• Discoverability is determining what actions are possible and how to do them
• Understandability is determining what everything means (what is it? how should it be used? what do the controls and settings mean?)
• Industrial design is the study of function, value and appearance of products and systems
• Interaction design is the study of human-product interaction, usability and understanding
• Experience design is the study of the quality and enjoyment of an experience (products, events, etc)

• Named after the author Don Norman
• Norman doors are doors that don't open like you'd expect.
• They're an example of failed discoverability.
• Doors should be easy to understand, a push plate or grab handle in the right place is enough to signal how to open and close (you don't need to label them push/pull).

• Design is concerned with how things work, how they are controlled and the nature of the interaction between the people and the thing. We design things for people, so we need to understand people, technology and how they interact.
• Machines are simple and rigid, making them unforgiving (if we provide the wrong rules or inputs). When working with machines we must be precise and accurate. We design knowing that users are humans and humans make errors. Designers need to focus on where things go wrong, not just the happy path.
• The Human-Centered design philosophy focuses attention on understanding people and their needs (primarily through observation). Equal importance is assigned to the problem definition (what is the right problem) and the solution definition. It also relies on the rapid testing of ideas. HCD overcomes two common issues: You're often asked to work on the wrong problem. Often people aren't able to articulate their needs well.

• Affordances are things an agent-object pair can do, a list of possible interactions. A chair has no affordances (only properties). A 'chair & human' have affordances: e.g.sit on, stand on, pick up, pull out.
• Designers have a habit of assigning affordances to objects, but they are relationships, an affordance mush have an object and an agent.
• Object-Agent pairs can have anti-affordances. Take (person-glass), there's an affordance of transparency but an anti-affordance of being able walk through it.
• For designs to be effective, affordances and anti-affordances have to be discoverable. If an affordance can't be perceived a signifier is needed. Visible affordances provide strong clues to the operation of things. When an affordance is but not present issues occur. Information pickup is the term given to what we can perceive from all our senses.

• Signifiers communicate the structure and operation of the device to the people that use it. They come in many forms (marks, sounds, languages). and can be deliberate (handles) or unintentional (subtle hand marks on a door).
• Signifiers can turn an affordance into a perceived affordance by signalling possible actions, and how to complete them. Signifiers must themselves be perceivable, else they're unable to function.
• Signifiers are more important than affordances, as they communicate how to use the design.

• Mapping is important to the layout of controls and displays.
• Designers should try to achieve a clear mapping between the control > action > intended result.
• Mappings work best when aligned to a conceptual model. With mappings, accuracy is less important than understandability.
• Principles of mapping include grouping and proximity. Grouping: related controls should be grouped together. Proximity: controls should be close to the item being controlled.
• Example of natural mapping: light switches in the same pattern as the lights, seat adjustment controls in the shape of a seat. Cultures have their own mappings, they're not always the same between cultures.

• Feedback is the system letting you know it's working on your request or communicating the result of the action. Control and Information theory is an entire area of study in itself. Feedback can come in many forms and act through any sense (visual, audible, touch, etc).
• Feedback is essential, but it has to be done correctly, appropriately

• A model is an internal explanation (often simplified) of how something works. It doesn’t have to be complete or accurate, just useful. Clues to how things work come from: Signifiers, affordances, constraints and mappings.
• Simplified models are only valuable if their assumptions hold true. Good models help us predict how something will behave, and predict the effects of our actions. Models are often inferred, shared, or learnt by experience.

Advice for Designers

• Don't blame those that fail. Their difficulties are a sign of what can be improved.
• Eliminate error messages, instead, provide help and guidance
• Make it possible to correct problems
• Don't impede a users progress, allow them to continue towards completing their task
• Never make people start over. Assume people are partially correct, let them make corrections
• Don't criticise unless you can do better!

• It's important to understand what a user is thinking when they interact with your design. However, much of human behaviour is subconscious. Often we are bad at predicting how people will behave.
• Learning things often requires conscious thought, but many actions can be offloaded to the subconscious once learnt, this allows us to focus on higher level goals and planning (learning to ride a bike).
• Cognition: is our attempt to make sense of the world, our understanding
• Emotion: assigns value - is something safe or dangerous? desirable or not?
• We can't separate cognition and emotions, actions have expectations and expectations trigger emotions.

• How people think when performing a task.
• First they form a goal, then they move through 3 stages of execution, followed by the 3 stages of evaluation.

• Each stage of execution and evaluation happens at a different level of processing:
• Reflective: Goal setting and planning activity. Slow conscious reasoning and decision making.
• Behavioural: Expectations and learned skills. Largely subconscious. Actions and controls.
• Visceral: Sensing and muscle control. The unconscious lizard brain, emotional and fast.
• The designers job is to bridge the Gulf of Execution (figuring out how to use something) with feedforward, and the Gulf of Evaluation (figuring out the current state of something) with feedback.
• All three levels of processing work together to determine a persons cognitive and emotional state. Higher-level reflective cognition can trigger lower-level emotions and vice versa. The reflective level though is where memories are formed, and memories are what drives us to recommend a product (or not). For frequent or routine actions, much of the action cycle is subconscious.

• Often the design is faulty, and others will make the same errors. Designers should aim to minimise inappropriate actions, through affordances, signifiers, mapping and constraints.If the user still makes an inappropriate action, maximise the chance its discovered and rectified. People get interrupted and disturbed whilst completing a task, expect them to leave things incomplete or lose their place.

• Endogenous knowledge requires learning - it can be ephemeral, here now, gone later.
• Environmental knowledge doesn't require learning - but can be more difficult to use. It also requires on the continued presence of the knowledge in the environment.
• We can design devices to guide behaviour, the guidance doesn't have to be precise.
• E.g: constraints can limit behaviour by limiting the number of choices (natural, cultural conventions)
• The most usable devices are understandable on first use, provide enough information in the environment and users will be able to learn how to operate the device.
• Declarative knowledge: facts and rules → is easy to write down
• Procedural knowledge: may be harder to articulate and is best taught through demonstration (e.g learning to play an instrument).
• We can use cues to provide information to the user (signifiers, mappings, physical constraints). Even physical layout can convey information (people naturally organise things spatially: putting things in piles, grouping things).
• We have to differentiate the desired action from the other choices a user can make. People naturally look for distinguishing features amongst options.
• Confusion is often a function of history, when rules change people get confused.
• What appears good in principle, can fail when introduced to the world.
• Constraints reduce choices, making things easier to follow and learn.

• Often systems and schemas are defined without considering the limitation of humans memory.
• Short-term memory (working memory) contains material currently being thought about. It is very fragile, limited. So don't count on anything retained in short term memory. Fades with both time and the number of things stored.
• Long-Term Memory is your interpretation of past events. Takes time and effort to store something and retrieve it. Our memory is almost unlimited, although subject to bias and distortion.
• It's difficult to learn knowledge with no underlying meaning or structure like the letters of the alphabet (arbitrary knowledge). There's often no way to sense check arbitrary knowledge. Good conceptual models can provide meanings to arbitrary things. Pilots don't rely on memory, they use checklists and quickly enter information or write it down.
• Prospective Memory: the memory for intentions. Key Components:
• Signal: knowing you have to remember something
• Message: remembering the information itself
• Trigger: time based or location based can help

Talking about memories with friends sparks more recollection.

• Physical limitations to the possible operations. More effective if they're easy to see and interpret.
• Cultural. Cultures have a set of allowable actions. Guidelines for cultural behaviour are represented in the mind by schemas, containing rules and information necessary for interpreting situations and guiding behaviour. Form scripts that we follow (or frames). Cultural constraints change with time
• Semantic: Only certain combinations make sense. Given what is there already, the remaining options are limited. Study of meaning. Semantic constraints rely on the of the situation to control the set of possible actions. Rely upon our knowledge of the system and the world.
• Logical constraint. There is a logical relationship between the spatial or functional layout of components and the things that they affect. E.g You take something apart, put it back together again, and there's a part left on the table. You know you've made a mistake.

• The new way has to be much better than the old.
• If the new is only slightly better, then it's better to be consistent.

People care about temperature and rate of flow. BUT water enters the sink from 2 pipes, a hot and a cold. There are a number of ways to configure controls to help the user. You can either have one control that does two things, or two controls that do a single thing. You can also choose to fix something, like flow rate or temperature.

• When there are two controls there are 4 mapping problems...
• Which knob controls the hot, which the cold? How do you change the temperature without affecting the flow rate? How do you change the flow without affecting the temperature? Which direction increases the water flow?
• The simple one control faucet still has 4 mapping problems. What dimension is temperature? What direction is hotter? What dimension if flow? What direction means more?
• The mapping problems are solved through cultural conventions or constraints. Left should be hot, right should be cold. Clockwise to shut off. Plumbing designers suffer from a lack of consistency though, which makes every new shower a challenge. Can you learn by trail and error? Just by evaluation. Well yes, but temperature can take 5 seconds to adjust, so if you go the wrong way, it can take 10 seconds to get back to a comfortable setting.

They should be simple, but they can violate many design principles... Visible affordances and signifiers, Discoverability and Immediacy of feedback.

Objects without speakers can still have great audio feedback (toast popping up, the click of light switch). When materials interact sounds are generated, so it feels natural to get some sound when doing an action. Getting sound design right: Sound can annoy and distract as easily as it can aid. When sound is expected, and silence occurs, then there can be problems.

The sound of a car signifies its presence, we've learnt that you can hear cars coming. Electric car makers are looking at adding the sound back for safety (especially on acceleration, at speed there's often enough tire noise).

Design considerations for electric vehicle sounds. Alert to presence, hint at orientation and speed, don't be annoying, don't contest with other street noises (sirens, horns) and should brands be able to have their own sounds as differentiators or should they be all standardised for consistency.

• 80% of industrial accidents are logged as human error
• People are often punished for making mistakes
• Errors occur for many reasons. They're more common when tasks require people to behave in an unnatural way (staying alert, being precise, being accurate, for long periods whilst multitasking).

• We can’t eliminate human error. So we should design for it. If the system lets you make the error then it is badly designed.
• Don’t neglect designing things that aren’t on the ‘happy path’
• When many people have the same issue, another cause should be found.
• We can't fix problems unless people admit that they exist.
• Problems can come from the system, procedures or social pressures. People aren't good at precision, monotony and time stress.
• We should treat all failures the same way. Avoid assigning blame and find the underlying cause. We should workout why it happened and update the design of the product so it can't happen again, or to reduce the impact of it happening again.

• Admit → investigate → make changes.
• Toyota Production System encourages reporting and root causing of errors by all
• Poka-Yoke is an error proofing process (at Toyota)
• Anything that makes it harder to make an error
• Making sure the right conditions exist before a step in the process is executed
• This makes it a preventative form of quality control, since errors are detected and then rectified before they occur.
• Example: Microwave doesn't work if the door is open
• NASA Aviation Safety Reporting System: pilots submit semi-anonymous reports about errors they'd seen in others. Neutral party.

• Accidents often have multiple causes. Often errors or latent causes go unnoticed until a number of them line up and allow an accident to happen.
• Well designed systems are resilient to failure.
• Attempts to find the cause often fail → There can be multiple causes but also multiple actions that could have prevented the accident. You need to understand the system, and make the highest impact actions to increase reliability.
• Ways to reduce accidents:
• add more slices of cheese
• reduce the number of holes (or make them smaller)
• add alerts when several holes have lined up.

• Human Error: Any deviation from appropriate behaviour
• Error: General term for all wrong actions (Two categories of error: slips and mistakes)
• Deliberate deviations: people knowingly take risks if they feel that guidelines slow them down
• Routine violations: when noncompliance is so frequent it's ignored.

Social and Institutional Pressures have a strong influence on behaviour, they lead to misinterpretation, mistakes, and accidents.

• Novices are more likely to make mistakes than slip. Experts are more likely to make slips.

Resilience Engineering: Designing systems and procedures so they are able to respond to problems as they arise. Technique: deliberately cause errors in small tests and simulations.

Understand the causes of error. Design to minimise them.

Principles

• Constrain actions to prevent errors
• Make operations reversible (UNDO)

• Show confirmation and error messages
• Make the item being acted upon more prominent
• Check inputs are in a sensible range
• Minimise slips 1 → Make actions and controls as dissimilar as possible
• Minimise slips 2 → Eliminate modes (or make them unmissable) to avoid mode error
• Provide feedback - show the new state - show an undo button
• Reduce interruptions where possible (consider pausing notifications whilst doing a task)

• Make it easier for people to discover errors. Make them easier to correct

• Norman’s consulting law: never solve the problem you're asked to solve
• Often we focus on the wrong problem. It takes work to discover the real problem
• The secret to success in design is to understand what the real problem is.

• Solving the right problem. Focusing on needs of individuals and jobs to be done. Building products that are understandable, usable and enjoyable.
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HCD has a four stage model (arranged as a cycle)
1. Observation.
• The best way to understand the nature of the problem is direct observation and interaction with future users
• Go to them, observe their actions and attempt to understand their true needs, motives and interests.
• Applied ethnography: faster than traditional research and has an emphasis on building products to help. Studies focus on the activities that people do, and how they accomplish tasks.
• Focus on the intended customer in the intended market. If you're targeting a sub-culture, then you need to study that group.
2. Idea Generation.
• Generating potential solutions (ideation)
• Used in both phases of the double diamond.
• Inducing creativity while brainstorming. Generate as many ideas as possible and avoid fixating on one or two too early.
• Be creative without regard for constraints. Hold back from criticising ideas, even your own. Avoid premature dismissal. Question everything, stupid questions are great. Question the obvious. Dig deeper and profound insight can follow.
3. Prototyping.
• The only way to know if an idea is reasonable is to test it.
• Build something quickly, get feedback as soon as possible.
• Using tricks (like mimicking a backed system a.k.a. the Wizard of Oz technique) you can valuable insight before building a working solution. You can even use a competitor product as a prototype. They can be used in both phases of the double diamond, to help define the problem and the solution.
4. Testing.
• Gather a small group of your target population. Have them use the prototypes in a realistic way, in a realistic environment. Take video recordings. Ask them to think out loud or explain their thought process after the fact.
• Study 5 people in each round, review the results and refine the process before going again. Don't do more than 5 without iterating on the script or product.
• Product Andrew: My model is test until you're bored, and they become predictable.
• CE marking and usability testing may require more participants and a more stable product and script.
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On iteration and the HCD cycle
• The speed at which a team can learn determines its success. We want to be continually refining and enhancing the product, by rapid prototyping and testing. Fail frequently, fail fast. Reframe failure as learning experience, If everything works, little is learned.
• Repeated observation and study can help get the requirements right (which is the hardest part of design). With each cycle ideas become clearer, specifications better defined, prototypes become more effective.
• People aren't great at identifying and communicating their higher level needs. They often don't grasp fully the number of deviations and special cases they encounter.

• Allows freedom to explore the problem and solution space. It doesn't guarantee success, you may converge on the wrong solution and have to repeat the process. Future iterations will be much faster though
• When using the double diamond approach, visible progress only comes towards the end of the project, which can be unsettling.
• The Design Problem: satisfying a number of constraints and concerns: shape, form, cost, reliability, effectiveness, understandability, usability, aesthetics, proud to own, joy to use
• Phase 1: Problem Definition: Solving the right problem
• Discovery: Diverging to examine fundamental issues, questioning the problem
• Definition: Converging upon a single problem statement
• Phase 2: Solution Proposal: Solving the problem in the right way
• Develop: Diverging to explore a wide variety of potential solutions
• Deliver: Converging on a proposed solution

• Design research → collecting deep insight on a small number of people. What people need and how they use it. Qualitative and observational.
• Market research → collecting shallow insight on a large number of people. What people buy, how they make purchasing decisions. Quantitative & Surveys.
• insight is shallow because what people say they do and what they do are different

• HCD is a focus on the individuals, ensuring the product fits real needs and is understandable, useful and joyful to use.
• BUT people vary widely - if you're making a product for the world that's a problem.
• A focus on activity (vs people) helps define a product and its structure.
• People have activities in common - and are willing to learn things that are essential to their lives

• Iterative methods are best suited for early design and smaller projects
• It doesn't scale to 100's or 1000's of people.
• Some waterfall methods have stage gates.
• Stage gates can be useful if you don't waste time prepping for them. The best methods contain a mixture of iterations and stage gates.
• The hard part of larger projects is communicating, organising and synchronising people across different departments. The longer the length of the project, the harder it gets (stakeholders and requirements shift). Often knowledge is implicit, in people's heads

• The fundamental principles of designing for people are the same across all domains. The design process must address numerous constraints.
• Products have conflicting requirements: put work in to understand them, get representation for each in the room
• Competing requirements is what makes design hard. It's easier to get to a viable solution if you have a team with representation from a mix of disciplines, and they work together as a single unit.

• There is no such thing as the average person
• You’ll exclude people even when you design for the percentiles
• Design for interests and skill levels. Don't be trapped by overly general, inaccurate stereotypes.

Most people don't want to advertise their imperfections or admit it to themselves.

• The OXO peeler designed for those with arthritis was a better peeler for everyone (the curb-cut effect).

• It’s often the best way to design for everyone’s needs. Allow people to adjust things. Fixed solutions will always fail for some, flexible solutions have a chance.

• Complexity is often essential. Don't make something more complex than it needs to be, tame complexity with a good conceptual model. Avoid confusion though

• Standardisation is a type of cultural constraint. Provides a breakthrough in usability!
• Benefits from technological advances are either brought about by the technology itself, or by standardisation.
• Establishing Standards is laborious. Easy in a company, much harder in an industry or international body. Standards are often a compromise and sometimes take so long to agree they can be irrelevant at launch. Standards are necessary, and help with interoperability.

• Hide critical components
• Use unnatural mappings
• Make the actions physically difficult to do
• Require precise timing and physical manipulation
• Do not give any feedback
• Use unnatural mappings

• There are a limited number of ways to compete: price, features, quality, aesthetics.
• The speed of product development is an important factor for companies in competition.
• Time pressure sometimes crowds out iteration and exploration.
• Often things are independently discovered or developed at the same time (Zeitgeist: spirit of time)
• Concerns that companies have:
• Speed of development
• Cutting costs
• The competition
• The need to satisfy multiple customers
• The question becomes: How do you best focus and deploy your resources?

• Product teams have a tendency to add more features than they remove. Users and stakeholders rarely ask for features to be removed
• Pressure to add features comes from
• Requests from existing customers
• Competing products adding them
• To acquire new customers (satisfied customers, rate of acquisition slows as you approach saturation)
• Quest of product teams to expand and provide more value
• To acquire new customers (satisfied customers, rate of acquisition slows as you approach saturation)

Continually adding features makes it hard to:

• Understand & communicate
• Use
• Maintain
• Improve

Is when you're matching the features of your competitor, leaving the customer no reason to choose either product. A better strategy might be to

• Make improvements where you're already strong
• Keep the product consistent, coherent and understandable
• Meet the needs of the people who use the product

Quality comes from continued focus on the customer.

• New technology unlocks new possibilities, entire industries can be remade following a new application of technology.
• People and culture change at a slower rate to technology. It can take decades for a technology to be adopted. However, the rate at which technologies are adopted is increasing.
• Most radical ideas fail. Most large companies don't tolerate failure well. They tend to be more conservative in adopting new technologies. Startups are more able to take risk. Most fail, but risk is asymmetrical as the upside is much larger. Startups are even more risky that we perceive, due to survivorship bias, Timing must be right, most companies that are early fail. Heavy momentum of legacy inhibits change.

• Originally chosen to benefit the mechanics of a typewriter. QWERTY layout helped the mechanical type bars approach one another at large angles, minimising the chance of collision. Otherwise it was designed for speed, attempting to make common letter pairs be split across the right and left hand.
• The DVORAK layout is slightly faster than QWERTY. However, the switching costs are high, and the difference isn't large enough to justify the time investment for most people
• Chord keyboards are faster still but aren't suitable for beginners and they require more training. They're only used in rare circumstances like transcribing court documents.

• Most innovation comes from incremental improvements to existing products, continual testing and refinement. The gains per cycle are often small, but the cumulative effect is dramatic (e.g iPhone, cars).
• Radical innovations that result in step changes or paradigm shifts are much less frequent. It may still take decades for a radical innovation to be adopted and for its transformational effect on industry to be realised.

• Human evolution is measured on a scale of thousands of years. Human culture takes decades or centuries to change.
• The world has become reliant on technology. Socrates thought books would mean people would stop thinking for themselves and debating.
• Humans and machines are both more powerful when augmenting each other. The power of an unaided mind is highly overrated, it is things that make us smart.
• Technology has freed us from the trivial. We don't have to find food, we don't have to remember numbers or directions, we're free to focus on other things.

• Production
• Emissions while in use
• Extending the duration of use
• Repairability and right to repair
• End of life

Do incremental generations encourage over consumption? Maybe a subscription model would incentivise more durable goods