Why We Love Diagrams: The Psychology Behind Data Visualization

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The greatest value of a picture is when it forces us to notice what we never expected to see. John W. Tukey - Exploratory Data Analysis, 1977.  

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The Beginning

Data visualization seems a modern concept, but human beings have been using illustrations to convey meaning since civilization began. The earliest visualizations were a device to transfer information, for example the cave paintings at Lascaux, or the Dunhuang Star Chart from the Tang Dynasty. For a long time, data visualization was limited to maps, and it was only between the 17^th and 19^th centuries that significant advances in data visualization were made. Rene Descartes invented the two-dimensional coordinate to present mathematical data graphically. And then William Playfair, a Scottish political economist, often regarded as the father of the data visualization field, invented the line and bar charts, and later the pie chart and circle graph.  

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The effectiveness of any visualization hinges not just on the data itself but on our understanding of how the human mind perceives and processes visual information. Let's explore the psychology behind data visualization and find out how cognitive processes influence the interpretation of visual data. By understanding the interplay between visual perception and cognitive psychology, creators can design data visualizations that are not only informative but also intuitive and compelling for their audiences.

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All In The Eyes

The brain takes 13 milliseconds to process an image. By leveraging the brain’s natural ability to process visual stimuli and recognize patterns to extract insights, any data scientist can create memorable visualizations. Visualizations are extremely effective because they shift the balance between perception and cognition.  

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Seeing (visual perception) is handled by the visual cortex in the back of the brain. Sensory input from the eyes reaches the visual cortex and is processed extremely quickly and efficiently. It does not require working memory and is autonomous. Thinking (cognition) is handled predominantly by the cerebral cortex in the front of the brain, and acquires knowledge and understanding through thought, experience, and the senses. This means that traditional presentations of data require conscious thinking to make sense. But data visualizations shift the balance towards significant use of visual perception, making it far easier to identify relationships and prioritize information to derive meaning from what we see.  

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For example, consider the impact of color in visualizations. Warm colors (e.g., red, orange) can draw attention to critical data points, while cool colors (e.g., blue, green) might be used to indicate background information. This use of color taps into our pre-attentive processing, enabling viewers to quickly grasp the most important parts of the visualization.

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Image source: www.data-pilot.com

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The image above shows the critical points for a retail business, such as 5 top-selling products and customer segmentation by gender and age. The bright colors, labels and numbers condense the complex information into easy to comprehend insights. It is evident from the line graph that Product C is the highest selling item (February onwards), while Product E has a seasonality factor and sells only in February and June. Additionally, the pie chart and bar chart show that males in the age group 18 – 25 are the most loyal customers. Such a representation of key performance indicators shows the health of the business and makes it easier to take decisions, such as which product to stock up on, and which advertising campaign to invest in.

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Pro tip: Request consultation with a Data Pilot expert to know more about data visualization tools.

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Remember The Viz

Understanding how memory works is crucial for designing data visualizations that stick with the audience. Memory is broadly categorized into two types: short-term (or working) memory and long-term memory. Short-term memory has limited capacity and duration, capable of holding about 7±2 items for 20-30 seconds without rehearsal. Which means data visualizations need to be designed for quick comprehension without overloading the viewer. Long-term memory is where information is stored more permanently. Connecting newer information with existing knowledge in long-term memory can facilitate better understanding and recall of data visualizations.

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Elements such as color, shape, and spatial arrangements can enhance memory retention. For instance, using consistent color schemes for similar data types across visualizations can help viewers quickly recall the meaning of those colors in future encounters. Structured and hierarchical information presentation aligns with how our memory organizes information, making it easier for viewers to store and retrieve information.

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Look at the two charts below. The one on the left is cluttered, full of “chart junk.” The unnecessary 3-D design, and the forced perspective prevents the audience from seeing the towers’ actual heights. The callouts overlap, the towers’ transparency doesn’t provide any information to the viewer and the beveled edges and shadows are distracting. The one on the right, however, shows the same data in a clean, well-organized bar chart that the audience can easily follow.

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Image source: researchgate.net

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Make Some Meaning

The human brain will attempt to simplify complex images with several elements by subconsciously organizing them to see structure and pattern. This idea is the basis of the Gestalt principles. These principles enrich data visualizations and help designers create meaningful patterns to enhance viewer engagement. Knowing the Gestalt psychology of data visualization is important for designers since their implementation can influence the aesthetics, functionality, and user-friendliness of any design.  

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• Similarity: Elements that are similar can be grouped together (even if they are not next to each other) by color, shape, or size. The chart below shows the use of color to help someone more readily identify which groups the displayed data belong with. If all the colors were the same, it would be difficult to see which location had what number of customers.  

Image source: www.data-pilot.com

• Continuity: The natural gaze pathway follows the smoothest path when viewing lines, regardless of how they were drawn. This can help guide the visitor’s eye to follow a line. Horizontal sliders are one such example, as are related product listings on sites like Amazon.
• Closure: How cool is it that the brain will fill in the gaps missing in an image to create a whole? This principle is often applied in logo design but on a website, it can be used to show an image fading off a user’s screen to indicate that there is more to be found if they swipe right!
• Proximity: Objects placed close to one another are perceived as a group. This principle helps organize information, create focal points, and establish relationships between different elements.
• Figure-ground: The figure/ground principle explains how we differentiate an object (the figure) from its surrounding area (the ground). This distinction is fundamental in determining the focus of an image and is a powerful tool in creating depth and emphasis in visual compositions.
• Symmetry and order: Symmetry and order refer to our preference for balanced, harmonious compositions. Designs that incorporate these elements are perceived as stable and aesthetically pleasing, contributing to a sense of visual comfort and satisfaction.
• Common fate: While common fate was not originally included in Gestalt theory, it has since been added. This principle states that people will group together things that point to or are moving in the same direction. As animated effects become more prevalent in modern design, this principle comes in handy.

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A Good Story Matters

Storytelling in data visualization creates impact, as it adds emotion to the numbers and charts, leading to a compelling narrative that resonates with the audience on a deeper level. Emotional engagement enhances comprehension, retention, and drives action based on the presented data. With a story woven into the data, the abstract figures become relatable, and guide the viewer through a sequence of information. In an age when data is abundant, but attention is not, data visualization needs to be accompanied by a compelling narrative.  

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The value of storytelling makes visualizations memorable and actionable. When climate change data is presented as part of a narrative about its effect on individual lives or communities, the emotional connection with the issue and message remains intact long after it has been encountered. In fields of public health and social justice, data-driven stories can stir emotions to cause behavior change into supporting a cause or decide a new course of action.  

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Final Note

Data visualizations are created to make sense of large data sets. Understanding the key elements of human perception and the cognitive process is an essential part of designing insightful data visualizations. Data visualizations are utilized for a variety of purposes, and it’s important to note that it is not only reserved for use by data teams. Management also leverages it to convey organizational structure and hierarchy while data analysts and data scientists use it to discover and explain patterns and trends. Therefore, the aim must be to design the data visualization for maximum impact by eliminating information that may distract the viewers.

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By Tooba Shah.

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