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• Workflow diagram: Symbols, uses, and ex ...

Workflow diagram: Symbols, uses, and examples

A workflow diagram provides a visual overview of a business process or system. These diagrams help team members easily visualize their goals and deadlines, preventing potential bottlenecks. Find out how to create one of your own.

A workflow diagram can help prevent project deviations and bottlenecks by communicating goals and deadlines in a visual way. Whether you use a workflow diagram when onboarding new hires or to streamline use cases and testimonials, it’s a great way to visualize tasks and data flows.

From what it is to how to create one of your own, we’ve put together some of the most important facts to know about workflow diagrams and included helpful examples. 

What is a workflow diagram?

A workflow diagram—also known as a workflow chart—provides a graphic overview of a business process or system. Usually, you’ll use these diagrams to visualize complex projects after you’ve completed the initial research and project planning stages.Once you’ve created a workflow diagram, you will have a detailed view of high level tasks and dependencies based on the overall project timeline and objectives. 

Workflow diagram compared to other process mapping

Workflow diagrams share many aspects of other diagrams in the UML (Unified Modeling Language)—a standard language for specifying, visualizing, constructing, and documenting the artifacts of software systems. But workflow diagrams differ slightly from other process mapping and UML diagrams. Here, we compare them to some common types to show how:  

Business process mapping

Workflow diagrams are closely related to business process mapping . The difference between the two is that a process map typically outlines steps in detail while a workflow diagram gives a visual representation of them. The objective of a workflow diagram is to help team members understand their tasks, objectives, and roles and responsibilities within the project at a high level.

Process flowcharts

Workflows and flowcharts are often confused. While the two terms sound similar, a workflow is just one type of flowchart . You can also use flowcharts to visualize other processes, like PERT charts and process documentation .

Activity diagrams

Activity diagrams are another type of flowchart that outline the flow of a series of activities within a system. It’s used to translate a business system’s functions into more digestible information for those who don’t understand the backend workings as much. In other words, an activity diagram is an easy way to visualize technical processes. For example, in Asana , you could draw an activity diagram to create a project as follows:

User clicks the button to create a project 

New project launched

User customizes the project with different names and features

User saves the project and updates when needed

Data flow diagrams

Data flow diagrams follow the data through an operating system or process, whereas workflow diagrams follow the work itself. Instead of inputting actions, for example, in a data flow diagram you’d enter in metrics, results, or other data points that you want to portray.

When to use a workflow diagram

A workflow diagram is a visual representation of a process, either a new process you’re creating or an existing process you’re altering. For example:

A process to streamline your ecommerce customer journey.

A project to increase customer retention and satisfaction.

A process to automate and optimize manual tasks involving customer data. 

A workflow diagram comes between the business process map (which you’ll create before the project starts) and business process automation (which you’ll use to optimize and streamline processes). This is because your map provides detailed process steps that stakeholders need to begin work, while a workflow diagram is a high level visual representation that can help clarify overarching goals during the process.

The components of a workflow diagram

In order to understand how a workflow diagram works, you first need to understand the components that make up a workflow. These include inputs, outputs, and transformations, which all help to communicate deliverables in as little time as possible.

Once you understand these components, you’ll be able to properly read a workflow process diagram and create one of your own. The main components of a workflow diagram include:

Inputs: An action that impacts the following step

Transformations: An input change

Outputs: The outcome after the transformation 

These components are visualized by shapes and arrows, including:

Ovals: Represent the start and end points of a process. 

Rectangles: Represent instructions about actions and steps. 

Diamonds: Represent key decisions during the process build.

Circles: Represent a jump in actions and may indicate steps to bypass (in certain situations).

Arrows: Connectors that represent the dependency between all shapes and actions. 

Together, ‌these components instruct the reader how to follow the correct path and achieve the desired outcome.

Types of workflow diagrams

When it comes to visualizing processes, there are a few different workflow diagram formats that you can choose from. Each one offers unique advantages that can help you map out your next process. The type of diagram you choose will depend on the process you’re working on and your needs for that process. 

From process flows to swimlanes, here are the four different types of workflow diagrams you can use for your workflow analysis.

1. Process flow diagram

A process flow diagram tool is the standard design for workflows. In this diagram, all components are mapped out chronologically, making it a basic visual representation of a process. This type of diagram provides a general overview of individual tasks and objectives without getting into too much detail.

Best for: Teams that want a high level visual representation of a new process that is quickly comprehended by any stakeholder or department.

2. Swimlane diagram

A swimlane diagram is also a popular workflow layout, though swimlanes differ quite significantly from process flow diagrams. A swimlane diagram breaks down your workflow into smaller flows or units. These flows are interconnected but separated to highlight interactions and possible inefficiencies. This creates visibility and offers a deeper dive into the overall process workflow.

Best for: Teams working on complex processes with many layers that are interrelated but independent. 

3. Business process modeling notation (BPMN) diagram

BPMN uses uniform notations that both business and technical stakeholders can easily interpret. It is a type of unified modeling language which uses standardized symbols to communicate different steps.

BPMN diagrams focus on the information that is received internally and how that information is interpreted. This is why it's most commonly used for internal process changes that don't impact external customers.

Best for: Teams working on process improvements in different departments. 

4. Supplier, input, process, output, customers (SIPOC) diagram

SIPOC is a type of swimlane diagram that focuses on analyzing multiple different parts of a workflow. 

Unlike a traditional diagram that organizes data in sequential order, a SIPOC diagram prioritizes who creates and receives the process data. SIPOC focuses on how the data is being received internally as well as externally which is why it's used for processes associated with customer experience.

Best for: Teams looking to focus on how data is being received internally and externally.

How to create a workflow diagram (with example)

To create a workflow diagram, begin putting together the main components of your process. To do this, bring together your inputs, outputs, transformations, and your main process deliverables. 

Map workflow components out on your diagram by using arrows, circles, rectangles, ovals, and diamonds to represent each data point. 

1. Select your type of workflow

To select the workflow type that’s best for you, consider the functions needed for your process. Is it a complex process with multiple stakeholders best fit for a swimlane diagram? Or is it a relatively simple process that’s best suited for a simplistic process flow diagram?

While you can adjust your workflow as you go, it's easier to decide on the type of workflow up front. This way, you know exactly how complex or simple your workflow is.

2. Determine your start and end points

Next, determine your workflow start and end points (represented by ovals on your diagram).

To determine these points, consider when your process begins and when it ends. Is there an action that triggers the process? Likewise, is there an action or step that ends the process? These data points will help effectively communicate when the process begins and ends.

3. Gather necessary information

To gather information, connect with stakeholders to understand each piece of the process. This may include a kickoff meeting with various departments and leaders to gather the details and approvals needed to begin constructing your workflow diagram.

Since each process differs, the information you need to gather will also vary. Consider the steps required to complete the process, the stakeholders who will be involved, and any other significant details that will help inform readers.

4. Eliminate inefficiencies

The final step before constructing your visual workflow is to consider and eliminate any inefficiencies that may arise. Make sure you analyze inefficiencies before designing your workflow so you can prevent any issues—rather than dealing with them in real time. The specific inefficiencies will vary, but they can include a lack of resources, issues with product development, or any other obstacle that could arise during the process.

Document these inefficiencies in a change log under your change control process . This way you will be able to communicate these problems to stakeholders, prioritize inefficiencies, and track whether they've been resolved. 

5. Design your workflow

Finally, begin constructing your workflow. Gather the unit information, data points, and efficiencies and map them on the diagram you chose in step one. Since each process is different and each diagram is constructed differently, yours will likely be unique in its design. Here’s just one example of what a workflow diagram might look like:

Once your workflow is designed, review it with your stakeholders to ensure it's accurate and appropriate for the situation. This is a great way to ensure all inefficiencies have been accounted for and resources have been specified properly.

Use workflows to map out processes

Visualizing workflows can help you effectively communicate deliverables to stakeholders and leadership. Plus, it’s a great way to align multiple different departments on a given process. 

To take your workflows one step further, try workflow management software. From task automation to streamlined communication, Asana can help.

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19 Best Process Mapping Tools to Visually Manage Work

In the dynamic landscape of modern businesses, optimizing workflows is crucial. Process mapping tools serve as the guiding compass, enabling organizations to visualize, streamline, and enhance their operational efficiencies. 

From intricate software platforms to intuitive diagramming solutions, these tools empower teams to chart out complex processes, fostering clarity, collaboration, and continual improvement. 

In this article, we will delve into the top 19 process mapping tools, exploring their functionalities and impact on driving organizational success.

We will cover:

• The list of tools

What is process mapping?

Why are process mapping tools important, features to look for in process mapping tools, process street.

Process Street offers a seamless platform for process mapping, aiding in visualizing workflows and procedures. Its intuitive interface allows step-by-step documentation, defining roles, and incorporating rich media. With its templates and collaborative features, teams can streamline operations, track progress, and improve efficiency across various industries and tasks.

Key features:

• Checklist automation: Create and automate dynamic checklists for recurring processes.
• Conditional logic : Customize workflows with conditional logic based on responses.
• Collaborative workflows: Facilitate team collaboration on tasks and processes.
• Integration capabilities: Seamlessly integrate with tons of apps and tools.
• Task assignment: Assign and track responsibilities within processes.
• Data collection: Gather and store data through forms within checklists.
• Reporting and analytics: Generate insights with analytics on process performance.
• Version control: Maintain and track versions of checklists for auditing.
• API access: Connect with other applications through APIs.
• Document storage: Attach and manage documents relevant to processes.
• Intuitive interface makes it user-friendly.
• Offers secure data handling and storage.
• It has a mobile application.
• Custom automated workflows can be made with AI .
• It has lots of premade workflow templates to choose from.
• There is no free plan.

Pricing: Process Street pricing page .

Canva simplifies process mapping by providing customizable templates, shapes, and design elements. Users can create visual process maps with ease, utilizing its drag-and-drop interface to illustrate workflows, add annotations, and integrate branding. Its collaborative features facilitate team input, making it versatile for mapping diverse procedures and workflows.

• Image library: Extensive collection of stock photos, icons, and illustrations.
• Brand kit: Stores brand assets for consistent branding.
• Social media integration: Direct sharing to social media platforms.
• Animations: Allows the creation of animated graphics and presentations.
• Print and digital designs: Supports designs for both online and print media.
• Offers various design templates for different purposes.
• Allows easy editing of elements, colors, and fonts.
• Supports sharing and teamwork on design projects.
• Provides basic features at no cost.
• Some templates lack customization options.
• There are limitations in the number of brand assets in the free version.

Pricing: Canva pricing page .

Nifty streamlines process mapping through its project management interface, enabling task breakdowns and timelines for visualizing workflows. Its collaborative boards, task dependencies, and customizable workflows empower teams to map processes, assign tasks, and track progress seamlessly. With integrations and a user-friendly layout, it aids in efficient process management.

• Task management: Create, assign, and organize tasks.
• Project milestones: Set and track project milestones.
• Collaborative workspaces: Shared spaces for team collaboration.
• Gantt charts: Visualize project timelines and dependencies.
• Team chat: Real-time communication within the platform.
• Offers a free version.
• Monitors time spent on tasks and projects.
• Syncs tasks and milestones with calendars.
• Offers pre-built templates for various project types.
• There are limited integration capabilities.
• It takes time to get team members used to it.

Pricing: Nifty pricing page .

Lucidchart is a versatile cloud-based platform known for its intuitive interface, enabling seamless creation of diagrams and visual representations. Renowned for collaborative capabilities, it facilitates team brainstorming and process mapping. Its adaptability across industries makes it a go-to choice for illustrating complex concepts and workflows.

• Shape libraries: Extensive libraries for symbols and shapes.
• Diagram creation: Tools for creating flowcharts, mind maps, org charts, etc.
• Presentation mode: Ability to present diagrams.
• Collaborative editing: Simultaneous editing by multiple users.
• Revision history: Track changes made to diagrams.
• Ability to link shapes and objects.
• Embed diagrams in websites or documents.
• Capability to work on diagrams offline.
• Ability to customize colors, fonts, and styles.
• There is an object limit in the free version.

Pricing: Lucidchart pricing page .

Miro , a collaborative online whiteboarding platform, is often used for product management. It offers visual tools for brainstorming, creating product roadmaps, and organizing user story maps. Miro enhances product development by enabling cross-functional teams to collaborate, ideate, and plan, fostering better communication and innovation in the product management process.

• Flowchart creation: Create and customize flowcharts.
• Innovation management: Keep track of ideas and documents.
• Product roadmap tools: Design project roadmaps with whiteboards.
• Project management: Use flow charts to design project management processes.
• It has a robust set of product features.
• Excellent for idea sharing.
• Supports video conferencing.
• It’s difficult to use with a trackpad.

Pricing: Miro pricing page .

MindMeister

MindMeister serves as an effective process mapping tool by offering a visual platform for brainstorming and organizing ideas. Its mind mapping capabilities enable users to outline workflows, connect processes, and illustrate relationships. Collaborative features empower teams to collectively design, refine, and visualize intricate processes for enhanced clarity and efficiency.

• Mind mapping: Creation of visual mind maps for brainstorming.
• Collaboration: Real-time collaboration for teams.
• Templates: Pre-built templates for various mind map types.
• Customizable styles: Options to customize map styles, colors, etc.
• Export/import: Capability to import/export mind maps in various formats.
• Offers the ability to present mind maps.
• Converts map items into actionable tasks.
• Adds notes and attachments to map items.
• Embeds maps in websites or documents.
• It’s not possible to increase the size of images.

Pricing: MindMeister pricing page .

Pipefy excels as a process mapping tool by offering customizable workflows within a visual interface. It enables mapping complex processes, automating tasks, and managing workflows efficiently. With its drag-and-drop system, forms, and automation, teams can visually map, optimize, and streamline their processes for improved productivity and clarity.

• Customizable workflows: Tailor processes to specific needs.
• Visual process mapping: Drag-and-drop interface for mapping workflows.
• Automation: Automate tasks and workflows.
• Forms and fields: Create customizable forms and fields.
• Task management: Track and manage tasks within processes.
• Offers pre-built templates for various workflows.
• Monitors and manages task deadlines.
• Allows users to attach and manage files within processes.
• The data analysis is poor.
• It’s not possible to create workflow approval processes.

Pricing: Pipefy pricing page .

ClickUp is a robust project management platform acclaimed for its versatility and customization. It streamlines workflows and fosters collaboration across teams. Known for its adaptability to various work styles, ClickUp offers a comprehensive solution for task management and team communication, enhancing productivity and project organization.

• Multiple views: Kanban boards, lists, calendars, and Gantt charts for varied project perspectives.
• Time tracking: Monitor time spent on tasks and projects.
• Team collaboration: Comments, mentions, and real-time collaboration on tasks.
• Goals and OKRs: Set and track objectives and key results.
• Document management: File attachments, document editing, and version control.
• Offers a wide range of features catering to diverse project manager needs.
• Allows for the creation of automated workflows, enhancing efficiency.
• Offers a free plan ideal freelancers and small businesses .
• Users have reported occasional lags or performance issues, especially with large data sets.

Pricing: ClickUp pricing page .

GitMind serves as a versatile process mapping tool, aiding in the creation of clear visual diagrams for workflows, decision trees, and organizational processes. Its user-friendly interface and collaborative features empower teams to brainstorm, plan, and structure complex processes, fostering transparency and coherence in project management and strategy development.

• Diagram creation: Tools for creating various diagram types.
• Templates: Pre-built templates for different diagramming needs.
• Customizable styles: Options to customize diagram styles, colors, etc.
• Allows simultaneous editing by multiple users.
• Offers options to share and publish diagrams.
• There are a lot of limitations in the free version.

Pricing: GitMind pricing page .

Creately empowers seamless visual collaboration, offering a dynamic platform for creating diverse diagrams and models. Renowned for its intuitive interface, it fosters team brainstorming, aiding in process mapping, flowcharting, and wireframing. With real-time collaboration, it enhances clarity and efficiency in project planning and problem-solving, elevating teamwork experiences.

• Diagram creation: Tools for creating various types of diagrams.
• Real-time collaboration: Simultaneous editing and collaboration.
• Templates: Pre-built templates for different diagram types.
• Integration: Compatibility with various apps and platforms.
• Tracks changes and versions of diagrams.
• Very affordable for different budgets.
• Allows the ability to embed diagrams in websites or documents.
• They don’t offer many tutorials on using the software.

Pricing: Creately pricing page .

Edraw stands as a versatile diagramming tool, renowned for its user-friendly interface and extensive template library. It empowers users to create a wide array of diagrams and visuals, aiding in process mapping, flowcharting, and organizational charts. Its intuitive design fosters efficient communication and visualization of complex concepts, enhancing productivity.

• Diagram creation: Tools for various types of diagrams.
• Access controls: Permissions and access settings.
• Notes and attachments: Add notes and attachments to diagrams.
• Offline access: Capability to work on diagrams offline.
• Search and filter: Search and filter capabilities within diagrams.
• Offers pre-built templates for different diagram needs.
• Has compatibility across different operating systems.
• Provides a drag-and-drop interface.
• The paid plans are a bit pricey.
• There’s no ability to combine templates.

Pricing: Edraw pricing page .

Visual Paradigm

Visual Paradigm stands as an advanced modeling tool, renowned for its comprehensive suite of modeling capabilities. It empowers users to create intricate diagrams and models, aiding in software development, system design, and business process analysis. With its intuitive interface, it fosters efficient visualization and planning of complex systems and workflows.

• Unified modeling language (UML) support: Tools for various UML diagrams.
• Diagram creation: Create diverse diagrams for software and systems.
• Business process modeling: Tools for business process analysis and modeling.
• Team collaboration: Real-time collaboration for multiple users.
• Requirement management: Capture and manage project requirements.
• Has features for Agile methodologies.
• Generates code from models and vice versa.
• Has prototyping and wireframing capabilities.
• There is no free version.
• It is not suitable for freelancers.

Pricing: Visual Paradigm pricing page .

Google Drawings

Google Drawings offers a simple yet effective platform for process mapping, enabling users to create visual diagrams and flowcharts. Its intuitive interface and basic shapes empower individuals to outline workflows, visualize processes, and illustrate relationships. Collaborative features allow team input, making it accessible for various mapping needs.

• Basic shapes: Tools for creating various shapes.
• Text editing: Capability to add and edit text.
• Lines and connectors: Tools for drawing lines and connectors.
• Image insertion: Ability to add and manipulate images.
• Color and fill options: Customizable color and fill settings.
• Has tools for freehand drawing.
• Has the capability to work with multiple layers.
• Gives users the ability to add comments and annotations.
• There are options to group and ungroup elements.
• There are no project management options.

Pricing: It’s free!

Gliffy serves as a robust process mapping tool, offering a user-friendly platform to create visual diagrams and flowcharts. Its intuitive interface and extensive shape library enable the seamless mapping of workflows, facilitating team collaboration. With its collaborative features and templates, it aids in illustrating and optimizing complex processes efficiently.

• Templates: Pre-built templates for different diagram needs.
• Customizable styles: Options to modify diagram styles, colors, etc.
• Drag-and-drop interface: Intuitive tools for easy diagram creation.
• Real-time collaboration: Simultaneous editing by multiple users.
• Capability to import/export diagrams in various formats.
• Has compatibility with various apps and platforms.
• Offers permissions and access settings.
• Provides features for managing teams and users.
• Users have to pay for each integration.

Pricing: Gliffy pricing page .

Cacoo functions as a versatile process mapping tool, providing a collaborative platform for creating visual diagrams and flowcharts. Its intuitive interface and extensive shape library empower users to map workflows, collaborate in real time, and illustrate complex processes. It facilitates team coordination and clarity in process visualization and optimization.

• Diagram embedding: Embed diagrams in websites or documents.
• Allows users to access and edit diagrams on mobile devices.
• Offers the capability to work on diagrams offline.
• Has collaboration features for comments and feedback.
• Usage is limited in certain plans.

Pricing: Cacoo pricing page .

Visme stands as a versatile visual content creation platform, offering intuitive tools to craft engaging presentations, infographics, and diagrams. Its user-friendly interface and diverse templates empower users to convey complex information effectively. Renowned for its design flexibility, it aids in creating impactful visuals for diverse communication needs.

• Presentation creation: Tools for creating interactive presentations.
• Infographic design: Features for designing infographics.
• Data visualization: Tools for presenting data visually.
• Templates: Pre-built templates for various visual content.
• Customizable designs: Options to customize styles, colors, etc.
• Has a diverse library of icons and images.
• Offers the ability to create interactive elements.
• Has tools for creating charts and graphs.
• It’s difficult to create custom measurements.

Pricing: Visme pricing page .

Draw.io functions as an intuitive and versatile diagramming tool, offering a robust platform for creating diverse diagrams and flowcharts. Renowned for its user-friendly interface and compatibility, it facilitates easy visualization and planning, aiding in illustrating complex ideas and processes effectively for various industries and tasks.

• Export/import: Capability to import/export diagrams in various formats.
• Has search and filter capabilities within diagrams.
• It lacks many features similar products have.

Google Docs

Google Docs serves as a rudimentary process mapping tool, albeit limited in visual capabilities. Its tables, shapes, and text features allow basic process mapping by creating tables for steps, inserting shapes for workflows, and organizing text to outline procedures. Collaborative editing enhances collective mapping efforts within documents.

• Text editing: Tools for creating and editing text.
• Collaborative editing: Real-time editing by multiple users.
• Formatting options: Customization of text styles, fonts, etc.
• Tables: Tools for creating and organizing tables.
• Comments and suggestions: Collaboration features for feedback.
• It costs nothing.
• It’s good for basic process mapping.
• It cannot do more than basic process mapping.

Microsoft Excel

Microsoft Excel can function as a basic process mapping tool by employing cells, tables, and shapes to outline sequential steps and workflows. Its grid structure allows the organization of processes, with cells used to detail each step, albeit lacking advanced visual mapping features. Macros and formulas enhance functional mapping capabilities.

• Cells and grid structure: Basic structure for organizing data.
• Tables and formatting: Tools for structuring data and content.
• Shapes and drawing tools: Basic shapes for visual representation.
• Formulas and functions: Mathematical functions for calculations.
• Conditional formatting: Highlighting cells based on conditions.
• Organizes and filters data.
• Controls data input with validation rules.
• Establishes relationships between cells.
• You can create graphs .
• It can only be used for basic process mapping.
• You have to subscribe to all of Microsoft 365’s suite of products to access it.

Pricing: Microsoft 365 pricing page .

Process mapping is a visual representation of how a process works, from beginning to end. It is a powerful tool used to understand and improve the flow of work in any organization, by highlighting areas of inefficiency, redundancy, or waste. Process mapping allows for a clear and concise depiction of how a process functions, making it easier to identify areas for improvement and optimization.

There are different types of detailed process maps, such as flowcharts, network diagrams, swimlane diagrams, and value stream mapping (VSM), which are used depending on the complexity and nature of the process being analyzed.

Flowcharts are the most common type of process mapping and are used to document and visualize the steps and decision points within a process. Swimlane diagrams, on the other hand, are used to show the interplay between different departments or individuals involved in a process.

Finally, value stream mapping and network diagrams are a method used to analyze and improve the flow of materials and information required to bring a product or service to a customer.

Process mapping is not only a useful tool for streamlining and improving operational processes and process documentation, but it also allows for greater understanding and communication of how work is done within an organization.

By creating a visual representation of a process, employees, managers, and stakeholders can better grasp how their work fits into the larger picture and identify where improvements can be made.

Different types of process mapping diagrams

There are several different types of process maps, each serving a specific purpose in visualizing and analyzing different aspects of a process. Some of the most common types include:

A flowchart is a visual representation of the steps and decisions in a process, using different shapes and arrows to illustrate the flow of activities.

Swimlane diagram

A swimlane diagram , also known as a cross-functional flowchart, shows the interactions between different departments, roles, or individuals involved in a process, often using lanes to denote each party’s responsibilities.

Value stream map

A value stream map focuses on understanding and improving the flow of materials and information in a specific process, with a particular emphasis on identifying non-value-added activities and reducing waste.

Business process model and notation (BPMN) diagram

BPMN diagrams use standardized symbols and notation to depict the sequence of activities, events, and decisions in a process, including the various participants and their interactions.

Gantt chart

While not exclusively a process mapping diagram, Gantt charts are often used to visualize the timeline and dependencies of tasks within a project or process, helping to plan and monitor progress effectively.

Process mapping templates and tools are important for a variety of reasons.

First and foremost, they provide a visual representation of a business or organizational process, allowing for a clear and detailed understanding of how tasks are completed and how information flows within the process. This visual representation can help to identify inefficiencies, redundancies, and bottlenecks within the process, and can be used to streamline and optimize workflow.

In addition to providing a visual representation, process mapping tools also allow for the documentation and standardization of processes. This can be incredibly valuable for businesses looking to maintain consistency and quality in their operations, as well as for those seeking to comply with industry regulations and standards. Standardized processes can also make it easier for new employees to quickly integrate into a team and understand their role within the larger context of the organization.

Process mapping tools can also facilitate communication and collaboration within an organization. By providing a clear and easily understandable representation of a process, these tools can help to align different teams and departments around shared goals and objectives, and can foster a sense of transparency and accountability within the organization.

Furthermore, process mapping tools can be used to support continuous improvement efforts within an organization. By providing a visual representation of a process, these tools can help to identify areas for improvement and encourage experimentation and innovation. This can result in a more agile and adaptable organization, better able to respond to changing market conditions and customer needs.

When choosing a process mapping software, it is important to consider the following features:

Intuitive user interface

The tool should have an easy-to-use interface that allows for drag-and-drop functionality and customizable symbols and shapes.

Collaboration capabilities

Look for a tool that allows for multiple users to work on the same process map simultaneously and offers real-time collaboration features.

Integration with other systems

Choose a tool that integrates with other software or systems, such as ERP, CRM, or BPM software for seamless data sharing and process automation.

Customization options

The tool should offer customization options for colors, styles, and templates to create professional-looking process maps.

Data visualization

Look for features that allow for data visualization, such as charts, diagrams, and graphs to represent process metrics and performance.

Version control and history tracking

The tool should provide version control and history tracking to keep track of changes made to process maps and allow for easy rollback if needed.

Security and permissions

Ensure that the tool offers security features such as role-based access control and data encryption to protect sensitive process information.

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Visualizations That Really Work

• Scott Berinato

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

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

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

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

Idea in Brief

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

Tools Are Fine…

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

…But Strategy Is Key

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

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

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

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Process mapping is a method that promotes a better understanding of processes and helps organizations identify areas for improvement.

Process mapping visually represents a workflow, allowing team to understand a process and its components more clearly. There are a variety of process maps, and you may know one by a different name, such as a flowchart, a detailed process map, a document map,  a high-level process map, a rendered process map, a swimlane, a value-added chain diagram, a value-stream map, a flow diagram, a process flowchart, a process model or a workflow diagram. These visual diagrams are usually a component of a company’s business process management (BPM).

A process map outlines the individual steps within a process, identifying task owners and detailing expected timelines. They are particularly helpful in communicating processes among stakeholders and revealing areas of improvement. Most process maps start at a macro level and then provide more detail as necessary.

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There are several different types of process maps. Some of mapping techniques include:

• Basic flowcharts are visual maps, which provides the basic details of a process such as inputs and outputs.
• Deployment maps , also known as cross-functional flowcharts, display the relationships between different teams. These maps often use swimlane diagrams to illustrate how a process flows across the company, making it easier to spot bottlenecks or redundancies.
• Detailed process maps show a drill-downed version of a process, containing details around any sub-processes.
• High-level process maps , also known as value-chain or top-down maps, show a macro view of a process, including key process elements such as a supplier, input, process, output, or customer (SIPOC).
• Rendered process maps represent a current state and/or future state processes to show areas for potential process improvement.
• A value stream map (VSM) is a lean six sigma technique, which documents the steps required to develop a product or service to an end user.

Process maps use visual representations, such as basic symbols to describe each element in the process. Some of the most common symbols are arrows, circles, diamonds, boxes, ovals and rectangles. These symbols can come from the Business Process Model and Notation (BPMN) or Unified Modeling Language (UML) (link resides outside IBM), which are graphical methods of notation for process maps.

Most organizations will need to use only a few of the most common symbols to complete a process map. Some of these symbols include:

• A rectangle is used to represent a specific process and its activities and functions.
• An arrow is used to show both the direction of flow and the connection between steps.
• An oval is often used to show the beginning or end points of a process flow.
• A diamond is used to indicate a decision point. The process will continue by following a predefined path depending on the decision.
• A rectangle with one end rounded is often used as a delay symbol, showing a pause in the process before the flow continues.

When developing your own business process map, you’ll want to leverage this methodology:

• Choose a process to focus on. To make the biggest impact, you may want to prioritize a process that’s struggling to achieve outcomes or a process that impacts customer satisfaction.
• Get the right people involved. Gather those who have deep knowledge of the process that you’re looking to optimize. These subject matter experts (SMEs) will help you determine the critical information within the entire process, such as stakeholders, sequence of steps, timelines, resources, etc. They can also highlight some of the problem areas, such as bottlenecks and redundancies, which may compromise efficiency. During this stage of the process, you want to document all relevant information around the process.  
• Outline the process map. During this step, you’ll want to determine where the current process starts and end and the sequence of steps in between. While the level of detail can vary, information around inputs, outputs, metrics, and stakeholders are typically included.
• Use basic flowchart symbols to enhance the process map. Refine the current process map by leveraging basic flowchart symbols. Process mapping software is generally used to complete this step.
• Get feedback. Validate the enhanced process map with team members to confirm accurate process documentation, ensuring that steps are not repeating or missing. When stakeholders have agreed on the process steps within the current state, start to solicit feedback around potential process optimizations. This can involve the elimination of steps for simplification purposes or the incorporation of new ones to allow for more collaboration or quality assurance.  
• Implement and observe the impact of process changes. Conduct a proof of concept (POC) with a subset of the team prior to scaling any changes broadly across the organization. This minimizes the risk to the business, and it provides the opportunity to incorporate additional feedback to optimize the process, allowing management to transition to a new process at scale more smoothly. Regular monitoring of processes will allow for continuous improvement over time.

The primary purpose of business process mapping is to assist organizations in becoming more efficient and effective at achieving a specific task or goal. It does this by providing greater transparency around decision-making and process flow which in turn helps to identify redundancies and bottlenecks within and between processes. Since process maps leverage visual cues and symbols, they make it easier to communicate a process to a broad audience. This can lead to increased engagement, as long-form documentation can be more tedious for both owners to create and for end users to consume.

By leveraging pre-made templates within process mapping software, teams can easily collaborate and brainstorm ways to streamline work processes, enabling business process improvement. In doing so, businesses can also better address specific challenges, such as employee onboarding and retention or declining sales.

Some specific benefits of process mapping include:

• Better enablement for scenario tests and assessments
• Increased standardization and awareness of roles and responsibilities
• Easier identification vulnerable aspects of a process
• Improved team performance and employee satisfaction
• Shorter learning curve for employees during training

Ditch the sticky notes on the way to seeing and understanding better business processes.

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Learn about process mining, a method of applying specialized algorithms to event log data to identify trends, patterns, and details of how a process unfolds.

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• Open access
• Published: 19 July 2015

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

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

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

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

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

Conclusions

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

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

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

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

Scientific practices and science education

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

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

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

Visualization as an epistemic object

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

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

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

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

Methods and Results: Merging Visualization and scientific practices in science

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

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

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

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

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

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

X-ray chrystallography of DNA

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Visual illustration of apparatus

Video processing of experimental set-up

Computer software for video input and variable recording

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

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

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

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

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

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

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

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

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

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

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

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

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

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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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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Decision making with visualizations: a cognitive framework across disciplines

• Lace M. Padilla   ORCID: orcid.org/0000-0001-9251-5279 1 , 2 ,
• Sarah H. Creem-Regehr 2 ,
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Visualizations—visual representations of information, depicted in graphics—are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns). However, findings from different domains are rarely shared across domains though there may be domain-general principles underlying visualizations and their use. The limited cross-domain communication may be due to a lack of a unifying cognitive framework. This review aims to address this gap by proposing an integrative model that is grounded in models of visualization comprehension and a dual-process account of decision making. We review empirical studies of decision making with static two-dimensional visualizations motivated by a wide range of research goals and find significant direct and indirect support for a dual-process account of decision making with visualizations. Consistent with a dual-process model, the first type of visualization decision mechanism produces fast, easy, and computationally light decisions with visualizations. The second facilitates slower, more contemplative, and effortful decisions with visualizations. We illustrate the utility of a dual-process account of decision making with visualizations using four cross-domain findings that may constitute universal visualization principles. Further, we offer guidance for future research, including novel areas of exploration and practical recommendations for visualization designers based on cognitive theory and empirical findings.

Significance

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

Introduction

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

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

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

Decision-making frameworks

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

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

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

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

Visualization cognition

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

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

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

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

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

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

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

A proposed integrated model of decision making with visualizations

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

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

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

Empirical studies of visualization decision making

Review method.

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

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

Bottom-up attention

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

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

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

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

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

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

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

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

• Visual-spatial biases

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

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

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

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

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

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

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

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

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

Cognitive fit

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

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

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

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

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

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

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

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

Either type 1 and/or 2

Knowledge-driven processing.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Review summary

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

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

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

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

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

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

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

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

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

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

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

Conclusions

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

Change history

02 september 2018.

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

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

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

Ancker, J. S., Senathirajah, Y., Kukafka, R., & Starren, J. B. (2006). Design features of graphs in health risk communication: A systematic review. Journal of the American Medical Informatics Association , 13 (6), 608–618.

Article   Google Scholar  

Baddeley, A. D., & Hitch, G. (1974). Working memory. Psychology of Learning and Motivation , 8 , 47–89.

Bailey, K., Carswell, C. M., Grant, R., & Basham, L. (2007). Geospatial perspective-taking: how well do decision makers choose their views? ​In  Proceedings of the Human Factors and Ergonomics Society Annual Meeting  (Vol. 51, No. 18, pp. 1246-1248). Los Angeles: SAGE Publications.

Balleine, B. W. (2007). The neural basis of choice and decision making. Journal of Neuroscience , 27 (31), 8159–8160.

Bandlow, A., Matzen, L. E., Cole, K. S., Dornburg, C. C., Geiseler, C. J., Greenfield, J. A., … Stevens-Adams, S. M. (2011). Evaluating Information Visualizations with Working Memory Metrics. In HCI International 2011–Posters’ Extended Abstracts , (pp. 265–269).

Chapter   Google Scholar  

Belia, S., Fidler, F., Williams, J., & Cumming, G. (2005). Researchers misunderstand confidence intervals and standard error bars. Psychological Methods , 10 (4), 389.

Bertin, J. (1983). Semiology of graphics: Diagrams, networks, maps . ​Madison: University of Wisconsin Press.

Boone, A., Gunalp, P., & Hegarty, M. (in press). Explicit versus Actionable Knowledge: The Influence of Explaining Graphical Conventions on Interpretation of Hurricane Forecast Visualizations. Journal of Experimental Psychology: Applied .

Brügger, A., Fabrikant, S. I., & Çöltekin, A. (2017). An empirical evaluation of three elevation change symbolization methods along routes in bicycle maps. Cartography and Geographic Information Science , 44 (5), 436–451.

Caffò, A. O., Picucci, L., Di Masi, M. N., & Bosco, A. (2011). Working memory components and virtual reorientation: A dual-task study. In Working memory: capacity, developments and improvement techniques , (pp. 249–266). Hauppage: Nova Science Publishers.

Google Scholar  

Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: using vision to think .  San Francisco: Morgan Kaufmann Publishers Inc.

Castro, S. C., Strayer, D. L., Matzke, D., & Heathcote, A. (2018). Cognitive Workload Measurement and Modeling Under Divided Attention. Journal of Experimental Psychology: General .

Cheong, L., Bleisch, S., Kealy, A., Tolhurst, K., Wilkening, T., & Duckham, M. (2016). Evaluating the impact of visualization of wildfire hazard upon decision-making under uncertainty. International Journal of Geographical Information Science , 30 (7), 1377–1404.

Connor, C. E., Egeth, H. E., & Yantis, S. (2004). Visual attention: Bottom-up versus top-down. Current Biology , 14 (19), R850–R852.

Cowan, N. (2017). The many faces of working memory and short-term storage. Psychonomic Bulletin & Review , 24 (4), 1158–1170.

Dennis, A. R., & Carte, T. A. (1998). Using geographical information systems for decision making: Extending cognitive fit theory to map-based presentations. Information Systems Research , 9 (2), 194–203.

Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: Oscillations and synchrony in top–down processing. Nature Reviews Neuroscience , 2 (10), 704–716.

Engle, R. W., Kane, M. J., & Tuholski, S. W. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. ​ In A. Miyake & P. Shah (Eds.),  Models of working memory: Mechanisms of active maintenance and executive control  (pp. 102-134). New York: Cambridge University Press.

Epstein, S., Pacini, R., Denes-Raj, V., & Heier, H. (1996). Individual differences in intuitive–experiential and analytical–rational thinking styles. Journal of Personality and Social Psychology , 71 (2), 390.

Evans, J. S. B. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology , 59 , 255–278.

Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science , 8 (3), 223–241.

Fabrikant, S. I., Hespanha, S. R., & Hegarty, M. (2010). Cognitively inspired and perceptually salient graphic displays for efficient spatial inference making. Annals of the Association of American Geographers , 100 (1), 13–29.

Fabrikant, S. I., & Skupin, A. (2005). Cognitively plausible information visualization. In Exploring geovisualization , (pp. 667–690). Oxford: Elsevier.

Fagerlin, A., Wang, C., & Ubel, P. A. (2005). Reducing the influence of anecdotal reasoning on people’s health care decisions: Is a picture worth a thousand statistics? Medical Decision Making , 25 (4), 398–405.

Feeney, A., Hola, A. K. W., Liversedge, S. P., Findlay, J. M., & Metcalf, R. (2000). How people extract information from graphs: Evidence from a sentence-graph verification paradigm. ​In  International Conference on Theory and Application of Diagrams  (pp. 149-161). Berlin, Heidelberg: Springer.

Frownfelter-Lohrke, C. (1998). The effects of differing information presentations of general purpose financial statements on users’ decisions. Journal of Information Systems , 12 (2), 99–107.

Galesic, M., & Garcia-Retamero, R. (2011). Graph literacy: A cross-cultural comparison. Medical Decision Making , 31 (3), 444–457.

Galesic, M., Garcia-Retamero, R., & Gigerenzer, G. (2009). Using icon arrays to communicate medical risks: Overcoming low numeracy. Health Psychology , 28 (2), 210.

Garcia-Retamero, R., & Galesic, M. (2009). Trust in healthcare. In Kattan (Ed.), Encyclopedia of medical decision making , (pp. 1153–1155). Thousand Oaks: SAGE Publications.

Gattis, M., & Holyoak, K. J. (1996). Mapping conceptual to spatial relations in visual reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition , 22 (1), 231.

PubMed   Google Scholar  

Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology , 62 , 451–482.

Gigerenzer, G., Todd, P. M., & ABC Research Group (2000). Simple Heuristics That Make Us Smart . ​Oxford: Oxford University Press.

Grounds, M. A., Joslyn, S., & Otsuka, K. (2017). Probabilistic interval forecasts: An individual differences approach to understanding forecast communication. Advances in Meteorology , 2017,  1-18.

Harel, J. (2015, July 24, 2012). A Saliency Implementation in MATLAB. Retrieved from http://www.vision.caltech.edu/~harel/share/gbvs.php

Hegarty, M. (2011). The cognitive science of visual-spatial displays: Implications for design. Topics in Cognitive Science , 3 (3), 446–474.

Hegarty, M., Canham, M. S., & Fabrikant, S. I. (2010). Thinking about the weather: How display salience and knowledge affect performance in a graphic inference task. Journal of Experimental Psychology: Learning, Memory, and Cognition , 36 (1), 37.

Hegarty, M., Friedman, A., Boone, A. P., & Barrett, T. J. (2016). Where are you? The effect of uncertainty and its visual representation on location judgments in GPS-like displays. Journal of Experimental Psychology: Applied , 22 (4), 381.

Hegarty, M., Smallman, H. S., & Stull, A. T. (2012). Choosing and using geospatial displays: Effects of design on performance and metacognition. Journal of Experimental Psychology: Applied , 18 (1), 1.

Hoffrage, U., & Gigerenzer, G. (1998). Using natural frequencies to improve diagnostic inferences. Academic Medicine , 73 (5), 538–540.

Hollands, J. G., & Spence, I. (1992). Judgments of change and proportion in graphical perception. Human Factors: The Journal of the Human Factors and Ergonomics Society , 34 (3), 313–334.

Huang, Z., Chen, H., Guo, F., Xu, J. J., Wu, S., & Chen, W.-H. (2006). Expertise visualization: An implementation and study based on cognitive fit theory. Decision Support Systems , 42 (3), 1539–1557.

Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence , 20 (11), 1254–1259.

Joslyn, S., & LeClerc, J. (2013). Decisions with uncertainty: The glass half full. Current Directions in Psychological Science , 22 (4), 308–315.

Kahneman, D. (2011). Thinking, fast and slow . (Vol. 1). New York: Farrar, Straus and Giroux.

Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In Heuristics and biases: The psychology of intuitive judgment , (p. 49).

Kahneman, D., & Tversky, A. (1982). Judgment under Uncertainty: Heuristics and Biases , (1st ed., ). Cambridge; NY: Cambridge University Press.

Book   Google Scholar  

Kane, M. J., Bleckley, M. K., Conway, A. R. A., & Engle, R. W. (2001). A controlled-attention view of working-memory capacity. Journal of Experimental Psychology: General , 130 (2), 169.

Keehner, M., Mayberry, L., & Fischer, M. H. (2011). Different clues from different views: The role of image format in public perceptions of neuroimaging results. Psychonomic Bulletin & Review , 18 (2), 422–428.

Keller, C., Siegrist, M., & Visschers, V. (2009). Effect of risk ladder format on risk perception in high-and low-numerate individuals. Risk Analysis , 29 (9), 1255–1264.

Keren, G., & Schul, Y. (2009). Two is not always better than one: A critical evaluation of two-system theories. Perspectives on Psychological Science , 4 (6), 533–550.

Kinkeldey, C., MacEachren, A. M., Riveiro, M., & Schiewe, J. (2017). Evaluating the effect of visually represented geodata uncertainty on decision-making: Systematic review, lessons learned, and recommendations. Cartography and Geographic Information Science , 44 (1), 1–21. https://doi.org/10.1080/15230406.2015.1089792 .

Kinkeldey, C., MacEachren, A. M., & Schiewe, J. (2014). How to assess visual communication of uncertainty? A systematic review of geospatial uncertainty visualisation user studies. The Cartographic Journal , 51 (4), 372–386.

Kriz, S., & Hegarty, M. (2007). Top-down and bottom-up influences on learning from animations. International Journal of Human-Computer Studies , 65 (11), 911–930.

Kunz, V. (2004). Rational choice . Frankfurt: Campus Verlag.

Lallanilla, M. (2014, April 24, 2014 10:15 am). Misleading Gun-Death Chart Draws Fire. https://www.livescience.com/45083-misleading-gun-death-chart.html

Lee, J., & Bednarz, R. (2009). Effect of GIS learning on spatial thinking. Journal of Geography in Higher Education , 33 (2), 183–198.

Liu, L., Boone, A., Ruginski, I., Padilla, L., Hegarty, M., Creem-Regehr, S. H., … House, D. H. (2016). Uncertainty Visualization by Representative Sampling from Prediction Ensembles.  IEEE transactions on visualization and computer graphics, 23 (9), 2165-2178.

Lobben, A. K. (2004). Tasks, strategies, and cognitive processes associated with navigational map reading: A review perspective. The Professional Geographer , 56 (2), 270–281.

Lohse, G. L. (1993). A cognitive model for understanding graphical perception. Human Computer Interaction , 8 (4), 353–388.

Lohse, G. L. (1997). The role of working memory on graphical information processing. Behaviour & Information Technology , 16 (6), 297–308.

Marewski, J. N., & Gigerenzer, G. (2012). Heuristic decision making in medicine. Dialogues in Clinical Neuroscience , 14 (1), 77–89.

PubMed   PubMed Central   Google Scholar  

McCabe, D. P., & Castel, A. D. (2008). Seeing is believing: The effect of brain images on judgments of scientific reasoning. Cognition , 107 (1), 343–352.

McKenzie, G., Hegarty, M., Barrett, T., & Goodchild, M. (2016). Assessing the effectiveness of different visualizations for judgments of positional uncertainty. International Journal of Geographical Information Science , 30 (2), 221–239.

Mechelli, A., Price, C. J., Friston, K. J., & Ishai, A. (2004). Where bottom-up meets top-down: Neuronal interactions during perception and imagery. Cerebral Cortex , 14 (11), 1256–1265.

Meilinger, T., Knauff, M., & Bülthoff, H. H. (2008). Working memory in wayfinding—A dual task experiment in a virtual city. Cognitive Science , 32 (4), 755–770.

Meyer, J. (2000). Performance with tables and graphs: Effects of training and a visual search model. Ergonomics , 43 (11), 1840–1865.

Munzner, T. (2014). Visualization analysis and design . Boca Raton, FL: CRC Press.

Nadav-Greenberg, L., Joslyn, S. L., & Taing, M. U. (2008). The effect of uncertainty visualizations on decision making in weather forecasting. Journal of Cognitive Engineering and Decision Making , 2 (1), 24–47.

Nayak, J. G., Hartzler, A. L., Macleod, L. C., Izard, J. P., Dalkin, B. M., & Gore, J. L. (2016). Relevance of graph literacy in the development of patient-centered communication tools. Patient Education and Counseling , 99 (3), 448–454.

Newman, G. E., & Scholl, B. J. (2012). Bar graphs depicting averages are perceptually misinterpreted: The within-the-bar bias. Psychonomic Bulletin & Review , 19 (4), 601–607. https://doi.org/10.3758/s13423-012-0247-5 .

Okan, Y., Galesic, M., & Garcia-Retamero, R. (2015). How people with low and high graph literacy process health graphs: Evidence from eye-tracking. Journal of Behavioral Decision Making .

Okan, Y., Garcia-Retamero, R., Cokely, E. T., & Maldonado, A. (2012). Individual differences in graph literacy: Overcoming denominator neglect in risk comprehension. Journal of Behavioral Decision Making , 25 (4), 390–401.

Okan, Y., Garcia-Retamero, R., Galesic, M., & Cokely, E. T. (2012). When higher bars are not larger quantities: On individual differences in the use of spatial information in graph comprehension. Spatial Cognition and Computation , 12 (2–3), 195–218.

Padilla, L., Hansen, G., Ruginski, I. T., Kramer, H. S., Thompson, W. B., & Creem-Regehr, S. H. (2015). The influence of different graphical displays on nonexpert decision making under uncertainty. Journal of Experimental Psychology: Applied , 21 (1), 37.

Padilla, L., Quinan, P. S., Meyer, M., & Creem-Regehr, S. H. (2017). Evaluating the impact of binning 2d scalar fields. IEEE Transactions on Visualization and Computer Graphics , 23 (1), 431–440.

Padilla, L., Ruginski, I. T., & Creem-Regehr, S. H. (2017). Effects of ensemble and summary displays on interpretations of geospatial uncertainty data. Cognitive Research: Principles and Implications , 2 (1), 40.

Pashler, H. (1994). Dual-task interference in simple tasks: Data and theory. Psychological Bulletin , 116 (2), 220.

Patterson, R. E., Blaha, L. M., Grinstein, G. G., Liggett, K. K., Kaveney, D. E., Sheldon, K. C., … Moore, J. A. (2014). A human cognition framework for information visualization. Computers & Graphics , 42 , 42–58.

Pinker, S. (1990). A theory of graph comprehension. In Artificial intelligence and the future of testing , (pp. 73–126).

Ratliff, K. R., & Newcombe, N. S. (2005). Human spatial reorientation using dual task paradigms . Paper presented at the Proceedings of the Annual Cognitive Science Society.

Reyna, V. F., Nelson, W. L., Han, P. K., & Dieckmann, N. F. (2009). How numeracy influences risk comprehension and medical decision making. Psychological Bulletin , 135 (6), 943.

Riveiro, M. (2016). Visually supported reasoning under uncertain conditions: Effects of domain expertise on air traffic risk assessment. Spatial Cognition and Computation , 16 (2), 133–153.

Rodríguez, V., Andrade, A. D., García-Retamero, R., Anam, R., Rodríguez, R., Lisigurski, M., … Ruiz, J. G. (2013). Health literacy, numeracy, and graphical literacy among veterans in primary care and their effect on shared decision making and trust in physicians. Journal of Health Communication , 18 (sup1), 273–289.

Rosenholtz, R., & Jin, Z. (2005). A computational form of the statistical saliency model for visual search. Journal of Vision , 5 (8), 777–777.

Ruginski, I. T., Boone, A. P., Padilla, L., Liu, L., Heydari, N., Kramer, H. S., … Creem-Regehr, S. H. (2016). Non-expert interpretations of hurricane forecast uncertainty visualizations. Spatial Cognition and Computation , 16 (2), 154–172.

Sanchez, C. A., & Wiley, J. (2006). An examination of the seductive details effect in terms of working memory capacity. Memory & Cognition , 34 (2), 344–355.

Schirillo, J. A., & Stone, E. R. (2005). The greater ability of graphical versus numerical displays to increase risk avoidance involves a common mechanism. Risk Analysis , 25 (3), 555–566.

Shah, P., & Freedman, E. G. (2011). Bar and line graph comprehension: An interaction of top-down and bottom-up processes. Topics in Cognitive Science , 3 (3), 560–578.

Shah, P., Freedman, E. G., & Vekiri, I. (2005). The Comprehension of Quantitative Information in Graphical Displays . In P. Shah (Ed.) & A. Miyake, The Cambridge Handbook of Visuospatial Thinking (pp. 426-476). New York: Cambridge University Press.

Shah, P., & Miyake, A. (1996). The separability of working memory resources for spatial thinking and language processing: An individual differences approach. Journal of Experimental Psychology: General , 125 (1), 4.

Shen, M., Carswell, M., Santhanam, R., & Bailey, K. (2012). Emergency management information systems: Could decision makers be supported in choosing display formats? Decision Support Systems , 52 (2), 318–330.

Shipstead, Z., Harrison, T. L., & Engle, R. W. (2015). Working memory capacity and the scope and control of attention. Attention, Perception, & Psychophysics , 77 (6), 1863–1880.

Simkin, D., & Hastie, R. (1987). An information-processing analysis of graph perception. Journal of the American Statistical Association , 82 (398), 454–465.

Sloman, S. A. (2002). Two systems of reasoning. ​ In T. Gilovich, D. Griffin, & D. Kahneman (Eds.),  Heuristics and biases : The psychology of intuitive judgment (pp. 379-396). New York: Cambridge University Press.

Smelcer, J. B., & Carmel, E. (1997). The effectiveness of different representations for managerial problem solving: Comparing tables and maps. Decision Sciences , 28 (2), 391.

St. John, M., Cowen, M. B., Smallman, H. S., & Oonk, H. M. (2001). The use of 2D and 3D displays for shape-understanding versus relative-position tasks. Human Factors , 43 (1), 79–98.

Stanovich, K. E. (1999). Who is rational? Studies of individual differences in reasoning . New York City: Psychology Press.

Stenning, K., & Oberlander, J. (1995). A cognitive theory of graphical and linguistic reasoning: Logic and implementation. Cognitive Science , 19 (1), 97–140.

Stone, E. R., Sieck, W. R., Bull, B. E., Yates, J. F., Parks, S. C., & Rush, C. J. (2003). Foreground: Background salience: Explaining the effects of graphical displays on risk avoidance. Organizational Behavior and Human Decision Processes , 90 (1), 19–36.

Stone, E. R., Yates, J. F., & Parker, A. M. (1997). Effects of numerical and graphical displays on professed risk-taking behavior. Journal of Experimental Psychology: Applied , 3 (4), 243.

Trueswell, J. C., & Papafragou, A. (2010). Perceiving and remembering events cross-linguistically: Evidence from dual-task paradigms. Journal of Memory and Language , 63 (1), 64–82.

Tversky, B. (2005). Visuospatial reasoning. In K. Holyoak and R. G. Morrison (eds.), The Cambridge Handbook of Thinking and Reasoning , (pp. 209-240). Cambridge: Cambridge University Press.

Tversky, B. (2011). Visualizing thought. Topics in Cognitive Science , 3 (3), 499–535.

Tversky, B., Corter, J. E., Yu, L., Mason, D. L., & Nickerson, J. V. (2012). Representing Category and Continuum: Visualizing Thought . Paper presented at the International Conference on Theory and Application of Diagrams, Berlin, Heidelberg.

Vessey, I., & Galletta, D. (1991). Cognitive fit: An empirical study of information acquisition. Information Systems Research , 2 (1), 63–84.

Vessey, I., Zhang, P., & Galletta, D. (2006). The theory of cognitive fit. In Human-computer interaction and management information systems: Foundations , (pp. 141–183).

Von Neumann, J. (1953). Morgenstern, 0.(1944) theory of games and economic behavior . Princeton, NJ: Princeton UP.

Vranas, P. B. M. (2000). Gigerenzer's normative critique of Kahneman and Tversky. Cognition , 76 (3), 179–193.

Wainer, H., Hambleton, R. K., & Meara, K. (1999). Alternative displays for communicating NAEP results: A redesign and validity study. Journal of Educational Measurement , 36 (4), 301–335.

Waters, E. A., Weinstein, N. D., Colditz, G. A., & Emmons, K. (2006). Formats for improving risk communication in medical tradeoff decisions. Journal of Health Communication , 11 (2), 167–182.

Waters, E. A., Weinstein, N. D., Colditz, G. A., & Emmons, K. M. (2007). Reducing aversion to side effects in preventive medical treatment decisions. Journal of Experimental Psychology: Applied , 13 (1), 11.

Wilkening, J., & Fabrikant, S. I. (2011). How do decision time and realism affect map-based decision making? Paper presented at the International Conference on Spatial Information Theory.

Zhu, B., & Watts, S. A. (2010). Visualization of network concepts: The impact of working memory capacity differences. Information Systems Research , 21 (2), 327–344.

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

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

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

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

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

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

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

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28 Process Infographic Examples with Design Tips

By Sara McGuire , May 08, 2020

Have you ever tried to summarize a complex process, or break one down into clear, concise steps for others to follow? It’s not always easy, but visuals can help. Specifically, designing a process infographic.

In this guide, we’ll share 28 customizable process infographic templates and design tips to help you get started, even if you’re a total beginner.

What is a process infographic?

Process infographics are a specific type of infographic , designed to visualize, summarize and simplify processes. They’re perfect for visualizing marketing strategies, new employee onboarding, customer journey maps, product guides, recipes and more.

Process infographic examples & design tips

• Use a circle layout to show a cyclical process
• Follow a simple step-by-step layout for straightforward processes
• Save space in your process infographic template by using an S-shaped layout
• Visualize steps with icons and illustrations
• Incorporate images to visualize your process
• Create a mind map for processes that don’t follow a specific order
• Color code phases of your process
• Pick fonts that reflect the theme of your process infographic
• Use arrows to give your process infographic template flow
• Use a flow chart to visualize a workflow or a process with multiple paths
• Create an infographic that lists the tools needed for a process

How to use a process infographic template:

• Click the infographic template that fits the process you want to visualize. Some templates are free, some require a small fee to use.
• You’ll enter our process infographics maker, an online drag and drop tool that’s perfect for design newbies.
• Add, remove, and rearrange steps in the infographic.
• Paste your own text and import your own images into the infographic.
• Customize the  colors , fonts and  icons  to make the infographic design your own.

Here’s a sneak peak of what the Venngage infographic editor looks like:

Keep reading for our 28 process infographic templates you can customize right now using Venngage’s intuitive online tool.

1. Use a circle layout to show a cyclical process

If you have a process that repeats, or one that involves regular iteration and improvement, then a circle infographic layout can work well. Clearly indicate where the “starting” point it and use numbered steps or arrows to indicate that direction the process follows.

For example, take a look at this circular process infographic template for product design:

If your process is more of a chicken-and-egg situation, then a circle with no break makes more sense. Take a look at how this cyclical process infographic template uses different colors to help each step stand out on its own:

This process infographic template is another example of using circles as a guide for your clients:

2. Follow a simple step-by-step layout for straightforward processes

Sometimes, it’s best to not overcomplicate your design. If you want to visualize a straightforward process, then a simple infographic with numbered steps can be very effective.

Use a clear, bold header for your infographic. Then, clearly number your steps. To emphasize each step, you may want to use a different color for the numbers. Take a look at how the orange numbers stand out against the blue background in this process infographic template:

Brand your infographic using Venngage’s in-editor My Brand Kit tool–just click any of our templates to access this feature. Add your brand colors with one click or import your business or consulting logo.

Or take a look at how this writing process infographic template uses a simple color gradient to visualize progress. Color should be used strategically in your infographic to make the information easier to understand:

At times, your infographic may have a lot of information. The easiest way to simplify the information so that it’s not overwhelming is to add clear steps. This infographic uses colors, sections, and numbered steps to create a seamless infographic:

Another example is using color shades to flip between each step of the infographic. This keeps the infographic more engaging and easy to follow:

3. Save space in your process infographic template by using an S-shaped layout

You only have so much space on your page. A hack for fitting more steps into one infographic is to use an S-shaped layout (you could also call this a road layout or a snake layout).

The S layout in this process infographic template looks like a winding road. Perfect for visualizing a customer journey:

Click the template above to enter our online customer journey map maker. Customize the template to your liking–no design experience required.

4. Visualize steps with icons and illustrations

At Venngage, we’re big fans of icons. In fact, we have a library of over 40,000 icons which you can use in your infographics. In the process infographic template below, icons are used to illustrate each step of a lengthy process. This not only adds visual interest, it prevents the design from becoming too text heavy.

The recipe infographic below similarly showcases its ingredients with illustrated icons:

In addition to using icons on their own, you can also combine icons to create your own custom illustrations. Take this job hiring process infographic template, for example, which uses icon illustrations to visualize what each phase of the process entails:

If you don’t have to resources to hire a graphic designer, you might be at a loss for how you can create your own illustrations.

That’s where icons can be a lifesaver. Simply search for icons depicting the different pieces of the scene you have in mind. Then, arrange them into a scene the way you would arrange pieces of a collage.

For example, look at how convincing the illustrations are in this hack infographic:

This process infographic highlights the best technology practices in the classroom. The 3D looking icons jump off the infographic and grab your attention before anything else:

5. Incorporate images to visualize your process

While icons are awesome visuals to include in your infographics, sometimes they aren’t quite realistic enough. That’s why you may want to include more realistic images to illustrate important information.

To incorporate images into your design, look for images that have a transparent background. That way, the background of the image won’t stick out from the background of your infographic.

If an image you want to use doesn’t have a transparent background, you can incorporate it into you design by using a border around the image or an image frame .

For example, this recipe infographic uses images to illustrate what each of the ingredients are:

It’s also useful to include a picture of the results of your process. Here’s another example of a recipe infographic, this time with a shot of the finished dish:

But this tip doesn’t just have to apply to a recipe. You could include a a mock-up of a finished product or an image of a picture depicting your goals being hit.

This process infographic uses stock photos to help stress its points:

6. Create a mind map for processes that don’t follow a specific order

A mind map connects ideas and shows where different ideas branch out.

Maybe you want to show a broad overview of a process. Or maybe the process you’re visualizing doesn’t follow a specific order. In both cases, a mind map can show readers options for steps they can take. (Plus, Venngage makes it easy to create a mind map in a pinch).

For example, this social media marketing  infographic template shows multiple options you could tackle first. A brief description under the mind map offers some important contextual information:

A mind map infographic is also a great way to share key information that readers should keep top-of-mind as they carry out a process. For example, this infographic visualizes six important tips for writing compelling emails:

7. Color code phases of your process

When it comes to visualizing information, your color choices should be about more than just looking nice. You should think about how colors can make information easier to understand.

For example, you can color code different phases or steps in your process. This will help make your process easier to follow, and will show how particular steps are grouped together.

For example, this hiring process infographic template uses different colors to sort the process into three sections:

You could also customize the above template to visualize your company’s employee onboarding process.

Using bright colors to brighten an otherwise boring process is a good way to highlight each step of a process. This hiring process infographic is grey but uses color to bring out each step to draw your attention:

Here’s another example of color coding steps in a process:

8. Pick fonts that reflect the theme of your process infographic

The fonts you choose can affect how the information in your infographic is perceived. Certain fonts look more old fashioned, like serif fonts or script fonts. Meanwhile, certain fonts look more modern and forward-thinking.

Think about who the audience of your infographic will be, and what mood you want your infographic to convey.

For example, are you visualizing a process for your customers? In that case, you may want to use a font that looks friendly and approachable. Or perhaps you want to use a more traditional font to show that your company is reliable.

The header font for each step in this design process infographic template is friendly and a bit playful. In this case, this reflects the idea of making clients happy:

This infographic looks more innovative and technical, doesn’t it?

9. Use arrows to give your process infographic template flow

In design, visual cues are things like arrows, images of fingers pointing, or images where someone is looking in a specific direction. Visual cues help to direct how people read your infographic.

When designing your process infographic, think about how you want your information to flow on the page. You can create flow by connecting steps in a process with a line, or by using arrows to point readers towards the next step.

Take a look at how arrows help the information in this process infographic flow:

The arrows used in this process infographic guides your eyes visually without needing to read the information, the text is used adds details to the information:

Here’s another example of an infographic that uses arrows. The arrows make it possible for the steps to jump back and forth across the page while still making sense:

Or, for a more subtle approach, you could incorporate an arrow shape into your section backgrounds. Like in this process infographic template, where each section “points” to the next:

Another subtle example of arrows being used to guide the eyes is with this e-learning infographic:

10. Use a flow chart to visualize a workflow or a process with multiple paths

Here’s an example of a simplified process flow chart for inbound marketing :

A flow chart can make processes with multiple paths and supporting processes easier to follow. They’re also handy for simplifying workflows and breaking complex processes down into steps.

Typically, a flow chart uses a box (or other shape) to visualize a step in a process, with lines or arrows pointing to the next step.

11. Create an infographic that lists the tools needed for a process

Infographics make for great cheat sheets. Why not also create an infographic to remind readers about tools they will need to successfully see a process through?

A simple list infographic layout works well for this. You can use icons or images to illustrate each tool, to help eliminate confusion. Here’s one example:

Here’s another beginner-friendly infographic template you can use to show a list. Here’s a creative design hack: use icons in the place of bullet points:

Process infographic templates

If you’re working within a tight design budget–and you don’t have much design experience yourself–designing an infographic might seem kind of intimidating.

That’s why it’s a good idea to start with a  process infographic template , like this one:

More infographic design guides for your everyday needs:

• 40+ Timeline Templates, Examples and Design Tips
• 30+ Comparison Infographic Templates for Product, Marketing & More (+ Design Tips)
• 12 Survey Infographic Templates and Essential Data Visualization Tips
• 25+ Statistical Infographic Templates To Help Visualize Your Data

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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.

Access your free e-book today.

What Is Data Visualization?

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

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

Data Visualization Techniques

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

Here are some important data visualization techniques to know:

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

1. Pie Chart

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

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

2. Bar Chart

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

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

3. Histogram

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

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

4. Gantt Chart

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

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

5. Heat Map

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

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

6. A Box and Whisker Plot

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

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

7. Waterfall Chart

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

8. Area Chart

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

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

9. Scatter Plot

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

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

10. Pictogram Chart

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

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

11. Timeline

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

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

12. Highlight Table

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

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

13. Bullet Graph

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

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

14. Choropleth Maps

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

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

15. Word Cloud

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

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

16. Network Diagram

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

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

17. Correlation Matrix

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

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

Other Data Visualization Options

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

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

Tips For Creating Effective Visualizations

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

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

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

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

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

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

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

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

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

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

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5. Visual Representation

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

Introduction to Visual Representation by Alan Blackwell

Alan Blackwell on applying theories of Visual Representation

• 5.1 Typography and text

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

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

Author/Copyright holder: Unknown (pending investigation). Copyright terms and licence: Unknown (pending investigation). See section "Exceptions" in the copyright terms below.

Figure 5.1 : Contemporary example from the grid system website

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

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

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

• 5.1.1 Summary

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

thegridsystem.org

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

• 5.2 Maps and graphs

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

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

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

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

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

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

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

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

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

Author/Copyright holder: Courtesy of Wikipedia. Copyright terms and licence: Unknown (pending investigation). See section "Exceptions" in the copyright terms below.

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

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

Figure 5.11 : The Whirlwind computer at the MIT Lincoln Laboratory

• 5.2.1 Summary

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

Where to learn more:

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

• 5.3 Schematic drawings

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

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

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

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

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

• 5.3.1 Summary

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

Engineering draughting textbooks

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

• 5.4 Pictures

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

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

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

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

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

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

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

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

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

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

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

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

• 5.4.1 Summary

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

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

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

• 5.5 Node-and-link diagrams

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

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

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

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

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

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

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

• 5.5.1 Summary

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

Diagrammatic representation books

Lowe , Ric (1992): Successful Instructional Diagram.

• 5.6 Icons and symbols

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

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

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

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

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

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

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

• 5.6.1 Summary

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

Napoles , Veronica (1987): Corporate Identity Design.

• 5.7 Visual metaphor

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

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

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

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

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

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

Figure 5.26 : Apple Lisa

• 5.7.1 Summary

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

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

• 5.8 Unified theories of visual representation

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

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

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

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

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

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

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

• 5.9 Where to learn more

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

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

• 5.9.0.1 IV - International Conference on Information Visualization

2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998

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

2008 2006 2004 2002 2000

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

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

• 5.9.0.4 InfoVis - IEEE Symposium on Information Visualization

2005 2004 2003 2002 2001 2000 1999 1998 1997 1995

• 5.10 References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• 5 Visual Representation

Human-Computer Interaction: The Foundations of UX Design

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5.10 commentary by ben shneiderman.

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

5.11 Commentary by Clive Richards

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

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

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

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

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

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

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

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

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

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

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

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

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

5.12 Commentary by Peter C-H. Cheng

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

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

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

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

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

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

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

5.13 Commentary by Brad A. Myers

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

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

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• Visual Representation of Process

In most organisations, you will find that while they have a process, nobody seems to know it exactly, or even where to go to find it. The problem, it seems is with the way in which processes are documented. Process documents are usually lamented over at the time of their writing, then shelved without much thought at all. The reason for this I believe is that there is primarily only two times when a process document is actually referenced:

• When a new employee joins the team, and is shown how things are done
• When a higher manager asks “how does your team operate”

In my mind, I would much prefer a simpler process flow that is actually used by staff, even if it doesn’t cover every possible eventuality along the way. The visual process document provides the most effective way of presenting the flow of how we go about completing our tasks. Its typically printable on one page (though it might have to be A3), it’s pinnable to your office cubicle, and sometimes as importantly, can be pasted into powerpoint presentations for the business.

So how do you present your testing process?

About the Author

Joel Deutscher is an experienced performance test consultant, passionate about continuous improvement. Joel works with Planit's Technical Testing Services as a Principal Consultant in Sydney, Australia. You can read more about Joel on LinkedIn .

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Joel Deutscher is an experienced performance test consultant, passionate about continuous improvement. Joel works with Planit's Technical Testing Services as a Principal Consultant in Sydney, Australia.

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Visual Representation of Design Process: Research Projects in Communication Design

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• Daniela Oliveira 10 ,
• Daniel Raposo 9 , 10 ,
• José Silva 9 , 10 &
• João Neves 9 , 10  

Part of the book series: Springer Series in Design and Innovation ((SSDI,volume 12))

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This study results of the application of the visual thinking methods through a non-interventionist methodology divided in two steps: exploratory interviews and exploratory descriptive diagrams. The research in this field has as main objectives to demonstrate that using visual thinking methods can help in a holistic comprehension of the methods and work processes of the projects, to simplify the complex information of each project and find key concepts common to all of the case studies. This research emerges because of the struggle in assess a detailed holistic perspective about the projects and his components, as the structure and organization of information during the project, since that the designer works in several projects at the same time. In this way, the study intends to evidence the importance that the organization of the information and his visual representation assumes in the development of the new knowledge, in the establishment of relations through the holistic view that visual thinking methods provides.

Furthermore, this study demonstrate that visual thinking is fundamental to a better comprehension of the structure and organization of the design process, as well as powerful tool to achieve findings in design and project methodology fields.

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Cash, P., Stanković, T., Štorga, M.: Using visual information analysis to explore complex patterns in the activity of designers. Des. Stud. 35 , 1–28 (2014). https://doi.org/10.1016/j.destud.2013.06.001

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Edkins, A.J., Kurul, E., Maytorena-Sanchez, E., Rintala, K.: The application of cognitive mapping methodologies in project management research. Int. J. Proj. Manag. 25 , 762–772 (2007). https://doi.org/10.1016/j.ijproman.2007.04.003

Eppler, M.J.: A comparison between concept maps, mind maps, conceptual diagrams, and visual metaphors as complementary tools for knowledge construction and sharing. Inf. Vis. 5 , 202–210 (2006). https://doi.org/10.1057/palgrave.ivs.9500131

Papanek, V.: Design for the Real World: Human Ecology and Social Change. Thames & Hudson, London (1985)

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Rodríguez Estrada, F.C., Davis, L.S.: Improving visual communication of science through the incorporation of graphic design theories and practices into science communication. Sci. Commun. 37 , 140–148 (2015). https://doi.org/10.1177/1075547014562914

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Oliveira, D., Raposo, D., Silva, J., Neves, J. (2021). Visual Representation of Design Process: Research Projects in Communication Design. In: Martins, N., Brandão, D. (eds) Advances in Design and Digital Communication . Digicom 2020. Springer Series in Design and Innovation , vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-61671-7_56

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Initial Thoughts

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

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

What evidence-based mathematics practices can teachers employ?

• Page 4: Explicit, Systematic Instruction

Page 5: Visual Representations

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

Research Shows

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

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

How does this practice align?

High-leverage practice (hlp).

• HLP15 : Provide scaffolded supports

CCSSM: Standards for Mathematical Practice

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

Number Lines

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

Common Uses : addition, subtraction, counting

Strip Diagrams

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

Common Uses : addition, fractions, proportions, ratios

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

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

Graphs/Charts

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

Common Uses : comparing numbers, statistics, ratios, algebra

Graphic Organizers

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

Common Uses : algebra, geometry

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

Elementary Example

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

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

High School Example

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

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

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

Manipulatives

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

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

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

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

Concrete-Representational-Abstract Framework

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

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

For Your Information

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

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

Kim Paulsen, EdD Associate Professor, Special Education Vanderbilt University

View Transcript

Transcript: Kim Paulsen, EdD

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

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

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Quantitative Biology > Neurons and Cognition

Title: cocog: controllable visual stimuli generation based on human concept representations.

Abstract: A central question for cognitive science is to understand how humans process visual objects, i.e, to uncover human low-dimensional concept representation space from high-dimensional visual stimuli. Generating visual stimuli with controlling concepts is the key. However, there are currently no generative models in AI to solve this problem. Here, we present the Concept based Controllable Generation (CoCoG) framework. CoCoG consists of two components, a simple yet efficient AI agent for extracting interpretable concept and predicting human decision-making in visual similarity judgment tasks, and a conditional generation model for generating visual stimuli given the concepts. We quantify the performance of CoCoG from two aspects, the human behavior prediction accuracy and the controllable generation ability. The experiments with CoCoG indicate that 1) the reliable concept embeddings in CoCoG allows to predict human behavior with 64.07\% accuracy in the THINGS-similarity dataset; 2) CoCoG can generate diverse objects through the control of concepts; 3) CoCoG can manipulate human similarity judgment behavior by intervening key concepts. CoCoG offers visual objects with controlling concepts to advance our understanding of causality in human cognition. The code of CoCoG is available at \url{ this https URL }.

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