Après les deux chapitres introductifs, The Design of Everyday Things‌ devient rapidement un ouvrage de psychologie cognitive. Après tout, la première édition était parue sous le titre ‌The Psychology of Everyday Things. Pas que ce soit un problème : les concepts scientifiques sont toujours illustrés par des exemples évocateurs, pour former une passionnante introduction à la conception centrée sur l’utilisateur.

Avec le bénéfice du recul, The Design of Everyday Things‌ peut être lu comme un petit manuel du design industriel contemporain, notamment dans le domaine de l’informatique1. Ses critiques peuvent aisément être appliquées aux pires produits récents d’Apple, par exemple, qui semblent avoir « oublié » les conseils de Norman. Concentré sur les objets tangibles, The Design of Everyday Things ignore presque entièrement les logiciels et les services, qui profiteraient pourtant d’une réflexion sur les affordances et les signifieurs.

Notes archivistiques

En double. Un exemplaire neuf, avec mes annotations. Un exemplaire d’occasion, vierge.


Découvrir et comprendre, p. 3 :

Two of the most important characteristics of good design are discoverability and understanding. Discoverability: Is it possible to even figure out what actions are possible and where and how to perform them? Understanding: What does it all mean? How is the product supposed to be used? What do all the different controls and settings mean?

L’introduction du terme « affordance » dans le champ du design, p. 11 :

The term affordance refers to the relationship between a physical object and a person (or for that matter, any interacting agent, whether animal or human, or even machines and robots). An affordance is a relationship between the properties of an object and the capabilities of the agent that determine just how the object could possibly be used. A chair affords (‘is for’) support and, therefore, affords sitting. Most chairs can also be carried by a single person (they afford lifting), but some can only be lifted by a strong person or by a team of people. If young or relatively weak people cannot lift a chair, then for these people, the chair does not have that affordance, it does not afford lifting.

The presence of an affordance is jointly determined by the qualities of the object and the abilities of the agent that is interacting. This relational definition of affordance gives considerable difficulty to many people. We are used to thinking that properties are associated with objects. But affordance is not a property. An affordance is a relationship. Whether an affordance exists depends upon the properties of both the object and the agent.

Affordances et signifieurs, p. 14 :

Affordances determine what actions are possible. Signifiers communicate where the action should take. We need both.

People need some way of understanding the product or service they wish to use, some sign of what it is for, what is happening, and what the alternative actions are. People search for clues, for any sign that might help them cope and understand. It is the sign that is important, anything that might signify meaningful information. Designers need to provide these clues. What people need, and what designers must provide, are signifiers. Good design requires, among other things, good communication of the purpose, structure, and operation of the device to the people who use it. That is the role of the signifier.

Ce que les gens veulent vraiment, p. 43 :

Harvard Business School marketing professor Theodore Levitt once pointed out, ‘People don’t want to buy a quarter-inch drill. They want a quarter-inch hole!’ Levitt’s example of the drill implying that the goal is really a hole is only partially correct, however. When people go to a store to buy a drill, that is not their real goal. But why would anyone want a quarter-inch hole? Clearly that is an intermediate goal. Perhaps they wanted to hang shelves on the wall. Levitt stopped too soon.

Once you realize that they don’t really want the drill, you realize that perhaps they don’t really want the hole, either: they want to install their bookshelves. Why not develop methods that don’t require holes? Or perhaps books that don’t require bookshelves. (Yes, I know: electronic books, e-books.)

Tout fonctionne parfaitement dans la conspiration du silence, p. 61 :

Suppose I try to use an everyday thing, but I can’t. Who is at fault: me or the thing? We are apt to blame ourselves, especially if others are able to use it. Suppose the fault really lies in the device, so that lots of people have the same problems. Because everyone perceives the fault to be his or her own, nobody wants to admit to having trouble. This creates a conspiracy of silence, where the feelings of guilt and helplessness among people are kept hidden.

La lente disparition des signifieurs porteurs de connaissance, p. 111 :

As we move away from many physical aids, such as printed books and magazines, paper notes, and calendars, much of what we use today as knowledge in the world will become invisible. Yes, it will all be available on display screens, but unless the screens always show this material, we will have added to the burden of memory in the head. We may not have to remember all the details of the information stored away for us, but we will have to remember that it is there, that it needs to be redisplayed at the appropriate time for use or for reminding.

Les choses nous rendent intelligents, p. 112 :

In an earlier book, Things That Make Us Smart, I argued that it is this combination of technology and people that creates super-powerful beings. Technology does not make us smarter. People do not make technology smart. It is the combination of the two, the person plus the artifact, that is smart. Together, with our tools, we are a powerful combination. On the other hand, if we are suddenly without these external devices, then we don’t do very well. In many ways, we do become less smart.

Si rien d’autre ne marche, standardisez, p. 155 :

If all else fails, standardize. Standardization is indeed the fundamental principle of desperation: when no other solution appears possible, simply design everything the same way, so people only have to learn once. If all makers of faucets could agree on a standard set of motions to control amount and temperature (how about up and down to control amount – up meaning increase – and left and right to control temperature, left meaning hot?), then we could all learn the standards once, and forever afterward use the knowledge for every new faucet we encountered.

La vraie définition du « skeuomorphisme », je me rends compte que j’ai longtemps utilisé les mots de Norman sans connaitre son œuvre, p. 159 :

Skeuomorphic is the technical term for incorporating old, familiar ideas into new technologies, even though they no longer play a functional role. Skeuomorphic designs are often comfortable for traditionalists, and indeed the history of technology shows that new technologies and materials often slavishly imitate the old for no apparent reason except that is what people know how to do. Early automobiles looked like horse-driven carriages without the horses (which is also why they were called horseless carriages); early plastics were designed to look like wood; folders in computer file systems often look the same as paper folders, complete with tabs. One way of overcoming the fear of the new is to make it look like the old. This practice is decried by design purists, but in fact, it has its benefits in easing the transition from the old to the new. It gives comfort and makes learning easier. Existing conceptual models need only be modified rather than replaced. Eventually, new forms emerge that have no relationship to the old, but the skeuomorphic designs probably helped the transition.

Le besoin de meilleures interfaces personne-machine, p. 185 :

A more powerful approach is to develop intelligent computer systems, using good search and appropriate reasoning techniques (artificial-intelligence decision-making and problem-solving). The difficulties here are in establishing the interaction of the people with the automation: human teams and automated systems have to be thought of as collaborative, cooperative systems. Instead, they are often built by assigning the tasks that machines can do to the machines and leaving the humans to do the rest. This usually means that machines do the parts that are easy for people, but when the problems become complex, which is precisely when people could use assistance, that is when the machines usually fail. (I discuss this problem extensively in The Design of Future Things.)

Les conséquences de la panne des automatisations, p. 213 :

When automation works, it is wonderful, but when it fails, the resulting impact is usually unexpected and, as a result, dangerous. Today, automation and networked electrical generation systems have dramatically reduced the amount of time that electrical power is not available to homes and businesses. But when the electrical power grid goes down, it can affect huge sections of a country and take many days to recover. With self-driving cars, I predict that we will have fewer accidents and injuries, but that when there is an accident, it will be huge.

L’« erreur humaine » comme erreur des interfaces personne-machine, p. 215 :

Difficulties arise when we do not think of people and machines as collaborative systems, but assign whatever tasks can be automated to the machines and leave the rest to people. This ends up requiring people to behave in a machine-like fashion, in ways that differ from human capabilities. We expect people to monitor machines, which means keeping alert for long periods, something we are bad at. We require people to do repeated operations with the extreme precision and accuracy required by machines, again something we are not good at. When we divide up the machine and human components of a task in this way, we fail to take advantage of human strengths and capabilities but instead rely upon areas where we are genetically, biologically unsuited. Yet, when people fail, they are blamed.

What we call ‘human error’ is often simply a a human action that is inappropriate for the needs of technology. As a result, it flags a deficit in our technology. It should not be thought of as error. We should eliminate the concept of error: instead, we should realize that people can use assistance in translating their goals and plans into the appropriate form for technology.

Quantitatif ou qualitatif, p. 225 :

The virtues of the use of big data for market research are frequently touted. The deficiencies are seldom noted, except for concerns about invasions of personal privacy. In addition to privacy issues, the real problem is that numerical correlations say nothing of people’s real needs, of their desires, and of the reasons for their activities. As a result, these numerical data can give a false impression of people. But the use of big data and market analytics is seductive: no travel, little expense, and huge numbers, sexy charts, and impressive statistics, all very persuasive to the executive team trying to decide which new products to develop. After all, what would you trust – neatly presented, colorful charts, statistics, and significance levels based on millions of observations, or the subjective impressions of a motley crew of design researchers who worked, slept, and ate in remote villages, with minimal sanitary facilities and poor infrastructure?

Complexe et compliqué, p. 247 :

Someone else’s kitchen looks complicated and confusing, but your own kitchen does not. The same can probably be said for every room in the home. Notice that this feeling of confusion is really one of knowledge. My kitchen looks confusing to you, but not to me. In turn, your kitchen looks confusing to me, but not to you. So the confusion is not in the kitchen: it is in the mind. ‘Why can’t things be made simple?’ goes the cry. Well, one reason is that life is complex, as are the tasks we encounter. Our tools must match the tasks.

Le bon vieil argument socratique, p. 285 :

Once technology can do our arithmetic, can remember for us, and can tell us how to behave, then we have no need to learn these things. But the instant the technology goes away, we are left helpless, unable to do any basic functions. We are now so dependent upon technology that when we are deprived, we suffer. We are unable to make our own clothes from plants and animal skins, unable to grow and harvest crops or catch animals. Without technology, we would starve or freeze to death. Without cognitive technologies, will we fall into an equivalent state of ignorance?

Heureusement facile à déminer, p. 286 :

Reliance on technology is a benefit to humanity. With technology, the brain gets neither better nor worse. Instead, it is the task that changes. Human plus machine is more powerful than either human or machine alone.

  1. Norman a travaillé chez Apple dans les années suivant la publication de DOET, d’abord comme « architecte de l’expérience utilisateur », puis comme vice-président de l’Advanced Technology Group. ↩︎