The interface speaks for itself — or it doesn't work.
Don Norman's "The Design of Everyday Things" (1988) laid out a small set of design principles that became the foundation of HCI. The premise: a well-designed object tells you what it does, how to use it, and whether you got it right — without a manual.
Action
Affordances
The properties of an object that suggest how it can be used. A handle affords pulling, a button affords pushing, a knob affords turning. In software, affordances are mostly perceived — a button "looks clickable" because its shape echoes a physical one. When the affordance lies, the user fails.
Shape suggests action
Defined
Suggests how to use
In UI
Perceived affordance
Fails when
Shape misleads
Cue
Signifiers
Norman added "signifier" to the second edition because "affordance" had been overloaded. A signifier is the visible signal — a label, an underline, an arrow — that points to where action is possible. Affordance is the underlying capability; signifier is what the user can actually see.
Tell the user where to act
Defined
The visible signal
Examples
Underline · arrow · label
Pairs with
Affordance
Layout
Mapping
The relationship between controls and their effects. Light switches that match the position of the bulbs they control; stove knobs aligned with their burners; volume sliders that go up when sound goes up. Bad mapping is why hotel showers and laundry machines are the running joke of UX.
Controls match what they affect
Defined
Control ↔ effect alignment
Classic case
Stove knobs
Fails when
User has to remember which is which
Response
Feedback
Every action gets a visible, audible, or tactile response. Without feedback, the user is left guessing whether anything happened, and starts re-clicking, re-submitting, and double-paying. Spinners, toasts, ripple effects, haptic taps, sound — small cues, huge confidence.
Did it work? Tell me.
Defined
Show that it happened
Channels
Visual · auditory · haptic
Without it
Double-clicking · double-charging
Limits
Constraints
Limits that prevent invalid actions before they happen. A USB plug that only fits one way, a date picker that won't let you pick before today, a tax form that grays out fields irrelevant to your filing status. Good constraints prevent errors invisibly; bad ones just frustrate.
Prevent invalid before it happens
Kinds
Physical · logical · cultural
Effect
Errors prevented quietly
Watch for
Over-constraint = frustration
Mental model
Conceptual Model
The simplified picture of how the thing works that the user carries in their head. The folder metaphor on the desktop, the page metaphor in a browser, the chain of messages in chat. When the system's actual behavior diverges from the user's conceptual model, every action becomes a guess.
User's model · system's behavior
What it is
User's simplified picture
Built from
Past experience · metaphor
Diverges
Every action is a guess
Discoverability
Visibility & Discoverability
Whatever can be done should be visible. Hidden controls, ⌘-only shortcuts, and "if you knew where to look" features all violate this. The user shouldn't have to remember; the interface should show. Discoverability is what turns a powerful product into a usable one.
Show what's possible
Defined
Show what's possible
Don't rely on
Memory
Bad sign
"You have to know"
Sameness
Consistency
The same thing should always look the same and behave the same. Across screens, across products, and against the platform conventions users have already learned. Inconsistency forces users to constantly re-learn, undermining every other principle on this page.
One pattern, used everywhere
Across
Screens · products · platforms
Pairs with
Jakob's Law
Cost of breaking
Re-learning everywhere
✦
Norman's Principles in the Age of AI
The conversational interface re-poses every one of Norman's questions — what can I do, did it work, what does it know about my model?
✦ AI Era
The Gulf of Execution Returns
Norman's "gulf of execution" — the distance between what you want to do and how to do it — was nearly closed by direct manipulation interfaces. A blank chat box reopens it. There's no signifier for what the model can do. Show the affordances back to the user: examples, suggested prompts, capability hints.
Re-signify a blank prompt
Reopened
Gulf of execution
Fix
Suggested prompts · examples
Apply
Visibility · signifiers
✦ AI Era
Feedback for Generated Output
When the model returns an answer, the user's gulf of evaluation reopens — "did it understand me, did it use the right sources?" Norman's feedback principle still applies. Surface the reasoning, the sources, the confidence. A clean answer with no scaffolding feels fast but breaks trust on first error.