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What gets confused depends heavily upon history: the aspects that have allowed us to distinguish among the objects in the past. When the rules for discrimination change, people can become confused and make errors. With time, they will adjust and learn to discriminate just fine and may even forget the initial period of confusion. The problem is that in many circumstances, especially one as politically charged as the size, shape, and color of currency, the public’s outrage prevents calm discussion and does not allow for any adjustment time. Consider this as an example of design principles
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Constraints are powerful tools for the designer:
Arbitrary knowledge can be classified as the simple remembering of things that have no underlying meaning or structure.
This also applies to the learning of the arbitrary key sequences, commands, gestures, and procedures of much of our modern technology: This is rote learning, the bane of modern existence.
Rote learning creates problems. First, because what is being learned is arbitrary, the learning is difficult: it can take considerable time and effort. Second, when a problem arises, the memorized sequence of actions gives no hint of what has gone wrong, no suggestion of what might be done to fix the problem. Although some things are appropriate to learn by rote, most are not. Alas, it is still the dominant method of instruction in many school systems, and even for much adult training.
When things make sense, they correspond to knowledge that we already have, so the new material can be understood, interpreted, and integrated with previously acquired material. Now we can use rules and constraints to help understand what things go together. Meaningful structure can organize apparent chaos and arbitrariness.
The design implications are clear: provide meaningful structures. Perhaps a better way is to make memory unnecessary: put the required information in the world. This is the power of the traditional graphical user interface with its old-fashioned menu structure. When in doubt, one could always examine all the menu items until the desired one was found.
The most effective way of helping people remember is to make it unnecessary.
One way to simplify thought is to use simplified models, approximations to the true underlying state of affairs. Science deals in truth, practice deals with approximations. Practitioners don’t need truth: they need results relatively quickly that, although inaccurate, are “good enough” for the purpose to which they will be applied.
There are five memory slots in short-term memory. Each time a new item is added, it occupies a slot, knocking out whatever was there beforehand. Is this model true? No, not a single memory researcher in the entire world believes this to be an accurate model of STM. But it is good enough for applications. Make use of this model, and your designs will be more usable.
Conceptual models are powerful explanatory devices, useful in a variety of circumstances. They do not have to be accurate as long as they lead to the correct behavior in the desired situation.
When precision is required, use a calculator. That’s what machines are good for: providing great precision. For most purposes, estimates are good enough.
here is a simpler way to dramatically enhance both memory and accuracy: write things down. Writing is a powerful technology: why not use it? Use a pad of paper, or the back of your hand. Write it or type it. Use a phone or a computer. Dictate it. This is what technology is for.
Effective memory uses all the clues available: knowledge in the world and in the head, combining world and mind.
There are two different aspects to a reminder: the signal and the message. Just as in doing an action we can distinguish between knowing what can be done and knowing how to do it, in reminding we must distinguish between the signal—knowing that something is to be remembered, and the message—remembering the information itself.
The ideal reminder has to have both components: the signal that something is to be remembered, and then the message of what it is.
The tradeoff is that to use our knowledge in the head, we have to be able to store and retrieve it, which might require considerable amounts of learning. Knowledge in the world requires no learning, but can be more difficult to use. And it relies heavily upon the continued physical presence of the knowledge; change the environment and the knowledge might be lost.
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.
Natural mappings are those where the relationship between the controls and the object to be controlled (the burners, in this case) is obvious. Depending upon circumstances, natural mappings will employ spatial cues.
With a good natural mapping, the relationship of the controls to the burner is completely contained in the world; the load on human memory is much reduced. With a bad mapping, however, a burden is placed upon memory, leading to more mental effort and a higher chance of error.
What is natural depends upon point of view, the choice of metaphor, and therefore, the culture. The design difficulties occur when there is a switch in metaphors.
chapter focuses upon the knowledge in the world: how designers can provide the critical information that allows people to know what to do, even when experiencing an unfamiliar device or situation.
Constraints are powerful clues, limiting the set of possible actions. The thoughtful use of constraints in design lets people readily determine the proper course of action, even in a novel situation.
Physical constraints are made more effective and useful if they are easy to see and interpret, for then the set of actions is restricted before anything has been done.
Why does inelegant design persist for so long? This is called the legacy problem, and it will come up several times in this book. Too many devices use the existing standard—that is the legacy.
Note that a superior solution would be to solve the fundamental need—solving the root need. After all, we don’t really care about keys and locks: what we need is some way of ensuring that only authorized people can get access to whatever is being locked. Instead of redoing the shapes of physical keys, make them irrelevant.
Cultural constraints are likely to change with time.
Semantics is the study of meaning. Semantic constraints are those that rely upon the meaning of the situation to control the set of possible actions.
Semantic constraints rely upon our knowledge of the situation and of the world. Such knowledge can be a powerful and important clue. But just as cultural constraints can change with time, so, too, can semantic ones. Extreme sports push the boundaries of what we think of as meaningful and sensible. New technologies change the meanings of things. And creative people continually change how we interact with our technologies and one another.
An interlock forces operations to take place in proper sequence. Microwave ovens and devices with interior exposure to high voltage use interlocks as forcing functions to prevent people from opening the door of the oven or disassembling the devices without first turning off the electric power: the interlock disconnects the power the instant the door is opened or the back is removed.
Real, natural sound is as essential as visual information because sound tells us about things we can’t see, and it does so while our eyes are occupied elsewhere. Natural sounds reflect the complex interaction of natural objects:
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.
If the number of accidents blamed upon human error were 1 to 5 percent, I might believe that people were at fault. But when the percentage is so high, then clearly other factors must be involved. When something happens this frequently, there must be another underlying factor.
Physical limitations are well understood by designers; mental limitations are greatly misunderstood. We should treat all failures in the same way: find the fundamental causes and redesign the system so that these can no longer lead to problems.
But it doesn’t cure the problem: the same error will occur over and over again. Instead, when an error happens, we should determine why, then redesign the product or the procedures being followed so that it will never occur again or, if it does, so that it will have minimal impact.
Root cause analysis is the name of the game: investigate the accident until the single, underlying cause is found.
Trying to find the cause of an accident sounds good but it is flawed for two reasons. First, most accidents do not have a single cause: there are usually multiple things that went wrong, multiple events that, had any one of them not occurred, would have prevented the accident.
Root cause analysis is intended to determine the underlying cause of an incident, not the proximate cause. The Japanese have long followed a procedure for getting at root causes that they call the “Five Whys,”
The Five Whys do not guarantee success. The question why is ambiguous and can lead to different answers by different investigators. There is still a tendency to stop too soon, perhaps when the limit of the investigator’s understanding has been reached. It also tends to emphasize the need to find a single cause for an incident, whereas most complex events have multiple, complex causal factors. Nonetheless, it is a powerful technique.
When people err, change the system so that type of error will be reduced or eliminated. When complete elimination is not possible, redesign to reduce the impact.
One big problem is that the natural tendency to blame someone for an error is even shared by those who made the error, who often agree that it was their fault. People do tend to blame themselves when they do something that, after the fact, seems inexcusable.
If the system lets you make the error, it is badly designed. And if the system induces you to make the error, then it is really badly designed.
Why do people err? Because the designs focus upon the requirements of the system and the machines, and not upon the requirements of people.
People are creative, constructive, exploratory beings. We are particularly good at novelty, at creating new ways of doing things, and at seeing new opportunities. Dull, repetitive, precise requirements fight against these traits.
Errors are not the only type of human failures. Sometimes people knowingly take risks. When the outcome is positive, they are often rewarded. When the result is negative, they might be punished.
The human error examined here is unintentional: deliberate violations, by definition, are intentional deviations that are known to be risky, with the potential of doing harm.
A slip occurs when a person intends to do one action and ends up doing something else.
There are two major classes of slips: action-based and memory-lapse. In action-based slips, the wrong action is performed. In lapses, memory fails, so the intended action is not done or its results not evaluated.
A mistake occurs when the wrong goal is established or the wrong plan is formed. From that point on, even if the actions are executed properly they are part of the error, because the actions themselves are inappropriate—they are part of the wrong plan. With a mistake, the action that is performed matches the plan: it is the plan that is wrong.
In a rule-based mistake, the person has appropriately diagnosed the situation, but then decided upon an erroneous course of action: the wrong rule is being followed. In a knowledge-based mistake, the problem is misdiagnosed because of erroneous or incomplete knowledge. Memory-lapse mistakes take place when there is forgetting at the stages of goals, plans, or evaluation.
