distinction. J. Cogn. Neurosci. 28 , 680–692 (2016).

Download references

Author information

Authors and affiliations.

Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognition, Nijmegen, The Netherlands

Samuel Noorman, David A. Neville & Irina Simanova

You can also search for this author in PubMed   Google Scholar

Contributions

S.N., D.N. and I.S. planned and designed the study, S.N. conducted the experiments and data analysis under supervision of I.S., S.N. and I.S. wrote the main manuscript text, all authors reviewed the manuscript.

Corresponding author

Correspondence to Irina Simanova .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Noorman, S., Neville, D.A. & Simanova, I. Words affect visual perception by activating object shape representations. Sci Rep 8 , 14156 (2018). https://doi.org/10.1038/s41598-018-32483-2

Download citation

Received : 22 May 2018

Accepted : 07 September 2018

Published : 20 September 2018

DOI : https://doi.org/10.1038/s41598-018-32483-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Facilitate Object Recognition
• Similar Classification Rates
• Word-picture Pairs
• Picture Onset

This article is cited by

Action-outcome delays modulate the temporal expansion of intended outcomes.

• Rohan R. Donapati
• Anuj Shukla
• Raju S. Bapi

Scientific Reports (2024)

Does knowledge influence visual attention? A comparative analysis between archaeologists and naïve subjects during the exploration of Lower Palaeolithic tools

• María Silva-Gago
• Annapaola Fedato
• Emiliano Bruner

Archaeological and Anthropological Sciences (2022)

An Efficient Framework for Video Documentation of Bladder Lesions for Cystoscopy: A Proof-of-Concept Study

• Okyaz Eminaga
• T. Jessie Ge
• Joseph C. Liao

Journal of Medical Systems (2022)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• 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

76k Accesses

78 Citations

13 Altmetric

Metrics details

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.

Achieve. (2013). The next generation science standards (pp. 1–3). Retrieved from http://www.nextgenscience.org/ .

Google Scholar  

Barber, J, Pearson, D, & Cervetti, G. (2006). Seeds of science/roots of reading . California: The Regents of the University of California.

Bungum, B. (2008). Images of physics: an explorative study of the changing character of visual images in Norwegian physics textbooks. NorDiNa, 4 (2), 132–141.

Bybee, RW. (2014). NGSS and the next generation of science teachers. Journal of Science Teacher Education, 25 (2), 211–221. doi: 10.1007/s10972-014-9381-4 .

Article   Google Scholar  

Chambers, D. (1983). Stereotypic images of the scientist: the draw-a-scientist test. Science Education, 67 (2), 255–265.

Cochran-Smith, M. (2004). The problem of teacher education. Journal of Teacher Education, 55 (4), 295–299. doi: 10.1177/0022487104268057 .

Conway, PF, Murphy, R, & Rath, A. (2009). Learning to teach and its implications for the continuum of teacher education: a nine-country cross-national study .

Crick, F. (1988). What a mad pursuit . USA: Basic Books.

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 , 189–216.

Dori, YJ, Tal, RT, & Tsaushu, M. (2003). Teaching biotechnology through case studies—can we improve higher order thinking skills of nonscience majors? Science Education, 87 (6), 767–793. doi: 10.1002/sce.10081 .

Duschl, RA, & Bybee, RW. (2014). Planning and carrying out investigations: an entry to learning and to teacher professional development around NGSS science and engineering practices. International Journal of STEM Education, 1 (1), 12. doi: 10.1186/s40594-014-0012-6 .

Duschl, R., Schweingruber, H. A., & Shouse, A. (2008). Taking science to school . Washington DC: National Academies Press.

Erduran, S, & Jimenez-Aleixandre, MP (Eds.). (2008). Argumentation in science education: perspectives from classroom-based research . Dordrecht: Springer.

Eurydice. (2012). Developing key competencies at school in Europe: challenges and opportunities for policy – 2011/12 (pp. 1–72).

Evagorou, M, Jimenez-Aleixandre, MP, & Osborne, J. (2012). “Should we kill the grey squirrels?” A study exploring students’ justifications and decision-making. International Journal of Science Education, 34 (3), 401–428. doi: 10.1080/09500693.2011.619211 .

Faraday, M. (1852a). Experimental researches in electricity. – Twenty-eighth series. Philosophical Transactions of the Royal Society of London, 142 , 25–56.

Faraday, M. (1852b). Experimental researches in electricity. – Twenty-ninth series. Philosophical Transactions of the Royal Society of London, 142 , 137–159.

Gilbert, JK. (2010). The role of visual representations in the learning and teaching of science: an introduction (pp. 1–19).

Gilbert, J., Reiner, M. & Nakhleh, M. (2008). Visualization: theory and practice in science education . Dordrecht, The Netherlands: Springer.

Gooding, D. (2006). From phenomenology to field theory: Faraday’s visual reasoning. Perspectives on Science, 14 (1), 40–65.

Gooding, D, Pinch, T, & Schaffer, S (Eds.). (1993). The uses of experiment: studies in the natural sciences . Cambridge: Cambridge University Press.

Hogan, K, & Maglienti, M. (2001). Comparing the epistemological underpinnings of students’ and scientists’ reasoning about conclusions. Journal of Research in Science Teaching, 38 (6), 663–687.

Knorr Cetina, K. (1999). Epistemic cultures: how the sciences make knowledge . Cambridge: Harvard University Press.

Korfiatis, KJ, Stamou, AG, & Paraskevopoulos, S. (2003). Images of nature in Greek primary school textbooks. Science Education, 88 (1), 72–89. doi: 10.1002/sce.10133 .

Latour, B. (2011). Visualisation and cognition: drawing things together (pp. 1–32).

Latour, B, & Woolgar, S. (1979). Laboratory life: the construction of scientific facts . Princeton: Princeton University Press.

Lehrer, R, & Schauble, L. (2012). Seeding evolutionary thinking by engaging children in modeling its foundations. Science Education, 96 (4), 701–724. doi: 10.1002/sce.20475 .

Longino, H. E. (2002). The fate of knowledge . Princeton: Princeton University Press.

Lynch, M. (2006). The production of scientific images: vision and re-vision in the history, philosophy, and sociology of science. In L Pauwels (Ed.), Visual cultures of science: rethinking representational practices in knowledge building and science communication (pp. 26–40). Lebanon, NH: Darthmouth College Press.

Lynch, M. & S. Y. Edgerton Jr. (1988). ‘Aesthetic and digital image processing representational craft in contemporary astronomy’, in G. Fyfe & J. Law (eds), Picturing Power; Visual Depictions and Social Relations (London, Routledge): 184 – 220.

Mendonça, PCC, & Justi, R. (2013). An instrument for analyzing arguments produced in modeling-based chemistry lessons. Journal of Research in Science Teaching, 51 (2), 192–218. doi: 10.1002/tea.21133 .

National Research Council (2000). Inquiry and the national science education standards . Washington DC: National Academies Press.

National Research Council (2012). A framework for K-12 science education . Washington DC: National Academies Press.

Nersessian, NJ. (1984). Faraday to Einstein: constructing meaning in scientific theories . Dordrecht: Martinus Nijhoff Publishers.

Book   Google Scholar  

Nersessian, NJ. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In RN Giere (Ed.), Cognitive Models of Science (pp. 3–45). Minneapolis: University of Minnesota Press.

Nersessian, NJ. (2008). Creating scientific concepts . Cambridge: The MIT Press.

Osborne, J. (2014). Teaching scientific practices: meeting the challenge of change. Journal of Science Teacher Education, 25 (2), 177–196. doi: 10.1007/s10972-014-9384-1 .

Osborne, J. & Dillon, J. (2008). Science education in Europe: critical reflections . London: Nuffield Foundation.

Papaevripidou, M, Constantinou, CP, & Zacharia, ZC. (2007). Modeling complex marine ecosystems: an investigation of two teaching approaches with fifth graders. Journal of Computer Assisted Learning, 23 (2), 145–157. doi: 10.1111/j.1365-2729.2006.00217.x .

Pauwels, L. (2006). A theoretical framework for assessing visual representational practices in knowledge building and science communications. In L Pauwels (Ed.), Visual cultures of science: rethinking representational practices in knowledge building and science communication (pp. 1–25). Lebanon, NH: Darthmouth College Press.

Philips, L., Norris, S. & McNab, J. (2010). Visualization in mathematics, reading and science education . Dordrecht, The Netherlands: Springer.

Pocovi, MC, & Finlay, F. (2002). Lines of force: Faraday’s and students’ views. Science & Education, 11 , 459–474.

Richards, A. (2003). Argument and authority in the visual representations of science. Technical Communication Quarterly, 12 (2), 183–206. doi: 10.1207/s15427625tcq1202_3 .

Rothbart, D. (1997). Explaining the growth of scientific knowledge: metaphors, models and meaning . Lewiston, NY: Mellen Press.

Ruivenkamp, M, & Rip, A. (2010). Visualizing the invisible nanoscale study: visualization practices in nanotechnology community of practice. Science Studies, 23 (1), 3–36.

Ryu, S, Han, Y, & Paik, S-H. (2015). Understanding co-development of conceptual and epistemic understanding through modeling practices with mobile internet. Journal of Science Education and Technology, 24 (2-3), 330–355. doi: 10.1007/s10956-014-9545-1 .

Sarkar, S, & Pfeifer, J. (2006). The philosophy of science, chapter on experimentation (Vol. 1, A-M). New York: Taylor & Francis.

Schwartz, RS, Lederman, NG, & Abd-el-Khalick, F. (2012). A series of misrepresentations: a response to Allchin’s whole approach to assessing nature of science understandings. Science Education, 96 (4), 685–692. doi: 10.1002/sce.21013 .

Schwarz, CV, Reiser, BJ, Davis, EA, Kenyon, L, Achér, A, Fortus, D, et al. (2009). Developing a learning progression for scientific modeling: making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46 (6), 632–654. doi: 10.1002/tea.20311 .

Watson, J. (1968). The Double Helix: a personal account of the discovery of the structure of DNA . New York: Scribner.

Watson, J, & Berry, A. (2004). DNA: the secret of life . New York: Alfred A. Knopf.

Wickman, PO. (2004). The practical epistemologies of the classroom: a study of laboratory work. Science Education, 88 , 325–344.

Wu, HK, & Shah, P. (2004). Exploring visuospatial thinking in chemistry learning. Science Education, 88 (3), 465–492. doi: 10.1002/sce.10126 .

Download references

Acknowledgements

The authors would like to acknowledge all reviewers for their valuable comments that have helped us improve the manuscript.

Author information

Authors and affiliations.

University of Nicosia, 46, Makedonitissa Avenue, Egkomi, 1700, Nicosia, Cyprus

Maria Evagorou

University of Limerick, Limerick, Ireland

Sibel Erduran

University of Tampere, Tampere, Finland

Terhi Mäntylä

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Maria Evagorou .

Additional information

Competing interests.

The authors declare that they have no competing interests.

Authors’ contributions

ME carried out the introductory literature review, the analysis of the first case study, and drafted the manuscript. SE carried out the analysis of the third case study and contributed towards the “Conclusions” section of the manuscript. TM carried out the second case study. All authors read and approved the final manuscript.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0 ), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 29 September 2014

Accepted : 16 May 2015

Published : 19 July 2015

DOI : https://doi.org/10.1186/s40594-015-0024-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Visual representations
• Epistemic practices
• Science learning

• X (Twitter)

Painting Pictures with Data: The Power of Visual Representations

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 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

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 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

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.

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

Related Posts

Top 5 Fishbone Diagram Templates You Need To Know About!

Mastering Your Mind: Exploring Effective Visualization Techniques

The Power of an Idea Map: Your Guide to Creative Thinking & Organizing Ideas

Innovation Unleashed: Mind Mapping vs Brainstorming in the Generation of Game-Changing Ideas

The Key to Success with Ingredients for a Fulfilling Life

Cracking the Code to Creative Thinking: Ignite Your Brain and Unleash Your Ideas

Write a comment cancel reply.

Save my name, email, and website in this browser for the next time I comment.

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Visualizations That Really Work

• Scott Berinato

Not long ago, the ability to create smart data visualizations (or dataviz) was a nice-to-have skill for design- and data-minded managers. But now it’s a must-have skill for all managers, because it’s often the only way to make sense of the work they do. Decision making increasingly relies on data, which arrives with such overwhelming velocity, and in such volume, that some level of abstraction is crucial. Thanks to the internet and a growing number of affordable tools, visualization is accessible for everyone—but that convenience can lead to charts that are merely adequate or even ineffective.

By answering just two questions, Berinato writes, you can set yourself up to succeed: Is the information conceptual or data-driven? and Am I declaring something or exploring something? He leads readers through a simple process of identifying which of the four types of visualization they might use to achieve their goals most effectively: idea illustration, idea generation, visual discovery, or everyday dataviz.

This article is adapted from the author’s just-published book, Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations.

Know what message you’re trying to communicate before you get down in the weeds.

Idea in Brief

Knowledge workers need greater visual literacy than they used to, because so much data—and so many ideas—are now presented graphically. But few of us have been taught data-visualization skills.

Tools Are Fine…

Inexpensive tools allow anyone to perform simple tasks such as importing spreadsheet data into a bar chart. But that means it’s easy to create terrible charts. Visualization can be so much more: It’s an agile, powerful way to explore ideas and communicate information.

…But Strategy Is Key

Don’t jump straight to execution. Instead, first think about what you’re representing—ideas or data? Then consider your purpose: Do you want to inform, persuade, or explore? The answers will suggest what tools and resources you need.

Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-have skill. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. That’s changed. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense of the work they do.

• Scott Berinato is a senior editor at Harvard Business Review and the author of Good Charts Workbook: Tips Tools, and Exercises for Making Better Data Visualizations and Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations .

Partner Center

• Reviews / Why join our community?
• For companies
• Frequently asked questions

Information Visualization

What is information visualization.

Information visualization is the process of representing data in a visual and meaningful way so that a user can better understand it. Dashboards and scatter plots are common examples of information visualization. Via its depicting an overview and showing relevant connections, information visualization allows users to draw insights from abstract data in an efficient and effective manner.

Information visualization plays an important role in making data digestible and turning raw information into actionable insights. It draws from the fields of human-computer interaction, visual design, computer science, and cognitive science, among others. Examples include world map-style representations, line graphs, and 3-D virtual building or town plan designs.

The process of creating information visualization typically starts with understanding the information needs of the target user group. Qualitative research (e.g., user interviews) can reveal how, when, and where the visualization will be used. Taking these insights, a designer can determine which form of data organization is needed for achieving the users’ goals. Once information is organized in a way that helps users understand it better—and helps them apply it so as to reach their goals—visualization techniques are the next tools a designer brings out to use. Visual elements (e.g., maps and graphs) are created, along with appropriate labels, and visual parameters such as color, contrast, distance, and size are used to create an appropriate visual hierarchy and a visual path through the information.

Information visualization is becoming increasingly interactive, especially when used in a website or application. Being interactive allows for manipulation of the visualization by users, making it highly effective in catering to their needs. With interactive information visualization, users are able to view topics from different perspectives, and manipulate their visualizations of these until they reach the desired insights. This is especially useful if users require an explorative experience.

Questions related to Information Visualization

There are many types of information visualization . And different types cater to diverse needs. The most common forms include charts, graphs, diagrams, and maps. Charts, like bar graphs, succinctly display data trends. Diagrams, such as flowcharts, convey processes. Maps visually represent spatial information, enhancing geographical insights. 

Each type serves a unique purpose, offering a comprehensive toolkit for effective information representation.

Information visualization and data visualization share a connection but diverge in scope. Data visualization centers on graphically representing raw data using charts or graphs. Information visualization extends beyond raw data, embracing a comprehensive array of contextual details and intricate datasets. It strives for a complete presentation, often employing interactivity to convey insights. 

Data visualization concentrates on visually representing data points. Conversely, information visualization adopts a holistic approach. It considers the context for deeper comprehension and decision-making. 

This video illustrates this concept using a routine example. It highlights the creative process and the importance of capturing and structuring ideas for effective communication.

• Transcript loading…

Information visualization and infographics play unique roles. Human memory is visual, often remembering images and patterns more than raw data. Information visualization capitalizes on this aspect. It simplifies complex data through graphics for better understanding. 

This article gives valuable insights into the properties of human memory and their significance for information visualization .

Infographics portray information in engaging formats, often for storytelling or marketing. Both use visuals, but information visualization prioritizes clarity for users and turning data into usable insights. However, the latter focuses on effective communication and engagement.

No, Information Design and data visualization are distinctive in their objectives and applications. Information Design is a broader concept. It helps organize and present information to improve communication in the bigger picture. It considers the text, images, and layout to convey information effectively. 

On the other hand, data visualization translates raw data into graphical representations. It extracts meaningful insights and patterns. The approach focuses on visual elements to simplify the analysis of complex datasets.

Information visualization is a process that transforms complex data into easy-to-understand visuals. The seven stages include: 

Data collection: Gathering relevant data from diverse sources to form the basis for visualization.

Data analysis: Examining and processing the collected data to identify patterns, trends, and insights.

Data pre-processing: Cleaning and organizing the data to make it suitable for visualization.

Visual representation: Choosing appropriate visualization techniques to represent data accurately and effectively.

Interaction design: Developing user-friendly interfaces that allow meaningful interaction with the visualized data.

Interpretation: Enabling users to interpret and derive insights from the visualized information.

Evaluation: Assessing the effectiveness of the visualization in conveying information and meeting objectives.

This article provides a comprehensive overview of the data analysis process and explores key techniques for analysis. 

Information visualization helps people understand data and make decisions. It turns complicated data into easy-to-understand visuals. This makes it easier to see patterns and get a good overall picture. It also helps people communicate by showing information in a visually exciting way. Visualizations empower individuals to interact with data, enhancing engagement and enabling deeper exploration. Additionally, visual representations facilitate easier retention and recall of information.

Data visualization has advantages and disadvantages. One big challenge is misinterpretation. The visualization of data can be misleading if presented inappropriately. It can also lead to false conclusions, especially for those who do not understand the information.

Another major problem is too much information, as this article explains: Information Overload, Why it Matters, and How to Combat It . A crowded or complex visualization can overwhelm users and make communicating difficult.

Also, making good visualizations takes time and skill. This can sometimes be challenging for newbies.

Data visualization is a powerful tool. Creating valuable and impactful visualizations requires a combination of skills. You must understand the data, choose suitable visualization methods, and tell a compelling story . All this requires a good understanding of data and design, as explained in this video.

Interpreting complex data and choosing compelling visualizations can be challenging for beginners. However, leveraging available resources and enhancing skills can simplify data visualization despite the occasional difficulty.

Check out this course to learn more about Information Visualization . The course also explains the connection between the eye and the brain in creating images. It looks at the history of information visualization, how it has evolved, and common mistakes that you must avoid in visual perception.

It will teach you how to design compelling information visualizations and use various techniques for your projects.

Literature on Information Visualization

Here’s the entire UX literature on Information Visualization by the Interaction Design Foundation, collated in one place:

Learn more about Information Visualization

Take a deep dive into Information Visualization with our course Information Visualization .

Information visualization skills are in high demand, partly thanks to the rise in big data. Tech research giant Gartner Inc. observed that digital transformation has put data at the center of every organization. With the ever-increasing amount of information being gathered and analyzed, there’s an increasing need to present data in meaningful and understandable ways.

In fact, even if you are not involved in big data, information visualization will be able to help in your work processes as a designer. This is because many design processes—including conducting user interviews and analyzing user flows and sales funnels—involve the collation and presentation of information. Information visualization turns raw data into meaningful patterns, which will help you find actionable insights. From designing meaningful interfaces, to processing your own UX research, information visualization is an indispensable tool in your UX design kit.

This course is presented by Alan Dix, a former professor at Lancaster University in the UK. A world-renowned authority in the field of human-computer interaction, Alan is the author of the university-level textbook Human-Computer Interaction . “Information Visualization” is full of simple but practical lessons to guide your development in information visualization. We start with the basics of what information visualization is, including its history and necessity, and then walk you through the initial steps in creating your own information visualizations. While there’s plenty of theory here, we’ve got plenty of practice for you, too.

All open-source articles on Information Visualization

Information overload, why it matters and how to combat it.

• 1.1k shares
• 4 years ago

Visual Representation

How to Design an Information Visualization

How to Visualize Your Qualitative User Research Results for Maximum Impact

• 3 years ago

Preattentive Visual Properties and How to Use Them in Information Visualization

• 5 years ago

How to Conduct Focus Groups

The Properties of Human Memory and Their Importance for Information Visualization

• 7 years ago

Information Visualization – A Brief Introduction

Visual Mapping – The Elements of Information Visualization

Guidelines for Good Visual Information Representations

How to Show Hierarchical Data with Information Visualization

Information Visualization – An Introduction to Multivariate Analysis

• 8 years ago

How to Display Complex Network Data with Information Visualization

Information Visualization – Who Needs It?

Vision and Visual Perception Challenges

Information Visualization an Introduction to Transformable Information Representations

The Principles of Information Visualization for Basic Network Data

The Continuum of Understanding and Information Visualization

• 6 years ago

Information Visualization – A Brief Pre-20th Century History

Information Visualization an Introduction to Manipulable Information Representations

Open Access—Link to us!

We believe in Open Access and the  democratization of knowledge . Unfortunately, world-class educational materials such as this page are normally hidden behind paywalls or in expensive textbooks.

If you want this to change , cite this page , link to us, or join us to help us democratize design knowledge !

Privacy Settings

Our digital services use necessary tracking technologies, including third-party cookies, for security, functionality, and to uphold user rights. Optional cookies offer enhanced features, and analytics.

Experience the full potential of our site that remembers your preferences and supports secure sign-in.

Governs the storage of data necessary for maintaining website security, user authentication, and fraud prevention mechanisms.

Enhanced Functionality

Saves your settings and preferences, like your location, for a more personalized experience.

Referral Program

We use cookies to enable our referral program, giving you and your friends discounts.

Error Reporting

We share user ID with Bugsnag and NewRelic to help us track errors and fix issues.

Optimize your experience by allowing us to monitor site usage. You’ll enjoy a smoother, more personalized journey without compromising your privacy.

Analytics Storage

Collects anonymous data on how you navigate and interact, helping us make informed improvements.

Differentiates real visitors from automated bots, ensuring accurate usage data and improving your website experience.

Lets us tailor your digital ads to match your interests, making them more relevant and useful to you.

Advertising Storage

Stores information for better-targeted advertising, enhancing your online ad experience.

Personalization Storage

Permits storing data to personalize content and ads across Google services based on user behavior, enhancing overall user experience.

Advertising Personalization

Allows for content and ad personalization across Google services based on user behavior. This consent enhances user experiences.

Enables personalizing ads based on user data and interactions, allowing for more relevant advertising experiences across Google services.

Receive more relevant advertisements by sharing your interests and behavior with our trusted advertising partners.

Enables better ad targeting and measurement on Meta platforms, making ads you see more relevant.

Allows for improved ad effectiveness and measurement through Meta’s Conversions API, ensuring privacy-compliant data sharing.

LinkedIn Insights

Tracks conversions, retargeting, and web analytics for LinkedIn ad campaigns, enhancing ad relevance and performance.

LinkedIn CAPI

Enhances LinkedIn advertising through server-side event tracking, offering more accurate measurement and personalization.

Google Ads Tag

Tracks ad performance and user engagement, helping deliver ads that are most useful to you.

Share Knowledge, Get Respect!

or copy link

Cite according to academic standards

Simply copy and paste the text below into your bibliographic reference list, onto your blog, or anywhere else. You can also just hyperlink to this page.

New to UX Design? We’re Giving You a Free ebook!

Download our free ebook The Basics of User Experience Design to learn about core concepts of UX design.

In 9 chapters, we’ll cover: conducting user interviews, design thinking, interaction design, mobile UX design, usability, UX research, and many more!

Illustrated Vocabulary

Before reading

This strategy teaches students to identify the components that make up a word’s meaning and to understand relationships among words that share components. Visual representation supports students’ vocabulary recall.

• Choose vocabulary words or have students identify unfamiliar words from the text. From those, select words that have more than one clear word part, like an affix or root.
• Working individually or in pairs, have students break the word up into its parts. For each part, students should find the meaning and illustrate it with images representing the part’s meaning.  
• Have students record the information and their illustrations on an index card (see sample).

Connection to anti-bias education

Illustrated vocabulary allows students to integrate visualization and personal meaning into their learning. Such student-centered instructional strategies contribute to inclusive classrooms where students feel comfortable talking about how they see things.  

Sample illustrated vocabulary:

• Student sensitivity.
• The Power of Advertising and Girls' Self-Image

Print this Strategy

Please note that some Strategies may contain linked PDF handouts that will need to be printed individually. These are listed in the sidebar of this page.

• Google Classroom

Sign in to save these resources.

Login or create an account to save resources to your bookmark collection.

Get the Learning for Justice Newsletter

We use essential cookies to make Venngage work. By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts.

Manage Cookies

Cookies and similar technologies collect certain information about how you’re using our website. Some of them are essential, and without them you wouldn’t be able to use Venngage. But others are optional, and you get to choose whether we use them or not.

Strictly Necessary Cookies

These cookies are always on, as they’re essential for making Venngage work, and making it safe. Without these cookies, services you’ve asked for can’t be provided.

Show cookie providers

• Google Login

Functionality Cookies

These cookies help us provide enhanced functionality and personalisation, and remember your settings. They may be set by us or by third party providers.

Performance Cookies

These cookies help us analyze how many people are using Venngage, where they come from and how they're using it. If you opt out of these cookies, we can’t get feedback to make Venngage better for you and all our users.

• Google Analytics

Targeting Cookies

These cookies are set by our advertising partners to track your activity and show you relevant Venngage ads on other sites as you browse the internet.

• Google Tag Manager
• Infographics
• Daily Infographics
• Popular Templates
• Accessibility
• Graphic Design
• Graphs and Charts
• Data Visualization
• Human Resources
• Beginner Guides

Blog Graphic Design Visual Presentation: Tips, Types and Examples

Visual Presentation: Tips, Types and Examples

Written by: Krystle Wong Sep 28, 2023

So, you’re gearing up for that big presentation and you want it to be more than just another snooze-fest with slides. You want it to be engaging, memorable and downright impressive. 

Well, you’ve come to the right place — I’ve got some slick tips on how to create a visual presentation that’ll take your presentation game up a notch. 

Packed with presentation templates that are easily customizable, keep reading this blog post to learn the secret sauce behind crafting presentations that captivate, inform and remain etched in the memory of your audience.

Click to jump ahead:

What is a visual presentation

15 effective tips to make your visual presentations more engaging, 6 major types of visual presentation you should know , what are some common mistakes to avoid in visual presentations, visual presentation faqs, 5 steps to create a visual presentation with venngage.

A visual presentation is a communication method that utilizes visual elements such as images, graphics, charts, slides and other visual aids to convey information, ideas or messages to an audience. 

Visual presentations aim to enhance comprehension engagement and the overall impact of the message through the strategic use of visuals. People remember what they see, making your point last longer in their heads. 

Without further ado, let’s jump right into some great visual presentation examples that would do a great job in keeping your audience interested and getting your point across.

In today’s fast-paced world, where information is constantly bombarding our senses, creating engaging visual presentations has never been more crucial. To help you design a presentation that’ll leave a lasting impression, I’ve compiled these examples of visual presentations that will elevate your game.

1. Use the rule of thirds for layout

Ever heard of the rule of thirds? It’s a presentation layout trick that can instantly up your slide game. Imagine dividing your slide into a 3×3 grid and then placing your text and visuals at the intersection points or along the lines. This simple tweak creates a balanced and seriously pleasing layout that’ll draw everyone’s eyes.

2. Get creative with visual metaphors

Got a complex idea to explain? Skip the jargon and use visual metaphors. Throw in images that symbolize your point – for example, using a road map to show your journey towards a goal or using metaphors to represent answer choices or progress indicators in an interactive quiz or poll.

3. Engage with storytelling through data

Use storytelling magic to bring your data to life. Don’t just throw numbers at your audience—explain what they mean, why they matter and add a bit of human touch. Turn those stats into relatable tales and watch your audience’s eyes light up with understanding.

4. Visualize your data with charts and graphs

The right data visualization tools not only make content more appealing but also aid comprehension and retention. Choosing the right visual presentation for your data is all about finding a good match. 

For ordinal data, where things have a clear order, consider using ordered bar charts or dot plots. When it comes to nominal data, where categories are on an equal footing, stick with the classics like bar charts, pie charts or simple frequency tables. And for interval-ratio data, where there’s a meaningful order, go for histograms, line graphs, scatterplots or box plots to help your data shine.

In an increasingly visual world, effective visual communication is a valuable skill for conveying messages. Here’s a guide on how to use visual communication to engage your audience while avoiding information overload.

5. Employ the power of contrast

Want your important stuff to pop? That’s where contrast comes in. Mix things up with contrasting colors, fonts or shapes. It’s like highlighting your key points with a neon marker – an instant attention grabber.

6. End with a powerful visual punch

Your presentation closing should be a showstopper. Think a stunning clip art that wraps up your message with a visual bow, a killer quote that lingers in minds or a call to action that gets hearts racing.

7. Tell a visual story

Structure your slides like a storybook and create a visual narrative by arranging your slides in a way that tells a story. Each slide should flow into the next, creating a visual narrative that keeps your audience hooked till the very end.

Icons and images are essential for adding visual appeal and clarity to your presentation. Venngage provides a vast library of icons and images, allowing you to choose visuals that resonate with your audience and complement your message. 

8. Show the “before and after” magic

Want to drive home the impact of your message or solution? Whip out the “before and after” technique. Show the current state (before) and the desired state (after) in a visual way. It’s like showing a makeover transformation, but for your ideas.

9. Add fun with visual quizzes and polls

To break the monotony and see if your audience is still with you, throw in some quick image quizzes or polls. It’s like a mini-game break in your presentation — your audience gets involved and it makes your presentation way more dynamic and memorable.

10. Use visuals wisely

Your visuals are the secret sauce of a great presentation. Cherry-pick high-quality images, graphics, charts and videos that not only look good but also align with your message’s vibe. Each visual should have a purpose – they’re not just there for decoration. 

11. Utilize visual hierarchy

Employ design principles like contrast, alignment and proximity to make your key info stand out. Play around with fonts, colors and placement to make sure your audience can’t miss the important stuff.

12. Engage with multimedia

Static slides are so last year. Give your presentation some sizzle by tossing in multimedia elements. Think short video clips, animations, or a touch of sound when it makes sense, including an animated logo . There are tons of video and clip creator tools like HubSpot or Adobe But remember, these are sidekicks, not the main act, so use them smartly.

13. Interact with your audience

Turn your presentation into a two-way street. Start your presentation by encouraging your audience to join in with thought-provoking questions, quick polls or using interactive tools. Get them chatting and watch your presentation come alive.

When it comes to delivering a group presentation, it’s important to have everyone on the team on the same page. Venngage’s real-time collaboration tools enable you and your team to work together seamlessly, regardless of geographical locations. Collaborators can provide input, make edits and offer suggestions in real time. 

14. Incorporate stories and examples

Weave in relatable stories, personal anecdotes or real-life examples to illustrate your points. It’s like adding a dash of spice to your content – it becomes more memorable and relatable.

15. Nail that delivery

Don’t just stand there and recite facts like a robot — be a confident and engaging presenter. Lock eyes with your audience, mix up your tone and pace and use some gestures to drive your points home. Practice and brush up your presentation skills until you’ve got it down pat for a persuasive presentation that flows like a pro.

Venngage offers a wide selection of professionally designed presentation templates, each tailored for different purposes and styles. By choosing a template that aligns with your content and goals, you can create a visually cohesive and polished presentation that captivates your audience.

Looking for more presentation ideas ? Why not try using a presentation software that will take your presentations to the next level with a combination of user-friendly interfaces, stunning visuals, collaboration features and innovative functionalities that will take your presentations to the next level. 

Visual presentations come in various formats, each uniquely suited to convey information and engage audiences effectively. Here are six major types of visual presentations that you should be familiar with:

1. Slideshows or PowerPoint presentations

Slideshows are one of the most common forms of visual presentations. They typically consist of a series of slides containing text, images, charts, graphs and other visual elements. Slideshows are used for various purposes, including business presentations, educational lectures and conference talks.

2. Infographics

Infographics are visual representations of information, data or knowledge. They combine text, images and graphics to convey complex concepts or data in a concise and visually appealing manner. Infographics are often used in marketing, reporting and educational materials.

Don’t worry, they are also super easy to create thanks to Venngage’s fully customizable infographics templates that are professionally designed to bring your information to life. Be sure to try it out for your next visual presentation!

3. Video presentation

Videos are your dynamic storytellers. Whether it’s pre-recorded or happening in real-time, videos are the showstoppers. You can have interviews, demos, animations or even your own mini-documentary. Video presentations are highly engaging and can be shared in both in-person and virtual presentations .

4. Charts and graphs

Charts and graphs are visual representations of data that make it easier to understand and analyze numerical information. Common types include bar charts, line graphs, pie charts and scatterplots. They are commonly used in scientific research, business reports and academic presentations.

Effective data visualizations are crucial for simplifying complex information and Venngage has got you covered. Venngage’s chart templates enable you to create engaging charts, graphs,and infographics that enhance audience understanding and retention, leaving a lasting impression in your presentation.

5. Interactive presentations

Interactive presentations involve audience participation and engagement. These can include interactive polls, quizzes, games and multimedia elements that allow the audience to actively participate in the presentation. Interactive presentations are often used in workshops, training sessions and webinars.

Venngage’s interactive presentation tools enable you to create immersive experiences that leave a lasting impact and enhance audience retention. By incorporating features like clickable elements, quizzes and embedded multimedia, you can captivate your audience’s attention and encourage active participation.

6. Poster presentations

Poster presentations are the stars of the academic and research scene. They consist of a large poster that includes text, images and graphics to communicate research findings or project details and are usually used at conferences and exhibitions. For more poster ideas, browse through Venngage’s gallery of poster templates to inspire your next presentation.

Different visual presentations aside, different presentation methods also serve a unique purpose, tailored to specific objectives and audiences. Find out which type of presentation works best for the message you are sending across to better capture attention, maintain interest and leave a lasting impression. 

To make a good presentation , it’s crucial to be aware of common mistakes and how to avoid them. Without further ado, let’s explore some of these pitfalls along with valuable insights on how to sidestep them.

Overloading slides with text

Text heavy slides can be like trying to swallow a whole sandwich in one bite – overwhelming and unappetizing. Instead, opt for concise sentences and bullet points to keep your slides simple. Visuals can help convey your message in a more engaging way.

Using low-quality visuals

Grainy images and pixelated charts are the equivalent of a scratchy vinyl record at a DJ party. High-resolution visuals are your ticket to professionalism. Ensure that the images, charts and graphics you use are clear, relevant and sharp.

Choosing the right visuals for presentations is important. To find great visuals for your visual presentation, Browse Venngage’s extensive library of high-quality stock photos. These images can help you convey your message effectively, evoke emotions and create a visually pleasing narrative. 

Ignoring design consistency

Imagine a book with every chapter in a different font and color – it’s a visual mess. Consistency in fonts, colors and formatting throughout your presentation is key to a polished and professional look.

Reading directly from slides

Reading your slides word-for-word is like inviting your audience to a one-person audiobook session. Slides should complement your speech, not replace it. Use them as visual aids, offering key points and visuals to support your narrative.

Lack of visual hierarchy

Neglecting visual hierarchy is like trying to find Waldo in a crowd of clones. Coupling this with video transcription can make your presentation more comprehensive and engaging. Use size, color and positioning to emphasize what’s most important. Guide your audience’s attention to key points so they don’t miss the forest for the trees.

Ignoring accessibility

Accessibility isn’t an option these days; it’s a must. Forgetting alt text for images, color contrast and closed captions for videos can exclude individuals with disabilities from understanding your presentation. 

Relying too heavily on animation

While animations can add pizzazz and draw attention, overdoing it can overshadow your message. Use animations sparingly and with purpose to enhance, not detract from your content.

Using jargon and complex language

Keep it simple. Use plain language and explain terms when needed. You want your message to resonate, not leave people scratching their heads.

Not testing interactive elements

Interactive elements can be the life of your whole presentation, but not testing them beforehand is like jumping into a pool without checking if there’s water. Ensure that all interactive features, from live polls to multimedia content, work seamlessly. A smooth experience keeps your audience engaged and avoids those awkward technical hiccups.

Presenting complex data and information in a clear and visually appealing way has never been easier with Venngage. Build professional-looking designs with our free visual chart slide templates for your next presentation.

What is a visual presentation?

A visual presentation is a method of presenting information through visual aids such as slides, images, charts and videos. It enhances understanding and retention by illustrating key points and data visually. Visual presentations are commonly used in meetings, lectures, and conferences to engage and inform the audience effectively.

What is the role of storytelling in visual presentations?

Storytelling plays a crucial role in visual presentations by providing a narrative structure that engages the audience, helps them relate to the content and makes the information more memorable.

What software or tools can I use to create visual presentations?

You can use various software and tools to create visual presentations, including Microsoft PowerPoint, Google Slides, Adobe Illustrator, Canva, Prezi and Venngage, among others.

What is the difference between a visual presentation and a written report?

The main difference between a visual presentation and a written report is the medium of communication. Visual presentations rely on visuals, such as slides, charts and images to convey information quickly, while written reports use text to provide detailed information in a linear format.

How do I effectively communicate data through visual presentations?

To effectively communicate data through visual presentations, simplify complex data into easily digestible charts and graphs, use clear labels and titles and ensure that your visuals support the key messages you want to convey.

Are there any accessibility considerations for visual presentations?

Accessibility considerations for visual presentations include providing alt text for images, ensuring good color contrast, using readable fonts and providing transcripts or captions for multimedia content to make the presentation inclusive.

Most design tools today make accessibility hard but Venngage’s Accessibility Design Tool comes with accessibility features baked in, including accessible-friendly and inclusive icons.

How do I choose the right visuals for my presentation?

Choose visuals that align with your content and message. Use charts for data, images for illustrating concepts, icons for emphasis and color to evoke emotions or convey themes.

How can I adapt my visual presentations for online or virtual audiences?

To adapt visual presentations for online or virtual audiences, focus on concise content, use engaging visuals, ensure clear audio, encourage audience interaction through chat or polls and rehearse for a smooth online delivery.

What is the role of data visualization in visual presentations?

Data visualization in visual presentations simplifies complex data by using charts, graphs and diagrams, making it easier for the audience to understand and interpret information.

How do I choose the right color scheme and fonts for my visual presentation?

Choose a color scheme that aligns with your content and brand and select fonts that are readable and appropriate for the message you want to convey.

How can I measure the effectiveness of my visual presentation?

Measure the effectiveness of your visual presentation by collecting feedback from the audience, tracking engagement metrics (e.g., click-through rates for online presentations) and evaluating whether the presentation achieved its intended objectives.

Follow the 5 simple steps below to make your entire presentation visually appealing and impactful:

1. Sign up and log In: Log in to your Venngage account or sign up for free and gain access to Venngage’s templates and design tools.

2. Choose a template: Browse through Venngage’s presentation template library and select one that best suits your presentation’s purpose and style. Venngage offers a variety of pre-designed templates for different types of visual presentations, including infographics, reports, posters and more.

3. Edit and customize your template: Replace the placeholder text, image and graphics with your own content and customize the colors, fonts and visual elements to align with your presentation’s theme or your organization’s branding.

4. Add visual elements: Venngage offers a wide range of visual elements, such as icons, illustrations, charts, graphs and images, that you can easily add to your presentation with the user-friendly drag-and-drop editor.

5. Save and export your presentation: Export your presentation in a format that suits your needs and then share it with your audience via email, social media or by embedding it on your website or blog .

So, as you gear up for your next presentation, whether it’s for business, education or pure creative expression, don’t forget to keep these visual presentation ideas in your back pocket.

Feel free to experiment and fine-tune your approach and let your passion and expertise shine through in your presentation. With practice, you’ll not only build presentations but also leave a lasting impact on your audience – one slide at a time.

Discover popular designs

Infographic maker

Brochure maker

White paper online

Newsletter creator

Flyer maker

Timeline maker

Letterhead maker

Mind map maker

Ebook maker

How To Visualize Words

• Success Team
• January 1, 2023

Transcribe, Translate, Analyze & Share

Join 170,000+ incredible people and teams saving 80% and more of their time and money. Rated 4.9 on G2 with the best AI video-to-text converter and AI audio-to-text converter , AI translation and analysis support for 100+ languages and dozens of file formats across audio, video and text.

Get a 7-day fully-featured trial!

How To Visualize Words: A Guide For Small & Medium Sized Businesses, Marketers, Qualitative Researchers, Customer Experience Managers, Market Researchers, Product Researchers, SEO Specialists, Business Analysts, Data Scientists, Academic Researchers and Business Owners

Visualizing words is a powerful tool that can help you better understand and communicate complex ideas. It can also help you to better understand the relationships between words and concepts. By visualizing words, you can create a visual representation of the meaning of a word or phrase, making it easier to remember and recall. In this article, we will discuss how to visualize words, the benefits of visualizing words, and some tips for getting started.

What Is Visualizing Words?

Visualizing words is a process of creating a visual representation of the meaning of a word or phrase. This can be done in a variety of ways, such as creating a diagram, drawing a picture, or using a mind map. Visualizing words can help you to better understand the relationships between words and concepts, as well as to better remember and recall information. It can also help to make complex ideas easier to understand and communicate.

Benefits of Visualizing Words

Visualizing words can have many benefits, including:

• Improving understanding of complex ideas
• Improving memory and recall
• Helping to communicate ideas more effectively
• Helping to identify relationships between words and concepts
• Making it easier to brainstorm and come up with new ideas

Tips for Visualizing Words

Here are some tips for visualizing words:

• Start with a simple diagram. Start by creating a simple diagram or mind map of the words you want to visualize. This will help you to organize your thoughts and make it easier to visualize the relationships between words and concepts.
• Use colors. Colors can be used to represent different ideas or concepts. For example, you could use blue to represent a concept and red to represent another. This can help to make your visualizations more visually appealing and easier to understand.
• Use symbols. Symbols can also be used to represent different ideas or concepts. For example, you could use a star to represent a concept and a heart to represent another. This can help to make your visualizations more visually appealing and easier to understand.
• Be creative. Don’t be afraid to get creative with your visualizations. You can use different shapes, sizes, and colors to represent different ideas or concepts. This can help to make your visualizations more visually appealing and easier to understand.
• Keep it simple. Try to keep your visualizations as simple as possible. Too many details can make it difficult to understand the relationships between words and concepts.

Visualizing words can be a powerful tool for improving understanding and communication. It can help to make complex ideas easier to understand and communicate, as well as to better remember and recall information. By following the tips outlined above, you can start to visualize words and reap the benefits of this powerful tool.

Get a 7-day fully-featured trial of Speak! No card required.

Trusted by 150,000+ incredible people and teams

Save 80% & more of your time and costs!

Use Speak’s powerful AI to transcribe, analyze, automate and produce incredible insights for you and your team.

The bag of (visual) words model

Let me start off by saying,  “You’ll want to pay attention to this lesson.”

The bag of visual words (BOVW) model is one of the  most important concepts in all of computer vision. We use the bag of visual words model to classify the contents of an image . It’s used to build  highly scalable (not to mention,  accurate)  CBIR systems. We even use the bag of visual words model when classifying texture via  textons .

So what exactly is a bag of visual words? And how do we construct one?

In the remainder of this lesson, we’ll review the basics of the bag of visual words model along with the general steps required to build one.

Objectives:

In this lesson, we will:

• Learn about the bag of visual words model.
• Review the required steps to build a bag of visual words.

What is a bag of visual words?

As the name implies, the “bag of visual words” concept is actually taken from the “bag of words” model from the field of Information Retrieval (i.e., text-based search engines) and text analysis.

The general idea in the bag of words model is to represent “documents” (i.e. webpages, Word files, etc.) as a collection of important keypoints while  totally disregarding the order the words appear in.

Documents that share a large number of the same keywords, again, regardless of the order the keywords appear in, are considered to be  relevant to each other .

Furthermore, since we are totally disregarding the order of the words in the document, we call this representation a “bag of words” rather than a “list of words” or “array of words”:

Treating a document as a “bag of words” allows us to efficiently analyze and compare documents since we do not have to store any information regarding the order and locality of words to each other — we simply count the number of times a word appears in a document, and then use the frequency counts of each word as a method to quantify the document.

In computer vision, we can apply the same concept — only now instead of working with keywords, our “words” are now image patches and their associated feature vectors:

When reading the computer vision literature, it’s common to see the terms  bag of words and  bag of visual words used interchangeably. Both terms refer to the same concept — representing an image as a collection of unordered image patches; however, I prefer to use the term  bag of visual words to alleviate any chance of ambiguity. Using the term  bag of words can become especially confusing if you are building a system that fuses  both text and image data — imagine the struggle and confusion that can result in trying to explain which “bag” model you are referring to! Because of this, I will use the term  bag of visual words , sometimes abbreviated as BOVW throughout the rest of this course.

In Information Retrieval and text analysis, recording the number of times a given word appears in a document is trivially easy — you simply count them and construction a histogram of word occurrences:

Here, we can see the word  “like”  is used twice. The word  “teaches” is used only once. And both the words  “computer” and  “vision” are used four times each. If we were to completely ignore the raw text and simply examine the word occurrence histogram, we could easily determine that this blob of text discusses the topic of “computer vision.”

However, applying the bag of words concept in computer vision is not as simple.

How exactly do you count the number of times an “image patch” appears in an image? And how do you ensure the same “vocabulary” of visual words is used for each image in your dataset?

We’ll discuss the vocabulary problem in more detail later in this lesson, but for the time being let’s assume that through some black-box algorithm that we can construct a large vocabulary (also referred to as a  dictionary or  codebook ; I’ll be using all three terms interchangeably) of possible visual words in our dataset.

Given our dictionary of possible visual words, we can then quantify and represent an image as a histogram which simply counts the number of times each visual word appears . This histogram is our actual  bag of visual words:

At the top of this figure, we have three input images of a  face ,  bicycle , and violin . We then extract image patches from each of these objects ( middle ). Then, on the bottom we take the image patches and use them to construct a histogram that “counts” the number of times each of these image patches appears. These histograms are our actual bag of visual words representation. We call this representation a “bag” because we completely throw out any ordering and locality of image patches and simply tabulate the number of times each type of patch appears.

Notice how the  face image has substantially more face patch counts than the other two images. Similarly, the bicycle  histogram has many more bicycle patches. And finally, the  violin image reports many more violin-like image patches in its histogram.

Building a bag of visual words

Building a bag of visual words can be broken down into a three-step process:

Step #1: Feature extraction

• Step #2: Codebook construction
• Step #3: Vector quantization.

We will cover each of these steps in detail over the next few lessons, but for the time being, let’s perform a high-level overview of each step.

The first step in building a bag of visual words is to perform feature extraction by extracting descriptors from each image in our dataset.

Feature extraction can be accomplished in a variety of ways including: detecting keypoints and extracting SIFT features from salient regions of our images; applying a grid at regularly spaced intervals (i.e., the  Dense keypoint detector) and extracting another form of local invariant descriptor; or we could even extract mean RGB values from random locations in the images.

The point here is that for each image inputted, we receive multiple feature vectors out:

Step #2: Dictionary/Vocabulary construction

Now that we have extracted feature vectors from each image in our dataset, we need to construct our vocabulary of possible visual words.

Vocabulary construction is normally accomplished via the k-means clustering algorithm  where we cluster the feature vectors obtained from  Step #1 .

The resulting cluster centers (i.e., centroids) are treated as our  dictionary  of visual words.

Step #3: Vector quantization

Given an arbitrary image (whether from our original dataset or not), we can quantify and abstractly represent the image using our bag of visual words model by applying the following process:

• Extract feature vectors from the image in the same manner as  Step #1 above.
• For each extracted feature vector, compute its nearest neighbor in the dictionary created in Step #2  — this is normally accomplished using the Euclidean Distance.
• Take the set of nearest neighbor labels and build a histogram of length  k (the number of clusters generated from k-means), where the  i ‘th value in the histogram is the frequency of the  i ‘th visual word. This process in modeling an object by its distribution of prototype vectors is commonly called  vector quantization .

An example of constructing a bag of visual words from an image can be seen below:

Finally, given the bag of visual words representation for  all images in our dataset, we can apply common machine learning or CBIR algorithms to classify and retrieve images based on their visual contents.

Simply put, the bag of visual words model allows us to take highly discriminate descriptors (such as SIFT), which result in  multiple feature vectors per image, and  quantize them into a single histogram based on our dictionary. Our image is now represented by a  single  histogram of discriminative features which can be fed into other machine learning, computer vision, and CBIR algorithms.

We started this lesson by discussing the  bag of words model in text analysis and Information Retrieval. The bag of words models a document by simply counting the number of times a given keyword occurs, irrespective of the ordering of the keywords in the document. The word “bag” is used to describe this method since we ignore the ordering of the keywords.

The same type of concept can be applied in computer vision by counting the number of pre-set image patches occurring in an image — this model is called a  bag of visual words .

From there, we discussed the three steps required to construct a bag of visual words, namely: (1) feature extraction; (2) codebook construction, normally via k-means; and (3) vector quantization.

The next four lessons in this module are dedicated to exploring and constructing the bag of visual words model in more detail. First up,  extracting keypoints and local invariant descriptors .

Understanding Without Words: Visual Representations in Math, Science and Art

• First Online: 02 November 2021

Cite this chapter

• Kathleen Coessens 5 ,
• Karen François 6 &
• Jean Paul Van Bendegem 7  

Part of the book series: Educational Research ((EDRE,volume 11))

320 Accesses

As knowledge can be condensed in different non-verbal ways of representation, the integration of graphic and visual representations and design in research output helps to expand insight and understanding. Layers of visual charts, maps, diagrams not only aim at synergizing the complexity of a topic with visual simplicity, but also to guide a personal search for and insights into knowledge. However, from research over graphic representation to interpretation and understanding implies a move that is scientific, epistemic, artistic and, last but not least, ethical. This article will consider these four aspects from both the side of the researcher and the receiver/interpreter from three different perspectives. The first perspective will consider the importance of visual representations in science and its recent developments. As a second perspective, we will analyse the discussion concerning the use of diagrams in the philosophy of mathematics. A third perspective will be from an artistic perspective on diagrams, where the visual tells us (sometimes) more than the verbal.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Visual Reasoning in Science and Mathematics

Diagrams in Mathematics: On Visual Experience in Peirce

Why Do Mathematicians Need Diagrams? Peirce’s Existential Graphs and the Idea of Immanent Visuality

This is the school typically associated with the mathematician David Hilbert. Although he himself saw formalism as a particular strategy to solve certain specific mathematical questions such as the consistency of arithmetic, nevertheless in the hands mainly of the French Bourbaki group it became an overall philosophy and the famous expression that mathematics is a game of meaningless signs was born. See (Detlefsen, 2005 ).

This seemingly simple graph consisting of 10 vertices and 15 edges is nevertheless of supreme importance in graph theory because of the impressive list of properties it possesses. Starikova ( 2017 ) presents a nice and thorough analysis of the graph (in order to discuss its aesthetic qualities). We just mention that the graph has 120 symmetries.

To be found at http://mathworld.wolfram.com/PetersenGraph.html , consulted Sunday, 17 September 2017.

A famous example is a proof of Augustin Cauchy wherein he made the mistake of inverting the quantifiers. A statement of the form ‘For all x, there is a y such that …’ was interpreted as ‘There is a y, such that for all x …’, which is a stronger statement. It is interesting to mention that this case was already (partially) studied by Imre Lakatos, see (Lakatos, 1976 , Appendix 1), who is often seen as the founding father of the study of mathematical practices.

That being said, the interest in the topic is growing. We just mention (Giaquinto, ), (Manders, ), (Giardino, ) and (Carter, 2010 ) as initiators. Of special interest is the connection that is being made between the philosophical approach and the opportunities offered by cognitive science to study the multiple ways that diagrams can be used an interpreted, see (Mumma & Hamami, 2013 ).

It is interesting that, under the same topic, David Bridges (this volume) develops a similar point of view on arts-based research for education. While Bridges questions the ambiguity of the potential and use of artistic means and expressions as research, we rather consider artistic expressions as enriching methods for knowledge construction, opening new insights by their complexity and layeredness.

Alsina, C., & Nelsen, R. B. (2006). Math made visual . The Mathematical Association of America.

Book   Google Scholar  

Bottici, C. (2014). Imaginal politics: Images beyond the imagination and beyond the imaginary . Columbia University Press.

Carter, J. (2010). Diagrams and proofs in analysis. International Studies in the Philosophy of Science, 24 (1), 1–14.

Article   Google Scholar  

Coessens, K. (2010). Visual praxis: Moving the body, the world and the self. Applied Semiotics/Sémiotique Appliquée, Issue: Translating Culture/Traduire La Culture, 9 (24), 112–143.

Google Scholar  

Detlefsen, M. (2005). Formalism. In S. Shapiro (Ed.), The Oxford handbook of philosophy of mathematics and logic (pp. 236–317). OUP.

Chapter   Google Scholar  

Doruff, S. (2011). Diagrammatic praxis. Journal for Artistic Research , (0). http://www.jar-online.net/diagrammatic-praxis/ . Last accessed 15 Feb 2018.

European Science Foundation (ESF) (2011). The European code of conduct for research integrity . Strasbourg: Ireg

Eisner, E., & Day, D. (2004). Handbook of research and policy in art education . Taylor and Francis Inc.

François, K., & Van Bendegem, J. P. (2010). Ethical-political aspects of statistical literacy. In Proceedings of the ICOTS-8 Eight International Conference on Teaching Statistics , Slovenia, Ljubljana, 11–16 July 2010. https://iase-web.org/documents/papers/icots8/ICOTS8_C258_FRANCOIS.pdf . Last accessed 19 Aug 2017.

Frans, J. (2017). Mathematical explanation. A philosophical analysis of the explanatory value of mathematical proofs. In Unpublished dissertation submitted to fulfil the requirements for the degree of Doctor in Philosophy . Brussels: Vrije Universiteit Brussel.

Gates, P. (2018). The importance of diagrams, graphics and other visual representations in STEM teaching. In R. Jorgensen & K. Larkin (Eds.), STEM education in the junior secondary. The state of play (pp. 169–196). Singapore: Springer.

Giardino, V. (2010). Intuition and visualization in mathematical problem solving. Topoi, 29 (1), 29–39.

Giaquinto, M. (2007). Visual thinking in mathematics . Oxford University Press.

Huff, D. (1954). How to lie with statistics . Norton.

Jones, K. (2012). Connecting research with communities through performative social science. The Qualitative Report, 17 (18), 1–8.

Klee, P. (1972/1953). Pedagogical Sketchbook . New York: Praeger Publishers.

Kostelanetz, R. (1975). Essaying essays: Alternative forms of exposition . Out of London Press.

Lakatos, I. (1976). Proofs and refutations. The logic of mathematical discovery . Cambridge University Press.

Latour, B., & Weibel, P. (Eds.). (2005). Making things public. Atmospheres of democracy . The MIT Press.

Manders, K. (2008). The Euclidean diagram. In P. Mancosu (Ed.), The philosophy of mathematical practice (pp. 80–133). OUP.

Mcniff, S. (2008). Art-based research. In J. Knowles & A. Cole (Eds.), Handbook of the arts in qualitative research: perspectives, methodologies, examples, and issues . California, US: Sage.

Mumma, J., & Hamami, Y. (2013). Prolegomena to a cognitive investigation of euclidean diagrammatic reasoning. Journal of Logic, Language and Information, 22 (4), 421–448.

Nelsen, R. B. (1993). Proofs without words II: More exercises in visual thinking . Mathematical Association of America.

Nelsen, R. B. (2000). Proofs without words: Exercises in visual thinking . Mathematical Association of America.

Neurath, M., & Cohen, R. S. (Eds.). (1973). Otto Neurath: empiricism and sociology. Vienna circle collection (vol. 1). Reidel.

Ranciere, J. (2009). The emancipated spectator , trans. G. Elliott. London: Verso.

Rodda, M. (2014). The diagrammatic spectator. Ephemera—Theory & Politics in Organization, 14 (2), 221–244.

Starikova, I. (2017). Aesthetic preferences in mathematics: A case study. In Philosophia mathematica . https://doi.org/10.1093/philmat/nkx014 . Last accessed 15 Feb 2018.

Stenning, K. (2002). Seeing reason: Image and language in learning to think . OUP.

Suppes, P., Esiner, E., Stanley, J., & Grebbe, M. (1998). The vision thing: Educational research and AERA in the 21st century—part 5: A vision for educational research and AERA in the 21st century. Educational Researcher, 27 (9), 33–35.

Tufte, E. R. (1991). Dequantification in scientific visualization: Is this science or television . Yale University.

Tufte, E. R. (2001a). Envisioning information (1990). Cheshire, CT: Graphics Press.

Tufte, E. R. (2001b). The visual display of quantitative information (1983) (2nd ed.). Graphics Press.

Uebel, T. (2005). Political philosophy of science in logical empiricism. Studies in History and Philosophy of Science., 36 (4), 754–773.

Uebel, T. (2010). What is right about Carnap, Neurath and the left Vienna circle thesis: A refutation. Studies in History and Philosophy of Science, 41 , 214–221.

Wallman, K. (1993). Enhancing statistical literacy: Enriching our society. Journal of the American Statistical Association, 88 (421), 1–8.

Download references

Acknowledgements

Thanks to Joachim Frans (2017) who directed my attention to the work of Nelsen (1993, 2000) in his inspiring Ph.D. thesis on ‘Mathematical explanation’.

Author information

Authors and affiliations.

Logic and Philosophy of Science (CLPS), Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussel, Belgium

Kathleen Coessens

Vrije Universiteit Brussel, Room 5B425, Pleinlaan 2, B-1050, Brussels, Belgium

Karen François

Vrije Universiteit Brussel, Pleinlaan 2, B-1050, Brussel, Belgium

Jean Paul Van Bendegem

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Karen François .

Editor information

Editors and affiliations.

KU Leuven, Leuven, Belgium

Paul Smeyers

Marc Depaepe

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Coessens, K., François, K., Van Bendegem, J.P. (2021). Understanding Without Words: Visual Representations in Math, Science and Art. In: Smeyers, P., Depaepe, M. (eds) Production, Presentation, and Acceleration of Educational Research: Could Less be More?. Educational Research, vol 11. Springer, Singapore. https://doi.org/10.1007/978-981-16-3017-0_9

Download citation

DOI : https://doi.org/10.1007/978-981-16-3017-0_9

Published : 02 November 2021

Publisher Name : Springer, Singapore

Print ISBN : 978-981-16-3016-3

Online ISBN : 978-981-16-3017-0

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

What's the opposite of Meaning of the word Words that rhyme with Sentences with the word Translate to Find Words Use * for blank tiles (max 2) Use * for blank spaces Find the of Pronounce the word in Find Names     Appearance ✓ Use device theme   ✓ Dark theme ✓ Light theme ? ? Here's a list of from our that you can use instead. Use * for blank tiles (max 2) Use * for blank spaces bottom_desktop desktop:[300x250] go Word Tools Finders & Helpers Apps More Synonyms Copyright WordHippo © 2024 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 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 Strip Diagrams Definition : A bar divided into rectangles that accurately represent quantities noted in the problem. Common Uses : addition, fractions, proportions, ratios Definition : Simple drawings of concrete or real items (e.g., marbles, trucks). Common Uses : counting, addition, subtraction, multiplication, division Graphs/Charts Definition : Drawings that depict information using lines, shapes, and colors. Common Uses : comparing numbers, statistics, ratios, algebra 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 Triangles equilateral – all sides are same length – all angles 60° isosceles – two sides are same length – two angles are the same scalene – no sides are the same length – no angles are the same right – one angle is 90°(right angle) – opposite side of right angle is longest side (hypotenuse) obtuse – one angle is greater than 90° acute – all angles are less than 90° 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. 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. 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 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. 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 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. arXiv's Accessibility Forum starts next month! Help | Advanced Search Quantitative Biology > Neurons and Cognition Title: universal dimensions of visual representation. Abstract: Do neural network models of vision learn brain-aligned representations because they share architectural constraints and task objectives with biological vision or because they learn universal features of natural image processing? We characterized the universality of hundreds of thousands of representational dimensions from visual neural networks with varied construction. We found that networks with varied architectures and task objectives learn to represent natural images using a shared set of latent dimensions, despite appearing highly distinct at a surface level. Next, by comparing these networks with human brain representations measured with fMRI, we found that the most brain-aligned representations in neural networks are those that are universal and independent of a network's specific characteristics. Remarkably, each network can be reduced to fewer than ten of its most universal dimensions with little impact on its representational similarity to the human brain. These results suggest that the underlying similarities between artificial and biological vision are primarily governed by a core set of universal image representations that are convergently learned by diverse systems. Subjects: Neurons and Cognition (q-bio.NC); Computer Vision and Pattern Recognition (cs.CV) Cite as: [q-bio.NC]   (or [q-bio.NC] for this version)   Focus to learn more arXiv-issued DOI via DataCite Submission history Access paper:. HTML (experimental) Other Formats References & Citations Google Scholar Semantic Scholar BibTeX formatted citation Bibliographic and Citation Tools Code, data and media associated with this article, recommenders and search tools. Institution arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Information Author Services Initiatives You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader. All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess . Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers. Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal. Original Submission Date Received: . Active Journals Find a Journal Proceedings Series For Authors For Reviewers For Editors For Librarians For Publishers For Societies For Conference Organizers Open Access Policy Institutional Open Access Program Special Issues Guidelines Editorial Process Research and Publication Ethics Article Processing Charges Testimonials Preprints.org SciProfiles Encyclopedia Article Menu Subscribe SciFeed Recommended Articles Google Scholar on Google Scholar Table of Contents Find support for a specific problem in the support section of our website. Please let us know what you think of our products and services. Visit our dedicated information section to learn more about MDPI. JSmol Viewer Semantic interaction meta-learning based on patch matching metric. 1. Introduction Click here to enlarge figure 2. Related Works 3. methodology, 3.1. framework, 3.2. self-supervised pretraining, 3.3. patch matching metric strategy, 3.3.1. gcn-based patch embedding construction, 3.3.2. patch matching metric, 3.4. label-assisted channel semantic interaction strategy, 4. experiments, 4.1. implementation details, 4.2. experiments of different adjacency matrix, 4.3. selecting hyperparameters of patch-level matching metric, 4.4. few-shot image classification experiments, 4.5. ablation experiments, 4.6. selecting helpful semantic extractors, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest. Lai, N.; Kan, M.; Han, C.; Song, X.; Shan, S. Learning to learn adaptive classifier–predictor for few-shot learning. IEEE Trans. Neural Netw. Learn. Syst. 2020 , 32 , 3458–3470. [ Google Scholar ] [ CrossRef ] [ PubMed ] Doersch, C.; Gupta, A.; Zisserman, A. Crosstransformers: Spatially-aware few-shot transfer. Adv. Neural Inf. Process. Syst. 2020 , 33 , 981–993. [ Google Scholar ] Chen, Y.; Liu, Z.; Xu, H.; Darrell, T.; Wang, X. Meta-baseline: Exploring simple meta-learning for few-shot learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 9062–9071. [ Google Scholar ] Kang, S.; Hwang, D.; Eo, M.; Kim, T.; Rhee, W. Meta-learning with a geometry-adaptive preconditioner. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 24 June 2023; pp. 16080–16090. [ Google Scholar ] Zhang, C.; Cai, Y.; Lin, G.; Shen, C. Deepemd: Differentiable earth mover’s distance for few-shot learning. IEEE Trans. Pattern Anal. Mach. Intell. 2022 , 45 , 5632–5648. [ Google Scholar ] [ CrossRef ] [ PubMed ] Finn, C.; Abbeel, P.; Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 6–11 August 2017; pp. 1126–1135. [ Google Scholar ] Snell, J.; Swersky, K.; Zemel, R. Prototypical networks for few-shot learning. In Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA, 4–9 December 2017. [ Google Scholar ] Sung, F.; Yang, Y.; Zhang, L.; Xiang, T.; Torr, P.H.; Hospedales, T.M. Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 1199–1208. [ Google Scholar ] Gidaris, S.; Komodakis, N. Dynamic few-shot visual learning without forgetting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 4367–4375. [ Google Scholar ] Hiller, M.; Ma, R.; Harandi, M.; Drummond, T. Rethinking generalization in few-shot classification. Adv. Neural Inf. Process. Syst. 2022 , 35 , 3582–3595. [ Google Scholar ] Chen, H.; Li, H.; Li, Y.; Chen, C. Sparse spatial transformers for few-shot learning. Sci. China Inf. Sci. 2023 , 66 , 210102. [ Google Scholar ] [ CrossRef ] Li, A.; Huang, W.; Lan, X.; Feng, J.; Li, Z.; Wang, L. Boosting few-shot learning with adaptive margin loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2020; pp. 576–584. [ Google Scholar ] Yang, F.; Wang, R.; Chen, X. Sega: Semantic guided attention on visual prototype for few-shot learning. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 3–8 January 2022; pp. 1056–1066. [ Google Scholar ] Yan, K.; Bouraoui, Z.; Wang, P.; Jameel, S.; Schockaert, S. Aligning visual prototypes with bert embeddings for few-shot learning. In Proceedings of the 2021 International Conference on Multimedia Retrieval, Taipei, Taiwan, 16–19 November 2021; pp. 367–375. [ Google Scholar ] Xing, C.; Rostamzadeh, N.; Oreshkin, B.; Pinheiro, P.O.O. Adaptive cross-modal few-shot learning. In Proceedings of the Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vancouver, BC, Canada, 8–14 December 2019. [ Google Scholar ] Chen, W.; Si, C.; Zhang, Z.; Wang, L.; Wang, Z.; Tan, T. Semantic prompt for few-shot image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2023; pp. 581–591. [ Google Scholar ] Zhou, J.; Wei, C.; Wang, H.; Shen, W.; Xie, C.; Yuille, A.; Kong, T. ibot: Image bert pre-training with online tokenizer. arXiv 2021 , arXiv:2111.07832. [ Google Scholar ] Bao, H.; Dong, L.; Piao, S.; Wei, F. Beit: Bert pre-training of image transformers. arXiv 2021 , arXiv:2106.08254. [ Google Scholar ] Ye, H.-J.; Hu, H.; Zhan, D.-C.; Sha, F. Few-shot learning via embedding adaptation with set-to-set functions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2020; pp. 8808–8817. [ Google Scholar ] Hou, R.; Chang, H.; Ma, B.; Shan, S.; Chen, X. Cross attention network for few-shot classification. In Proceedings of the Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vancouver, BC, Canada, 8–14 December 2019. [ Google Scholar ] Kipf, T.N.; Welling, M. Semi-supervised classification with graph convolutional networks. arXiv 2016 , arXiv:1609.02907. [ Google Scholar ] Li, W.; Wang, L.; Xu, J.; Huo, J.; Gao, Y.; Luo, J. Revisiting local descriptor based image-to-class measure for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2019; pp. 7260–7268. [ Google Scholar ] Radford, A.; Kim, J.W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learnin, Virtual Event, 18–24 July 2021; pp. 8748–8763. [ Google Scholar ] Vinyals, O.; Blundell, C.; Lillicrap, T.; Wierstra, D. Matching networks for one shot learning. In Proceedings of the Advances in Neural Information Processing Systems 29 (NIPS 2016), Barcelona, Spain, 5–10 December 2016. [ Google Scholar ] Ren, M.; Triantafillou, E.; Ravi, S.; Snell, J.; Swersky, K.; Tenenbaum, J.B.; Larochelle, H.; Zemel, R.S. Meta-learning for semi-supervised few-shot classification. arXiv 2018 , arXiv:1803.00676. [ Google Scholar ] Bertinetto, L.; Henriques, J.F.; Torr, P.H.; Vedaldi, A. Meta-learning with differentiable closed-form solvers. arXiv 2018 , arXiv:1805.08136. [ Google Scholar ] Oreshkin, B.; López, P.R.; Lacoste, A. Tadam: Task dependent adaptive metric for improved few-shot learning. In Proceedings of the Advances in Neural Information Processing Systems 31 (NeurIPS 2018), Montréal, QC, Canada, 3–8 December 2018. [ Google Scholar ] Touvron, H.; Cord, M.; Douze, M.; Massa, F.; Sablayrolles, A.; Jégou, H. Training data-efficient image transformers & distillation through attention. In Proceedings of the 38th International Conference on Machine Learning, Virtual Event, 18–24 July 2021; pp. 10347–10357. [ Google Scholar ] Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; Guo, B. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 10012–10022. [ Google Scholar ] Loshchilov, I.; Hutter, F. Decoupled weight decay regularization. arXiv 2017 , arXiv:1711.05101. [ Google Scholar ] Zhang, X.; Meng, D.; Gouk, H.; Hospedales, T.M. Shallow bayesian meta learning for real-world few-shot recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 651–660. [ Google Scholar ] Yang, F.; Wang, R.; Chen, X. Semantic guided latent parts embedding for few-shot learning. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 3–7 January 2023; pp. 5447–5457. [ Google Scholar ] Hu, S.X.; Moreno, P.G.; Xiao, Y.; Shen, X.; Obozinski, G.; Lawrence, N.D.; Damianou, A. Empirical bayes transductive meta-learning with synthetic gradients. arXiv 2020 , arXiv:2004.12696. [ Google Scholar ] Afrasiyabi, A.; Lalonde, J.-F.; Gagné, C. Associative alignment for few-shot image classification. In Proceedings of the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, 23–28 August 2020; Proceedings, Part V 16. Springer: Berlin/Heidelberg, Germany, 2020; pp. 18–35. [ Google Scholar ] Dong, B.; Zhou, P.; Yan, S.; Zuo, W. Self-promoted supervision for few-shot transformer. In Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel, 23–27 October 2022; Springer: Berlin/Heidelberg, Germany, 2022; pp. 329–347. [ Google Scholar ] Reimers, N.; Gurevych, I. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv 2019 , arXiv:1908.10084. [ Google Scholar ] Pennington, J.; Socher, R.; Manning, C.D. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25–29 October 2014; pp. 1532–1543. [ Google Scholar ] Environment Parameters Operating System Windows 10 Enterprise 64-bit CPU Intel Core i9 12900K Memory DDR4 64 GB GPU Nvidia RTX 3090 Python 3.7 Cuda 11.1 Pytorch 1.7.1 Type Normalization Vit-Small Swin-Tiny 5W1S 5W5S 5W1S 5W5S simple adjacency matrix random 68.55 ± 0.64 83.07 ± 0.43 71.17 ± 0.62 84.03 ± 0.43 symmetry 68.68 ± 0.64 83.76 ± 0.41 71.17 ± 0.62 84.01 ± 0.42 our adjacency matrix random 69.56 ± 0.65 84.01 ± 0.40 symmetry 71.50 ± 0.62 83.97 ± 0.44 Type Normalization Vit-Small Swin-Tiny 5W1S 5W5S 5W1S 5W5S simple adjacency matrix random 71.10 ± 0.61 85.12 ± 0.51 70.15 ± 0.92 86.03 ± 0.51 symmetry 71.29 ± 0.60 85.67 ± 0.50 70.37 ± 0.72 86.07 ± 0.50 our adjacency matrix random 72.37 ± 0.70 87.69 ± 0.44 symmetry 71.09 ± 0.72 86.64 ± 0.49 Dataset Vit-Small Swin-Tiny 5W1S 5W5S 5W1S 5W5S Mini-ImageNet 1 196 9 49 Tiered-ImageNet 9 9 9 49 CIFAR-FS 1 25 9 49 FC100 1 25 9 49 Method Backbone ≈Params Mini-ImageNet Tiered-ImageNet 5W1S 5W5S 5W1S 5W5S MAML [ ] ResNet-12 12.5 M 58.60 ± 0.61 69.54 ± 0.56 59.82 ± 0.56 73.17 ± 0.56 DynamicFSL [ ] ResNet-12 12.5 M 62.81 ± 0.27 78.97 ± 0.18 68.35 ± 0.31 83.52 ± 0.21 DeepEMD-Bert [ ] ResNet-12 12.5 M 67.03 ± 0.791 83.68 ± 0.6 73.76 ± 0.72 87.51 ± 0.75 SSFormers [ ] ResNet-12 12.5 M 67.25 ± 0.24 82.75 ± 0.20 72.52 ± 0.25 86.61 ± 0.18 LPE-Glove [ ] ResNet-12 12.5 M 68.28 ± 0.43 78.88 ± 0.33 72.03 ± 0.49 83.76 ± 0.37 SIB [ ] WRN-28-10 36.5 M 70.00 ± 0.60 79.20 ± 0.40 70.01 ± 0.54 84.13 ± 0.54 Align [ ] WRN-28-10 36.5 M 65.92 ± 0.60 82.85 ± 0.55 74.40 ± 0.68 86.61 ± 0.59 MetaQDA [ ] WRN-28-10 36.5 M 67.83 ± 0.64 84.28 ± 0.69 74.33 ± 0.65 ProtoNet-Swin Swin-Tiny 29.0 M 67.28 ± 0.67 82.56 ± 0.44 70.68 ± 0.71 85.81 ± 0.47 SUN [ ] Visformer-S 12.4 M 67.80 ± 0.45 83.25 ± 0.30 72.99 ± 0.50 86.74 ± 0.33 SP-CLIP [ ] Visformer-T 10.0 M 72.31 ± 0.40 83.42 ± 0.30 78.03 ± 0.46 88.55 ± 0.32 FewTURE [ ] Swin-Tiny 29.0 M 70.48 ± 0.62 84.41 ± 0.41 76.32 ± 0.87 88.70+0.44 PatSiML-ViT (ours) Vit-Small 22.0 M 72.26 ± 0.57 85.39 ± 0.43 74.74 ± 0.69 88.90 ± 0.48 PatSiML-Swin (ours) Swin-Tiny 29.0 M 89.51 ± 0.46 Method Backbone ≈Params CIFAR-FS FC100 5W1S 5W5S 5W1S 5W5S DynamicFSL [ ] ResNet-12 12.5M 61.68 ± 0.26 78.97 ± 0.18 40.81 ± 0.56 56.64 ± 0.58 SSFormers [ ] ResNet-12 12.5M 74.50 ± 0.21 86.61 ± 0.23 43.72 ± 0.21 58.92 ± 0.61 SIB [ ] WRN-28-10 36.5M 80.00 ± 0.60 85.30 ± 0.40 MetaQDA [ ] WRN-28-10 36.5M 75.83 ± 0.88 88.79 ± 0.70 ProtoNet-Swin Swin-Tiny 29.0M 71.24 ± 0.45 82.47 ± 0.43 42.13 ± 0.67 57.11 ± 0.62 SUN [ ] Visformer-S 12.4M 78.37 ± 0.46 88.84 ± 0.32 SP-CLIP [ ] Visformer-T 10.0M 82.18 ± 0.40 88.24 ± 0.32 48.53 ± 0.38 61.55 ± 0.41 FewTURE [ ] Swin-Tiny 29.0M 77.76 ± 0.81 88.90 ± 0.59 47.68 ± 0.78 63.81 ± 0.75 PatSiML-ViT Vit-Small 22.0M 82.83 ± 0.61 90.48 ± 0.44 50.61 ± 0.59 64.09 ± 0.62 PatSiML-Swin Swin-Tiny 29.0M 81.72 ± 0.59 90.72 ± 0.38 50.42 ± 0.58 65.03 ± 0.57 No. Self-Supervised Pretraining Patch Matching Metric Channel Semantic Interaction Instructions (A) ✓ - - Removal of patch matching metric and channel semantic interaction strategy using ProtoNet. (B) ✓ ✓ - Removing channel semantic interaction. (C) ✓ - ✓ Removing the patch matching metric strategy and using ProtoNet’s matching metric with channel semantic interaction for class prototypes. (D) - ✓ ✓ Replacing self-supervised pretraining and with supervised pretraining. (E) ✓ ✓ ✓ PatSiML. No. Vit-Small Swin-Tiny 5W1S 5W5S 5W1S 5W5S A 66.83 ± 0.66 81.96 ± 0.45 67.28 ± 0.67 82.56 ± 0.44 B 69.82 ± 0.65 (↑2.99) 85.33 ± 0.41 (↑3.37) 72.13 ± 0.62 (↑4.85) 85.41 ± 0.41 (↑2.85) C 68.63 ± 0.66 (↑1.80) 82.87 ± 0.45 (↑0.91) 69.63 ± 0.67 (↑2.35) 83.01 ± 0.44 (↑0.45) D 52.14 ± 0.60 (↓14.69) 71.40 ± 0.45 (↓10.56) 55.18 ± 0.65 (↓12.10) 67.65 ± 0.45 (↓14.91) E No. Vit-Small Swin-Tiny 5W1S 5W5S 5W1S 5W5S A 70.32 ± 0.78 82.35 ± 0.50 70.68 ± 0.71 85.81 ± 0.47 B 74.00 ± 0.73 (↑4.32) 88.26 ± 0.45 (↑2.09) 75.31 ± 0.70 (↑4.63) 87.64 ± 0.49 (↑1.83) C 71.78 ± 0.71 (↑1.46) 83.54 ± 0.49 (↑1.19) 71.98 ± 0.71 (↑1.30) 86.92 ± 0.47 (↑1.11) D 59.42 ± 0.65 (↓10.90) 75.34 ± 0.55 (↓7.01) 64.94 ± 0.72 (↓5.74) 77.85 ± 0.45 (↓7.96) E Back-Bone Semantic Extractor Mini-ImageNet Tiered-ImageNet 5W1S 5W5S 5W1S 5W5S Vit-Small - 69.82 ± 0.65 85.33 ± 0.41 74.00 ± 0.73 88.26 ± 0.45 CLIP SBERT 71.96 ± 0.60 85.15 ± 0.49 74.20 ± 0.68 88.76 ± 0.52 GloVe 71.78 ± 0.59 85.06+0.39 74.68 ± 0.72 88.01 ± 0.51 Swin-Tiny - 72.13 ± 0.62 85.41 ± 0.41 75.31 ± 0.70 87.64 ± 0.48 CLIP SBERT 73.60 ± 0.57 84.08 ± 0.44 78.24 ± 0.68 88.97 ± 0.46 GloVe 72.37 ± 0.60 84.10 ± 0.44 77.73 ± 0.67 89.22 ± 0.44 The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Share and Cite Wei, B.; Wang, X.; Su, Y.; Zhang, Y.; Li, L. Semantic Interaction Meta-Learning Based on Patch Matching Metric. Sensors 2024 , 24 , 5620. https://doi.org/10.3390/s24175620 Wei B, Wang X, Su Y, Zhang Y, Li L. Semantic Interaction Meta-Learning Based on Patch Matching Metric. Sensors . 2024; 24(17):5620. https://doi.org/10.3390/s24175620 Wei, Baoguo, Xinyu Wang, Yuetong Su, Yue Zhang, and Lixin Li. 2024. "Semantic Interaction Meta-Learning Based on Patch Matching Metric" Sensors 24, no. 17: 5620. https://doi.org/10.3390/s24175620 Article Metrics Article access statistics, further information, mdpi initiatives, follow mdpi. Subscribe to receive issue release notifications and newsletters from MDPI journals Help & FAQ Bidirectionally synchronized Domain Specific Language and its visual representation as a basis for extensible Low-Code Development Platform Hryhorii Popov School of Computing and Engineering Department of Computer Science Student thesis : Master's Thesis Date of Award 21 May 2024 Original language English Supervisor (Main Supervisor) & Vladimir Vishnyakov (Co-Supervisor) File : application/pdf, 8.88 MB Type : Thesis 267 IMAGES Visual Poetry Visual Words Visual Word Wall Words : File Folder Heaven PPT Word2Vec: TensorFlow Vector Representation Of Words Image representation using bag-of-visual-words. VIDEO Kotaku STILL Claiming LGBTQ Representation In Gaming ISN'T GOOD ENOUGH They Want Link To Be AUTISTIC Now!? Shakespeare's Sonnet 18: Shall I compare thee to a summer’s day? #sonnetswilliamshakespeare #poetry Dive into Visualization Visual Literacy: teaching with images and videos [Advancing Learning Webinar] How to analyse visuals at an A+ standard in Analysing Argument (Language Analysis) COMMENTS Visuwords Go Word Spelunking! Still not sure what Visuwords™ is about? Hit that explore button and pull up something random.. Explore; Learn More What is visual representation? » Design Match Defining Visual Representation: Visual representation is the act of conveying information, ideas, or concepts through visual elements such as images, charts, graphs, maps, and other graphical forms. It's a means of translating the abstract into the tangible, providing a visual language that transcends the limitations of words alone. What is Visual Representation? Visual representation simplifies complex ideas and data and makes them easy to understand. Without these visual aids, designers would struggle to communicate their ideas, findings and products. For example, it would be easier to create a mockup of an e-commerce website interface than to describe it with words. 17 Important Data Visualization Techniques 15. Word Cloud. A word cloud, or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their ... How Words Are Represented in the Brain The nature of the visual representation for words has been fiercely debated for over 150 y. We used direct brain stimulation, pre- and postsurgical behavioral measures, and intracranial electroencephalography to provide support for, and elaborate upon, the visual word form hypothesis. This hypothesis states that activity in the left midfusiform ... Visual Word Recognition Visual Word Recognition. Kathleen Rastle, in Neurobiology of Language, 2016. 21.4 Visual Word Recognition and the Reading System. This chapter has put forward an understanding of visual word recognition based on a hierarchical analysis of visual features, letters, subword units (e.g., morphemes), and, ultimately, orthographic representations of whole words. Visual Word Visual words, as used in image retrieval systems, refer to small parts of an image that carry some kind of information related to the features (such as the color, ... Based on this kind of image representation, it is possible to use text retrieval techniques to design an image retrieval system. However, since all text retrieval systems depend ... Words affect visual perception by activating object shape The results show that hearing a word activates representations of its referent's shape, which interacts with the visual processing of a subsequent picture within 100 ms from its onset ... 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 ... The what, when, where, and how of visual word recognition A long-standing debate in reading research is whether printed words are perceived in a feedforward manner on the basis of orthographic information, with other representations such as semantics and phonology activated subsequently, or whether the system is fully interactive and feedback from these representations shapes early visual word recognition. Visual Representations: Unleashing the Power of Data Visualization Here are a handful of different types of data visualization tools that you can begin using right now. 1. Spider Diagrams. Use this template. 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. Visualizations That Really Work Visualizations That Really Work. Know what message you're trying to communicate before you get down in the weeds. by. Scott Berinato. From the Magazine (June 2016) HBR Staff. Summary. Not long ... What is Information Visualization? "Use a picture. It's worth a thousand words." Tess Flanders, Journalist and Editor, Syracuse Post Standard, 1911. Journalists have known for a very long time that some ideas are simply too awkward to communicate in words and that a visual representation can help someone understand concepts that might otherwise be impossible to explain. Illustrated Vocabulary Sample illustrated vocabulary: This image illustrates the word geologist. The object on the left next to the glasses is a magnifying glass indicating close study. Add to a Learning Plan. In this visual strategy, students divide vocabulary words into parts and draw illustrations to represent the separate meaning of each part. Visual Presentation: Tips, Types and Examples 4. Charts and graphs. Charts and graphs are visual representations of data that make it easier to understand and analyze numerical information. Common types include bar charts, line graphs, pie charts and scatterplots. They are commonly used in scientific research, business reports and academic presentations. (PDF) Effective Use of Visual Representation in Research and Teaching Visu al information plays a fundamental role in our understanding, more than any other form of information (Colin, 2012). Colin (2012: 2) defines. visualisation as "a graphica l representation ... How To Visualize Words Visualizing words is a process of creating a visual representation of the meaning of a word or phrase. This can be done in a variety of ways, such as creating a diagram, drawing a picture, or using a mind map. Visualizing words can help you to better understand the relationships between words and concepts, as well as to better remember and ... The bag of (visual) words model Building a bag of visual words. Building a bag of visual words can be broken down into a three-step process: Step #1: Feature extraction. Step #2: Codebook construction. Step #3: Vector quantization. We will cover each of these steps in detail over the next few lessons, but for the time being, let's perform a high-level overview of each step. Understanding Without Words: Visual Representations in Math, Science The first one considered the importance of visual representations in science and its recent debate in education. It was already shown by philosophers of the Wiener Kreis that visual representation could serve for a better understanding and dissemination of knowledge to the broader public. As knowledge can be condensed in different non-verbal ... Visual Representation synonyms Visual Representation synonyms - 1 539 Words and Phrases for Visual Representation. graphic representation. n. graphical representation. pictorial representation. n. visual presentation. n. graph. What is another word for visual representation Synonyms for visual representation include representation, graph, map, chart, figure, diagram, plan, grid, histogram and nomograph. Find more similar words at ... 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 ... Bag of Visual Words in a Nutshell Bag-of-visual-words (BOVW) Bag of visual words (BOVW) is commonly used in image classification. Its concept is adapted from information retrieval and NLP's bag of words (BOW). In bag of words (BOW), we count the number of each word appears in a document, use the frequency of each word to know the keywords of the document, and make a frequency ... [2408.12804v1] Universal dimensions of visual representation View PDF HTML (experimental) Abstract: Do neural network models of vision learn brain-aligned representations because they share architectural constraints and task objectives with biological vision or because they learn universal features of natural image processing? We characterized the universality of hundreds of thousands of representational dimensions from visual neural networks with ... Semantic Interaction Meta-Learning Based on Patch Matching Metric This strategy merges word embeddings with patch-level visual features across the channel dimension, utilizing a sophisticated language model to combine semantic understanding with visual information. ... C.D. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ... Bidirectionally synchronized Domain Specific Language and its visual LMS manifests novel features by combining rich text editing of a domain-specific language (DSL) with rich diagram editing of a visual representation, with textual language serving as a source of truth for LMS models.In the future, LMS can be further enhanced by being extracted from the demo example into a generic library, reusing Langium-based ... What does the word "science" evoke? Social representation of science Concerning inter-representation relationships, comparisons among SRs identified a strong connection among these three objects, indicating the existence of a coherent representation system where ... assignment essay homework paper phd report resume review speech thesis