ux-psychology-review

name

ux-psychology-review

description

Apply design psychology — cognitive biases and behavioral principles — to review UI/UX work and generate ideas, recommendations, and critiques grounded in named principles. Use this skill whenever the user asks for a UX review, design critique, conversion analysis, onboarding/checkout/pricing/paywall feedback, "why might users drop off here," "how do I get users to X," or any product-design task where human psychology matters. Also trigger when the user wants to brainstorm UX improvements, evaluate a flow against best practices, name the principle behind a pattern they've seen, or check a design for dark patterns. Don't wait for the user to say "psychology" — design reviews and UX recommendations are this skill's bread and butter even when phrased casually ("does this signup flow look ok?", "ideas for our pricing page", "review this dashboard").

UX Psychology Review

A working knowledge base of 106 cognitive biases and design principles for reviewing UI/UX work and generating recommendations grounded in named psychology. Adapted from growth.design's "Psychology of Design" framework, which builds on Buster Benson's cognitive bias codex.

What this skill does

Two jobs, often blended:

1. Review — Given a design (screenshot, description, flow, or live URL), diagnose which principles it leverages well, which it violates, and which dark patterns it risks.
2. Generate — Given a UX problem ("how do we improve activation", "what should the pricing page do"), propose concrete, principle-backed ideas.

Both modes name principles explicitly. A recommendation like "add social proof near the CTA — this leverages the Bandwagon Effect and reduces decision anxiety" is more useful than an unattributed gut call, because the user can challenge it, extend it, or check it against literature.

The framework: 4 stages of user cognition

Buster Benson's codex sorts cognitive biases by the problem the brain is trying to solve. Every time someone interacts with your product, they:

1. Filter information — too much input, the brain ignores most of it. (Visual hierarchy, Hick's Law, Cognitive Load, Selective Attention, Banner Blindness…)
2. Search for meaning — gaps in the input get filled with assumptions and stories. (Mental Models, Familiarity Bias, Halo Effect, Authority Bias, Confirmation Bias…)
3. Act within time pressure — decisions get rushed; shortcuts win. (Loss Aversion, Sunk Cost, Default Effect, Scarcity, Reciprocity…)
4. Store what mattered — memory is selective and reconstructive. (Peak-End Rule, Serial Position Effect, Zeigarnik Effect, Spacing Effect…)

Good design respects all four. Bad design fights one or more.

How to work through a request

When reviewing a design

1. Get the artifact in front of you. If the user shared a URL, fetch it. If a screenshot, look carefully. If only a description, ask one or two specific questions before guessing — designs are visual, and a vague description leads to vague feedback.

2. Identify the user's goal at this surface. Onboarding? Checkout? Re-engagement? Pricing decision? You can't review a design without knowing what success looks like.

3. Walk the four stages. For each, note which principles are in play:

   • Filter: Where does the eye go? Is the hierarchy clear? Is there cognitive overload?
   • Meaning: What story will the user tell themselves? Do labels/icons match their mental models?
   • Action: What's the easiest path? What friction exists? What's the cost of choosing wrong?
   • Memory: What will they remember tomorrow? What's the peak? The end?

4. Output a structured review. Use this shape:

   • Strengths — 2-4 principles the design uses well (named, with the specific element)
   • Issues — 2-5 principles being violated or missed (named, with the specific element and why it matters)
   • Dark pattern flags — anything that risks crossing the line (see Dark Patterns below)
   • Recommendations — concrete changes, each tied to a named principle

When generating ideas

1. Restate the problem in terms of user behavior. "How do we improve activation?" → "What makes a new user reach their first 'aha' moment faster?"
2. Pick 3-5 principles most relevant to that behavior. Use the use-case index in references/by-use-case.md.
3. For each, propose one concrete tactic. Tactic + principle + rationale. Skip principles that don't yield a concrete idea — don't pad.
4. Flag tradeoffs. Many principles have "when it backfires" notes. If a recommendation could backfire, say so.

When the user names a single principle

Just answer their question. Pull the entry from the relevant reference file, give them the definition, examples, and tips. Don't run the whole review framework.

The full index — all 106 principles

Each principle is listed with its category, a one-line summary, and the reference file where the full entry lives. Read the reference file when you need the definition, examples, "when to apply," "when it backfires," and citations for a specific principle.

🙈 Filter information (the user's brain ignores most of what you put in front of it)

Full entries: references/01-filter-information.md

#	Principle	One-liner
1	Hick's Law	More choices → slower decisions. Reduce options or chunk them.
2	Visual Hierarchy	Size, position, contrast, and whitespace tell the eye what matters.
3	Cognitive Load	Every element costs mental effort. Strip what doesn't earn its place.
4	Selective Attention	Users see what they're looking for and miss the rest.
5	Banner Blindness	Anything that looks like an ad gets skipped, even when relevant.
6	Fitts's Law	Bigger and closer targets are faster to hit. Make CTAs accordingly.
7	Anchoring Bias	The first number/option seen frames everything that follows.
8	Centre-Stage Effect	Items in the middle of a row are perceived as the default/best.
9	Contrast	Difference draws attention. Use it deliberately, not decoratively.
10	Von Restorff Effect	The element that stands out from a set is the one remembered.
11	Aesthetic-Usability Effect	Pretty designs feel easier to use, even when they aren't.
12	Progressive Disclosure	Hide complexity until the user needs it.
13	Signifiers	Visual cues that hint at how to interact (underlines, shadows, hover states).
14	Law of Proximity	Things close together are perceived as related.
15	Law of Similarity	Things that look alike are perceived as a group.
16	Law of Common Region	Items inside the same boundary are grouped, even if far apart.
17	Law of Closure	The brain completes incomplete shapes — useful for icons, hurtful for cut-off content.
18	Visual Anchors	Eye-leading elements (arrows, gaze, lines) that guide attention.
19	Juxtaposition	Place two elements together to create comparison or association.
20	Framing Effect	The same fact lands differently depending on how it's worded ("90% fat free" vs "10% fat").
21	Priming Effect	Prior stimuli shape later perception. What the user saw 10 seconds ago matters.
22	Nudge	Subtle defaults and cues that steer without coercion.
23	External Trigger	Notifications, emails, banners that prompt return.
24	Internal Trigger	The user's own emotional state (boredom, anxiety) that prompts return.
25	Spark Effect	Tiny animations and microinteractions that delight without distracting.
26	Tesler's Law	Every system has irreducible complexity. Don't push it onto the user.
27	Decoy Effect	Adding a clearly-worse option makes a target option look better.
28	Attentional Bias	Users notice what aligns with their current concerns and ignore the rest.
29	Confirmation Bias	Users seek evidence that supports what they already believe.
30	Expectations Bias	Prior expectations color the perceived experience, sometimes more than reality.
31	Survivorship Bias	Studying only "winners" hides the lessons in failures.
32	Empathy Gap	We underestimate how much our future emotional state will differ from our current one.
33	Feedback Loops (Retroaction)	Visible response to action reduces uncertainty and builds confidence.
34	Curiosity Gap	A teased-but-incomplete piece of information demands closure.

👀 Search for meaning (users invent stories to fill gaps in what they see)

Full entries: references/02-search-for-meaning.md

#	Principle	One-liner
35	Mental Model	Users arrive with prior beliefs about how things work. Match those, or pay the cost.
36	Familiarity Bias	Familiar = trusted. Reusing patterns reduces friction.
37	Skeuomorphism	Mimicking real-world objects helps users transfer expectations.
38	Halo Effect	One strong positive trait spreads to color overall perception.
39	Authority Bias	Endorsements, certifications, and expert framing carry disproportionate weight.
40	Social Proof	"Others did this" reassures the hesitant.
41	Bandwagon Effect	The pull to do what the crowd does, even against personal preference.
42	Aha! Moment	The first concrete value the user feels — make it fast.
43	Spotlight Effect	Users assume they're being watched/judged more than they are.
44	Hindsight Bias	After the fact, outcomes seem inevitable. Useful for confidence-building UX.
45	Survey Bias	Asking the question changes the answer. Question design shapes data.
46	Cheerleader Effect	Items presented as a group look more attractive than alone.
47	Singularity Effect	One unique element commands disproportionate attention.
48	Scarcity	Rare = valuable. Often abused; use real, verifiable scarcity only.
49	Streisand Effect	Suppressing information amplifies it.
50	Cashless Effect	Abstracting payment (cards, points, in-app currency) reduces felt cost.
51	Pseudo-set Framing	Showing progress toward a "set" (3 of 5 done) drives completion.
52	Storytelling Effect	Narrative is remembered and trusted more than facts alone.
53	Fluency Heuristic	Easy to process = perceived as more true, more valuable.
54	Pratfall Effect	A small visible flaw makes something more relatable, not less.
55	Barnum Effect	Vague personality descriptions feel personally accurate.
56	Picture Superiority Effect	Images are remembered far better than words.
57	Self-Reference Effect	"Your" beats "the." Personalized framing sticks.
58	IKEA Effect	Effort invested in something raises its perceived value.
59	Endowment Effect	Once owned, things feel more valuable than equivalent unowned ones.
60	Curse of Knowledge	Experts forget what it's like not to know. Designers especially.
61	False Consensus	Designers overestimate how many users think like them.
62	In-group Bias	Users trust voices that signal membership in their tribe.
63	Negativity Bias	Bad experiences/reviews weigh more than good ones.

⏰ Act within time pressure (decisions are rushed and shortcut-driven)

Full entries: references/03-act-within-time.md

#	Principle	One-liner
64	Loss Aversion	Losing $10 hurts more than gaining $10 pleases. Frame accordingly.
65	Sunk Cost Fallacy	Past investment makes users reluctant to abandon a path.
66	Default Effect	Whatever's pre-selected is what most users will keep.
67	Status Quo Bias	Change feels risky. Inertia keeps users where they are.
68	Reciprocity	Free value creates a felt obligation to return the favor.
69	Commitment & Consistency	Small initial commitments lead to larger ones.
70	Foot-in-the-Door	Yes to a small ask makes yes to a bigger ask easier.
71	Door-in-the-Face	An over-large ask makes a moderate ask feel reasonable.
72	Goal-Gradient Effect	Effort accelerates near the finish line. Show progress.
73	Endowed Progress Effect	Pre-filled progress (2 of 10 stamps free) speeds completion.
74	Investment Loop	Users invest (data, customization, friends) → switching cost rises.
75	Variable Reward	Unpredictable rewards drive engagement (and addiction).
76	Hyperbolic Discounting	Now beats later, even when later is much better.
77	Temptation Bundling	Pairing a "should" with a "want" raises adherence.
78	Decision Fatigue	Each choice depletes will. Reduce choices late in a flow.
79	Choice Overload	Too many options paralyze. Default + filter + recommend.
80	Paradox of Choice	More options often reduce satisfaction with the chosen one.
81	Reactance	Users push back against perceived loss of autonomy.
82	Forer/Barnum (Action)	"This was made for you" framing increases buy-in.
83	Labor Illusion	Visible effort (loading bars, "searching 1,200 hotels…") makes results feel valuable.
84	Occam's Razor	Simpler explanations and paths are preferred.
85	Friction	Strategic difficulty: where to add it, where to remove it.
86	Confirmation (Action)	Make destructive actions take a second step.
87	Convenience Bias	Easier > better, in the moment.
88	Optimism Bias	Users overestimate good outcomes ("I'll cancel before the trial ends").
89	Planning Fallacy	Users underestimate how long tasks take, including yours.
90	Risk Aversion	Faced with equal expected value, users prefer the certain option.

💾 Store what mattered (memory is selective and reconstructed)

Full entries: references/04-store-what-mattered.md

#	Principle	One-liner
91	Peak-End Rule	An experience is remembered by its peak moment and its end.
92	Serial Position Effect	First and last items in a list are remembered best.
93	Primacy Effect	First impressions disproportionately shape lasting judgment.
94	Recency Effect	The most recent item/interaction is most available in memory.
95	Zeigarnik Effect	Unfinished tasks linger in memory better than completed ones.
96	Spacing Effect	Spread-out exposure beats massed exposure for retention.
97	Method of Loci / Spatial Memory	Spatial layout helps recall. Consistent placement matters.
98	Chunking	Group information into 5-9 unit clusters; users can hold those in working memory.
99	Miller's Law	"7 ± 2" working memory limit. Don't ask the user to hold more.
100	Storytelling Recall	Stories are remembered as wholes; bullet lists are not.
101	Mere Exposure Effect	Repeated exposure increases liking, even without explicit reasoning.
102	Google Effect	If users can look it up, they don't memorize it. Don't expect them to.
103	Levels of Processing	Deep engagement (think, do, teach) outlasts shallow (read, watch).
104	Picture Superiority (Memory)	Visuals outlast text in long-term recall.
105	Tesler's Law (Memory side)	Complexity the user has to remember is complexity you failed to absorb.
106	Method of Repetition	Spaced repetition cements habits — for better and worse.

Dark patterns: when these principles hurt users

Most of these principles are neutral tools. Some, deployed against the user's interest, become dark patterns — and reviewing for them is part of this skill's job.

The line is roughly: Is the principle being used to help the user reach their goal, or to extract from the user against their goal?

Common conversions to watch for:

Principle	Ethical use	Dark pattern variant
Scarcity	Real low-stock warnings	Fake countdown timers, fabricated "only 2 left!"
Loss Aversion	Save-progress nudges	"You'll lose your discount forever!" pressure
Default Effect	Sensible pre-selection	Pre-checked email opt-ins, hidden subscriptions
Sunk Cost	Show progress earned	Trapping users in cancellation mazes ("but you've earned 47 points!")
Social Proof	Real testimonials	Fake reviews, inflated user counts
Reciprocity	Genuine free value	Manufactured "gifts" with strings attached
Authority	Real credentials	Fake awards, manufactured expert quotes
Friction	Reduce on goal paths	Add to cancellation, refunds, account deletion
Confirmshaming	(no ethical version)	"No thanks, I hate saving money" decline buttons
Forced Continuity	(no ethical version)	Auto-renew with hidden cancel, free trials with no reminder

When reviewing, flag these explicitly — even if the user didn't ask. A "Dark pattern flags" line in the review with zero items is a reassuring signal too.

When the user asks for ideas, prefer ethical framings. If a tactic only works as a dark pattern (confirmshaming, forced continuity), say so and suggest a non-coercive alternative that still moves the metric.

How to use the reference files

Each category file has the full entry for every principle in that category, in this shape:

## [Number]. [Principle Name]
**Category:** [Filter / Meaning / Time / Memory]
**Also known as:** [aliases if any]
**Use cases:** [pricing, onboarding, retention, …]

**Definition.** [1-2 sentences, plain English.]

**Why it works.** [The cognitive/emotional mechanism.]

**Examples in the wild.**
- [Concrete UX example with what it does and why]
- [Second example, different surface]

**When to apply.** [Specific UX situations where this is the right tool.]

**When it backfires.** [Failure modes, dark-pattern adjacency, overuse.]

**Citations.**
- [Source 1]
- [Source 2]

There's also references/by-use-case.md — a cross-index from common UX problems (pricing, onboarding, paywall, cart abandonment, retention, error states…) to the principles most relevant. Use this when generating ideas: pick the use case, look up the relevant principles, then pull their full entries.

Output style

• Name principles when you use them. "This works because of the Peak-End Rule" beats "users will remember this."
• Be concrete. "Move the testimonial above the CTA" beats "consider social proof."
• Don't list everything. A review of 15 principles is unreadable. Pick the 5-7 that matter most for this design.
• Disagree when warranted. If the user pushes a principle that's wrong for their context, say so and explain. The skill exists to be useful, not validating.
• Cite when uncertain. If you're stretching beyond what the reference files contain, say "I'm extrapolating here" and note it.

Notes on sources

This knowledge base draws from:

• growth.design — the original 106-principle framework (the structure and category approach come from here)
• Buster Benson's cognitive bias codex — the 4-stage organizing model
• Nielsen Norman Group, Interaction Design Foundation — applied UX research
• Behavioral economics literature — Kahneman, Thaler, Cialdini for the underlying mechanisms
• Wikipedia + The Decision Lab — for cross-checking definitions

Specific citations live in each principle's entry. When the literature disagrees on a definition or effect size, note the disagreement rather than picking one.