visual representation of 500 words

WORD PRESCRIPTION

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Do you find setting a word count for your content difficult because you’re not sure what it actually translates to onto a page? Here’s an idea of what specific word counts look like on a page - as well as on a PDF with Arial size 11 font.

P.s. A.A. Milne is one of our favourites - so here’s one of his famous Winnie The Pooh stories used as our example. Enjoy! We’ve included the link to the rest of the story at the bottom of the page.

You could tell that Christopher Robin had something important to say from the way he clasped his knees tightly and wriggled his toes. Everybody gathered round and looked at him expectantly. “I’ve heard that her Majesty ...” Christopher Robin began. “Oh,” squeaked Piglet in a state of great excitement.

You could tell that Christopher Robin had something important to say from the way he clasped his knees tightly and wriggled his toes. Everybody gathered round and looked at him expectantly. “I’ve heard that her Majesty ...” Christopher Robin began. “Oh,” squeaked Piglet in a state of great excitement. “Her Royal Highness ...” he went on. “Quite so, quite so,” agreed Rabbit. “The Queen of England ...” he said quickly before anyone else could interrupt him. “Oh, The Queen,” said Pooh Bear, much relieved. “The other people you mentioned sounded much too tall and fearsome, but The Queen is quite different.”

Pooh had once sent a letter and was told to stick on a small picture of The Queen. It stuck more to his nose than to the letter, but it told the postman that it was Most Urgent and that The Queen Says It Must Be Sent and so he was sure it had been. “As I was saying,” said Christopher Robin, passing Pooh a honey sandwich so that he might continue speaking, “Her Majesty The Queen is celebrating an important birthday, her ninetieth birthday. And we should too. Celebrate it, that is, by giving her a present.”

“I had a present once,” sighed Eeyore wistfully. “Two, in fact, if I may boast a little. One was rather small and damp and the other somewhat larger and sticky. But I don’t like to complain. A present is Something and to have two is Something Else.”

Both Pooh and Piglet blushed slightly. They had a memory of a wonderful balloon and a large jar of the best honey that had begun as Exceedingly Good presents and then, due to various mishaps, had

become Rather Disappointing presents, but which were present nevertheless and as Eeyore had said – that was Something.

“The question is,” said Rabbit importantly, “what do Queens like best?” “Honey, I should think,” sighed Pooh, looking at the small, sticky crumb where the honey sandwiches had once been. “I’ve heard,” said Christopher Robin, who knew a great deal about faraway places like the other side of the Forest and London, “that The Queen has a grand tea every day in her palace, with buttered toast and crumpets, so I shouldn’t think we’d need to give her anything to eat. Her present should be something to treasure.”

“I’ve never had much luck finding treasure,” sighed Pooh. “But I did once find the North Pole. Do you suppose The Queen might like that?”. The friends thought this an excellent idea, but it wasn’t long before they realised that finding the North Pole once was a very fine thing but that finding it again was an altogether different thing. Suddenly the Forest seemed to be full of sticks that could or could not be the North Pole. “This will never do,” announced Rabbit. “Do ...” mumbled Pooh. “That brings to mind a little hum which I’d like to hum if it was felt that a hum was called for at such a time of thoughtfulness.”

And without waiting for a reply, he began: The Queen lived in her palace, as Queens often do. Doing all those busy things that busy Queens do. But The Queen could never know, as you and I do, That doing nothing much can be the BEST thing to do. So from a forest far away, for your special day, We’re sending you some quiet and a little time to play. And quiet there was. The sort of quiet that makes the tip of your

nose turn a sunset-shade of pink.

“Bear,” announced Christopher Robin solemnly. “That hum is fit for a Queen. That hum shall be The Queen’s present. Owl shall write it out, and you and I and Eeyore will deliver it to Buckingham Palace. And Piglet must come too because London is a very big place indeed and even small animals, if they are very good friends, can make everything alright.”

And so it was decided and Owl was called for. Owl fussed here and fussed there and used up a good deal of time, paper and ink but at last, it was done, and everyone admired it. Kanga, who knew how important presentation was, especially for Queens, took the hum, rolled it and tied a thick vine around it. Into the vine she twisted wild heather, columbine, buttercups, meadowsweet, thyme and lastly a thistle, kindly donated by Eeyore. Christopher Robin also found a beautiful, bright red balloon, which he thought The Queen might enjoy on grey days.

And so it was decided and Owl was called for. Owl fussed here and fussed there and used up a good deal of time, paper and ink but at last it was done, and everyone admired it. Kanga, who knew how important presentation was, especially for Queens, took the hum, rolled it and tied a thick vine around it. Into the vine she twisted wild heather, columbine, buttercups, meadowsweet, thyme and lastly a thistle, kindly donated by Eeyore. Christopher Robin also found a beautiful, bright red balloon, which he thought The Queen might enjoy on grey days.

“Piglet, you should hold it,” he said. “That way, we won’t lose you in the crowd.” Piglet held on very tightly to the balloon. He wasn’t quite sure what a crowd was, something like a dark cloud perhaps, but in any case, he didn’t want to get lost in it and was pleased the balloon would help. So, the presents were ready and, side-by-side, Winnie-the-Pooh (Edward Bear, Bear of Very Little Brain, Brave Adventurer and Loyal

Friend), his small companion Piglet, Eeyore and Christopher Robin set off for London.

“Of course, London is on quite the other side,” remarked Christopher Robin as they were walking. “Of the sea?” asked Pooh, somewhat alarmed. “Not the sea, I shouldn’t think,” replied Christopher Robin,

whose Geography lessons so far had been mostly spent colouring in edgy bits. “But certainly the country or county. I’m not quite sure which. In any case, it is very far off, and we shall have to catch a train.” “I do hope it wants to be caught,” said Pooh, who was already a little out of puff from the walk. But the train was good enough to stop for them in the station so there was no catching to be done at all and there was plenty of time to climb aboard and find four comfy seats and then they were off!

“Would you be so kind as to stay very close by?” asked an anxious Piglet as they got off the train at Victoria Station.

Finish reading the story here : https://cdnvideo.dolimg.com/cdn_assets/c746f1893ff36c08a697a03e750688ae3bc4c498.pdf

All Winnie The Pooh content and images were taken from this source.

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Best Free Word Visualization Tools

A word cloud (or tag cloud ) is a word visualization that displays the most used words in a text from small to large, according to how often each appears.

They give a glance into the most important keywords in news articles, social media posts, and customer reviews, among other text. They can also provide interesting insights when comparing two texts against each other, like political speeches or product reviews.

Try MonkeyLearn’s word visualization tool to create your own professional word clouds for free. 

You can see that the largest words are the most used, and stopwords ( a, and, the , etc.) have been automatically removed. But MonkeyLearn’s powerful natural language processing (NLP) goes beyond this. It’s able to understand word combinations, like “easy sharing options” and “communications platform” that other word cloud tools may miss. 

MonkeyLearn’s word cloud tool also has the added benefit of scoring words for relevance. So, we can be sure that even the smaller words in the cloud are among the most important.

How to Generate Word Visualizations

1. choose and “clean” the dataset you want to use.

Find the text you’d like to use to create your word visualization. It can come from almost anywhere: tweets mentioning your company, online reviews, legal documents, books and stories, etc. 

Most word cloud tools allow you to enter text directly or cut and paste, and some allow you to upload a CSV file. Depending on where your data comes from, you may have to clean the text to remove special characters, irrelevant words, URL links, etc. 

2. Select the word visualization tool that best fits your needs

There are a number of great word cloud tools available. Here’s an intro to five of the best.

Top 4 Word Visualization Tools

• MonkeyLearn Word Cloud Generator | User-friendly, powerful AI
• Wordclouds.com | Visualizations in clipart-style shapes
• TagCrowd | Available in 15 languages
• ABCya! | Word visualization as a learning tool

1. MonkeyLearn

The MonkeyLearn word cloud generator is fun, easy to use, and powerful all at once. Simply paste text into the text box or upload a CSV file to create your word visualization in a snap. You can edit your text from right inside the browser, if you need to delete irrelevant words or add more text.

Change the text color, font, and size; or tweak color themes and word groupings. The tool also lists words by number of appearances and relevancy, to understand more about the importance of each word. Download your word cloud as a hi-res image (SVG or PNG) and a CSV file of your word list, showing relevancy score and word frequency.

You can try MonkeyLearn’s word cloud tool and more advanced text analysis tools, like the sentiment analyzer to sort your text into positive, negative, and neutral.

2. Wordclouds.com

WordClouds.com offers a clear and intuitive interface and a “Wizard” button that can walk you through all of the steps. The simple clipart-style shapes encourage users to create a piece of art to hang on the wall.

You can paste or enter text, enter a URL, or upload a file (even PDFs). Choose a shape, edit the word list, and change the size of the gap between words to get your visualization looking exactly like you want.

3. TagCrowd

Created by Daniel Steinbock, while a PhD student at Stanford, TagCrowd offers an interesting take on word visualization. You can paste text, upload a plain text file, or enter a URL.

The tool recommends use as “visual poetry,” as much as analytical research. It can be used in 15 languages, and custom algorithms allow TagCrowd to group together similar words, offering added insight into how the top words are used together.

ABCya!’s word cloud generator was created to inspire kids to learn simply by having fun playing with words. It’s so easy to use that even young kids can do it on their own: just click ‘Start,’ type or paste text, then hit ‘Create.’  

The playful shapes and colors help keep kids’ attention, and you can randomize the cloud and watch it morph into new shapes. ABCya! offers dozens of other fun learn games for kids, too.

3. Tutorial: create your word cloud in a few simple steps

1. upload your data.

Go to the MonkeyLearn word cloud generator . Paste text, enter it directly, or upload a CSV file. 

2. Click ‘Generate Cloud’

Your cloud will be created immediately.

3. Customize your word cloud

Click ‘Edit Text’ to return to the text box and add or remove words. You can change the size of the words and color schemes, choose a theme, or refresh the page to change the word grouping. 

The column on the right shows the number of times each word appears in the text and assigns a relevancy score to each word – you can switch to view by relevance or word frequencies. It also offers the option to click directly into other MonkeyLearn text analysis tools .

Slide the ‘Words Quantity’ bar in the upper right to change the total number of words that appear in your data visualization .

An example of the same word cloud featuring fewer words.

4. Download your word cloud

Click ‘Download’ in the upper right to download your word visualization as a super hi-res SVG or PNG image. Plus, download a CSV file for a list of the most used words in order of relevancy or number of appearances.

How to Improve Word Clouds with Text Analysis Tools

When it comes to performing serious text analysis, word visualization can only take you so far. Word clouds are a great introduction to the world of AI, but using more advanced, machine learning tools can get more from your text data. MonkeyLearn offers a suite of text analysis tools that can provide more accurate, comprehensive results and immediately actionable insights relevant to any business.

For an example of how to combine word clouds with other text analysis tools, we’ll first organize our data with topic classification. Topic classification is the process of assigning categories or “tags” to text according to its content. 

Using the same dataset of software reviews we used to create the word clouds above, we’ll apply machine learning topic classification to categorize each review to fall under “Customer Support,” “Usability,” or “Features.” This will give us the opportunity to separately analyze the reviews that fall into each category, then apply them to new word clouds. 

A review like, “ Mobile app needs a lot of improvement ,” for example would fall under Ease of Use . Take a look at the MonkeyLearn text classification tutorial to learn more.

Our new word visualizations by category:

Customer Support

We can see that users tend to relate certain applications, “Wrike,” “Kickserv,” etc., with certain topics. Some of the same words or similar words are common within each of the categories. However, distributing all the reviews into three categories does point out that certain words that didn’t appear in the original visualization, like “longer,” “small,” “glitches,” etc. are clearly important to the individual classifications.

Word clouds can be simple, fun, and insightful. Using word cloud tools, together with other text analysis tools, will both broaden and clarify your results. 

If you’d like to try out more advanced tools, MonkeyLearn offers a number of AI machine learning tools that can prove to be useful for any business:

• Sentiment Analyzer
• Name Extractor
• Keyword Extractor
• Intent and Email Classifier

Sign up to MonkeyLearn for free and try out some of the most advanced text analysis tools in the industry.

Inés Roldós

June 23rd, 2020

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Text Visualization: Word Clouds

Data visualization is usually related to graphical representations of numbers, but when the information to be displayed is textual you can use a word cloud.

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Data visualization is usually related to graphical representations of numbers, which allow for more precise analysis of information and identification of trends. But when the information displayed is textual, there are tools to visualize this type of information, and one of the best known is the word cloud .

What is a word cloud?

It is a graphical representation or image of a series of keywords or tags organized by color and size to form a figure. It is also known as a tag cloud or tag cloud.

Source: Word cloud created using Words Cloud from the Datasketch home page .

How is a word cloud used?

The word cloud allows you to identify the most relevant topics at a glance. It is often used in blogs and websites to help users find information more easily.

Another less common but tremendously helpful use for data journalism is its use as a resource to display textual information in articles.

Usage example: New York Times

In mid-February and in the wake of various global events, the Morning Consult Company surveyed 2200 Americans. They asked about the perception of inflation in the country and its perceived products.

According to the Gallup Group Index , inflation was one of the three biggest concerns at the moment, above unemployment.

The New York Times collects the results of this research and shows it using word clouds in its article “ Where 2200 Americans Have Noticed Inflation ”. They analyzed the most repeated terms in the participants' open-ended responses.

We should note that these data do not capture price increases due to the conflict between Russia and Ukraine. Still, they identified that the answers somehow show that citizens and states perceive inflation differently. According to the media, dozens of participants pointed out that inflation is in all products, but the most concerning areas are fuel and food. The image highlights in orange the categories that represent food and allows us to see the relevance of this issue.

Source: Bacon, Gas, and Essentials: Where 2,200 Americans Have Noticed Inflation | The New York Times .

Inflation affects most of those who have the least, and being clear about the areas that most concern people in their day-to-day lives can help make policy decisions and identify areas of impact that will help improve people’s lives.

It is transparent and straightforward visual information. It’s not explained in text, but graphically. It’s handy for creating relationships between concepts. You often don’t have a global vision of what terms you can use. So another key is to understand that it helps you identify words that you can relate to, generating new concepts.

Recommendations when creating word clouds

This tool has a great potential to show the most salient information of a text. For that, we must:

🎨 Explain what the colors mean in our visualizations.

The colors chosen can mean many things, such as the predominance of a term in the text, the relationship between different representations (in the example above, all the words in orange are food), or can be random, and the reader should know them beforehand.

📋 Explain what the differences in the size of the terms mean.

Usually, the size depends on how regularly the label appears, but this is not always the case, so explaining the size change can help our readers understand the text better.

Displays it

🔢 Add the number of times the word appeared

Differences in size and color can lead to errors in interpretation, so adding the number of times the term has repeated makes it easier to get an accurate picture of its importance.

✍🏽 Use only one type of word. It is not usually advisable to mix verbs with nouns or adjectives, as their meanings vary and lead to confusion.

🌀 Check that the words in your cloud make sense and, if necessary, eliminate empty terms such as articles or determiners.

3 tools to create your word cloud

We propose some tools to create tag clouds that we like the most and do not require downloading. We do not show them in any specific order of preference. You will have to try them and select the one you like the most.

Words cloud

Words cloud is one of the easiest tools from Datasketch. It’s free, and you only need to register. It allows you to create word clouds from sample texts, copy and paste, upload a file (doc/txt/pdf) or use a URL. Moreover, you can work in different languages. The options to customize your visualization are extensive. Among them, you can choose the shape, the color palette, the slant of the words, remove common words, or the font you want to use.

WordItOut identifies the most frequently occurring words and highlights them. It is pretty simple and allows some customization.

Word Art allows you to customize your word clouds with a wide range of image templates. If you are looking for originality in your clouds, this is probably a good option. It also allows you to add the word list directly in the tool and pick the shape, font, layout, and style.

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.

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Visual Representation

What is visual representation.

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

Alan Blackwell, cognition scientist and professor, gives a brief introduction to visual representation:

• Transcript loading…

We can see visual representation throughout human history, from cave drawings to data visualization :

Art uses visual representation to express emotions and abstract ideas.

Financial forecasting graphs condense data and research into a more straightforward format.

Icons on user interfaces (UI) represent different actions users can take.

The color of a notification indicates its nature and meaning.

Van Gogh's "The Starry Night" uses visuals to evoke deep emotions, representing an abstract, dreamy night sky. It exemplifies how art can communicate complex feelings and ideas.

© Public domain

Importance of Visual Representation in Design

Designers use visual representation for internal and external use throughout the design process . For example:

Storyboards are illustrations that outline users’ actions and where they perform them.

Sitemaps are diagrams that show the hierarchy and navigation structure of a website.

Wireframes are sketches that bring together elements of a user interface's structure.

Usability reports use graphs and charts to communicate data gathered from usability testing.

User interfaces visually represent information contained in applications and computerized devices.

This usability report is straightforward to understand. Yet, the data behind the visualizations could come from thousands of answered surveys.

© Interaction Design Foundation, CC BY-SA 4.0

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.

Visual representation simplifies the communication of designs. Without mockups, it would be difficult for developers to reproduce designs using words alone.

Types of Visual Representation

Below are some of the most common forms of visual representation designers use.

Text and Typography

Text represents language and ideas through written characters and symbols. Readers visually perceive and interpret these characters. Typography turns text into a visual form, influencing its perception and interpretation.

We have developed the conventions of typography over centuries , for example, in documents, newspapers and magazines. These conventions include:

Text arranged on a grid brings clarity and structure. Gridded text makes complex information easier to navigate and understand. Tables, columns and other formats help organize content logically and enhance readability.

Contrasting text sizes create a visual hierarchy and draw attention to critical areas. For example, headings use larger text while body copy uses smaller text. This contrast helps readers distinguish between primary and secondary information.

Adequate spacing and paragraphing improve the readability and appearance of the text. These conventions prevent the content from appearing cluttered. Spacing and paragraphing make it easier for the eye to follow and for the brain to process the information.

Balanced image-to-text ratios create engaging layouts. Images break the monotony of text, provide visual relief and illustrate or emphasize points made in the text. A well-planned ratio ensures neither text nor images overwhelm each other. Effective ratios make designs more effective and appealing.

Designers use these conventions because people are familiar with them and better understand text presented in this manner.

This table of funerals from the plague in London in 1665 uses typographic conventions still used today. For example, the author arranged the information in a table and used contrasting text styling to highlight information in the header.

Illustrations and Drawings

Designers use illustrations and drawings independently or alongside text. An example of illustration used to communicate information is the assembly instructions created by furniture retailer IKEA. If IKEA used text instead of illustrations in their instructions, people would find it harder to assemble the furniture.

IKEA assembly instructions use illustrations to inform customers how to build their furniture. The only text used is numeric to denote step and part numbers. IKEA communicates this information visually to: 1. Enable simple communication, 2. Ensure their instructions are easy to follow, regardless of the customer’s language.

© IKEA, Fair use

Illustrations and drawings can often convey the core message of a visual representation more effectively than a photograph. They focus on the core message , while a photograph might distract a viewer with additional details (such as who this person is, where they are from, etc.)

For example, in IKEA’s case, photographing a person building a piece of furniture might be complicated. Further, photographs may not be easy to understand in a black-and-white print, leading to higher printing costs. To be useful, the pictures would also need to be larger and would occupy more space on a printed manual, further adding to the costs.

But imagine a girl winking—this is something we can easily photograph. 

Ivan Sutherland, creator of the first graphical user interface, used his computer program Sketchpad to draw a winking girl. While not realistic, Sutherland's representation effectively portrays a winking girl. The drawing's abstract, generic elements contrast with the distinct winking eye. The graphical conventions of lines and shapes represent the eyes and mouth. The simplicity of the drawing does not draw attention away from the winking.

A photo might distract from the focused message compared to Sutherland's representation. In the photo, the other aspects of the image (i.e., the particular person) distract the viewer from this message.

© Ivan Sutherland, CC BY-SA 3.0 and Amina Filkins, Pexels License

Information and Data Visualization

Designers and other stakeholders use data and information visualization across many industries.

Data visualization uses charts and graphs to show raw data in a graphic form. Information visualization goes further, including more context and complex data sets. Information visualization often uses interactive elements to share a deeper understanding.

For example, most computerized devices have a battery level indicator. This is a type of data visualization. IV takes this further by allowing you to click on the battery indicator for further insights. These insights may include the apps that use the most battery and the last time you charged your device.

macOS displays a battery icon in the menu bar that visualizes your device’s battery level. This is an example of data visualization. Meanwhile, macOS’s settings tell you battery level over time, screen-on-usage and when you last charged your device. These insights are actionable; users may notice their battery drains at a specific time. This is an example of information visualization.

© Low Battery by Jemis Mali, CC BY-NC-ND 4.0, and Apple, Fair use

Information visualization is not exclusive to numeric data. It encompasses representations like diagrams and maps. For example, Google Maps collates various types of data and information into one interface:

Data Representation: Google Maps transforms complex geographical data into an easily understandable and navigable visual map.

Interactivity: Users can interactively customize views that show traffic, satellite imagery and more in real-time.

Layered Information: Google Maps layers multiple data types (e.g., traffic, weather) over geographical maps for comprehensive visualization.

User-Centered Design : The interface is intuitive and user-friendly, with symbols and colors for straightforward data interpretation.

The volume of data contained in one screenshot of Google Maps is massive. However, this information is presented clearly to the user. Google Maps highlights different terrains with colors and local places and businesses with icons and colors. The panel on the left lists the selected location’s profile, which includes an image, rating and contact information.

© Google, Fair use

Symbolic Correspondence

Symbolic correspondence uses universally recognized symbols and signs to convey specific meanings . This method employs widely recognized visual cues for immediate understanding. Symbolic correspondence removes the need for textual explanation.

For instance, a magnifying glass icon in UI design signifies the search function. Similarly, in environmental design, symbols for restrooms, parking and amenities guide visitors effectively.

The Interaction Design Foundation (IxDF) website uses the universal magnifying glass symbol to signify the search function. Similarly, the play icon draws attention to a link to watch a video.

How Designers Create Visual Representations

Visual language.

Designers use elements like color , shape and texture to create a communicative visual experience. Designers use these 8 principles:

Size – Larger elements tend to capture users' attention readily.

Color – Users are typically drawn to bright colors over muted shades.

Contrast – Colors with stark contrasts catch the eye more effectively.

Alignment – Unaligned elements are more noticeable than those aligned ones.

Repetition – Similar styles repeated imply a relationship in content.

Proximity – Elements placed near each other appear to be connected.

Whitespace – Elements surrounded by ample space attract the eye.

Texture and Style – Users often notice richer textures before flat designs.

The 8 visual design principles.

In web design , visual hierarchy uses color and repetition to direct the user's attention. Color choice is crucial as it creates contrast between different elements. Repetition helps to organize the design—it uses recurring elements to establish consistency and familiarity.

In this video, Alan Dix, Professor and Expert in Human-Computer Interaction, explains how visual alignment affects how we read and absorb information:

Correspondence Techniques

Designers use correspondence techniques to align visual elements with their conceptual meanings. These techniques include color coding, spatial arrangement and specific imagery. In information visualization, different colors can represent various data sets. This correspondence aids users in quickly identifying trends and relationships .

Color coding enables the stakeholder to see the relationship and trend between the two pie charts easily.

In user interface design, correspondence techniques link elements with meaning. An example is color-coding notifications to state their nature. For instance, red for warnings and green for confirmation. These techniques are informative and intuitive and enhance the user experience.

The IxDF website uses blue for call-to-actions (CTAs) and red for warnings. These colors inform the user of the nature of the action of buttons and other interactive elements.

Perception and Interpretation

If visual language is how designers create representations, then visual perception and interpretation are how users receive those representations. Consider a painting—the viewer’s eyes take in colors, shapes and lines, and the brain perceives these visual elements as a painting.

In this video, Alan Dix explains how the interplay of sensation, perception and culture is crucial to understanding visual experiences in design:

Copyright holder: Michael Murphy _ Appearance time: 07:19 - 07:37 _ Link: https://www.youtube.com/watch?v=C67JuZnBBDc

Visual perception principles are essential for creating compelling, engaging visual representations. For example, Gestalt principles explain how we perceive visual information. These rules describe how we group similar items, spot patterns and simplify complex images. Designers apply Gestalt principles to arrange content on websites and other interfaces. This application creates visually appealing and easily understood designs.

In this video, design expert and teacher Mia Cinelli discusses the significance of Gestalt principles in visual design . She introduces fundamental principles, like figure/ground relationships, similarity and proximity.

Interpretation

Everyone's experiences, culture and physical abilities dictate how they interpret visual representations. For this reason, designers carefully consider how users interpret their visual representations. They employ user research and testing to ensure their designs are attractive and functional.

Leonardo da Vinci's "Mona Lisa", is one of the most famous paintings in the world. The piece is renowned for its subject's enigmatic expression. Some interpret her smile as content and serene, while others see it as sad or mischievous. Not everyone interprets this visual representation in the same way.

Color is an excellent example of how one person, compared to another, may interpret a visual element. Take the color red:

In Chinese culture, red symbolizes luck, while in some parts of Africa, it can mean death or illness.

A personal experience may mean a user has a negative or positive connotation with red.

People with protanopia and deuteranopia color blindness cannot distinguish between red and green.

In this video, Joann and Arielle Eckstut, leading color consultants and authors, explain how many factors influence how we perceive and interpret color:

Learn More about Visual Representation

Read Alan Blackwell’s chapter on visual representation from The Encyclopedia of Human-Computer Interaction.

Learn about the F-Shaped Pattern For Reading Web Content from Jakob Nielsen.

Read Smashing Magazine’s article, Visual Design Language: The Building Blocks Of Design .

Take the IxDF’s course, Perception and Memory in HCI and UX .

Questions related to Visual Representation

Some highly cited research on visual representation and related topics includes:

Roland, P. E., & Gulyás, B. (1994). Visual imagery and visual representation. Trends in Neurosciences, 17(7), 281-287. Roland and Gulyás' study explores how the brain creates visual imagination. They look at whether imagining things like objects and scenes uses the same parts of the brain as seeing them does. Their research shows the brain uses certain areas specifically for imagination. These areas are different from the areas used for seeing. This research is essential for understanding how our brain works with vision.

Lurie, N. H., & Mason, C. H. (2007). Visual Representation: Implications for Decision Making. Journal of Marketing, 71(1), 160-177.

This article looks at how visualization tools help in understanding complicated marketing data. It discusses how these tools affect decision-making in marketing. The article gives a detailed method to assess the impact of visuals on the study and combination of vast quantities of marketing data. It explores the benefits and possible biases visuals can bring to marketing choices. These factors make the article an essential resource for researchers and marketing experts. The article suggests using visual tools and detailed analysis together for the best results.

Lohse, G. L., Biolsi, K., Walker, N., & Rueter, H. H. (1994, December). A classification of visual representations. Communications of the ACM, 37(12), 36+.

This publication looks at how visuals help communicate and make information easier to understand. It divides these visuals into six types: graphs, tables, maps, diagrams, networks and icons. The article also looks at different ways these visuals share information effectively.

​​If you’d like to cite content from the IxDF website , click the ‘cite this article’ button near the top of your screen.

Some recommended books on visual representation and related topics include:

Chaplin, E. (1994). Sociology and Visual Representation (1st ed.) . Routledge.

Chaplin's book describes how visual art analysis has changed from ancient times to today. It shows how photography, post-modernism and feminism have changed how we see art. The book combines words and images in its analysis and looks into real-life social sciences studies.

Mitchell, W. J. T. (1994). Picture Theory. The University of Chicago Press.

Mitchell's book explores the important role and meaning of pictures in the late twentieth century. It discusses the change from focusing on language to focusing on images in cultural studies. The book deeply examines the interaction between images and text in different cultural forms like literature, art and media. This detailed study of how we see and read visual representations has become an essential reference for scholars and professionals.

Koffka, K. (1935). Principles of Gestalt Psychology. Harcourt, Brace & World.

"Principles of Gestalt Psychology" by Koffka, released in 1935, is a critical book in its field. It's known as a foundational work in Gestalt psychology, laying out the basic ideas of the theory and how they apply to how we see and think. Koffka's thorough study of Gestalt psychology's principles has profoundly influenced how we understand human perception. This book has been a significant reference in later research and writings.

A visual representation, like an infographic or chart, uses visual elements to show information or data. These types of visuals make complicated information easier to understand and more user-friendly.

Designers harness visual representations in design and communication. Infographics and charts, for instance, distill data for easier audience comprehension and retention.

For an introduction to designing basic information visualizations, take our course, Information Visualization .

Text is a crucial design and communication element, transforming language visually. Designers use font style, size, color and layout to convey emotions and messages effectively.

Designers utilize text for both literal communication and aesthetic enhancement. Their typography choices significantly impact design aesthetics, user experience and readability.

Designers should always consider text's visual impact in their designs. This consideration includes font choice, placement, color and interaction with other design elements.

In this video, design expert and teacher Mia Cinelli teaches how Gestalt principles apply to typography:

Designers use visual elements in projects to convey information, ideas, and messages. Designers use images, colors, shapes and typography for impactful designs.

In UI/UX design, visual representation is vital. Icons, buttons and colors provide contrast for intuitive, user-friendly website and app interfaces.

Graphic design leverages visual representation to create attention-grabbing marketing materials. Careful color, imagery and layout choices create an emotional connection.

Product design relies on visual representation for prototyping and idea presentation. Designers and stakeholders use visual representations to envision functional, aesthetically pleasing products.

Our brains process visuals 60,000 times faster than text. This fact highlights the crucial role of visual representation in design.

Our course, Visual Design: The Ultimate Guide , teaches you how to use visual design elements and principles in your work effectively.

Visual representation, crucial in UX, facilitates interaction, comprehension and emotion. It combines elements like images and typography for better interfaces.

Effective visuals guide users, highlight features and improve navigation. Icons and color schemes communicate functions and set interaction tones.

UX design research shows visual elements significantly impact emotions. 90% of brain-transmitted information is visual.

To create functional, accessible visuals, designers use color contrast and consistent iconography. These elements improve readability and inclusivity.

An excellent example of visual representation in UX is Apple's iOS interface. iOS combines a clean, minimalist design with intuitive navigation. As a result, the operating system is both visually appealing and user-friendly.

Michal Malewicz, Creative Director and CEO at Hype4, explains why visual skills are important in design:

Learn more about UI design from Michal in our Master Class, Beyond Interfaces: The UI Design Skills You Need to Know .

The fundamental principles of effective visual representation are:

Clarity : Designers convey messages clearly, avoiding clutter.

Simplicity : Embrace simple designs for ease and recall.

Emphasis : Designers highlight key elements distinctively.

Balance : Balance ensures design stability and structure.

Alignment : Designers enhance coherence through alignment.

Contrast : Use contrast for dynamic, distinct designs.

Repetition : Repeating elements unify and guide designs.

Designers practice these principles in their projects. They also analyze successful designs and seek feedback to improve their skills.

Read our topic description of Gestalt principles to learn more about creating effective visual designs. The Gestalt principles explain how humans group elements, recognize patterns and simplify object perception.

Color theory is vital in design, helping designers craft visually appealing and compelling works. Designers understand color interactions, psychological impacts and symbolism. These elements help designers enhance communication and guide attention.

Designers use complementary , analogous and triadic colors for contrast, harmony and balance. Understanding color temperature also plays a crucial role in design perception.

Color symbolism is crucial, as different colors can represent specific emotions and messages. For instance, blue can symbolize trust and calmness, while red can indicate energy and urgency.

Cultural variations significantly influence color perception and symbolism. Designers consider these differences to ensure their designs resonate with diverse audiences.

For actionable insights, designers should:

Experiment with color schemes for effective messaging. 

Assess colors' psychological impact on the audience. 

Use color contrast to highlight critical elements. 

Ensure color choices are accessible to all.

In this video, Joann and Arielle Eckstut, leading color consultants and authors, give their six tips for choosing color:

Learn more about color from Joann and Arielle in our Master Class, How To Use Color Theory To Enhance Your Designs .

Typography and font choice are crucial in design, impacting readability and mood. Designers utilize them for effective communication and expression.

Designers' perception of information varies with font type. Serif fonts can imply formality, while sans-serifs can give a more modern look.

Typography choices by designers influence readability and user experience. Well-spaced, distinct fonts enhance readability, whereas decorative fonts may hinder it.

Designers use typography to evoke emotions and set a design's tone. Choices in font size, style and color affect the emotional impact and message clarity.

Designers use typography to direct attention, create hierarchy and establish rhythm. These benefits help with brand recognition and consistency across mediums.

Read our article to learn how web fonts are critical to the online user experience .

Designers create a balance between simplicity and complexity in their work. They focus on the main messages and highlight important parts. Designers use the principles of visual hierarchy, like size, color and spacing. They also use empty space to make their designs clear and understandable.

The Gestalt law of Prägnanz suggests people naturally simplify complex images. This principle aids in making even intricate information accessible and engaging.

Through iteration and feedback, designers refine visuals. They remove extraneous elements and highlight vital information. Testing with the target audience ensures the design resonates and is comprehensible.

Michal Malewicz explains how to master hierarchy in UI design using the Gestalt rule of proximity:

Answer a Short Quiz to Earn a Gift

Why do designers use visual representation?

• To guarantee only a specific audience can understand the information
• To replace the need for any form of written communication
• To simplify complex information and make it understandable

Which type of visual representation helps to compare data?

• Article images
• Line charts
• Text paragraphs

What is the main purpose of visual hierarchy in design?

• To decorate the design with more colors
• To guide the viewer’s attention to the most important elements first
• To provide complex text for high-level readers

How does color impact visual representation?

• It has no impact on the design at all.
• It helps to distinguish different elements and set the mood.
• It makes the design less engaging for a serious mood.

Why is consistency important in visual representation?

• It limits creativity, but allows variation in design.
• It makes sure the visual elements are cohesive and easy to understand.
• It makes the design unpredictable yet interesting.

Better luck next time!

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Literature on Visual Representation

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

Learn more about Visual Representation

Take a deep dive into Visual Representation with our course Perception and Memory in HCI and UX .

How does all of this fit with interaction design and user experience? The simple answer is that most of our understanding of human experience comes from our own experiences and just being ourselves. That might extend to people like us, but it gives us no real grasp of the whole range of human experience and abilities. By considering more closely how humans perceive and interact with our world, we can gain real insights into what designs will work for a broader audience: those younger or older than us, more or less capable, more or less skilled and so on.

“You can design for all the people some of the time, and some of the people all the time, but you cannot design for all the people all the time.“ – William Hudson (with apologies to Abraham Lincoln)

While “design for all of the people all of the time” is an impossible goal, understanding how the human machine operates is essential to getting ever closer. And of course, building solutions for people with a wide range of abilities, including those with accessibility issues, involves knowing how and why some human faculties fail. As our course tutor, Professor Alan Dix, points out, this is not only a moral duty but, in most countries, also a legal obligation.

Portfolio Project

In the “ Build Your Portfolio: Perception and Memory Project ”, you’ll find a series of practical exercises that will give you first-hand experience in applying what we’ll cover. If you want to complete these optional exercises, you’ll create a series of case studies for your portfolio which you can show your future employer or freelance customers.

This in-depth, video-based course is created with the amazing Alan Dix , the co-author of the internationally best-selling textbook  Human-Computer Interaction and a superstar in the field of Human-Computer Interaction . Alan is currently a professor and Director of the Computational Foundry at Swansea University.

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All open-source articles on Visual Representation

Data visualization for human perception.

The Key Elements & Principles of Visual Design

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Guidelines for Good Visual Information Representations

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Philosophy of Interaction

Information visualization – an introduction to multivariate analysis.

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Aesthetic Computing

How to represent linear data visually for information visualization.

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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.

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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.

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A Picture Is Worth 1,000 Words: The Importance of Data Visualization

Have you ever heard the saying, “A picture is worth 1,000 words”? This statement holds especially true in the field of Data Science. Let’s say you are a data scientist at a top Fortune company, dealing with budget portfolio optimizations worth millions of dollars annually for various clients. It is essential to effectively communicate your […]

Have you ever heard the saying, “A picture is worth 1,000 words”? This statement holds especially true in the field of Data Science. Let’s say you are a data scientist at a top Fortune company, dealing with budget portfolio optimizations worth millions of dollars annually for various clients. It is essential to effectively communicate your findings to clients or stakeholders to make an impact. This is where data visualization comes in. 

This is crucial when presenting findings to stakeholders or other members of your team who may not be familiar with the intricacies of your analysis. In addition to making data more digestible, data visualization can help you identify errors or inconsistencies in your data. 

By visualizing your data, you may notice patterns or outliers that you would have otherwise missed. Overall, data visualization is a powerful tool in the Data Science toolkit. It allows you to communicate your findings more effectively, identify patterns and outliers, and ultimately make better decisions based on your data.

Additionally, data visualization plays a crucial role in the process of data EDA, or exploratory data analysis. By presenting data in a visual format, patterns and trends can be quickly identified and insights can be gleaned from the data. This helps to make sense of complex data sets and can lead to more informed decision-making. Without effective data visualization, it can be difficult to fully understand the meaning behind the data, and valuable insights may be missed. Overall, data visualization is an integral part of the data EDA process and is essential for effectively analyzing and interpreting data.

Let’s take an example of housing price data along with factors that can impact its pricing [1], to understand how visualization can make your data speak. Below are some visualization plots that help portray meaningful insights. 

Correlation Matrices

Let’s say you want to know which factors have a similar impact on the pricing of a house, or simply which factors are correlated. The colored correlation matrix in Figure 1 gives you an overview of the correlation between various factors, and you can make the following inferences with just a glance:

• Year Built and Garage Year Built have an 83% correlation, as most people build their garages along with their houses.
• Sale Price and Overall Quality have a 79% correlation, as a better-quality house will usually command a higher price.
• Year Built and Overall Condition have a negative correlation of -0.38, as the condition of a house deteriorates with each passing year.

Count Plots

What if you want to know the pricing distribution by neighborhood? If you were to describe it verbally, it would be extremely difficult to retain the various numerical metrics, such as mean price, variance of price, etc., for each neighborhood. However, if you use a count plot, as in Figure 2, you can directly infer all the distribution data and compare the neighborhoods, all at one go.

You can tell from the figure which areas often experience higher sale prices and which areas typically experience lower sale prices. This enables you to determine the quality of the community. Additionally, you can see that the neighborhood’s mean sale price shows a significant amount of variation.

Now, if you want to understand the sales price trends over the years, and what factors may have contributed to them, you can refer to Figure 3. 

• The line plots show that the sales price has decreased over time, which indicates that the market has not been doing well. 
• Additionally, you can observe that if a home’s basement area is excessively large, the price of the home tends to be quite low. This may be because it is considered poorly constructed. 
• You can observe that overall quality and condition are directly correlated with sales price, as was previously expected.
• It’s interesting to note that prices for homes constructed before 1900 are greater than those constructed later; this could be because of the homes’ historical importance.

All these insights – deduced from the plots with just a glance – would otherwise have been difficult to extract based on numerical or verbal information.

Factor Plots

When multiple response and factor variables are plotted together, the resulting plot is called a factor plot. Any type of univariate or bivariate plot can be used as the basis for the underlying graphic. Let’s say you want to provide a timeline of housing construction in a community and also show the evolution of housing style over time. To communicate this in a textual manner, you would have to provide a detailed list or table, along with descriptions. However, in Figure 4, you can observe all the information in a single plot, along with additional insights.

The plot displays the year that houses were built, as well as the neighborhood in which they were built and the sort of houses that were constructed. It is clear that the oldest neighborhood in town was called Old Town. You can also see when the neighborhoods progressively developed, as well as the fact that some communities developed in a very quick amount of time. You may also notice that the majority of buildings with two stories were constructed after the 1980s.

All these samples help make clear the importance of a good visual and how you can effectively convey insights with visual representations.

Challenges of Data Visualization

However, it’s not always easy to visualize a given dataset. When it comes to data visualization in Data Science, there are several challenges that professionals in this field face. One of the main challenges is the sheer amount of data that needs to be analyzed and presented in a meaningful way. With so much data to work with, it can be difficult to know where to start and how to identify the most important insights. 

Another challenge is choosing the right visualizations to represent the data. Different types of data require different types of visualizations, and it can be a challenge to determine which type of visualization will be most effective for a particular dataset. Additionally, creating visualizations that are accurate and easy for non-technical users to understand can be difficult. 

Finally, ensuring the accuracy and reliability of data visualizations can be a challenge. It’s important to be diligent in checking and double-checking data to make sure that the visualizations accurately represent the information they are meant to convey. With these challenges in mind, data scientists must be meticulous in their approach to data visualization, in order to create useful and effective visualizations that can inform decision-making and drive business success.

[1] Thain, Tom.  House Prices – Advanced Regression Techniques.   Kaggle . 

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

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Blog Graphic Design 15 Effective Visual Presentation Tips To Wow Your Audience

15 Effective Visual Presentation Tips To Wow Your Audience

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 & why is it important?

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. 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.

4. 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.

5. 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. 

6. 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.

7. Add fun with visual quizzes and polls

To break the monotony and see if your audience is still with you, throw in some quick 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.

8. 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.

9. 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.

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 . 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 tools 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. 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 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.

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.

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.

Ultimately, creating a memorable visual presentation isn’t just about throwing together pretty slides. It’s about mastering the art of making your message stick, captivating your audience and leaving a mark.

Lucky for you, Venngage simplifies the process of creating great presentations, empowering you to concentrate on delivering a compelling message. 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.

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• 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

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• 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.

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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).

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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

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

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

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

Conclusions

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

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

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

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

Scientific practices and science education

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

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

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

Visualization as an epistemic object

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

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

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

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

Methods and Results: Merging Visualization and scientific practices in science

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

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

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

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

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

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

X-ray chrystallography of DNA

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Visual illustration of apparatus

Video processing of experimental set-up

Computer software for video input and variable recording

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

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

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

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

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

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

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

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

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

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

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

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

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

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 .

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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.

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

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