In a rapidly evolving field of augmented reality, driverless cars, and rich social media, is there any more mundane way to start a book on user interfaces than history? Probably not. And yet, for a medium that is invented entirely from imagination, the history of user interfaces is reminder that the interactive world we experience today could have been different. Files, keyboards, mice, and touchscreens—all of these concepts were invented to solve very real challenges with interacting with computers, and but for a few important people and places in the 20th century, we might have invented entirely different ways of interacting with computers ^{2 2}

Stuart K. Card, Thomas P. Moran (1986). User technology: From pointing to pondering. ACM Conference on the History of Personal Workstations.

Computing began as an idea: Charles Babbage, a mathematician, philosopher, and inventor in London in the early 19th century, began imagining a kind of mechanical machine that could automatically calculate mathematical formulas. He called it the “Analytical Engine”, and conceived of it as a device that would encode instructions for calculations. Taking arbitrary data encoded as input on punch cards, this engine would be able to quickly and automatically calculate formulas much faster than people. And indeed, at the time, people were the bottleneck: if a banker wanted a list of numbers to be added, they needed to hire a computer—a person that quickly and correctly performed arithmetic—to add them. Baggage’s “Analytical Engine” promised to do the work of (human) computers much more quickly and accurately. Babbage later mentored Ada Lovelace, the daughter of a wealthy aristocrat; Lovelace loved the beauty of mathematics, and was enamored by Babbage’s vision, publishing the first algorithms intended to executed by such a machine. Thus began the age of computing, shaped largely by values of profit but also the beauty of mathematics.

While no one ever succeeded in making a  mechanical  analytical engine, one hundred years later, electronic technology made  digital  ones possible ^{3 3} James Gleick and Rob Shapiro (2011). The Information . But Babbage’s vision of using punch cards for input remained intact: if you used a computer, it meant constructing programs out of individual machine instructions like add, subtract, or jump, and encoding them onto punch cards to be read by a mainframe. And rather than being driven by a desire to lower costs and increase profit in business, these computers were instruments of war, decrypting German secret messages and more quickly calculating ballistic trajectories.

As wars came to a close, some found the vision of computers as business and war machines too limiting.  Vannevar Bush , a science administrator who headed the U.S. Office of Scientific Research and Development, had a much bigger vision. He wrote a 1945 article in  The Atlantic Monthly , called “As We May Think.” In it, he envisioned a device called the “Memex” in which people could store all of their information, including books, records, and communications, and access them with speed and flexibility ^{1 1}

Vannevar Bush (1945). As we may think. The Atlantic Monthly, 176(1), 101-108.

Here is how Bush described the Memex:

Does this remind you of anything? The internet, hyperlinks, Wikipedia, networked databases, social media. All of it is there in this prescient description of human augmentation, including rich descriptions of the screens, levers, and other controls for accessing information.

J.C.R. Licklider, an American psychologist and computer scientist, was fascinated by Bush’s vision, writing later in his “Man-Computer Symbiosis” ^{4 4}

J. C. R. Licklider (1960). Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics.

He then talked about visions of a “thinking center” that “ will incorporate the functions of present-day libraries together with anticipated advances in information storage and retrieval  ,” connecting individuals and computers through desk displays, wall displays, speech production and recognition, and other forms of artificial intelligence. In his role in the United States Department of Defense Advanced Research Projects Agency and as a professor at MIT, Licklider funded and facilitated the research that eventually led to the internet and the graphical user interface.

One person that Bush and Licklider’s ideas influenced was MIT computer scientist  Ivan Sutherland . He followed this vision of human augmentation by exploring interactive sketching on computers, working on a system called Sketchpad for his dissertation ^{7 7}

Ivan Sutherland (1963). Sketchpad, A Man-Machine Graphical Communication System. Massachusetts Institute of Technology, Introduction by A.F. Blackwell & K. Rodden. Technical Report 574. Cambridge University Computer Laboratory.

Sketchpad and Bush and Licklider’s article inspired Douglas Engelbart to found the  Augmentation Research Center  at the Stanford Research Institute (SRI) in the early 1960’s.  Over the course of about six years, with funding from NASA and the U.S. Defense Department’s Advanced Research Projects Agency (known today as DARPA), Engelbart and his team prototyped the “oN-Line System” (or the NLS), which attempted to engineer much of Bush’s vision. NLS had networking, windows, hypertext, graphics, command input, video conferencing, the computer mouse, word processing, file version control, text editing, and numerous other features of modern computing. Engelbart himself demoed the system to a live audience in what is often called “The Mother of all Demos.” You can see the entire demonstration in the below above.

Engelbart’s research team eventually disbanded and many of them ended up at the Xerox Palo Alto Research Center (Xerox PARC). Many were truly inspired by the demo and wanted to use the freedom Xerox had given them to make the NLS prototype a reality. One of the key members of this team was  Alan Kay , who had worked with Ivan Sutherland and seen Engelbart’s demo. Kay was interested in ideas of objects, object-oriented programming, and windowing systems, and created the programming language and environment Smalltalk. He was a key member of a team at PARC that developed the Alto in the mid 1970’s. The Alto included the first operating system based on a graphical user interface with a desktop metaphor, and included WYSIWYG word processing, an email client, a vector graphics editor, a painting program, and a multi-player networked video game.

Now, these were richly envisioned prototypes, but they were not products. You couldn’t buy an Alto at a store. This changed when Steve Jobs visited Xerox PARC in 1979, where he saw a demo of the Alto’s GUI, its Smalltalk-based programming environment, and its networking. Jobs was particularly excited about the GUI, and recruited several of the Xerox PARC researchers to join Apple, leading to the Lisa and later Macintosh computers, which offered the first mass-market graphical user interfaces. Apple famously marketed the Macintosh in its 1984 advertisement, framing the GUI the first salvo in a war against Big Brother, a reference to the book,  1984 .

As the Macintosh platform expanded, it drew many innovators, including many Silicon Valley entreprenuers with ideas for how to make personal computers even more powerful. This included ideas like presentation software such as  PowerPoint , which grew out of the same Xerox PARC vision of what-you-see-is-what-you-get text and graphics manipulation.

Since the release of the Macintosh, companies like Apple, Microsoft, and now Google have driven much of the engineering of user interfaces, deviating little from the original visions inspired by Bush, Licklider, Sutherland, Engelbart, and Kay. But governments and industry continued to harvest basic research for new paradigms of interaction, including the rapid proliferation of capacitive touch screens in the early 2000’s in smartphones ^{6 6}

Brad A. Myers (1998). A brief history of human-computer interaction technology. ACM interactions.

Reflecting on this history, one of the most remarkable things is how powerful one vision was to catalyze an entire world’s experience with computers. Is there something inherently fundamental about networked computers, graphical user interfaces, and the internet that was inevitable? Or if someone else had written an alternative vision for computing, would we be having different interactions with computers and therefore different interactions with each other through computing? And is it still possible to imagine different futures of interactive computing that will shape the future yet? This history and these questions remind us that nothing about our interactions with computing is necessarily “true” or “right”: they’re just ideas that we’ve collectively built, shared, and learned—and they can change. In the coming chapters, we’ll uncover what  is   fundamental about user interfaces and explore alternative visions of interacting with computers that may require new fundamentals.

First  history  and now theory? What a way to start a practical book about user interfaces. But as social psychologist Kurt Lewin said, “There’s nothing as practical as a good theory” ^{6 6}

Lewin (1943). Theory and practice in the real world. The Oxford Handbook of Organization Theory.

Let’s start with  why  theories are practical. Theories, in essence, are  explanations  for what something is and how something works. These explanatory models of phenomena in the world help us not only comprehend the phenomena, but also predict what will happen in the future with some confidence, and perhaps more importantly, they give us a conceptual vocabulary to exchange ideas about the phenomena. A good theory about user interfaces would be practical because it would help explain what user interface are, and what governs whether they are usable, learnable, efficient, error-prone, etc.

HCI researchers have written a lot about theory, including theories from other disciplines, and new theories specific to HCI ^{5 , 9 5}

Jacko, J. A. (2012). Human computer interaction handbook: Fundamentals, evolving technologies, emerging applications. CRC Press.

What are interfaces?

Let’s begin with what user interfaces  are .  User interfacesuser interface: A special kind of software designed to map human activities in the physical world (clicks, presses, taps, speech, etc.) to functions defined in a computer program.  are  software and/or hardware that bridge the world of human action and computer action . The first world is the natural world of matter, motion, sensing, action, reaction, and cognition, in which people (and other living things) build models of the things around them in order to make predictions about the effects of their actions. It is a world of stimulus, response, and adaptation. For example, when an infant is learning to walk, they’re constantly engaged in perceiving the world, taking a step, and experiencing things like gravity and pain, all of which refine a model of locomotion that prevents pain and helps achieve other objectives. Our human ability to model the world and then reshape the world around us using those models is what makes us so adaptive to environmental change. It’s the learning we do about the world that allows us to survive and thrive in it. Computers, and the interfaces we use to operate them, are one of the things that humans adapt to.

The other world is the world of computing. This is a world ultimately defined by a small set of arithmetic operations such as adding, multiplying, and dividing, and an even smaller set of operations that control which operations happen next. These instructions, combined with data storage, input devices to acquire data, and output devices to share the results of computation, define a world in which there is only forward motion. The computer always does what the next instruction says to do, whether that’s reading input, writing output, computing something, or making a decision. Sitting atop these instructions are  functionsfunction: An idea from mathematics of using algorithms to map some input (e.g., numbers, text, or other data), to some output. Functions can be as simple as basic arithmetic (e.g.,  multiply , which takes two numbers and computes their product), or as complex as machine vision ( object recognition , which takes an image and a machine learned classifier trained on millions of images and produces a set of text descriptions of objects in the image. , which take input and produce output using some algorithms. Essentially all computer behavior leverages this idea of a function, and the result is that all computer programs (and all software) are essentially collections of functions that humans can invoke to compute things and have effects on data. All of this functional behavior is fundamentally deterministic; it is data from the world (content, clocks, sensors, network traffic, etc.), and increasingly, data that models the world and the people in it, that gives it its unpredictable, sometimes magical or intelligent qualities.

Now, both of the above are basic theories of people and computers. In light of this, what are user interfaces? User interfaces are mappings from the sensory, cognitive, and social human world to computational functions in computer programs. For example, a save button in a user interface is nothing more than an elaborate way of mapping a person’s physical mouse click or tap on a touch screen to a command to execute a function that will take some data in memory and permanently store it somewhere. Similarly, a browser window, displayed on a computer screen, is an elaborate way of taking the carefully rendered pixel-by-pixel layout of a web page, and displaying it on a computer screen so that sighted people can perceive it with their human perceptual systems. These mappings from physical to digital, digital to physical, are how we talk to computers.

Learning interfaces

If we didn’t have user interfaces to execute functions in computer programs, it would still be possible to use computers. We’d just have to program them using programming languages (which, as we discuss later in  Programming Interfaces , can be hard to learn). What user interfaces offer are more  learnable  representations of these functions, their inputs, their algorithms, and their outputs, so that a person can build mental models of these representations that allow them to sense, take action, and achieve their goals, as they do with anything in the natural world. However, there is nothing natural about user interfaces: buttons, scrolling, windows, pages, and other interface metaphors are all artificial, invented ideas, designed to help people use computers without having to program them. While this makes using computers easier, interfaces must still be learned.

Don Norman, in his book,  The Design of Everyday Things ^{8 8}

Don Norman (2013). The design of everyday things: Revised and expanded edition. Basic Books.

What is it that people need to learn to take action on an interface? Norman (and later Gaver ^{2 2}

Gaver, W.W. (1991). Technology affordances. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '91). New Orleans, Louisiana (April 27-May 2, 1991). New York: ACM Press, pp. 79-84.

How can a person know what affordances an interface has? That’s where the concept of a  signifiersignifier: Any indicator of an affordance in an interface.  becomes important. Signifiers are any sensory or cognitive indicator of the presence of an affordance. Consider, for example, how you know that a computer mouse can be clicked. It’s physical shape might evoke the industrial design of a button. It might have little tangible surfaces that entreat you to push your finger on them. A mouse could even have visual sensory signifiers, like a slowly changing colored surface that attempts to say, “ I’m interactive, try touching me. ” These are mostly sensory indicators of an affordance. Personal digital assistants like Alexa, in contrast, lack most of these signifiers. What about an Amazon Echo says, “ You can say Alexa and speak a command? ” In this case, Amazon relies on tutorials, stickers, and even television commercials to signify this affordance.

While both of these examples involve hardware, the same concepts of affordance and signifier apply to software too. Buttons in a graphical user interface have an affordance: if you click within their rectangular boundary, the computer will execute a command. If you have sight, you know a button is clickable because long ago you learned that buttons have particular visual properties such as a rectangular shape and a label. If you are blind, you might know a button is clickable because your screen reader announces that something is a “button” and reads its label. All of this requires you knowing that interfaces have affordances such as buttons that are signified by a particular visual motif. Therefore, a central challenge of designing a user interface is deciding what affordances an interface will have and how to signify that they exist.

But  execution  is only half of the story. Norman also discussed  gulfs of evaluationgulf of evaluation: Everything a person must learn in order to understand the effect of their action on an interface, including understanding error messages or a lack of response from an interface. , which are the gaps between the output of a user interface and a user’s goal. Once a person has performed some action on a user interface via some functional affordance, the computer will take that input and do something with it. It’s up to the user to then map that feedback onto their goal. If that mapping is simple and direct, then the gulf is a small. For example, consider an interface for printing a document. If after pressing a print button, the feedback was “ Your document was sent to the printer for printing, ” that would clearly convey progress toward the user’s goal, minimizing the gulf between the output and the goal. If after pressing the print button, the feedback was “ Job 114 spooled, ” the gulf is larger, forcing the user to know what a “ job ” is, what “ spooling ” is, and what any of that has to do with printing their document.

In designing user interfaces, there are many ways to bridge gulfs of execution and evaluation. One is to just teach people all of these affordances and help them understand all of the user interface’s feedback. A person might take a class to learn the range of tasks that can be accomplished with a user interface, steps to accomplish those tasks, concepts required to understand those steps, and deeper models of the interface that can help them potentially devise their own procedures for accomplishing goals. Alternatively, a person can read tutorials, tooltips, help, and other content, each taking the place of a human teacher, approximating the same kinds of instruction a person might give. There are entire disciplines of technical writing and information experience that focus on providing seamless, informative, and encouraging introductions to how to use a user interface. ^{7 7}

Linda Newman Lior (2013). Writing for Interaction: Crafting the Information Experience for Web and Software Apps. Morgan Kaufmann.

To many user interface designers, the need to explicitly teach a user interface is a sign of design failure. There is a belief that designs should be “self-explanatory” or “intuitive.” What these phrases actually mean are that the  interface  is doing the teaching rather than a person or some documentation. To bridge gulfs of  execution , a user interface designer might conceive of physical, cognitive, and sensory affordances that are quickly learnable, for example. One way to make them quickly learnable is to leverage  conventionsconvention: A widely-used and widely-learned interface design pattern (e.g., a web form, a login screen, a hamburger menu on a mobile website). , which are essentially user interface design patterns that people have already learned by learning other user interfaces. Want to make it easy to learn how to add an item to a cart on an e-commerce website? Use a button labeled “ Add to cart, ” a design convention that most people will have already learned from using other e-commerce sites.Alternatively, interfaces might even try to anticipate what people want to do, personalizing what’s available, and in doing so, minimizing how much a person has to learn. From a design perspective, there’s nothing inherently wrong with learning, it’s just a cost that a designer may or may not want to impose on a new user. (Perhaps learning a new interface affords new power not possible with old conventions, and so the cost is justified).

To bridge gulfs of  evaluation , a user interface needs to provide  feedbackfeedback: Output from a computer program intended to explain what action a computer took in response to a user’s input (e.g., confirmation messages, error messages, or visible updates).  that explains what effect the person’s action had on the computer. Some feedback is  explicit instruction  that essentially teaches the person what functional affordances exist, what their effects are, what their limitations are, how to invoke them in other ways, what new functional affordances are now available and where to find them, what to do if the effect of the action wasn’t desired, and so on. Clicking the “ Add to cart ” button, for example, might result in some instructive feedback like this:

Some feedback is implicit, suggesting the effect of an action, but not explaining it explicitly. For example, after pressing “ Add to cart ,” there might be an icon showing an abstract icon of an item being added to a cart, with some kind of animation to capture the person’s attention. Whether implicit or explicit, all of this feedback is still contextual instruction on the specific effect of the user’s input (and optionally more general instruction about other affordances in the user interface that will help a user accomplish their goal).

The result of all of this learning is a  mental modelmental model: A person’s beliefs about an interface’s affordances and how to operate them. ^{1 1}

Carroll, J. M., Anderson, N. S. (1987). Mental models in human-computer interaction: Research issues about what the user of software knows. National Academies.

Note that in this entire discussion, we’ve said little about tasks or goals. The broader HCI literature theorizes about those broadly ^{5 , 9 5}

Jacko, J. A. (2012). Human computer interaction handbook: Fundamentals, evolving technologies, emerging applications. CRC Press.

Using theory

You have some new concepts about user interfaces, and an underlying theoretical sense about what user interfaces are and why designing them is hard. Let’s recap:

• User interfaces bridge human goals and cognition to functions defined in computer programs.
• To use interfaces successfully, people must learn an interface’s affordances and how they can be used to achiever their goals.
• This learning must come from somewhere, either instruction by people, explanations in documentation, or teaching by the user interface itself.
• Most user interfaces have large gulfs of execution and evaluation, requiring substantial learning.

The grand challenge of user interface design is therefore trying to conceive of interaction designs that have  small  gulfs of execution and evaluation, while also offering expressiveness, efficiency, power, and other attributes that augment human ability.

These ideas are broadly relevant to all kinds of interfaces, including all those already invented, and new ones invented every day in research. And so these concepts are central to design. Starting from this theoretical view of user interfaces allows you to ask the right questions. For example, rather than trying to vaguely identify the most “intuitive” experience, you can systematically ask: “ Exactly what is our software’s functionality affordances and what signifiers will we design to teach it? ” Or, rather than relying on stereotypes, such as “ older adults will struggle to learn to use computers, ” you can be more precise, saying, “ This particular population of adults has not learned the design conventions of iOS, and so they will need to learn those before successfully utilizing this application’s interface. ”

Approaching user interface design and evaluation from this perspective will help you identify major gaps in your user interface design analytically and systematically, rather than relying only on observations of someone struggling to use an interface, or worse yet, stereotypes or ill-defined concepts such as “intuitive” or “user-friendly.” Of course, in practice, not everyone will know these theories, and other constraints will often prevent you from doing what you know might be right. That tension between theory and practice is inevitable, and something we’ll return to throughout this book.

In the last two chapters, we considered two fundamental perspectives on user interface software and technology: a  historical  one, which framed user interfaces as a form of human augmentation, and a  theoretical  one, which framed interfaces as a bridge between sensory worlds and computational world of inputs, functions, outputs, and state. A third and equally important perspective on interfaces is what role interfaces play in individual lives and society broadly. While user interfaces are inherently computational artifacts, they are also inherently sociocultural and sociopolitical entities.

Broadly, I view the social role of interfaces as a  mediating  one. Mediation is the idea that rather than two entities interacting directly, something controls, filters, transacts, or interprets interaction between two entities. For example, one can think of human-to-human interactions as mediated, in that our thoughts, ideas, and motivations are mediated by language. In that same sense, user interfaces can mediate human interaction with many things. Mediation is important, because, as Marshall McLuhan argued, media (which by definition mediates) can lead to subtle, sometimes invisible structural changes in society’s values, norms, and institutions ^{5 5}

Marshall McLuhan (1994). Understanding media: The extensions of man. MIT press.

In this chapter, we will discuss how user interfaces mediate access to three things: automation, information, and other humans. ^aa Computer interfaces might mediate other things too: these are just the three most prominent society. 

Mediating automation

As the theory in the last chapter claimed, interfaces primarily mediate computation. Whether it’s calculating trajectories in World War II or detecting faces in a collection of photos, the vast range of algorithms from computer science that process, compute, filter, sort, search, and classify information provide real value to the world, and interfaces are how people access that value.

Interfaces that mediate automation are much like  application programming interfacesapplication programming interface (API): A collection of code used for computing particular things, designed for reuse by others without having to understand the details of the computation. For example, an API might mediate access to facial recognition algorithms, date arithmetic, or trigonometric functions.  (APIs) that software developers use. APIs organize collections of functionality and data structures that encapsulate functionality, hiding complexities, and providing a simpler interface for developers to use to build applications. For example, instead of a developer having to learn how to build a machine learning classification algorithm themselves, they can use a machine learning API, which only requires the developer to provide some data and parameters, which the API uses to train the classifier. 

In the same way, user interfaces are simply often direct manipulation ways of providing inputs to APIs. For example, think about what operating a calculator app on a phone actually involves: it’s really a way of delegating arithmetic operations to a computer. Each operation is a function that takes some arguments (addition, for example, is a function that takes two numbers and returns their sum). In this example, the calculator is  literally  an interface to an API of mathematical functions that compute things for a person. But consider a very different example, such as the camera application on a smartphone. This is  also  an interface to an API, with a single function that takes as input all of the light into a camera sensor, a dozen or so configuration options for focus, white balance, orientation, etc., and returns a compressed image file that captures that moment in space and time. This even applies to more intelligent interfaces you might not think of as interfaces at all, such as driverless cars. A driverless car is basically one complex function that is called dozens of times a second that take a car’s location,  destination, and all of the visual and spatial information around the car via sensors as input, and computes an acceleration and direction for the car to move. The calculator, the camera, and the driverless car are really both just interfaces to APIs that expose a set of computations, and user interfaces are what we use to access this computation.

From this API perspective, user interfaces mediate access to APIs, and interaction with an interface is really identical, from a computational perspective, to executing a program that uses those APIs to compute. In the calculator, when you press the sequence of buttons “1”, “+”, “1”, “=”, you just wrote a program  add(1,1)  and get the value  2  in return. When you open the camera app, point it at your face for a selfie, and tap on the screen to focus, you’re writing the program  capture(focusPoint, sensorData)  and getting the image file in return. When you enter your destination in a driverless car, you’re really invoking the program  while(!at(destination)) { drive(destination, location, environment); } . From this perspective, interacting with user interfaces is really about executing “one-time use” programs that compute something on demand.

How is this mediation? Well, the most direct way to access computation would be to write computer programs and then execute them. That’s what people did in the 1960’s before there were graphical user interfaces, but even those were mediated by punchcards, levers, and other mechanical controls. Our more modern interfaces are better because we don’t have to learn as much to communicate to a computer what computation we want. But, we still have to learn interfaces that mediate computation: we’re just learning APIs and how to program with them in graphical, visual, and direct manipulation forms, rather than as code.

Of course, as this distance between what we want a computer to do and how it does it grows, so does fear about how much trust to put in computers to compute fairly. A rapidly growing body of research is considering what it means for algorithms to be fair, how people perceive fairness, and how explainable algorithms are ^{1 , 7 , 9 1}

Abdul, A., Vermeulen, J., Wang, D., Lim, B.Y., Kankanhalli, M (2018). Trends and trajectories for explainable, accountable, intelligible systems: an HCI research agenda. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

Mediating information

Interfaces aren’t just about mediating computation, however. They’re also about accessing information. Before software, other  humans  mediated our access to information. We asked friends for advice, we sought experts for wisdom, and when we couldn’t find people to inform us, we consulted librarians to help us find recorded knowledge, written by experts we didn’t have access to. Searching and browsing for information was always an essential human activity.

Computing changed this. Now we use software to search the web, to browse documents, to create information, and to organize it. Because computers allow us to store information and access it much more quickly than we could access information through people or documents, we started to build systems for storing, curating, and provisioning information on computers and access it through user interfaces. We took all of the old ideas from information science such as documents, metadata, searching, browsing, indexing, and other knowledge organization ideas, and imported those ideas into computing. Most notably, the  Stanford Digital Library Project  leveraged all of these old ideas from information science and brought them to computers. This inadvertently led to Google, which still views its core mission as organizing the world’s information, which is what libraries were originally envisioned to do. But there is a difference: librarians, card catalogs, and libraries in general view their core values as access and truth, Google and other search engines ultimately prioritize convenience and profit, often at the expense of access and truth.

The study of how to design user interfaces to optimally mediate access to information is usually called  information architectureinformation architecture: The study and practice of organizing information and inferfaces to support searching, browsing, and sensemaking. ^{8 8}

Rosenfeld, L., & Morville, P. (2002). Information architecture for the world wide web. O'Reilly Media, Inc..

In practice, user interfaces for information technologies can be quite simple. They might consist of a programming language, like  Google’s query language , in which you specify an information need (e.g.,  cute kittens ) that is satisfied with retrieval algorithms (e.g., an index of all websites, images, and videos that might match what that phrase describes, using whatever metadata those documents have). Google’s interface also includes systems for generating user interfaces that present the retrieved results (e.g., searching for flights might result in an interface for purchasing a matching flight). User interfaces for information technologies might instead be browsing-oriented, exposing metadata about information and facilitating navigation through an interface. For example, searching for a product on Amazon often involves setting filters and priorities on product metadata to narrow down a large set of products to those that match your criteria. Whether an interface is optimized for searching, browsing, or both, all forms of information mediation require metadata.

Mediating communication

While computation and information are useful, most of our socially meaningful interactions still occur with other people. That said, more of these interactions than ever are mediated by user interfaces. Every form of social media — messaging apps, email, video chat, discussion boards, chat rooms, blogs, wikis, social networks, virtual worlds, and so on — is a form of  computer-mediated communicationcomputer-mediated communication: Any form of communication that is conducted through some computational device, such as a phone, tablet, laptop, desktop, smart speaker, etc. ^{4 4} Fussell, S. R., & Setlock, L. D. (2014). Computer-mediated communication. Handbook of Language and Social Psychology. Oxford University Press, Oxford, UK, 471-490 .

What makes user interfaces that mediate communication different from those that mediate automation and information? Whereas mediating automation requires clarity about an API’s functionality and input requirements, and mediating information requires metadata to support searching and browsing, mediating communication requires  social context . For example, in their seminal paper, Gary and Judy Olson discussed the vast array of social context present in collocated synchronous interactions that have to be reified in computer-mediated communication ^{6 6}

Olson, G. M., & Olson, J. S. (2000). Distance matters. Human-Computer Interaction.

Many researchers in the field of computer-supported collaborative work have sought to find designs that better support social processes, including ideas of “social translucence,” which achieves similar context as collocation, but through new forms of visibility, awareness, and accountability ^{3 3}

Erickson, T., & Kellogg, W. A. (2000). Social translucence: an approach to designing systems that support social processes. ACM transactions on computer-human interaction (TOCHI), 7(1), 59-83.

These three types of mediation each require different architectures, different affordances, and different feedback to achieve their goals:

• Interfaces for computation have to teach a user about what computation is possible and how to interpret the results.
• Interfaces for information have to teach the kinds of metadata that conveys what information exists and what other information needs could be satisfied.
• Interfaces for communication have to teach a user about new social cues that convey the emotions and intents of the people in a social context, mirroring or replacing those in the physical world.

Of course, one interface can mediate many of these things. Twitter, for example, mediates information to resources, but it also mediates communication between people. Similarly, Google, with its built in calculator support, mediates information, but also computation. Therefore, complex interfaces might be doing many things at once, requiring even more careful teaching. Because each of these interfaces must teach different things, one must know foundations of  what  is being mediated to design effective interfaces. Throughout the rest of this book, we’ll review both the foundations of user interface implementation, but also how the subject of mediation constrains and influences what we implement.

Thus far, most of our discussion has focused on what user interfaces  are . I  described them theoretically  as  a mapping from the sensory, cognitive, and social human world to these collections of functions exposed by a computer program . While that’s true, most of the mappings we’ve discussed have been input via our fingers and output to our visual perceptual systems. Most user interfaces have largely ignored other sources of human action and perception. We can speak. We can control over 600 different muscles. We can convey hundreds of types of non-verbal information through our gaze, posture, and orientation. We can see, hear, taste, smell, sense pressure, sense temperature, sense balance, sense our position in space, and feel pain, among dozens of other senses. This vast range of human abilities is largely unused in interface design. 

This bias is understandable. Our fingers are incredibly agile, precise, and high bandwidth sources of action. Our visual perception is similarly rich, and one of the dominant ways that we engage with the physical world. Optimizing interfaces for these modalities is smart because it optimizes our ability to use interfaces. 

However, this bias is also unreasonable because not everyone can see, use their fingers precisely, or read text. Designing interfaces that can  only  be used if one has these abilities means that vast numbers of people simply can’t use interfaces ^{11 11}

Richard E. Ladner (2012). Communication technologies for people with sensory disabilities. Proceedings of the IEEE.

This might describe you or people you know. And in all likelihood, you will be disabled in one or more of these ways someday, as you age or temporarily, due to injuries, surgeries, or other situational impairments. And that means you’ll struggle or be unable to use the graphical user interfaces you’ve worked so hard to learn. And if you know no one that struggles with interfaces, it may be because they are stigmatized by their difficulties, not sharing their struggles and avoiding access technologies because they signal disability ^{14 14}

Kristen Shinohara and Jacob O. Wobbrock (2011). In the shadow of misperception: assistive technology use and social interactions. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

Of course, abilities vary, and this variation has different impacts on people’s ability to use interfaces. One of the most common forms of disability is blindness and low-vision. But even within these categories, there is diversity. Some people are completely blind, some have some sight but need magnification. Some people have color blindness, which can be minimally impactful, unless an interface relies heavily on colors that a person cannot distinguish. I am near-sighted, but still need glasses to interact with user interfaces close to my face. When I do not have my glasses, I have to rely on magnification to see visual aspects of user interface. And while the largest group of people with disabilities are those with vision issues, the long tail of other disabilities around speech, hearing, and motor ability, when combined, is just as large.

Accessibility

Of course, most interfaces assume that none of this variation exists. And ironically, it’s partly because the user interface toolkits we described in  the architecture chapter  embed this assumption deep in their architecture. Toolkits make it so easy to design graphical user interfaces that these are the only kind of interfaces designers make. This results in most interfaces being difficult or sometimes impossible for vast populations to use, which really makes no business sense ^{8 8}

Sarah Horton and David Sloan (2015). Accessibility for business and pleasure. ACM interactions.

Whereas universal interfaces work for everyone as is,  access technologiesaccess technology: Interfaces that are used in tadem with other intefaces to improve their poor accessibility.  are alternative user interfaces that attempt to make an existing user interface more universal. Access technologies include things like:

• Screen readers  convert text on a graphical user interface to synthesized speech so that people who are blind or unable to read can interact with the interface.
• Captions  annotate the speech and action in video as text, allowing people who are deaf or hard of hearing to consume the audio content of video.
• Braille , as shown in the image at the top of this chapter, is a tactile encoding of words for people who are visually impaired. 

Consider, for example, this demonstration of screen readers and a braille display:

Fundamentally, universal user interface designs are ones that can be operated via  any input and output modality . If user interfaces are really just ways of accessing functions defined in a computer program, there’s really nothing about a user interface that requires it to be visual or operated with fingers. Take, for example, an ATM machine. Why is it structured as a large screen with several buttons? A speech interface could expose identical banking functionality through speech and hearing. Or, imagine an interface in which a camera just looks at someone’s wallet and their face and figures out what they need: more cash, to deposit a check, to check their balance. The input and output modalities an interface uses to expose functionality is really arbitrary: using fingers and eyes is just easier for most people in most situations. 

Advances in Accessibility

The challenge for user interface designers then is to not only design the functionality a user interface exposes, but also design a myriad of ways of accessing that functionality through any modality. Unfortunately, conceiving of ways to use all of our senses and abilities is not easy. It took us more than 20 years to invent graphical user interfaces optimized for sight and hands. It’s taken another 20 years to optimize touch-based interactions. It’s not surprising that it’s taking us just as long or longer to invent seamless interfaces for speech, gesture, and other uses of our muscles, and efficient ways of perceiving user interface output through hearing, feeling, and other senses. 

These inventions, however, are numerous and span the full spectrum of modalities. For instance, access technologies like screen readers have been around since shortly after  Section 508  of the Rehabilitation Act of 1973, and have converted digital text into synthesized speech. This has made it possible for people who are blind or have low-vision to interact with graphical user interfaces. But now, interfaces go well beyond desktop GUIs. For example, just before the ubiquity of touch screens, the SlideRule system showed how to make touch screens accessible to blind users by reading labels of UI throughout multi-touch ^{10 10}

Shaun K. Kane, Jeffrey P. Bigham, Jacob O. Wobbrock (2008). Slide rule: making mobile touch screens accessible to blind people using multi-touch interaction techniques. ACM SIGACCESS Conference on Computers and Accessibility.

For decades, screen readers have only worked on computers, but recent innovations like  VizLens  (above) have combined machine vision and crowdsourcing to support arbitrary interfaces in the world, such as microwaves, refrigerators, ATMs, and other appliances ^{7 7}

Anhong Guo, Xiang 'Anthony' Chen, Haoran Qi, Samuel White, Suman Ghosh, Chieko Asakawa, Jeffrey P. Bigham (2016). VizLens: A robust and interactive screen reader for interfaces in the real world. ACM Symposium on User Interface Software and Technology (UIST).

With the rapid rise in popularity of the web, web accessibility has also been a popular topic of research. Problems abound, but one of the most notable is the inaccessibility of images. Images on the web often come without  alt  tags that describe the image for people unable to see. User interface controls often lack labels for screen readers to read. Some work shows that some of the the information needs in these descriptions are universal—people describing needing descriptions of people and objects in images—but other needs are highly specific to a context, such as subjective descriptions of people on dating websites ^{15 15}

Abigale Stangl, Meredith Ringel Morris, and Danna Gurari (2020). Person, Shoes, Tree. Is the Person Naked? What People with Vision Impairments Want in Image Descriptions. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

For people who are deaf or hard of hearing, videos, dialog, or other audio output interfaces are a major accessibility barrier to using computers or engaging in computer-mediated communication. Researchers have invented systems like  Legion , which harness crowd workers to provide real-time captioning of arbitrary audio streams with only a few seconds of latency ^{12 12}

Walter Lasecki, Christopher Miller, Adam Sadilek, Andrew Abumoussa, Donato Borrello, Raja Kushalnagar, Jeffrey Bigham (2012). Real-time captioning by groups of non-experts. ACM Symposium on User Interface Software and Technology (UIST).

For people who have motor impairments, such as  motor tremors , fine control over mouse, keyboards, or multi-touch interfaces can be quite challenging, especially for tasks like text-entry, which require very precise movements. Researchers have explored several ways to make interfaces more accessible for people without fine motor control.  EdgeWrite , for example, is a gesture set (shown above) that only requires tracing the edges and diagonals of a square ^{19 19}

Jacob O. Wobbrock, Brad A. Myers, John A. Kembel (2003). EdgeWrite: a stylus-based text entry method designed for high accuracy and stability of motion. ACM Symposium on User Interface Software and Technology (UIST).

Sight, hearing, and motor abilities have been the major focus of innovation, but an increasing body of work also considers neurodiversity. For example, autistic people, people with down syndrome, and people with dyslexia may process information in different and unique ways, requiring interface designs that support a diversity of interaction paradigms. As we have discussed, interfaces can be a major source of such information complexity. This has led to interface innovations that facilitate a range of aids, including images and videos for conveying information, iconographic, recognition-based speech generation for communication, carefully designed digital surveys to gather information in health contexts, and memory-aids to facilitate recall. Some work has found that while these interfaces can facilitate communication, they are often not independent solutions, and require exceptional customization to be useful ^{6 6}

Ryan Colin Gibson, Mark D. Dunlop, Matt-Mouley Bouamrane, and Revathy Nayar (2020). Designing Clinical AAC Tablet Applications with Adults who have Mild Intellectual Disabilities. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

Whereas all of the innovations above aimed to make particular types of information accessible to people with particular abilities, some techniques target accessibility problems at the level of software architecture. For example, accessibility frameworks and features like Apple’s  VoiceOver  in iOS are system-wide: when a developer uses Apple’s standard user interface toolkits to build a UI, the UI is automatically compatible with  VoiceOver , and therefore automatically screen-readable. Because it’s often difficult to convince developers of operating systems and user interfaces to make their software accessible, researchers have also explored ways of modifying interfaces automatically. For example,  Prefab  (above) is an approach that recognizes the user interface controls based on how they are rendered on-screen, which allows it to build a model of the UI’s layout ^{4 4}

Morgan Dixon and James Fogarty (2010). Prefab: implementing advanced behaviors using pixel-based reverse engineering of interface structure. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

While all of the ideas above can make interfaces more universal, they can also have other unintended benefits for people without disabilities. For example, it turns out screen readers are great for people with ADHD, who may have an easier time attending to speech than text. Making web content more readable for people with low-vision also makes it easier for people with situational impairments, such as dilated pupils after a eye doctor appointment. Captions in videos aren’t just good for people who are deaf and hard of hearing; they’re also good for watching video in quiet spaces. Investing in these accessibility innovations then isn’t just about impacting that 15% of people with disabilities, but also the rest of humanity.

If you don’t know the history of computing, it’s easy to overlook the fact that all of the interfaces that people used to control computers before the graphical user interface were  programming  interfaces. But that’s no longer true; why then talk about programming in a book about user interface technology? Two reasons: programmers are users too, and perhaps more importantly, user interfaces quite often have programming-like features embedded in them. For example, spreadsheets aren’t just tables of data; they also tend to contain formulas that perform calculations on that data. Or, consider the increasingly common trend of smart home devices: to make even basic use of them, one must often write basic programs to automate their use (e.g., turning on a light when one arrives home, or defining the conditions in which a smart doorbell sends a notification). Programming interfaces are therefore far from niche: they are the foundation of how all interfaces are built and increasingly part of using interfaces to control and configure devices.

But it’s also important to understand programming interfaces in order to understand why interactive interfaces — the topic of our  next chapter  — are so powerful. This chapter won’t teach you to code if you don’t already know how, but it will give you the concepts and vocabulary to understand the fundamental differences between these two interface paradigms, when one might be used over the other, and what challenges each poses to people using computers.

Properties of programming interfaces

In some ways, programming interfaces are like any other kind of user interface: they take input, present output, have affordances and signifiers, and present a variety of gulfs of evaluation and execution. They do this by using a collection of tools, which make up the “interface” a programmer users to write computer programs:

• Programming languages . These are interfaces that take computer programs, translate them into instructions that a computer can execute, and then execute them. Popular examples include languages like  Python  (depicted at the beginning of this chapter) and  JavaScript  (which is used to build interactive web sites) but also less well-known languages such as  `Scratch` , which youth often use to create interactive 2D animations and games, or  R , which statisticians and data scientists use to analyze data.
• Editors . These are editors, much like word processes, that programmers use to read and edit computer programs. Some languages are editor-agnostic (e.g., you can edit Python code in any text editor), whereas others come with dedicated editors (e.g., Scratch programs can only be written with the Scratch editor).

These two tools, along with many other optional tools to help streamline the many challenges that come with programming, are the core interfaces that people use to write computer programs. Programming, therefore, generally involves reading and editing code in an editor, and repeatedly asking a programming language to read the code to see if there are any errors in it, and then executing the program to see if it has the intended behavior. It often doesn’t, which requires people to debug their code, finding the parts of it leading the program to misbehave. This  edit-run-debug  cycle is the general experience of programming, slowly writing and revising a program until it behaves as intended (much like slowly sculpting a sculpture, writing an essay, or painting a painting).

While it’s obvious this is different from using a website or mobile application, it’s helpful to talk in more precise terms about exactly how it is different. In particular, there are three attributes in particular that make an interface a programming interface ^{3 3}

Blackwell, A. F. (2002). First steps in programming: A rationale for attention investment models. In Human Centric Computing Languages and Environments, 2002. Proceedings. IEEE 2002 Symposia on (pp. 2-10). IEEE.

• No direct manipulation . In interactive interfaces like a web page or mobile app, concrete physical actions by a user on some data — moving a mouse, tapping on an object — result in concrete, immediate feedback about the effect of those actions. For example, dragging a file from one folder to another immediately moves the file, both visually, and on disk; selecting and dragging some text in a word processor moves the text. In contrast, programming interfaces offer no such direct manipulation: one  declares  what a program should do to some data, and then only after the program is executed does that action occur (if it was declared correctly). Programming interfaces therefore always involve  indirect  manipulation, describing.
• Use of notation . In interactive interfaces, most interactions involve concrete representations of data and action. For example, a web form has controls for entering information, and a submit button for saving it. But in programming interfaces, references to data and actions always involves using some programming language notation to refer to data and actions. For example, to open a new folder in the Unix or Linux operating systems, one has to type  cd myFolder , which is a particular notation for executing the  cd  command (standing for “change directory” and specifying which folder to navigate to ( myFolder) . Programming interfaces therefore always involve specific rules for describing what a user wants the computer to do.
• Use of abstraction . An inherent side effect of using notations is that one must also use abstractions to refer to a computer’s actions. For example, when one uses the Unix command line program  mv file.txt ../  (which moves  file.txt  to its parent directory), they are using a command, a file name, and a symbolic reference  ../   to refer to the parent directory of the current directory. Commands, names, and relative path references are all abstractions in that they all abstractly  represent  the data and actions to be performed. Visual icons in a graphical user interface are also abstract, but because they support direct manipulation, such as moving files from one folder to another, they can be treated as concrete objects. Not so with programming interfaces, where everything is abstract.

Clearly, textual programming languages such as the popular  Python ,  Java  or  JavaScript  fit all all of the descriptions above. But what about programming interfaces like Excel spreadsheets? Thinking through each of the properties above, we see spreadsheets have many of the same properties. While one can directly manipulate data in cells, there are no direct manipulation ways to use formulas: those must be written in a notation, the Excel programming language. And the notation invokes abstract ideas like  sum  rows and columns. Spreadsheets blur the line between programming and interactive interfaces, however, by immediately executing formulas as soon as they are edited, recomputing everything in the spreadsheet. This gives a a similar sense of immediacy as direct manipulation, even though it is not direct.

Another interesting case is that of chat bots, including those with pre-defined responses like in help tools and Discord, as well as more sophisticated ones like ChatGPT and others that use large language models (LLMs) trained on all of the internet’s public documents. Is writing prompts a form of programming? They seem to satisfy all of the properties above. They do not support direct manipulation, because they require a descriptions of commands. They do use notation, just natural language instead of a formal notation like a programming language. And they definitely use abstraction, in that they rely on sequences of symbols that represent abstract ideas (otherwise known as  words ) to refer to what one wants a computer to do. If we accept the definition above, then yes, chat bots and voice interfaces are programming interfaces, and may entail similar challenges as more conventional programming interfaces do. And we shouldn’t be surprised: all large-language models do (along with more rudimentary approaches to responding to language) is take a series of symbols as inputs, and generate a series of symbols in response, just like any other function in a computer program.

Learning programming interfaces

Because programming interfaces are interfaces, they also have  gulfs of executiongulf of execution: Everything a person must learn in order to acheive their goal with an interface, including learning what the interface is and is not capable of and how to operate it correctly.  and  evaluationgulf of evaluation: Everything a person must learn in order to understand the effect of their action on an interface, including understanding error messages or a lack of response from an interface.  as well. Their gulfs are just much larger because of the lack of direct manipulation, the use of notation, and the centrality of abstraction. All of these create a greater distance between someone’s goal, which often involves delegating or automating some task with code.

At the level of writing code, the gulfs of execution are immense. Imagine, for example, you have an idea for an mobile app, and are starting at a blank code editor. To have any clue what to type, people must know 1) a programming language, including its notation, and the various rules for using the notation correctly; 2) basic concepts in computer science about data structures and algorithms,  and  3) how to operate the code editor, which may have many features designed assuming all of this prior knowledge. Its rare, therefore, that a person might just poke around an editor or programming language to learn how it works. Instead, learning usually requires good documentation, perhaps some tutorials, and quite possibly a teacher and an education system. The same can be true of complex interactive interfaces (e.g., learning  Photoshop ), but at least in the case of interactive interfaces, the functional affordances is explicitly presented in menus. With programming interfaces, affordances are invisible.

Once one has some code and executes it, programming interfaces pose similarly large gulfs of evaluation. Most programs start off defective in some way, not quite doing what a person intended: when the program misbehaves, or gives an error message, what is a programmer to do? To have any clue, people lean on their knowledge of programming language to interpret error messages, carefully analyze their code, and potentially use other tools like debuggers to understand where their instructions might have gone wrong. These debugging skills are similar to the troubleshooting skills required by in interactive interfaces (e.g., figuring out why Microsoft Word keeps autocorrecting something), but the solutions are often more than just unchecking a checkbox: they may involve revising or entirely rewriting a part of a program.

If these two gulfs were not enough, modern programming interfaces have come to introduce new gulfs. These new gulfs primarily emerge from the increasing use of  APIsapplication programming interface (API): A collection of code used for computing particular things, designed for reuse by others without having to understand the details of the computation. For example, an API might mediate access to facial recognition algorithms, date arithmetic, or trigonometric functions.  to construct applications by reusing other people’s code. Some of the gulfs that reuse impose include ^{11 11}

Amy J. Ko, Brad A. Myers, Htet Htet Aung (2004). Six learning barriers in end-user programming systems. IEEE Symposium on Visual Languages-Human Centric Computing (VL/HCC).

• Design gulfs . APIs make some things easy to create, while making other things hard or impossible. This gulf of execution requires programmers to know what is possible to express.
• Selection gulfs . APIs may offer a variety of functionality, but it’s not always clear what functionality might be relevant to implementing something. This gulf of execution requires programmers to read documentation, scour web forums, and talk to experts to find the API that might best serve their need.
• Use gulfs . Once someone find an API that might be useful for creating what they want, they have to learn how to  use  the API. Bridging this gulf of execution might entail carefully reading documentation, finding example code, and understanding how the example code works.
• Coordination gulfs . Once someone learns how to use one part of an API, they might need to use it in coordination with another part to achieve the behavior they want. Bridging this gulf of execution might require finding more complex examples, or creatively tinkering with the API to understand its limitations.
• Understanding gulfs . Once someone has written some code with an API, they may need to debug their code, but without the ability to see the API’s own code. This poses gulfs of evaluation, requiring programmers to carefully interpret and analyze API behavior, or find explanations of its behavior in resources or online communities. 

These many gulfs of execution and evaluation mean have two implications: 1) programming interfaces are hard to learn and 2) designing programming interfaces is about more than just programming language design. It’s about supporting a whole ecosystem of tools and resources to support learning.

If we return to our examination of chat bots, we can see that they also have the same gulfs. It’s not clear what one has to say to a chat bot to get the response one wants. Different phrases have different effects, in unpredictable ways. And once one does say something, if the results are unexpected, it’s not clear why the model produced the results it did, and what action needs to be taken to produce a different response. “Prompt engineering” may just be modern lingo for “programming” and “debugging”, just without all of the usual tools, documentation, and clarity of how to use them to get what you want.

Simplifying programming interfaces

Given all of the difficulties that programming interfaces pose, its reasonable to wonder why we use them at all, instead of just creating interactive interfaces for everything we want computers to do. Unfortunately, that simply isn’t possible: creating new applications requires programming; customizing the behavior of software to meet our particular needs requires programming; and automating any task with a computer requires programming. Therefore, many researchers have worked hard to reduce the gulfs in programming interfaces as much as possible, to enable more people to succeed in using them.

While there is a vast literature on programming languages and tools, and much of it focuses on bridging gulfs of execution and evaluation, in this chapter, we’ll focus on the contributions that HCI researchers have made to solving these problems, as they demonstrate the rich, under-explored design space of ways that people can program computers beyond using general purpose languages. Much of this work can be described as supporting  end-user programmingend-user programming: Any programming done as a means to accomplish some other goal (in contrast to software engineering, which is done for the sole purpose of creating software for others to use). , which is any programming that someone does as a means to accomplishing some other goal ^{13 13}

Amy J. Ko, Robin Abraham, Laura Beckwith, Alan Blackwell, Margaret Burnett, Martin Erwig, Chris Scaffidi, Joseph Lawrance, Henry Lieberman, Brad Myers, Mary Beth Rosson, Gregg Rothermel, Mary Shaw, Susan Wiedenbeck (2011). The state of the art in end-user software engineering. ACM Computing Surveys.

This vast range of domains in which programming interfaces can be used has lead to an abundance of unique interfaces. For example, several researchers have explored ways to automate interaction with user interfaces, to take repetitive tasks and automate them. One such system is Sikuli (above), which allows users to use screenshots of user interfaces to write scripts that automate interactions ^{28 28}

Tom Yeh, Tsung-Hsiang Chang, Robert C. Miller (2009). Sikuli: using GUI screenshots for search and automation. ACM Symposium on User Interface Software and Technology (UIST).

Another major focus has been supporting people interacting with data. Some systems like Vega above have offered new programming languages for declaratively specifying data visualizations ^{25 25}

Arvind Satyanarayan, Kanit Wongsuphasawat, Jeffrey Heer (2014). Declarative interaction design for data visualization. ACM Symposium on User Interface Software and Technology (UIST).

Some systems have attempted to support more ambitious automation, empowering users to create entire applications that better support their personal information needs. For example, many systems have combined spreadsheets with other simple scripting languages to enable users to write simple web applications with rich interfaces, using the spreadsheet as a database ^{2 , 6 2}

Edward Benson, Amy X. Zhang, David R. Karger (2014). Spreadsheet driven web applications. ACM Symposium on User Interface Software and Technology (UIST).

Perhaps the richest category of end-user programming systems are those supporting the creation of games. Hundreds of systems have provided custom programming languages and development environments for authoring games, ranging from simple game mechanics to entire general purpose programming languages to support games ^{10 10}

Caitlin Kelleher and Randy Pausch (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys.

Most innovations for programming interfaces have focused on bridging the gulf of execution. Fewer systems have focused on bridging gulfs of evaluation by supporting, testing, and debugging behaviors a user is trying to understand. One from my own research was a system called the Whyline ^{11 11}

Amy J. Ko, Brad A. Myers, Htet Htet Aung (2004). Six learning barriers in end-user programming systems. IEEE Symposium on Visual Languages-Human Centric Computing (VL/HCC).

One way to think about all of these innovations is as trying to bring all of the benefits of interactive interfaces — direct manipulation, no notation, and concreteness — to notations that inherently don’t have those properties, by augmenting programming  environments  with these features. This work blurs the distinction between programming interfaces and interactive interfaces, bringing the power of programming to broader and more diverse audiences. But the work above also makes it clear that there are limits to this blurring: no matter how hard we try,  describing  what we want rather than  demonstrating  it directly, always seems to be more difficult, and yet more powerful.

It’s hard to imagine, but the interactive digital world we have to today only become a reality in the early 1980’s. Before that, interacting with a computer was much like we described in the  previous chapter : carefully writing computer programs, one instruction at a time, and executing it to get a result. Nearly all of the things that define our modern world: the internet, social media, instant video streaming, messaging, and millions of apps, websites, and games — simply hadn’t been invited.

This is a reminder that nothing about the user interface designs we use today is fundamental or inevitable. Consider, for example, if researchers in the 1960’s had devoted their attention to making programming easier rather than inventing the graphical user interface. That alternate future might have created a world in which we were all coders rather than clickers.

What happened instead was a series of inventions by researchers tackling a foundational question: what if communicating with computers was less like carefully crafting instructions, and more like a conversation with a computer? In our first chapter on  history , we talked about Ivan Sutherland’s 1962 experiments with  Sketchpad , which envisioned pen-based interactions with constrained graphical objects, where a user could interactively create diagrams with tap and drag of a pen. Around the same time, Douglas Englebart began work on  NLS , which envisioned an entire system of commands, file systems, mice, keyboards, and the internet. Inspired by this work, Alan Kay joined Xerox PARC in 1970, envisioning graphical objects in virtual windowed worlds in  Smalltalk . All of these offered very different but converging visions for how people would engage computing interactively instead of through code, and elements of each of these systems emerged as the core components of modern graphical user interfaces.

Most of these ideas came together at Xerox PARC during the design and development of the Star. Its interface, shown at the beginning of this chapter, contained all of the elements you’re familiar with today. These elements are typically referred to with the acronym  WIMP , which stands for  Windows ,  Icons ,  Menus , and  Pointer . This paradigm, which leveraged a desktop metaphor full of files, programs, and interactive widgets such as buttons, scroll bars, toggles, and other controls, became the dominant paradigm for desktop computing. And the paradigm persists: even in the newest smartphone, tablet, and AR/VR operating systems, we still interact with windows, icons, menus and other widgets in nearly identical ways. We may use multi-touch or gesture interactions, but these are just other ways of pointing.

In this chapter, we’ll discuss each of these concepts, and the ideas that followed, describing the problems WIMP was trying to solve and that ideas emerged to solve these problems. Understanding this history and foundation will help us understand the interface innovations that have come since.

Windows

The first big idea that emerged at Xerox PARC was the concept of a  window . The fundamental problem that windows solved is  how to provide visual access to a potentially infinite amount of content larger than the size of a screen on a fixed size display . It’s hard to imagine a world before windows, and to appreciate how much they shape our interactions with computers today until you think about the world in terms of programming interfaces: prior to windows, the only way of seeing a computer’s output was in a long temporal log of textual output. The idea of using  two  dimensions to display program output, and to use pixels instead of characters,  and  to allow for an infinite canvas of pixels, was incomprehensible. 

Part of making windows work required inventing  scroll bars , which solve the problem of how to navigate an infinite canvas of content. This invention was far from straightforward. For example, in this “All the Widgets” video, you can see a wide range of alternatives for how windows and scroll bars could work:

Some were just directional, instructing the window to move up or down a content area, much like the swiping gestures we use on touchscreens today to scroll. Others used a scroll bar “knob” to control what part of a larger document the window would show, where the size of the knob was proportional to the amount of content visible in the window (this is the idea that we see in operating systems today). These many iterations eventually converged toward the scroll bars we use today, which are sized proportional to the amount of content visible, and are draggable.

Researchers have since explored many more advanced techniques for windows and scrolling, including forms of scrolling that are aware of the underlying content to support non-linear navigation paths ^{6 6}

Edward W. Ishak and Steven K. Feiner (2006). Content-aware scrolling. ACM Symposium on User Interface Software and Technology (UIST).

The invention of windows also required the invention of  window managers . The problem here was deciding how to lay out windows on to a fixed sized display. There were countless ideas for different arrangements. The Star had windows that could be resized, dragged, and overlapped, which the Macintosh adopted, and led to the interaction designs we all use today. But early versions of Microsoft Windows had  tiled  windows (as shown above), which were non-overlapping grids of windows. Other variations involved “stacks” of windows, and keyboard shortcuts for flipping between them.

Of course, Windows, macOS, and Ubuntu now have many advanced window management features, allowing the user to see a zoomed out view with all active windows and move them to different virtual desktops. Modern mobile operating systems such as iOS, Android, and Windows Phone all eschewed multiple windows for a paradigm of one full-screen window at a time with navigation features for moving between full-screen applications. Researchers in the 1980’s were behind many of these innovations ^{12 12}

Myers, B. A. (1988). A taxonomy of window manager user interfaces. IEEE Computer Graphics and Applications.

Icons

How to display infinite content was one problem; another challenge was how to represent all of the code and data stored inside of a computer’s memory. Prior to the WIMP interfaces, invisibility was the norm: to know what applications or data were available, one had to type a command to list programs and files. This forced users to have to remember these commands, but also to constantly request these listings in order to navigate and find the applications or data they needed.

The  Star  eliminated the burden of remembering commands and requesting listings by inventing  icons . With icons, all of these operations of seeing what was available, starting a program, or opening a file were mapped to a pointing device instead of a keyboard: double-clicking on a program to launch it, double-clicking on a document to open it, and dragging an icon to change its location in a file system. This also necessitated some notion of a “desktop,” on which program and document icons would be stored, providing a convenient place to start work. Again, none of these ideas  had  to work this way, and in fact, newer operating systems don’t: for many years, iOS did not expose a concept of files or a desktop. Instead, there were only icons for programs, and data is presented as living only “inside” that application. Of course, there are still files stored on the device, they are just managed by the application instead of by the user. Eventually, after much demand, Apple released a  Files  application to make files visible.

Menus and Forms

Programs have commands, which are essentially an API of functions that can be called to perform useful operations. The command lines that existed before WIMP interfaces required people to remember all of the commands available and how to property construct a command to execute them.  Menus  solved this problem by providing an always available visual list of commands, and forms for gathering data to execute them. 

The earliest menus were simple lists of commands that could be selected with a pointing device. Some menus were attached to the border of a window, others were anchored to the top of the screen, and others still were  contextual  and attached to a specific icon or object representing some data, document, or program. You see all of these different types of menus in modern interfaces, with wide variation in where a menu is invoked. But all menus still behave like the original  Star , and were mainstreamed with the introduction of the Macintosh, which borrowed the  Star ’s design.

A key part of menu design was handling commands that required more input than a click. For example, imagine a menu item labeled “Sign in...” that signs the user into a service, asking for an email address and password. WIMP interfaces needed a way to gather that input. The  Star  team invented  forms  to solve this problem. Most forms are displayed in popup windows that solicit input from users before executing a command, though they come in many forms, such as sheets or “wizards” with multiple screens of forms.

While menus and forms don’t seem like a major opportunity for innovation, researchers have long studied more effective and efficient forms. For example, rather than linear lists of labels, researchers have explored things like  hierarchical marking menus  that are radial, can be moved through without clicking, and can result in a memory for pointing trajectories for rapid selection of items ^{19 19}

Shengdong Zhao and Ravin Balakrishnan (2004). Simple vs. compound mark hierarchical marking menus. ACM Symposium on User Interface Software and Technology (UIST).

Pointers

None of the core WIMP actions — moving a window, opening an application, selecting a command — are possible without the last element of WIMP,  pointers . They solved a fundamental problem of interacting with a 2-dimensional display: how can a user indicate the window they want to move or resize, the icon they want to select, or the menu item they want to invoke? The key insight behind pointing is that so much about interacting with a computer requires a precise statement of  what  is being “discussed” in the dialog between the user and the computer. Pointers are a way of indicating the topic of discussion, just as pointing is in conversations between people. 

The power of this idea becomes apparent when we consider interfaces without pointers. Consider, for example, speech interfaces. How might you tell a computer that you want to delete a file? In a speech interface, we might have to say something like “Delete the file in the folder named ‘Documents’ that has the name ‘report.txt’”, and it would be up to the computer to search for such a file, ask for clarification if there was more than one match, return an error if nothing was found, and of course, deal with any speech recognition mistakes that it made. Pointers solve all of those problems with a single elegant interaction, borrowed from human embodied interaction. We will talk about  pointing  in more detail in a later chapter.

Widgets

One  can  build entire interfaces out of windows, icons, menus, and pointers. However, application designers quickly realized that users need to do more than just open files, folders, and programs: they also need to provide input, and do so without making mistakes.  Widgets  are how we do this: sliders, check boxes, text boxes, radio buttons, drop down menus, and the many other controls found in graphical user interfaces are generally designed to make it possible to precisely specify an input within a certain set of constraints:

• Sliders  provide a control for specifying continuous numeric values within a numeric range.
• Check boxes  provide an error-free mechanism for specifying binary values (and sometimes tertiary values, which are often represented by a dash).
• Text boxes  provide an interface for specifying string values, often with sophisticated error-prevention mechanisms such as form validation and user efficiency features such as auto-complete.
• Radio buttons  and  drop down menus  provide error-preventing interfaces for specifying categorical values.

Each one of these widgets has been carefully designed to allow rapid, error-free, efficient input of each of these data types, and none were immediately obvious.

Of course, since these early widgets were invented, researchers have discovered many other types of widgets designed for data types that don’t map well onto this small set of primitive widgets. For example, some researchers have designed widgets for selecting time values on non-linear scales ^{8 8}

Yuichi Koike, Atsushi Sugiura, Yoshiyuki Koseki (1997). TimeSlider: an interface to specify time point. ACM Symposium on User Interface Software and Technology (UIST).

Copy and paste

Another gap that the early inventors of WIMP interfaces noticed is that there was no easy way to move data between parts of WIMP interfaces. Prior to WIMP, copying information meant storing some information in a file, copying the file or concatenating its contents to another file, and then saving that file.  Copy and paste  brilliantly streamlined this data transfer process by simply creating a temporary storage place for data is not stored in a file. 

Researchers have explored many ways to improve the power of this feature, including techniques that have greater semantic awareness of the content being copied, allowing it to be parsed and pasted in more intelligent ways ^{17 17}

Jeffrey Stylos, Brad A. Myers, Andrew Faulring (2004). Citrine: providing intelligent copy-and-paste. ACM Symposium on User Interface Software and Technology (UIST).

Direct manipulation

Throughout WIMP interfaces, there is a central notion of  immediacy : one takes an action and gets a response. This idea, which we call  direct manipulation ^{5 5}

Hutchins, E. L., Hollan, J. D.,, Norman, D. A. (1985). Direct manipulation interfaces. Human-Computer Interaction.

• The object of interest is always represented visually (e.g., the file you want to move is presented on a screen).
• Operating on the object involves invoking commands through physical action rather than notation (e.g., click and drag the file from the current folder to a different folder instead of writing a command line command telling the computer to move it).
• Feedback on the effect of an operation is immediately visible and is reversible (e.g., as you drag, the file moves, and if you change your mind, you can just move it back).

Direct manipulation interfaces, which include things like drag and drop interactions, or gesture-based interactions is in the Minority Report movie depicted above, can be learned quickly, can be efficient to use, can prevent errors. And because they are reversible, they can support rapid error recovery. Because of these benefits, many researchers have tried to translate tasks that traditionally require programming or other complex sequences of operations into direct manipulation interfaces. Early work explored things like alignment guides in drawing programs ^{14 14}

Roope Raisamo and Kari-Jouko Räihä (1996). A new direct manipulation technique for aligning objects in drawing programs. ACM Symposium on User Interface Software and Technology (UIST).

Non-WIMP interfaces

While all of the interactive interface ideas above are probably deeply familiar to you, it is important to remember that they are not natural in any way. They are entirely invented, artificial designs that solve very specific problems of presenting information to users, getting data from users, and supporting command invocation. The only reason they  feel  natural is because we practice using them so frequently. In designing interfaces, it’s reasonable to leverage everyone’s long history of practice with these old ideas. However, it’s also reasonable to question them when dealing with new types of data or interaction.

Games are the perfect example of this. They may have WIMP ideas in home screens and settings like menus and buttons, but the game play itself, and even some aspects of game menus, may avoid many aspects of WIMP. Consider for example, the lack of pointers on many video game consoles: rather than pointing to something, navigation is often by a directional pad or analog stick, giving discrete or continuous input about which trajectory a player wants to navigate in some space, but not a particular target. Or, consider the presence of non-player characters in games: the goal is not to execute commands on those characters, but interact with them for information, fight them, or perhaps even avoid them, and these behaviors are often not triggered by selecting things and invoking commands, but by pressing buttons, coming near something, or other gestures. These interfaces are still graphical, and often still have all of the features of direct manipulation, but are not WIMP in their interface metaphor.

As should be clear from the history above, nothing about graphical user interfaces is natural: every single aspect of them was invented to solve a particular problem, and could have been invented differently. One might argue, however, that humans do have relatively fixed abilities, and so some aspects of interactive interfaces were inevitable (we point to things in the physical world, so why wouldn’t we point to things in the virtual world?). Even if this is the case, it still takes hard work to invent these ways. Only after we find great designs do they become so ubiquitous that we take them for granted.

While the previous chapter discussed many of the seminal interaction paradigms we have invented for interacting with computers, we’ve discussed little about how all of the widgets, interaction paradigms, and other user interface ideas are actually  implemented  as software. This knowledge is obviously important for developers who implement buttons, scroll bars, gestures, and so on, but is this knowledge important for anyone else?

I argue yes. Much like a violinist needs to know whether a bow’s strings are made from synthetic materials or Siberian horse tail, a precise understanding of user interface implementation allows designers to have a precise understanding of how to compose widgets into user experiences. This helps designers and engineers to:

• Analyze limitations of interfaces
• Predict edge cases in their behavior, and
• Discuss their behavior precisely.

Knowing, for example, that a button only invokes its command  after  the mouse button is released allows one to reason about the assumptions a button makes about  ability . The ability to hold a mouse button down, for example, isn’t something that all people have, whether due to limited finger strength, motor tremors that lead to accidental button releases, or other motor-physical limitations. These details allow designers to fully control what you make and how it behaves.

Knowledge of user interface implementation might also be important if you want to invent  new  interface paradigms. A low-level of user interface implementation knowledge allows you to see exactly how current interfaces are limited, and empowers you to envision new interfaces that don’t have those limitations. For example, when  Apple redesigned their keyboards to have shallower (and reviled) depth , their design team needed deeper knowledge than just “pushing a key sends a key code to the operating system.” They needed to know the physical mechanisms that afford depressing a key, the tactile feedback those mechanisms provide, and the auditory feedback that users rely on to confirm they’ve pressed a key. Expertise in these physical qualities of the hardware interface of a keyboard was essential to designing a new keyboard experience.

Precise technical knowledge of user interface implementation also allows designers and engineers to have a shared vocabulary to  communicate  about interfaces. Designers should feel empowered to converse about interface implementation with engineers, knowing enough to critique designs and convey alternatives. Without this vocabulary and a grasp of these concepts, engineers retain power over user interface design, even though they aren’t trained to design interfaces.

States and Events

There are many levels at which designers might want to understand user interface implementation. The lowest—code—is probably too low level to be useful for the purposes above. Not everyone needs to understand, for example, the source code implementations of all of Windows or macOS’s widgets. Instead, here we will discuss user interface implementation at the  architectural  level. In software engineering, architecture is a high level view of code’s behavior: how it is organized into different units with a shared purpose, how data moves between different units, and which of these units is in charge of making decisions about how to respond to user interactions.

To illustrate this notion of architecture, let’s return to the example of a graphical user interface button. We’ve all used buttons, but rarely do we think about exactly how they work. Here is the architecture of a simple button, depicted diagrammatically:

Computer scientists call this diagram a  state machine , which is an abstract representation of different  statestate: A particular mode or configuration of a user interface that can be changed through user input. For example, a button might be hovered over or not, or user on a web page might be logged in or not.  that a computer program might be in. State machines also indicate the inputs that the can receive that cause them to move between different states. The button state machine above has two possible states:  up  (on the left) and  down  (on the right). In this state machine, there are two inputs that can cause changes in states. In user interface toolkits, these inputs are usually call  eventsevent: A user input such as a mouse click, tap, drag, or verbal utterance that may trigger a state change in a user interface’s state. , because they are things that users do at a particular point in time. The first event is when the button receives a  mouse down  event, after a user clicks a mouse button while the pointer is over the button. This event causes the state machine to transition from the  up  state to the  down  state. The button stays in this state until it later receives a  mouse up  event from the user, when they release the mouse button; this causes the button to transition to the  up  state and also executes its command. This is about as simple as button statement machines get.

Representing a state machine in code involves translating the abstract logic as in the diagram above into a programming language and user interface toolkit. For example, here is an implementation of the button above in JavaScript, using the popular  React  framework:

To understand the code above, the comments marked with the text  //  are your guide. The render()  function at the bottom describes how the button should appear in its two different states: dark grey with the text “I’m down” when in the  down  state and light gray with the text “I’m up” in the  up  state. Notice how the  handleUp()  and  handleDown()  functions are assigned to the  onMouseUp  and  onMouseDown  event handlers. These functions will execute when the button receives those corresponding events, changing the button’s state (and its corresponding appearance) and executing  executeCommand() . This function could do anything — submit a form, display a message, compute something — but it currently does nothing. None of this implementation is magic — in fact, we can change the state machine by changing any of the code above, and produce a button with very different behavior. The rest of the details are just particular ways that JavaScript code must be written and particular ways that React expects JavaScript code to be organized and called; all can be safely ignored, as they do not affect the button’s underlying architecture, and could have been written in any other programming language with any other user interface toolkit.

The buttons in modern operating systems actually have much more complex state machines than the one above. Consider, for example, what happens if the button is in a  down  state, but the mouse cursor moves  outside  the boundary of the button and  then  a mouse up event occurs. When this happens, it transitions to the  up  state, but does  not  execute the button’s command. (Try this on a touchscreen: tap on a button, slide your finger away from it, then release, and you’ll see the button’s command isn’t executed). Some buttons have an  inactive  state, in which they will not respond to any events until made active. If the button supports touch input, then a button’s state machine also needs to handle touch events in addition to mouse events. And to be accessible to people who rely on keyboards, buttons also need to have a  focused  and  unfocused  state and respond to things like the space or enter key being typed while focused. All of these additional states and events add more complexity to a button’s state machine.

All user interface widgets are implemented in similar ways, defining a set of states and events that cause transitions between those states. Scroll bar handles respond to  mouse down ,  drag , and  mouse up  events, text boxes respond to  keypress  events, links respond to  mouse down  and  mouse up  events. Even text in a web browser or text editor response to  mouse down ,  drag , and  mouse up  events to enter a text selection state.

Model-View-Controller

State machines are only part of implementing a widget, the part that encodes the logic of how a widget responds to user input. What about how the widget is presented visually, or how bits of data are stored, such as the text in a text field or the current position of a scroll bar? 

The dominant way to implement these aspects of widgets, along with the state machines that determine their behavior, is to follow a  model-view-controllermodel-view-controller: A way of organizing a user interface implementation to separate how data is stored (the model), how data is presented (the view), and how data is changed in response to user input (the controller).  (MVC) architecture. One can think of MVC as a division of responsibilities in code between storing and retrieving data (the  model ), presenting data and listening for user input (the  view ), and managing the interaction between the data storage and the presentation using the state machine (the  controller ). This architecture is ubiquitous in user interface implementation.

To illustrate how the architecture works, let’s consider a non-user interface example. Think about the signs that are often displayed on gas station or movie theaters. Something is responsible for storing the content that will be shown on the signs; perhaps this is a piece of paper with a message, or a particularly organized gas station owner has a list of ideas for sign messages stored in a notebook. Whatever the storage medium, this is the  model . Someone else is responsible for putting the content on the signs based on whatever is in the model; this is the  view  responsibility. And, of course, someone is in charge of deciding when to retrieve a new message from the  model  and telling the person in charge of the view to update the sign. This person is the  controller . 

In the same way as in the example above, MVC architectures in user interfaces take an individual part of an interface (e.g., a button, a form, a progress bar, or even some complex interactive data visualization on a news website) and divide its implementation into these three parts. For example, consider the example below of a post on a social media site like Facebook:

In this interface:

• The  model  stores the  data  that a user interface is presenting. For example, in the figure above, this would be the comment that someone is typing and posting. In the case of social media, the model might include both the part of memory storing the comment being typed, but also the database stored on Facebook’s servers that persists the comment for later display. 
• The  view  visualizes the data in the model. For example, in the figure above, this includes the text field for the comment, but also the profile image, the privacy drop down menu, and the name. The view’s job is to render these controls, listen for input from the user (e.g., pressing the  post  button to submit the comment), and display any output the controller decides to provide (e.g., feedback about links in the post).
• The  controller  makes decisions about how to handle user input and how to update the model. In our comment example above, that includes validating the comment (e.g., it can’t be empty), and submitting the comment when the user presses enter or the  post  button. The controller gets and set data in the model when necessary and tells the view to update itself as the model changes.

View Hierarchies

If every individual widget in a user interface is its own self-contained model-view-controller architecture, how are all of these individual widgets composed together into a user interface? There are three big ideas that stitch together individual widgets into an entire interface.

First, all user interfaces are structured as  hierarchies  in which one widget can contain zero or more other “child” widgets, and each widget has a parent, except for the “root” widget (usually a window). For instance, here’s the Facebook post UI we were discussing earlier and its corresponding hierarchy:

Notice how there are some components in the tree above that aren’t visible in the UI (the “post”, the “editor”, the “special input” container). Each of these are essentially containers that group components together. These containers are used to give widgets a  layout , in which the children of each component are organized spatially according to some layout rule. For example, the special input widgets are laid out in a horizontal row within the special inputs container and the special inputs container itself is laid out right aligned in the “editor” container. Each component has its own layout rules that govern the display of its children.

Finally,  event propagation  is the process by which user interface events move from a physical device to a particular user interface component in a larger view hierarchy. Each device has its own process, because it has its own semantics. For instance:

• A  mouse  emits mouse move events and button presses and releases. All of these are emitted as discrete hardware events to the operating system. Some events are aggregated into  synthetic  events like a click (which is really a mouse press followed by a mouse release, but not a discrete event a mouse’s hardware). When the operating system receives events, it first decides which window will receive those events by comparing the position of the mouse to the position and layering of the windows, finding the topmost window that contains the mouse position. Then, the window decides which component will handle the event by finding the topmost component whose spatial boundaries contain the mouse. That event is then sent to that component. If the component doesn’t handle the event (e.g., someone clicks on some text in a web browser that doesn’t respond to clicks), the event may be  propagated  to its parent, and to its parent’s parent, etc, seeing if any of the ancestors in the component hierarchy want to handle the event. Every user interface framework handles this propagation slightly differently, but most follow this basic pattern.
• A  keyboard  emits key down and key up events, each with a  key code  that corresponds to the physical key that was pressed. As with a mouse, sequences are synthesized into other events (e.g., a key down followed by a key up with the same key is a key “press”). Whereas a mouse has a position, a keyboard does not, and so operating systems maintain a notion of  window focus  to determine which window is receiving key events, and then each window maintains a notion of  keyboard focus  to determine which component receives key events. Operating systems are then responsible for providing a visible indicator of which component has keyboard focus (perhaps giving it a border highlight and showing a blinking text caret). As with mouse events, if the component with focus does not handle a keyboard event, it may be propagated to its ancestors and handled by one of them. For example, when you press the escape key when a confirmation dialog is in focus, the button that has focus will ignore it, but the dialog window may interpret the escape key press as a “cancel”.
• A  touch screen  emits a stream of touch events, segmented by start, move, and end events. Other events include touch cancel events, such as when you slide your finger off of a screen to indicate you no longer want to touch. This low-level stream of events is converted by operating systems and applications into touch gestures. Most operating systems recognize a class of gestures and emit events for them as well, allowing user interface controls to respond to them.
• Even  speech  interfaces emit events. For example, digital voice assistants are continuously listening for activation commands such as “Hey Siri” or “Alexa.” After these are detected, they begin converting speech into text, which is then matched to one or more commands. Applications that expose a set of commands then receive events that trigger the application to process the command. Therefore, the notion of input events isn’t inherently tactile; it’s more generally about translating low-level inputs into high-level commands.

Every time a new input device has been invented, user interface designers and engineers have had to define new types of events and event propagation rules to decide how inputs will be handled by views within a larger view hierarchy.

Advances in Architecture

While the basic ideas presented above are now ubiquitous in desktop and mobile operating systems, the field of HCI has rapidly innovated beyond these original ideas. For instance, much of the research in the 1990’s focused on building more robust, scalable, flexible, and powerful user interface toolkits for building desktop interfaces. The  Amulet toolkit  was one of the most notable of these, offering a unified framework for supporting graphical objects, animation, input, output, commands, and undo ^{23 23}

Myers, Brad A., Richard G. McDaniel, Robert C. Miller, Alan S. Ferrency, Andrew Faulring, Bruce D. Kyle, Andrew Mickish, Alex Klimovitski, Patrick Doane (1997). The Amulet environment: New models for effective user interface software development. IEEE Transactions on Software Engineering.

Research in the 2000’s shifted to deepen these ideas. For example, some work investigated alternatives to component hierarchies such as  scene graphs ^{16 16}

Stéphane Huot, Cédric Dumas, Pierre Dragicevic, Jean-Daniel Fekete, Gerard Hégron (2004). The MaggLite post-WIMP toolkit: draw it, connect it and run it. ACM Symposium on User Interface Software and Technology (UIST).

Other research has looked beyond traditional WIMP interfaces, creating new architectures to support new media. The DART toolkit, for example, invented several abstractions for augmented reality applications ^{9 9}

Gandy, M., & MacIntyre, B. (2014). Designer's augmented reality toolkit, ten years later: implications for new media authoring tools. In Proceedings of the 27th annual ACM symposium on User interface software and technology (pp. 627-636).

While much of the work in user interface architecture has sought to contribute new architectural ideas for user interface construction, some have focused on ways of  modifying  user interfaces without modifying their underlying code. For example, one line of work has explored how to express interfaces abstractly, so these abstract specifications can be used to generate many possible interfaces depending on which device is being used ^{7 , 25 , 26 7}

W. Keith Edwards and Elizabeth D. Mynatt (1994). An architecture for transforming graphical interfaces. ACM Symposium on User Interface Software and Technology (UIST).

A smaller but equally important body of work has investigated ways of making interfaces easier to test and debug. Some of these systems expose information about events, event handling, and finite state machine state ^{15 15}

Scott E. Hudson, Roy Rodenstein, Ian Smith (1997). Debugging lenses: a new class of transparent tools for user interface debugging. ACM Symposium on User Interface Software and Technology (UIST).

Conclusion

Considering this body of work as a whole, there are some patterns that become clear: 

• Model-view-controller is a ubiquitous architectural style in user interface implementation.
• User interface toolkits are essential to making it easy to implement interfaces.
• New input techniques require new user interface architectures, and therefore new user interface toolkits.
• Interfaces can be automatically generated, manipulated, inspected, and transformed, but only within the limits of the architecture in which they are implemented.
• The architecture an interface is built in determines what is difficult to test and debug.

These “laws” of user interface implementation can be useful for making predictions about the future. For example, if someone proposes incorporating a new sensor in a device, subtle details in the sensor’s interactive potential may require new forms of testing and debugging, new architectures, and potentially new toolkits to fully leverage its potential. That’s a powerful prediction to be able to make and one that many organizations overlook when they ship new devices.

Thus far, we have focused on universal issues in user interface design and implementation. This chapter will be the first in which we discuss specific paradigms of interaction and the specific theory that underlies them. Our first topic will be  pointing .

What is pointing?

Fingers can be pretty handy (pun intended). When able, we use them to grasp nearly everything. We use them to communicate through signs and gesture. And at a surprisingly frequent rate each day, we use them to  point , in order to indicate, as precisely as we can, the identity of something (that person, that table, this picture, those flowers). As a nearly universal form of non-verbal communication, it’s not surprising then that pointing has been such a powerful paradigm of interaction with user interfaces (that icon, that button, this text, etc.).

Pointing is not strictly related to interface design. In fact, back in the early 1950s, Paul M. Fitts was very interested in modeling human performance of pointing. He began developing predictive models about pointing in order to help design dashboards, cockpits, and other industrial designs for manufacturing. His focus was on “aimed” movements, in which a person has a  target  they want to indicate and must move their pointing device (originally, their hand) to indicate that target. This kind of pointing is an example of a “closed loop” motion, in which a system (e.g., the human) can react to its evolving state (e.g., here the human’s finger is in space relative to its target). A person’s reaction is the continuous correction of their trajectory as they move toward a target. Fitts began measuring this closed loop movement toward targets, searching for a pattern that fit the data, and eventually found this law, which we call  Fitts’ Law :

Let’s deconstruct this equation:

• The formula computes the time to reach a target ( MT  refers to “motion time”). This is, simply, the amount of time it would take for a person to move their pointer (e.g., finger, stylus) to precisely indicate a target. Computing this time is the goal of Fitt’s law, as it allows us to take some set of design considerations and make a prediction about pointing speed.
• The  A  in the figure is how far one must move to reach the target (e.g. how far your finger has to move from where it is to reach a target on your phone’s touch screen. Imagine, for example, moving your hand to point to an icon on your smartphone; that’s a small  A . Now, imagine moving a mouse cursor on a wall-sized display from one side to the next. That would be a  large   A . The intuition behind accounting for the distance is that the larger the distance one must move to point, the longer it will take.
• The  W  is the size (or “width”) of the target. For example, this might be the physical length of an icon on your smartphone’s user interface, or the length of the wall in the example above. The units on these two measures don’t really matter as long as they’re the same, because the formula above computes the ratio between the two, canceling the units out. The intuition behind accounting for the size of the target is that the larger the target, the easier it is to point to (and the smaller the target, the harder it will be).
• The  a  coefficient is a user- and device-specific constant. It is some fixed constant minimum time to move; for example, this might be the time it takes for your desire to move your hand results in your hand actually starting to move, or any lag between moving a computer mouse and the movement starting on the computer.  The movement time is therefore, at a minimum,  a . This varies by person and device, accounting for things like reflexes and latency in devices.
• The  b  coefficient is a measure of how efficiently movement occurs. Imagine, for example, a computer mouse that weighs 5 pounds; that might be a large  b , making movement time slower. A smaller  b  might be something like using eye gaze in a virtual reality setting, which requires very little energy. This also varies by person and device, accounting for things like user strength and the computational efficiency of a digital pointing device.

This video illustrates some of these concepts visually:

So what does the formula  mean ? Let’s play with the algebra. When  A  (distance to target) goes up, time to reach the target increases. That makes sense, right? If you’re using a touch screen and your finger is far from the target, it will take longer to reach the target. What about  W  (size of target)? When that goes up, the movement time goes  down . That also makes sense, because easier targets (e.g., bigger icons) are easier to reach. If  a  goes up, the minimum movement time will go up. Finally, if  b  goes up, movement time will also increase, because movements are less efficient. The design implications of this are quite simple: if you want fast pointing, make sure 1) the target is close, 2) the target is big, 3) the minimum movement time is small, and 4) movement is efficient.

There is one critical detail missing from Fitt’s law: errors. You may have the experience, for example, of moving a mouse cursor to a target and missing it, or trying to tap on an icon on a touch screen, and missing it. These types of pointing “errors” are just as important is movement time, because if we make a mistake, we may have to do the movement all over again. Wobbrock et al. considered this gap and found that Fitts’ law itself actually strongly implies a speed accuracy tradeoff: the faster one moves during pointing, the less likely one is to successfully reach a target. However, they also showed experimentally that the particular error rates are more sensitive to some factors than others:  a  and  b  strongly influence error rates, and to a lesser extent, target size  W  matters more than target distance  A ^{16 16}

Jacob O. Wobbrock, Edward Cutrell, Susumu Harada, and I. Scott MacKenzie (2008). An error model for pointing based on Fitts' law. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

What does pointing have to do with user interfaces?

But there are some other interesting implications for user interface design in the algebraic extremes. For example, what if the target size is  infinite ? An example of this is the command menu in Apple Mac OS applications, which is always placed at the very top of a screen. The target in this case is the top of the screen, which is effectively infinite in size, because no matter how far past the top of the screen you point with a mouse, the operating system always constrains the mouse position to be within the screen boundaries. This makes the top of the screen (and really any side of the screen) a target of infinite size. A similar example is the Windows Start button, anchored to the corner of the screen. And according to Fitts’ Law, these infinite target sizes means effectively zero movement time. That’s why it’s so quick to reach the menu on Mac OS and the Start button on Windows: you can’t miss.

What’s surprising about Fitts’ Law is that, as far as we know, it applies to any kind of pointing: using a mouse, using a touch screen, using a trackball, using a trackpad, or reaching for an object in the physical world. That’s conceptually powerful because it means that you can use the idea of large targets and short distance to design interfaces that are efficient to use. As a mathematical model used for prediction, it’s less powerful: to really predict exactly how long a motion will take, you’d need to estimate a distribution of those  a  and  b  coefficients for a large group of possible users and devices. Researchers carefully studying motion might use it to do precise modeling, but for designers, the concepts it uses are more important.

Now that we have Fitts’ law as a conceptual foundation for design, let’s consider some concrete design ideas for pointing in interfaces. There are so many kinds: mice, styluses, touch screens, touch pads, joysticks, trackballs, and many other devices. Some of these devices are  direct pointingdirect pointing: Pointing devices in which the input for pointing and the corresponding feedback in response occur in the same physical place (e.g., a touchscreen).  devices (e.g., touch screens), in which input and output occur in the same physical place (e.g., a screen or some other surface). In contrast,  indirect pointingindirect pointing: Pointing devices in which the input for pointing and the corresponding feedback in response occur in different physical places (e.g., a mouse and a screen).  devices (e.g., a mouse, a touchpad, a trackball) provide input in a different physical place from where output occurs (e.g., input on a device, output on a non-interactive screen). Each has their limitations: direct pointing can result in  occlusion  where a person’s hand obscures output, and indirect pointing requires a person to attend to two different places.

There’s also a difference between  absoluteabsolute pointing: Pointing devices in which the physical coordinates of input are mapped directly onto the interface coordinate system (e.g., touchscreens).  and  relativerelative pointing: Pointing devices in which changes in the physical coordinates of input device are mapped directly  changes  to the current position in an interface coordinate system (e.g., mice).  pointing. Absolute pointing includes input devices where the physical coordinate space of input is mapped directly onto the coordinate space in the interface. This is how touch screens work (bottom left of the touch screen is bottom left of the interface). In contrast, relative pointing maps  changes  in a person’s pointing to changes in the interface’s coordinate space. For example, moving a mouse left an inch is translated to moving a virtual cursor some number of pixels left. That’s true regardless of where the mouse is in physical space. Relative pointing allows for variable  gain , meaning that mouse cursors can move faster or slower depending on a user’s preferences. In contrast, absolute pointing cannot have variable gain, since the speed of interface motion is tied to the speed of a user’s physical motion.

How can we make pointing better?

When you think about these two dimensions from a Fitts’ law perspective, making input more efficient is partly about inventing input devices that minimize the  a  and  b  coefficients. For example, researchers have invented new kinds of mice that have multi-touch on them, allowing users to more easily provide input during pointing movements ^{15 15}

Nicolas Villar, Shahram Izadi, Dan Rosenfeld, Hrvoje Benko, John Helmes, Jonathan Westhues, Steve Hodges, Eyal Ofek, Alex Butler, Xiang Cao, Billy Chen (2009). Mouse 2.0: multi-touch meets the mouse. ACM Symposium on User Interface Software and Technology (UIST).

Other innovations focus on software, and aim to increase target size or reduce travel distance. Many of these ideas are  target-agnostic  approaches that have no awareness about what a user might be pointing  to . Some target-agnostic techniques include things like mouse pointer acceleration, which is a feature that makes the pointer move faster if it determines the user is trying to travel a large distance ^{3 3}

Casiez, G., Vogel, D., Balakrishnan, R., & Cockburn, A. (2008). The impact of control-display gain on user performance in pointing tasks. Human-Computer Interaction.

Other pointing innovations are  target-aware , in that the technique needs to know the location of things that a user might be pointing to so that it can adapt based on target locations. For example, area cursors are the idea of having a mouse cursor represent an entire two-dimensional space rather than a single point, reducing the distance to targets. These have been applied to help users with motor impairments ^{7 7}

Leah Findlater, Alex Jansen, Kristen Shinohara, Morgan Dixon, Peter Kamb, Joshua Rakita, Jacob O. Wobbrock (2010). Enhanced area cursors: reducing fine pointing demands for people with motor impairments. ACM Symposium on User Interface Software and Technology (UIST).

Snapping is another target-aware technique commonly found in graphic design tools, in which a mouse cursor is constrained to a location based on nearby targets, reducing target distance. Researchers have made snapping work across multiple dimensions simultaneously ^{6 6}

Marianela Ciolfi Felice, Nolwenn Maudet, Wendy E. Mackay, Michel Beaudouin-Lafon (2016). Beyond Snapping: Persistent, Tweakable Alignment and Distribution with StickyLines. ACM Symposium on User Interface Software and Technology (UIST).

While target-aware techniques can be even more efficient than target-agnostic ones, making an operating system aware of targets can be hard, because user interfaces can be architected to process pointing input in such a variety of ways. Some research has focused on overcoming this challenge. For example, one approach reverse-engineered the widgets on a screen by analyzing the rendered pixels to identify targets, then applied targeted-aware pointing techniques like the bubble cursor ^{5 5}

Morgan Dixon, James Fogarty, Jacob Wobbrock (2012). A general-purpose target-aware pointing enhancement using pixel-level analysis of graphical interfaces. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

You might be wondering: all this work for faster pointing? Fitts’ law and its focus on speed  is  a very narrow way to think about the experience of pointing to computers. And yet, it is such a fundamental and frequent part of how we interact with computers; making pointing fast and smooth is key to allowing a person to focus on their task and not on the low-level act of pointing. This is particularly true of people with motor impairments, which interfere with their ability to precisely point: every incremental improvement in one’s ability to precisely point to a target might amount to hundreds or thousands of easier interactions a day, especially for people who depend on computers to communicate and connect with the world.

Just as pointing is a nearly ubiquitous form of  non-verbal  communication, text is a ubiquitous form of  verbal  communication. Every single character we communicate to friends, family, coworkers, and computers—every tweet, every Facebook post, every email—leverages some form of text-entry user interface. These interfaces have one simple challenge: support a person in translating the verbal ideas in their head into a sequence of characters that are stored, processed, and transmitted by a computer.

If you’re reading this, you’re already familiar with common text-entry interfaces. You’ve almost certainly used a physical keyboard, arranged with physical keys labeled with letters and punctuation. You’ve probably also used a virtual on-screen keyboard, like those in smartphones and tablets. You might even occasionally use a digital assistant’s speech recognition as a form of text entry. And because text-entry is so frequent a task with computers, you probably also have very strong opinions about what kinds of text-entry you prefer: some hate talking to Siri, some love it. Some still stand by their physical Blackberry keyboards, others type lightning fast on their Android phone’s virtual on-screen keyboard. And some like big bulky clicky keys, while others are fine with the low travel of most thin modern laptop keyboards.

What underlies these strong opinions? A large set of hidden complexities. Text entry needs to support letters, numbers, and symbols across nearly all of human civilization. How can text entry support the input of the 109,384 distinct symbols from all human languages encoded in  Unicode 6.0 ? How can text be entered quickly and error-free, and help users recover from entry errors? How can people  learn  text entry interfaces, just like they learn handwriting, keyboarding, speech, and other forms of verbal communication. How can people enter text in a way that doesn’t cause pain, fatigue, or frustration? Many people with injuries or disabilities (e.g., someone who is fully paralyzed) may find it excruciatingly difficult to enter text. The ever-smaller devices in our pockets and on our wrists only make this harder, reducing the usable surfaces for comfortable text entry.

The history of text entry interfaces predates computers (Silfverberg 2007). For example, typewriters like the Jewett No. 4 shown above had to solve the same problem as modern computer keyboards, but rather than storing the sequence of characters in computer memory, they were stored on a piece of paper with ink. Typewriters like the Jewett No. 4 and their QWERTY keyboard layout emerged during the industrial revolution when the demand for text increased.

Of course, the difference between mechanical text entry and computer text entry is that computers can do so much more to ensure fast, accurate, and comfortable experiences. Researchers have spent several decades exploiting computing to do exactly this. This research generally falls into three categories: techniques that leverage  discretediscrete text entry: Explicitly and unambiguously selecting characters, words, and phrases for entry (e.g., with a keyboard).  input, like the pressing of a physical or virtual key, techniques that leverage  continuouscontinuous text entry: Providing some ambiguous source of text (e.g., speech, gesture) and having an interface translate it into text.  input, like gestures or speech, and statistical techniques that attempt to  predictpredictive text entry: Providing input that suggests the desired text and having the interface predict what input might be intended.  the text someone is typing to  automate  text entry.

Discrete input

Discrete text input involves entering a single character or word at a time. We refer to them as discrete because of the lack of ambiguity in input: either a button is pressed and a character or word is generated, or it is not. The most common and familiar forms of discrete text entry are keyboards. Keyboards come in numerous shapes and sizes, both physical and virtual, and these properties shape the speed, accuracy, learnability, and comfort of each.

Keyboards can be as simple as 1-dimensional layouts of characters, navigated with a left, right, and select key. These are common on small devices where physical space is scarce. Multiple versions of the iPod, for example, used 1-dimensional text entry keyboards because of its one dimensional click wheel.

Two-dimensional keyboards like the familiar QWERTY layout are more common. And layout matters. The original QWERTY layout, for example, was designed to minimize mechanical failure, not speed or accuracy. The Dvorak layout was designed for speed, placing the most common letters in the home row and maximizes alternation between hands (Dvorak and Dealey 1936):

Not all two-dimensional keyboards have a 1-to-1 mapping from key to character. Some keyboards are virtual, with physical keys for moving an on-screen cursor:

Cell phones in the early 21st century used a  multitap  method in which each key on the 12-key numeric keyboard typically found on pre-smartphone cellphones mapped to three or four numbers or letters. To select the letter you wanted, you would press a key multiple times until the desired letter is displayed. A letter was entered when a new key was struck. If the next letter is on the same key as the previous letter, then the user must wait for a short timeout or hold down the key to commit the desired character. Some researchers sped up multitap techniques like this by using other sensors, such as tilt sensors, making it faster to indicate a character ^{15 15}

Daniel Wigdor and Ravin Balakrishnan (2003). TiltText: using tilt for text input to mobile phones. ACM Symposium on User Interface Software and Technology (UIST).

Some keyboards are eyes-free. For example, long used in courtrooms, stenographers who transcribe human speech in shorthand have used chorded keyboards called stenotypes:

With 2-4 years of training, some stenographers can reach 225 words per minute. Researchers have adapted these techniques to other encodings, such as braille, to support non-visual text entry for people who are blind or low-vision ^{3 3}

Shiri Azenkot, Jacob O. Wobbrock, Sanjana Prasain, Richard E. Ladner (2012). Input finger detection for nonvisual touch screen text entry in Perkinput. Graphics Interface (GI).

On-screen virtual keyboards like those found in modern smartphones introduce some degree of ambiguity into the notion of a discrete set of keys, because touch input can be ambiguous. Some researchers have leveraged additional sensor data to disambiguate which key is being typed, such as which finger is typically used to type a key ^{7 7}

Daewoong Choi, Hyeonjoong Cho, Joono Cheong (2015). Improving Virtual Keyboards When All Finger Positions Are Known. ACM Symposium on User Interface Software and Technology (UIST).

The primary benefit of the discrete input techniques above is that they can achieve relatively fast speeds and low errors because input is reliable: when someone presses a key, they probably meant to. But this is not always true, especially for people with motor impairments that reduce stability of motion. Moreover, there are many people that cannot operate a keyboard comfortably or at all, many contexts in which there simply isn’t physical or virtual space for keys, and many people who do not want to learn an entire new encoding for entering text.

Continuous input

Continuous input is an alternative to discrete input, which involves providing a stream of data as input, which the computer then translates into characters or words. This helps avoid some of the limitations above, but often at the expense of speed or accuracy. For example, popular in the late 1990’s, the Palm Pilot, seen in the video below, used a unistroke gesture alphabet for text entry. It did not require a physical keyboard, nor did it require space on screen for a virtual keyboard. Instead, users learned a set of gestures for typing letters, numbers, and punctuation.

As the video shows, this wasn’t particularly fast or error-free, but it was relatively learnable and kept the Palm Pilot small.

Researchers envisioned other improved unistroke alphabets. Most notably, the EdgeWrite system was designed to stabilize the motion of people with motor impairments by defining gestures that traced around the edges and diagonals of a square ^{16 16}

Jacob O. Wobbrock, Brad A. Myers, John A. Kembel (2003). EdgeWrite: a stylus-based text entry method designed for high accuracy and stability of motion. ACM Symposium on User Interface Software and Technology (UIST).

Whereas the unistroke techniques focus on entering one character at a time, others have explored strokes that compose entire words. Most notably, the SHARK technique allowed users to trace across multiple letters in a virtual keyboard layout, spelling entire words in one large stroke ^{9 9}

Per-Ola Kristensson and Shumin Zhai (2004). SHARK2: a large vocabulary shorthand writing system for pen-based computers. ACM Symposium on User Interface Software and Technology (UIST).

Researchers have built upon this basic idea, allowing users to use two hands instead of one for tablets ^{5 5}

Xiaojun Bi, Ciprian Chelba, Tom Ouyang, Kurt Partridge, Shumin Zhai (2012). Bimanual gesture keyboard. ACM Symposium on User Interface Software and Technology (UIST).

As interfaces move increasingly away from desktops, laptops, and even devices, researchers have investigated forms of text-entry that involve no direct interaction with a device at all. This includes techniques for tracking the position and movement of fingers in space for text entry ^{17 17}

Xin Yi, Chun Yu, Mingrui Zhang, Sida Gao, Ke Sun, Yuanchun Shi (2015). ATK: Enabling ten-finger freehand typing in air based on 3d hand tracking data. ACM Symposium on User Interface Software and Technology (UIST).

Other techniques leveraging spatial memory of keyboard layout for accurate text input on devices with no screens ^{6 6}

Xiang 'Anthony' Chen, Tovi Grossman, George Fitzmaurice (2014). Swipeboard: a text entry technique for ultra-small interfaces that supports novice to expert transitions. ACM Symposium on User Interface Software and Technology (UIST).

Handwriting and speech recognition have also long been a goal in research and industry. While both continue to improve, and speech recognition in particular becomes ubiquitous, both continue to be plagued by recognition errors. People are finding many settings in which these errors are tolerable (or even fun!), but they have yet to reach levels of accuracy to be universal, preferred methods for text entry.

Predictive input

The third major approach to text entry has been predictive input, in which a system simply guesses what a user wants to type based on some initial information. This technique has been used in both discrete and continuous input, and is relatively ubiquitous. For example, before smartphones and their virtual keyboards, most cellphones offered a scheme called T9, which would use a dictionary and word frequencies to predict the most likely word you were trying to type.

These techniques leverage Zipf’s law, an empirical observation that the most frequent words in human language are exponentially more frequent than the less frequent words. The most frequent word in English (“the”) accounts for about 7% of all words in a document, and the second most frequent word (“of”) is about 3.5% of words. Most words rarely occur, forming a long tail of low frequencies.

This law is valuable because it allows techniques like T9 to make predictions about word likelihood. Researchers have exploited it, for example, to increase the relevance of autocomplete predictions ^{11 11}

I. Scott MacKenzie, Hedy Kober, Derek Smith, Terry Jones, Eugene Skepner (2001). LetterWise: prefix-based disambiguation for mobile text input. ACM Symposium on User Interface Software and Technology (UIST).

The past, present, and future of text entry

Our brief tour through the history of text entry reveals a few important trends:

• There are  many  ways to enter text into computers and they all have speed-accuracy tradeoffs. 
• The vast majority of techniques focus on speed and accuracy, and not on the other experiential factors in text entry, such as comfort or accessibility. 
• There are many text entry methods that are inefficient, and yet ubiquitious (e.g., QWERTY); adoption therefore isn’t purely a function of speed and accuracy, but many other factors in society and history. 

As the world continues to age, and computing moves into every context of our lives, text entry will have to adapt to these shifting contexts and abilities. For example, we will have to design new ways of efficiently entering text in augmented and virtual realities, which may require more sophisticated ways of correcting errors from speech recognition. Therefore, while text entry may  seem  like a well-explored area of user interfaces, every new interface we invent demands new forms of text input.

Thus far, we have discussed two forms of input to computers:  pointing  and  text entry . Both are mostly sufficient for operating most forms of computers. But, as we discussed in our chapter on  history , interfaces have always been about augmenting human ability and cognition, and so researchers have pushed far beyond pointing and text to explore many new forms of input. In this chapter, we focus on the use of hands to interact with computers, including  touchscreens ,  pens ,  gestures , and  hand tracking . 

One of the central motivations for exploring hand-based input came from new visions of interactive computing. For instance, in 1991, Mark Weiser, who at the time was head of the very same Xerox PARC that led to the first GUI, wrote in Scientific American about a vision of  ubiquitous computing . ^{36 36}

Mark Weiser (1991). The Computer for the 21st Century. Scientific American 265, 3 (September 1991), 94-104.

Within this vision, input must move beyond the screen, supporting a wide range of embodied forms of computing. We’ll begin by focusing on input techniques that rely on hands, just as pointing and text-entry largely have: physically touching a surface, using a pen-shaped object to touch a surface, and moving the hand or wrist to convey a gesture. Throughout, we will discuss how each of these forms of interaction imposes unique gulfs of execution and evaluation. 

Touch

Perhaps the most ubiquitous and familiar form of hand-based input is using our fingers to touchscreens. The first touchscreens originated in the mid-1960’s. They worked similarly to modern touchscreens, just with less fidelity. The earliest screens consisted of an insulator panel with a resistive coating. When a conductive surface such as a finger made contact, it closed a circuit, flipping a binary input from off to on. It didn’t read position, pressure, or other features of a touch, just that the surface was being touched. Resistive touchscreens came next, and rather than using capacitance to close a circuit, it relied on pressure to measure voltage flow between X wires and Y wires, allowing a position to be read. In the 1980’s, HCI researcher  Bill Buxton ^aa Fun fact: Bill was my “academic grandfather”, meaning that he was my advisor’s advisor.  invented the first multi-touch screen while at the University of Toronto, placing a camera behind a frosted glass panel, and using machine vision to detect different black spots from finger occlusion. This led to several other advancements in sensing technologies that did not require a camera, and in the 1990’s, multi-touchscreens launched on consumer devices, including handheld devices like the  Apple Newton  and the  Palm Pilot . The 2000’s brought even more innovation in sensing technology, eventually making multi-touchscreens small enough to embed in the smartphones we use today. (See  ArsTechnica’s feature on the history of multi-touch  for more history).

As you are probably already aware, touchscreens impose a wide range of gulfs of execution and evaluation on users. On first use, for example, it is difficult to know if a surface is touchable. One will often see children who are used to everything being a touchscreen attempt to touch non-touchscreens, confused that the screen isn’t providing any feedback. Then, of course, touchscreens often operate via complex multi-fingered gestures. These have to be somehow taught to users, and successfully learned, before someone can successfully operate a touch interface. This learning requires careful feedback to address gulfs of evaluation, especially if a gesture isn’t accurately performed. Most operating systems rely on the fact that people will learn how to operate touchscreens from other people, such as through a tutorial at a store. 

While touchscreens might seem ubiquitous and well understood, HCI research has been pushing its limits even further. Some of this work has invented new types of touch sensors. For example, researchers have worked on materials that allow touch surfaces to be cut into arbitrary shapes and sizes other than rectangles. ^{23 23}

Simon Olberding, Nan-Wei Gong, John Tiab, Joseph A. Paradiso, Jürgen Steimle (2013). A cuttable multi-touch sensor. ACM Symposium on User Interface Software and Technology (UIST).

Other researchers have explored ways of more precise sensing of  how  a touchscreen is touched. Some have added speakers to detect how something was grasped or touched ^{25 25}

Makoto Ono, Buntarou Shizuki, Jiro Tanaka (2013). Touch & activate: adding interactivity to existing objects using active acoustic sensing. ACM Symposium on User Interface Software and Technology (UIST).

Commercial touchscreens still focus on single-user interface, only allowing one person at a time to touch a screen. Research, however, has explored many ways to differentiate between multiple people using a single touch-screen. One approach is to have users sit on a surface that determines their identity, differentiating touch input. ^{4 4}

Paul Dietz and Darren Leigh (2001). DiamondTouch: a multi-user touch technology. ACM Symposium on User Interface Software and Technology (UIST).

While these inventions have richly explored many possible new forms of interaction, there is so far very little appetite for touchscreen innovation in industry. Apple’s force-sensitive touchscreen interactions (called “3D touch”) is one example of an innovation that made it to market, but there are some indicators that Apple will abandon it after just a few short years of users not being able to discover it (a classic gulf of execution). 

Pens

In addition to fingers, many researchers have explored the unique benefits of pen-based interactions to support handwriting, sketching, diagramming, or other touch-based interactions. These leverage the skill of grasping a pen or pencil that many are familiar with from manual writing. Pens are similar to using a mouse as a pointing device in that they both involve pointing, but pens are critically different in that they involve direct physical contact with targets of interest. This directness requires different sensing technologies, provides more degrees of freedom for movement and input, and rel more fully on the hand’s complex musculature. 

Some of these pen-based interactions are simply replacements for fingers. For example, the Palm Pilot, popular in the 1990’s, required the use of a stylus for it’s resistive touch-screen, but the pens themselves were plastic. They merely served to prevent fatigue from applying pressure to the screen with a finger and to increase the precision of touch during handwriting or interface interactions. 

However, pens impose their own unique gulfs of execution and evaluation. For example, many pens are not active until a device is set to a mode to receive pen input. The Apple Pencil, for example, only works in particular modes and interfaces, and so it is up to a person to experiment with an interface to discover whether it is pencil compatible. Pens themselves can also have buttons and switches that control modes in software, which require people to learn what the modes control and what effect they have on input and interaction. Pens also sometimes fail to play well with the need to enter text, as typing is faster than tapping one character at a time with a pen. One consequence of these gulfs of execution and efficiency issues is that pens are often used for specific applications such as drawing or sketching, where someone can focus on learning the pen’s capabilities and is unlikely to be entering much text. 

Researchers have explored new types of pen interactions that attempt to break beyond these niche applications. For example, some techniques explore a user using touch input with a non-dominant hand, and pen with a dominant hand ^{8 , 13 8}

William Hamilton, Andruid Kerne, Tom Robbins (2012). High-performance pen + touch modality interactions: a real-time strategy game eSports context. ACM Symposium on User Interface Software and Technology (UIST).

Other pen-based innovations are purely software based. For example, some interactions improve handwriting recognition by allowing users to correct recognition errors while writing ^{28 28}

Michael Shilman, Desney S. Tan, Patrice Simard (2006). CueTIP: a mixed-initiative interface for correcting handwriting errors. ACM Symposium on User Interface Software and Technology (UIST).

Gestures

Whereas touch and pens involve traditional  pointing , gesture-based interactions involve recognizing patterns in hand movement. Some gestures still recognize a gesture from a time-series of points in a 2-dimensional plane, such as the type of multi-touch gestures such as pinching and dragging on a touchscreen, or symbol recognition in handwriting or text entry. This type of gesture recognition can be done with a relatively simple recognition algorithm. ^{38 38}

Jacob O. Wobbrock, Andrew D. Wilson, Yang Li (2007). Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes. ACM Symposium on User Interface Software and Technology (UIST).

Other gestures rely on 3-dimensional input about the position of fingers and hands in space. Some recognition algorithms seek to recognize discrete hand positions, such as when the user brings their thumb and forefinger together (a pinch gesture). ^{37 37}

Andrew D. Wilson (2006). Robust computer vision-based detection of pinching for one and two-handed gesture input. ACM Symposium on User Interface Software and Technology (UIST).

While all of these inventions are exciting in their potential, gestures have significant gulfs of execution and evaluation. How does someone learn the gestures? How do we create tutorials that give feedback on correct gesture “posture”? When someone performs a gesture incorrectly, how can someone undo it if it had an unintended effect? What if the undo gesture is performed incorrectly? These questions ultimately arise from the unreliability of gesture classification.

Hand Tracking

Gesture-based systems look at patterns in hand motion to recognize a set of discrete poses or gestures. This is often appropriate when the user wants to trigger some action, but it does not offer the fidelity to support continuous hand-based actions, such as physical manipulation tasks in 3D space that require continuous tracking of hand and finger positions over time. Hand tracking systems are better suited for these tasks because they treat the hand as a continuous input device, rather than a gesture as a discrete event, estimating in real-time the hand’s position and orientation. 

Most hand tracking systems use cameras and computer vision techniques to track the hand in space. These systems often rely on an approximate model of the hand skeleton, including bones and joints, and solve for the joint angles and hand pose that best fits the observed data. Researchers have used gloves with unique color patterns, shown above, to make the hand easier to identify and to simplify the process of pose estimation. ^{32 32}

Robert Y. Wang and Jovan Popović (2009). Real-time hand-tracking with a color glove. ACM Transactions on Graphics.

Since then, researchers have developed and refined techniques using depth cameras like the Kinect for tracking the hand without the use of markers or gloves. ^{20 , 22 , 31 , 33 20}

Mueller, F., Mehta, D., Sotnychenko, O., Sridhar, S., Casas, D., & Theobalt, C. (2017). Real-time hand tracking under occlusion from an egocentric rgb-d sensor. International Conference on Computer Vision.

For head-mounted virtual and augmented reality systems, a common way to track the hands is through the use of positionally tracked controllers. Systems such as the Oculus Rift or HTC Vive, use cameras and infrared LEDs to track both the position and orientation of the controllers. 

Like gesture interactions, the potential for classification error in hand tracking interactions can impose significant gulfs of execution and evaluation. However, because the applications of hand tracking often involve manipulation of 3D objects rather than invoking commands, the severity of these gulfs may be lower in practice. This is because object manipulation is essentially the same as direct manipulation: it’s easy to see what effect the hand tracking is having and correct it if the tracking is failing. 

While there has been incredible innovation in hand-based input, there are still many open challenges. They can be hard to learn for new users, requiring careful attention to tutorials and training. And, because of the potential for recognition error, interfaces need some way of helping people correct errors, undo commands, and try again. Moreover, because all of these input techniques use hands, few are accessible to people with severe motor impairments in their hands, people lacking hands altogether, or if the interfaces use visual feedback to bridge gulfs of evaluation, people lacking sight. In the next chapter, we will discuss techniques that rely on other parts of a human body for input, and therefore can be more accessible to people with motor impairments.

In the  last chapter , I introduced Weiser’s vision of ubiquitous computing, and argued that part of fulfilling it was fully exploiting the versatility of our hands. In this chapter, we explore all of the sources of human action other than hands — speech, eye gaze, limbs, and entire bodies — and the range of ways this action has been channeled as input to computers. As we shall see, many of the same gulfs of execution and evaluation arise with body-based input as does with hands. Part of this is that all body-based input is based in probabilistically recognizing the actions of muscles. When we speak or make other sounds, we use the muscles in our throat, mouth, and face. When we look with our eyes, we use muscles to shift our gaze, to blink, to squint, and keep our eyes closed. We use our muscles to move and arrange our limbs. Perhaps the only action we perform that isn’t driven by muscles is thought. Our brain drives our muscles through electrical signals, which are detectable through our skin through techniques such as EMG and EEG. All of these forms of action are central to our existence in a physical world. Researchers have explored how to leverage these muscle-based signals to act in virtual worlds.

Speech

Aside from our hands, one of the most versatile ways we use our muscles to engage with our environment is to use our voice. We use it to speak, to hum, to sing, and to make other non-verbal auditory sounds to communicate with humans and other living things. Why not computers?

Voice-based interactions have been a dream for decades, and long-imagined in science fiction. Only after decades of progress on speech recognition, spanning the early 1950’s at Bell Labs, to continued research today in academia, did voice interfaces become reliable enough for interaction. Before the ubiquitous digital voice assistants on smartphones and smart speakers, there were speech recognition programs that dictated text and phone-based interfaces that listened to basic commands and numbers. 

The general process for speech recognition begins with an audio sample. The computer records some speech, encoded as raw sound waves, just like that recorded in a voice memo application. Before speech recognition algorithms even try to recognize speech, they apply several techniques to “clean” the recording, trying to distinguish between background and foreground sound, removing background sound. They segment sound into utterances separated by silence, which can be more easily classified. They rely on large databases of phonetic patterns, which define the kinds of sounds that are used in a particular natural language. They use machine learning techniques to try to classify these phonetic utterances. And then finally, once these phonemes are classified, they try to recognize sequences of phonetic utterances as particular words. More advanced techniques used in modern speech recognition may also analyze an entire sequence of phonetic utterances to try to infer the most likely possible nouns, noun phrases, and other parts of sentences, based on the rest of the content of the sentence. 

Whether speech recognition works well depends on what data is used to train the recognizers. For example, if the data only includes people with particular accents, recognition will work well for those accents and not others. The same applies to pronunciation variations: for example, for a speech recognizer to handle both the American and British spellings pronunciations of “aluminum” and “aluminium”, both pronunciations need to be in the sample data. This lack of diversity in training data is a significant source of recognition failure. It is also a source of gulf of executions, is it is not always clear when speaking to a recognition engine what it is trained on and therefore what enunciation might be necessary to get it to properly recognize a word or phrase.

Of course, even accounting for diversity, technologies for recognizing speech are insufficient on their own to be useful. Early HCI research explored how to translate graphical user interfaces into speech-based interfaces to make GUIs accessible to people who were blind or low vision ^{16 16}

Elizabeth D. Mynatt and W. Keith Edwards (1992). Mapping GUIs to auditory interfaces. ACM Symposium on User Interface Software and Technology (UIST).

Voice can be useful far beyond giving simple commands. Researchers have explored its use in multi-modal contexts in which a user both points and gives speech commands to control complex interfaces ^{2 , 23 2}

Richard A. Bolt (1980). "Put-that-there": Voice and gesture at the graphics interface. In Proceedings of the 7th annual conference on Computer graphics and interactive techniques (SIGGRAPH '80).

Of course, in all of these voice interactions, there are fundamental gulfs of execution and evaluation. Early work on speech interfaces ^{25 25}

Nicole Yankelovich, Gina-Anne Levow, Matt Marx (1995). Designing SpeechActs: issues in speech user interfaces. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

Gaze

Another useful muscle in our bodies are those in our eyes that control our gaze. Gaze is inherently social information that refers to inferences about where someone is looking based on the position of their pupils relative to targets in a space. Gaze tracking technology usually involves machine vision techniques, using infrared illumination of pupils, then time series analysis and individual calibration to track pupil movements over time. These can have many useful applications in things like  virtual reality . For example, in a game, gaze detection might allow non-player characters to notice when you are looking at them, and respond to you. In the same way, gaze information might be propagated to a player’s avatar, moving it’s eyes in the player’s eyes move, helping other human players in social and collaborative games know when a teammate is looking your way. 

Gaze recognition techniques are similar in concept to speech recognition in that they rely on machine learning and large data sets. However, rather than training on speech utterances, these techniques use data sets of eyes to detect and track the movement of pupils over time. The quality of this tracking depends heavily on the quality of cameras, as pupils are small and their movements are even smaller. Pupil movement is also quite fast, and so cameras need to record at a high frame rate to monitor movement. Eyes also have very particular movements, such as  saccades , which are ballistic motions that abruptly shift from one point of fixation to another. Most techniques overcome the challenges imposed by these dynamic properties of eye movement by aggregating and averaging movement over time and using that as input.

Researchers have exploited gaze detection as a form of hands-free interaction, usually discriminating between looking and acting with  dwell-times : if someone looks at something long enough, it is interpreted as an interaction. For example, some work has used dwell times and the top and bottom of a window to support gaze-controlled scrolling through content ^{11 11}

Manu Kumar and Terry Winograd (2007). Gaze-enhanced scrolling techniques. ACM Symposium on User Interface Software and Technology (UIST).

Unfortunately, designing for gaze can require sophisticated knowledge of constraints on human perception. For example, one effort to design a hands-free, gaze-based drawing tool for people with motor disabilities ended up requiring the careful design of commands to avoid frustrating error handling and disambiguation controls ^{9 9}

Anthony J. Hornof and Anna Cavender (2005). EyeDraw: enabling children with severe motor impairments to draw with their eyes. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

Limbs

Whereas our hands, voices, and eyes are all very specialized for social interaction, the other muscles in our limbs are more for movement. That said, they also offer rich opportunities for interaction. Because muscles are activated using electrical signals, techniques like electromyography (EMG) can be used to sense muscle activity through electrodes placed on the skin. For example, this technique was used to sense motor actions in the forearm, discriminating between different grips to support applications like games ^{22 22}

T. Scott Saponas, Desney S. Tan, Dan Morris, Ravin Balakrishnan, Jim Turner, and James A. Landay (2009). Enabling always-available input with muscle-computer interfaces. ACM Symposium on User Interface Software and Technology (UIST).

Other ideas focused on the movement of the tongue muscles using optical sensors and a mouth retainer ^{21 21}

T. Scott Saponas, Daniel Kelly, Babak A. Parviz, and Desney S. Tan (2009). Optically sensing tongue gestures for computer input. ACM Symposium on User Interface Software and Technology (UIST).

While these explorations of limb-based interactions have all been demonstrated to be feasible to agree, as with many new interactions, they impose many gulfs of execution and evaluation that have not yet been explored. 

• How can users learn what a system is trying to recognize about limb movement? 
• How much will users be willing to train the machine learned classifiers used to recognize movement?
• When errors inevitably occur, how can we support people in recovering from error, but also improving classification in the future?

Body

Other applications have moved beyond specific muscles to the entire body and its skeletal structure. The most widely known example of this is the  Microsoft Kinect , shown in the image at the beginning of this chapter. The Kinect used a range of cameras and infrafred projectors to create a depth map of a room, including the structure and posture of the people in this room. Using this depth map, it build basic skeletal models of players, including the precise position and orientation of arms, legs, and heads. This information was then available in real-time for games to use as sources of input (e.g., mapping the skeletal model on to an avatar, using skeletal gestures to invoke commands).

But Kinect was just one technology that emerged from a whole range of explorations of body-based sensing. For example, researchers have explored whole-body gestures, allowing for hands-free interactions. This technique uses the human body as an antenna for sensing, requiring no instrumentation to the environment, and only a little instrumentation of the user ^{4 4}

Gabe Cohn, Daniel Morris, Shwetak Patel, Desney Tan (2012). Humantenna: using the body as an antenna for real-time whole-body interaction. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

Similar techniques sense a person tapping patterns on their body ^{3 3}

Xiang 'Anthony' Chen and Yang Li (2016). Bootstrapping user-defined body tapping recognition with offline-learned probabilistic representation. ACM Symposium on User Interface Software and Technology (UIST).

Whole body interactions pose some unique gulfs of execution and evaluation. For example, a false positive for a hand or limb-based gesture might mean unintentionally invoking a command. We might not expect these to be too common, as we often use our limbs only when we want to communicate. But we use our bodies all the time, moving, walking, and changing our posture. If recognition algorithms aren’t tuned to filter out the broad range of things we do with our body in every day life, the contexts in which body-based input might be used might be severely limited.

This survey of body-based input techniques show a wide range of possible ways of providing input. However, like any new interface ideas, there are many unresolved questions about how to train people to use them and how to support error recovery. There are also many questions about the contexts in which such human-computer interaction might be socially acceptable. For example, how close do we want to be with computers. New questions about  human-computer integration ^{14 14}

Florian Floyd Mueller, Pedro Lopes, Paul Strohmeier, Wendy Ju, Caitlyn Seim, Martin Weigel, Suranga Nanayakkara, Marianna Obrist, Zhuying Li, Joseph Delfa, Jun Nishida, Elizabeth M. Gerber, Dag Svanaes, Jonathan Grudin, Stefan Greuter, Kai Kunze, Thomas Erickson, Steven Greenspan, Masahiko Inami, Joe Marshall, Harald Reiterer, Katrin Wolf, Jochen Meyer, Thecla Schiphorst, Dakuo Wang, and Pattie Maes (2020). Next Steps for Human-Computer Integration. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

• Symbiosis  might entail humans and digital technology working together, in which software works on our behalf, and in return, we maintain and improve it. For example, examples above where computers exhibit agency of their own (e.g., noticing things that we miss, and telling us) are a kind of symbiosis. 
• Fusion  might entail using computers to extend our bodies and bodily experiences. Many of the techniques described above might be described as fusion, where they expand our abilities.

Do you want this future? If so, the maturity of these ideas are not quite sufficient to bring them to market. If not, what kind of alternative visions might you imagine to prevent these futures?

It’s easy to forget that computers didn’t always have screens. The original format for computer output was actually printed, not rendered, and as with even modern printers, printing was slow. It wasn’t until  Ivan Sutherland integrated a CRT screen with a computer in Sketchpad  that screens enabled interactive, immediate feedback experiences.

But display hardware alone was not enough to support the visual interfaces we used today. There was an entire set of new concepts that needed to be invented to make use of screens, ranging from graphics, typography, images, visualization, and animated versions of all of these media. And researchers continue to innovate in these spaces, including in screen technology itself. In this chapter, we’ll review screen technology, and then discuss how these media were translated to computer screens and further enhanced. 

Displays

To begin, let’s consider screens themselves. Some of the earliest screen technology used something called a CRT (Cathode Ray Tube), seen below in the image, and widely used throughout the 1980’s and 90’s for televisions, personal computers, and terminals. CRTs use a vacuum tube with an electron gun and a phosphorescent screen. The device moves the electron gun in a repetitive pattern called a “raster” scan across the two dimensions of the screen, causing the phosphorescent material to glow at whatever points it was active. To make color CRTs, three electron guns are used, one for red, green, and blue color. To determine what to draw on screen, computers stored in memory a long list of color values, and then hardware translated those color values at high frequency during the raster to determine when the electron guns were on and off. When this happens at a high frequency (generally 24 times a second or faster), we get the interactive screens we are used to today. 

The problem with CRTs was that they were  huge and heavy , making them practical only for desktop use. Display technology evolved to solve these problems, with liquid crystal displays (LCDs) making the next leap. LCDs, which are still quite common in devices today, are grids of red, green, and blue liquid crystals.  Liquid crystals  are a state of matter between liquid and solid with varying optical qualities. By placing this grid of liquid crystals on top of a big backlight, these crystals filter light in red, green, and blue at different intensities based on the current running through the liquid. (The video below shows in more detail exactly which materials are used to display varying colors). These crystals are tiny, allowing for screens that were flat and with much lower energy consumption than CRTs. This allowed for entirely new mobile devices like laptops and phones. 

The latest display technology,  light emitting diode  displays, are grids of semiconductors that individually emit their own light when activated. Because LEDs can light themselves, unlike an LCD, which requires a backlight, they can be even thinner and use even less energy. This makes them practical for even smaller devices, such as smartwatches, head-mounted displays for VR, and other devices with small, battery-powered displays.

While these advances in display quality might appear only to affect the quality of a picture, they have had dramatic effects on interfaces. For example, none of the mobile devices in use today would be possible with CRT technology. Screens would be far too big and far too inefficient to make mobile interaction possible. And some sizes of devices, like smartwatches, are only possible with LEDs: their thinness and energy efficiency make room for more battery, which is the critical limiting factor for such small devices. 

Researchers continue to innovate in display technology, especially with new forms of interaction in mind. For example, researchers have enhanced existing display technologies by making them transparent, allowing for new collaboration opportunities while managing privacy ^{14 14}

David Lindlbauer, Toru Aoki, Robert Walter, Yuji Uema, Anita Höchtl, Michael Haller, Masahiko Inami, Jörg Müller (2014). Tracs: transparency-control for see-through displays. ACM Symposium on User Interface Software and Technology (UIST).

Other researchers have experimented with projectors, making them track the movement of projection surfaces ^{12 12}

Johnny C. Lee, Scott E. Hudson, Jay W. Summet, Paul H. Dietz (2005). Moveable interactive projected displays using projector based tracking. ACM Symposium on User Interface Software and Technology (UIST).

Some have experimented with even smaller displays, such as low-energy displays without batteries ^{7 7}

Tobias Grosse-Puppendahl, Steve Hodges, Nicholas Chen, John Helmes, Stuart Taylor, James Scott, Josh Fromm, David Sweeney (2016). Exploring the Design Space for Energy-Harvesting Situated Displays. ACM Symposium on User Interface Software and Technology (UIST).

When we step back and consider the role that displays have played in shaping interfaces, the trend has fundamentally been in creating new  forms  of devices. Form, after all, dictates many things. Large CRTs were heavy and risky to move, and so people designed desks and workstations around which people sat to do work. Smaller LCD displays were fundamental to making mobile devices possible, and so we designed interfaces and interaction techniques that could be used sitting, standing, and even moving, like the tablets and smartphones many of us use today. More energy-efficient LED displays have allowed us to place computing on our wrists, faces, and feet, resulting in new categories of interactions. And the displays being invented in research promise to bring computing even closer to our bodies and our environments, perhaps even  in  our bodies and environments, via implants. This trend is clearly one of deep integration between visual displays of digital information, our built environment, and ourselves. 

Graphics

While displays can enable new interfaces, the content on displays is what makes them valuable, and the basis of all content in displays is  computer graphicsgraphics: The use of computer hardware to create static, dynamic, and interactive two and three dimensional imagery. . Graphics can be anything from basic graphical primitives like lines, rectangles, circles, polygons, and other shapes. In a way, all computer graphics are simulations, reconstructing complex visual scenes out of more primitive shapes, from powerful windowed operating systems to photorealistic scenes in computer generated animated movies. 

Despite all of this complexity, computer graphics have simple foundations. Because displays are organized as 2-dimensional arrays of pixels, graphical rendering is all about  coordinate systems . Coordinate systems have an x-axis, a y-axis, and at each point in a matrix of pixels, a color. Rendering a graphical primitive means specifying a location and color for that shape (and often whether that shape should be filled, or just an outline). 

To create more complex visual scenes, computer graphics involves  compositingcompositing: Layering visual imagery and filters, from top to bottom, to construct scenes from individual layers of graphics. , which entails layering graphical primitives in a particular order. Much like painting, compositing uses layers to construct objects with backgrounds, foregrounds, and other texture. This is what allows us to render buttons with backgrounds and text labels, scroll bars with depth and texture, and windows with drop shadows. 

Operating systems are typically in charge of orchestrating the interface compositing process. They begin with a blank canvas, and then, from back to front, render everything in a recursive object-oriented manner:

• The operating system first renders the background, such as a desktop wallpaper.
• Next, the operating system iterates through visible windows, asking each window to render its contents.
• Each window recursively traverses the hierarchy of user interface element, with each element responsible for compositing its visual appearance.
• After windows are rendered, the operating system composites its own system-wide interface controls, such as task bars, application switcher interfaces.
• Finally, the operating system renders the mouse cursor last, so that it is always visible.
• After everything is done rendering, the entire scene is rendered all at once on the display, so that the person viewing the display doesn’t see the scene partially rendered.

This entire compositing process happens anywhere from 30 to 120 times per second depending on the speed of the computer’s graphics hardware and the refresh rate of the display. The result is essentially the same as any animation, displaying one frame at a time, with each frame making minor adjustments to create the illusion of motion. Computer graphics therefore relies on our human ability to perceive graphical shapes as persistent objects over time. 

Just as important as shapes are images. Just like the screen itself, images are represented by 2-dimensional grids of pixels. As most computer users know, there are countless ways of storing and compressing this pixel data (bmp, pict, gif, tiff, jpeg, png). In the 1980’s and 90’s, these formats mattered for experience, especially on the web: if you stored pixels in order, uncompressed, an image in a browser would be rendered line by line, as it downloaded, but if you stored it out of order, you could render low-resolution versions of a picture as the entire image downloaded. The internet is fast enough today that these format differences don’t affect user experience as much. 

There are many techniques from computer graphics that ensure a high-level of graphical fidelity.  Transparency  is the idea of allowing colors to blend with each other, allowing some of an image to appear behind another.  Anti-aliasing  is the idea of smoothing the ragged edges of the 2D grid of pixels by making some pixels lighter, creating the illusion of a straight line.  Sub-pixel rendering  is a way of drawing images on a screen that leverages the physical properties of LCD screens to slightly increase resolution.  Double-buffering  is a technique of rendering a complete graphical scene off screen, then copying it all at once to the screen, to avoid flickering.  Graphics processing units  (GPUs) take common advanced graphics techniques and move them to hardware so that graphics are high-performance. 

All of these techniques and more are the foundation of  computer graphics , helping ensure that people can focus on content on their screens rather than the pixels that make them up. These concepts do become important, however, if you’re responsible for the graphic design portion of a user interface. Then, the pixels are something you need to design around, requiring deeper knowledge of how images are rendered on screens. 

Typography

While graphical primitives are the foundation of visual scenes, text are the basis of most information in interfaces. A huge part of even being able to operate user interfaces are the words we use to explain the semantics of user interface behavior. In the early days of command line interfaces, typography was rudimentary: just like screens were grids of pixels, text was presented as a grid of characters. This meant that the entire visual language of print, such as typefaces, fonts, font size, and other dimensions of typography, were fixed and inflexible:

Two things changed this. First, Xerox PARC, in its envisioning of graphical user interfaces, brought typography to the graphical user interface. The conduit for this was primarily its vision of word processing, which attempted to translate the ideas from print to the screen, bringing typefaces, fonts, font families, font sizes, font weights, font styles, ligature, kerning, baselines, ascents, descents, and other ideas to graphical user interfaces. Concepts like typefaces—the visual design—and fonts—a particular size and weight of a typeface—had been long developed in print, and were directly adapted to screen. This required answering questions about how to take ideas optimized for ink and paper and translate them to discrete 2-dimensional grids of pixels. Ideas like anti-aliasing and sub-pixel rendering mentioned above, which smooth the harsh edges of pixels, were key to achieving readability. 

The second decision that helped bring typography to user interfaces was Steve Jobs taking a calligraphy course at Reed College (calligraphy is like typography, but by hand). He saw that text could be art, that it could be expressive, and that it was central to differentiating the Mac from the full-text horrors of command lines. And so when he saw Xerox PARC’s use of typography and envisioned the Mac, type was  at the center of his vision . 

Parallel to these efforts was the need to represent all of the symbols in natural language. One of the first standards was ASCII, which represented the Roman characters and Arabic numbers in English, but nothing else.  Unicode  brought nearly the entire spectrum of symbols and characters to computing, supporting communication within and between every country on Earth. 

Research on the  technology  of typography often focuses on readability. For example, Microsoft, including researchers from Microsoft Research, developed a sub-pixel font rendering algorithm called ClearType, which they found significantly decreased average reading time ^{5 5}

Andrew Dillon, Lisa Kleinman, Gil Ok Choi, Randolph Bias (2006). Visual search and reading tasks using ClearType and regular displays: two experiments. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

Animation

While all of the static forms we’ve discussed above are powerful on their own, efforts to animate these forms offered to increase the expressive power of digital media. One early work investigated foundations of animation that might be brought from film animation, including principles of  solidity ,  exaggeration , and  reinforcement , which were long used to give life to static images ^{4 4}

Bay-Wei Chang and David Ungar (1993). Animation: from cartoons to the user interface. ACM Symposium on User Interface Software and Technology (UIST).

While this basic idea of animating interfaces was straightforward, finding ways to seamlessly implement animation into interfaces was not. Having parts of interfaces move required careful management of the position of interface elements over time, and these were incompatible with the notion of view hierarchies determining element positions at all times. Some of the earliest ideas involved defining  constraintsconstraint: A technique of software development in which the value of one thing is made to always be equal to some transofrmation of another value (e.g., the left edge of this square should always be aligned horizontally with the right edge of this circle.) , and letting those constraints determine position over time ^{17 17}

Brad A. Myers, Robert C. Miller, Rich McDaniel, Alan Ferrency (1996). Easily adding animations to interfaces using constraints. ACM Symposium on User Interface Software and Technology (UIST).

All of these research ideas are now ubiquitous in toolkits like Apple’s  Core Animation  and Google’s  Material Design , which make it easy to express animation states without having to manage low-level details of user interface rendering. 

Data

All of the techniques in the prior section come together in many complex 2D visualizations. One domain in particular that has leveraged displays, graphics, typography, and animation is data visualization. This has provoked questions about how to best render and interact with data sets. The field of data visualization (also known as information visualization) has explored these questions, building upon data visualization efforts in print ^{15 15}

Liu, Shixia, Weiwei Cui, Yingcai Wu, Mengchen Liu (2014). A survey on information visualization: recent advances and challenges. The Visual Computer 30, no. 12 (2014): 1373-1393.

The foundations of data visualization are relatively stable:

Each of these phases has its own interactive complexities. Data transformation often requires interaction in order to “wrangle” data into a structure suitable for visualization ( ^{10 10}

Sean Kandel, Jeffrey Heer, Catherine Plaisant, Jessie Kennedy, Frank van Ham, Nathalie Henry Riche, Chris Weaver, Bongshin Lee, Dominique Brodbeck, Paolo Buono (2011). Research directions in data wrangling: Visualizations and transformations for usable and credible data. Information Visualization.

Because of the complexity of this pipeline, actually rendering data visualizations has required more direct toolkit support for abstracting away some of the low-level complexities of these phases. 

Toolkits like  Protovis ^{1 1}

Michael Bostock and Jeffrey Heer (2009). Protovis: A Graphical Toolkit for Visualization. IEEE Transactions on Visualizations and Computer Graphics.

While the 2000’s saw 3D begin to dominate games and movies, 2D rendering is still at the heart of interactions with user interfaces. While much of the research in this space has moved on to interactive 3D experiences, the foundations built over the past fifty years remain with us. Will 2D always be the foundation of interfaces, or will we eventually all shift to pure 3D interfaces?

In some sense, this question is less about technology and more about media. Print, film, animation, games, and other genres of content have often shaped the types of experiences we have on computers. There is no sign that these diverse genres of media are going away; rather, we just continue to invent new media, and add it to an already complex array of visual content. For example, one could imagine a world that was more universally accessible, in which auditory content become more ubiquitous. Podcasts and increasing support for screen readers are one sign that while visual displays may reign, we may begin to broaden the senses we use to interact with computers. 



In the last chapter, we discussed how interfaces communicate data, content, and animations on two-dimensional screens. However, human visual perception is not limited to two dimensions: we spend most of our lives interacting in three-dimensional space. The prevalence of flat screens on our computers, smartwatches, and smartphones is primarily due to limitations in the technology. However, with advances in 3D graphics, display technology, and sensors, we’ve been able to build virtual and augmented reality systems that allow us to interact with technology in immersive 3D environments.

Computing has brought opportunities to create dynamic virtual worlds, but with this has come the need to properly map those virtual worlds onto our ability to perceive our environment. This poses a number of interdisciplinary research challenges that has seen renewed interest over the last decade. In this chapter, we’ll explore some of the current research trends and challenges in building virtual reality and augmented reality systems. 

Virtual reality

The goal of  virtual realityvirtual reality: Interfaces that attempt to achieve immersion and presence through audio, vidoe, and tactile illusion.  (VR) has always been the same as the 2D virtual realities we discussed in the previous chapter:  immersionimmersion: The illusory experience of interacting with virtual content for what represents, as opposed to the low-level light, sound, haptics and other sensory information from whic it is composed. . Graphical user interfaces, for example, can result in a degree of immersive flow when well designed. Movies, when viewed in movie theaters, are already quite good at achieving immersion. Virtual reality aims for  total  immersion, while integrating interactivity. 

But VR aims for more than immersion. It also seeks to create a sense of  presencepresence: The sense of being physically located in a place and space. . Presence is an inherent quality of being in the physical world. And since we cannot escape the physical world, there is nothing about strapping on a VR headset that inherently lends itself to presence: by default, our exerience will be standing in a physical space with our head covered by a computer. And so a chief goal of VR is creating such a strong illusion that users perceive the visual sensations of being in a virtual environment, actually engaged and psychologically present in the environment.

One of the core concepts behind achieving presence in VR is enabling the perception of 3D graphics by presenting a different image to each eye using stereo displays. By manipulating subtle differences between the image in each eye, it is possible to make objects appear at a particular depth. An early example using this technique was the  View-Master , a popular toy developed in the 1930s that showed a static 3D scene using transparencies on a wheel. 

One of the first virtual reality systems to be connected to a computer was Ivan Sutherland’s “Sword of Damocles”, shown below, and created in 1968. With his student Bob Sproull, the headmounted display they created was far too heavy for someone to wear. The computer generated basic wireframe rooms and objects. This early work was pure proof of concept, trying to imagine a device that could render  any  interactive virtual world. 

In the 1990s, researchers developed CAVE systems (cave automatic virtual environments) to explore fully immersive virtual reality. These systems used multiple projectors with polarized light and special 3D glasses to control the image seen by each eye. After careful calibration, a CAVE user will perceive a fully 3D environment with a wide field of view. 

Jaron Lanier, one of the first people to write and speak about VR in its modern incarnation, originally viewed VR as an “empathy machine” capable of helping humanity have experiences that they could not have otherwise. In  an interview with the Verge in 2017 , he lamented how much of this vision was lost in the modern efforts to engineer VR platforms:

While modern VR efforts have often been motivated by ideas of empathy, most of the research and development investment has focused on fundamental engineering and design challenges over content:

• Making hardware light enough to fit comfortably on someone’s head
• Untethering a user from cables, allowing freedom of movement
• Sensing movement at a fidelity to mirror movement in virtual space
• Ensuring users don’t hurt themselves in the physical world because of total immersion
• Devising new forms of input that work when a user cannot even see their hands
• Improving display technology to reduce simulator sickness
• Adding haptic feedback to further improve as sense of immersion

Most HCI research on these problems has focused on new forms of input and output. For example, researchers have considered ways of using the backside of a VR headset as touch input ^{10 , 12 , 18 , 25 10}

Jan Gugenheimer, David Dobbelstein, Christian Winkler, Gabriel Haas, Enrico Rukzio (2016). Facetouch: Enabling touch interaction in display fixed uis for mobile virtual reality. ACM Symposium on User Interface Software and Technology (UIST).

Some approaches to VR content have focused on somewhat creepy ways of exploiting immersion to steer users’ attention or physical direction of motion. Examples include using a haptic reflex on the head to steer users ^{15 15}

Yuki Kon, Takuto Nakamura, Hiroyuki Kajimoto (2017). HangerOVER: HMD-embedded haptics display with hanger reflex. ACM SIGGRAPH Emerging Technologies.

Other more mainstream applications have included training simulations and games ^{27 27}

Zyda, M. (2005). From visual simulation to virtual reality to games. Computer, 38(9), 25-32.

Augmented reality

 Augmented realityaugumented reality: An interface that layers on interactive virtual content into the physical world.  (AR), in contrast to virtual reality, does not aim for complete immersion in a virtual environment, but to alter reality to support human augmentation. ^aa Augmented reality and mixed reality are sometimes used interchangeably, both indicating approaches that superimpose information onto our view of the physical world. However, marketing has often defined mixed reality as augmented reality plus interaction with virtual components.  The vision for AR goes back to Ivan Sutherland (of  Sketchpad ) in the 1960’s, who dabbled with head mounted displays for augmentation. Only in the late 1990’s did the hardware and software sufficient for augmented reality begin to emerge, leading to research innovation in displays, projection, sensors, tracking, and, of course, interaction design ^{2 2}

Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B (2001). Recent advances in augmented reality. IEEE Computer Graphics and Applications.

Technical and design challenges in AR are similar to those in VR, but with a set of additional challenges and constraints. For one, the requirements of tracking accuracy and latency are much stricter, since any errors in rendering virtual content will be made more obvious by the physical background. For head-mounted AR systems, the design of displays and optics are challenging, since they must be able to render content without obscuring the user’s view of the real world. When placing virtual objects in a physical space, researchers have looked at how to match the lighting of the physical space so virtual objects look believable ^{16 , 22 16}

P. Lensing and W. Broll (2012). Instant indirect illumination for dynamic mixed reality scenes. IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

Interaction issues here abound. How can users interact with the same object? What happens if the source and target environments are not the same physical dimensions? What kind of broader context is necessary to support collaboration? Some research has tried to address some of these lower-level questions. For example, one way for remote participants to interact with a shared object is to make one of them virtual, tracking the real one in 3D ^{19 19}

Ohan Oda, Carmine Elvezio, Mengu Sukan, Steven Feiner, Barbara Tversky (2015). Virtual replicas for remote assistance in virtual and augmented reality. ACM Symposium on User Interface Software and Technology (UIST).

Some interaction research attempts to solve lower-level challenges. For example, many AR glasses have narrow field of view, limiting immersion, but adding further ambient projections can widen the viewing angle ^{5 5}

Hrvoje Benko, Eyal Ofek, Feng Zheng, Andrew D. Wilson (2015). FoveAR: Combining an Optically See-Through Near-Eye Display with Projector-Based Spatial Augmented Reality. ACM Symposium on User Interface Software and Technology (UIST).

From smartphone-based VR, to the more advanced augmented and mixed reality visions blending the physical and virtual worlds, designing interactive experiences around 3D output offers great potential for new media, but also great challenges in finding meaningful applications and seamless interactions. Researchers are still hard at work trying to address these challenges, while industry forges ahead on scaling the robust engineering of practical hardware.

There are also many open questions about how 3D output will interact with the world around it:

• How can people seamlessly switch between AR, VR, and other modes of use while performing the same task?
• How can VR engage groups when not everyone has a headset?
• What activities are VR and AR suitable for? What tasks are they terrible for?

These and a myriad of other questions are critical for determining what society chooses to do with AR and VR and how ubiquitous it becomes.

Whereas the three-dimensional output we discussed in  the previous chapter  can create entirely new virtual worlds, or enhanced versions of our own world, it’s equally important that software be able to interface with the physical world. One of the first research papers to recognize this was the seminal work  Tangible Bits: Towards Seamless Interfaces between People, Bits and Atoms . ^{8 8}

Hiroshi Ishii and Brygg Ullmer. (1997). Tangible bits: Towards seamless interfaces between people, bit, atoms. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

This basic idea, that the massive rift between our physical and digital worlds should be bridged by a diversity of new physical input and output media, dovetailed Weiser’s vision of ubiquitous computing. ^{19 19}

Mark Weiser (1991). The Computer for the 21st Century. Scientific American 265, 3 (September 1991), 94-104.

The impact of this vision was an explosion of research to create more physical input and output. In this chapter, we’ll discuss this research, and the history of physical computing that it build upon. 

Printing

Most people who’ve interacted with computers have used a printer at some point in their life to turn bits into atoms. The basic principle is simple: take a digital document and create a copy with paper and some kind of marking substance, such as spraying ink (inkjets), burning the paper (laser printers), or one of a variety of other approaches. 

Why was printing so important as computers first became ubiquitous?

For most of the 20th century, paper was the central medium for transmitting information. We used typewriters to create documents. We stored documents in file cabinets. We used photocopiers to duplicate documents. The very notion of a file and folder in graphical user interfaces mirrored the ubiquity of interacting with paper. Printers were a necessary interface between the nascent digital world and the dominant paper-based world of information. 

One of the earliest forms of digital printing was the stock ticker machine, which printed text on a thin strip of paper on which messages are recorded:



While ticker tape was the most ubiquitous before the advent of digital computers, printing devices had long been in the imagination of early computer scientists.  Charles Babbage  was an English mathematician and philosopher who first imagined the concept of a programmable digital computer in 1822. He also imagined, however, a mechanical printing device that could print the results of his imagined differencing machine. Eventually, people began to engineer these printing devices. For example, consider the dot matrix printer shown in the video below, which printed a grid of ink. These printers were ubiquitous in the 1980’s, and a general extension of both ticker tape printers and a deeper integration with digital printing from general purpose computers. This was the beginning of a much greater diversity of printing mediums we use today, which use toner, liquid ink, solid ink, or dye-sublimation. 

Most of these printing technologies have focused on 2-dimensional output because the documents we create on computers are primarily 2-dimensional. However, as the plastics industry evolved, and plastic extruders reshaped manufacturing, interest in democratizing access to 3D fabrication expanded. This led to the first 3D printer, described in a U.S. patent in 1984, which described a process for generating 3D objects by creating cross-sectional patterns of an object. ^{7 7}

Hull, C. (1984). Patent No. US4575330 A. United States of America.

While the basic idea of 3D printing is now well established, and the market for 3D printers is expanding, researchers have gone well beyond the original premise. Much of this exploration has been in exploring materials other than plastic. One example of this is an approach to printing  interactive electromechanical objects with wound in place coils . ^{17 17}

Huaishu Peng, François Guimbretière, James McCann, Scott Hudson (2016). A 3D Printer for Interactive Electromagnetic Devices. ACM Symposium on User Interface Software and Technology (UIST).

Another project explored printing with wool and wool blend yarn to create soft felt objects rather than just rigid plastic objects. ^{6 6}

Scott E. Hudson (2014). Printing teddy bears: a technique for 3D printing of soft interactive objects. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).

These new forms of printing pose new challenges in authoring. One cannot print arbitrary 3D shapes with 3D printers, and so this requires authors to understand the limitations of printing methods. This lack of understanding can lead to failed prints, which can be frustrating and expensive. Some researchers have explored “patching” 3D objects, calculating additional parts that must be printed and mounted onto an existing failed print to create the desired shape. ^{18 18}

Alexander Teibrich, Stefanie Mueller, François Guimbretière, Robert Kovacs, Stefan Neubert, Patrick Baudisch (2015). Patching Physical Objects. ACM Symposium on User Interface Software and Technology (UIST).