HCI Laws – Exam Study Sheet

Everything likely to show up on the exam: formulas, implications, examples, usability notes, and common pitfalls.

1) Fitts’ Law Motor behavior

TF = a + b · log2(2D/W) – pointing time rises with distance (D) and drops with larger targets (W). a,b are empirical constants. fileciteturn0file4

Design implications

  • Make frequently used targets large and place them close; edges & corners are fastest (cursor “pinning”). fileciteturn0file8
  • Touch: minimum comfortable target ≈ 10 mm; expand hit areas when possible. fileciteturn0file8
  • Use bigger buttons for primary actions; prioritize size by frequency of use. fileciteturn0file8

Limits / caveats

  • Models rapid, aimed 1‑D movement; not suited to typing, drawing, or complex gestures. fileciteturn0file6
  • Screen real estate forces trade‑offs; choose sizes systematically. fileciteturn0file6

Recognition & variations: ISO 9241‑9; derivations/variations summarized. fileciteturn0file8

2) Hick’s Law Decision time

TH = b · H, where H = log2(n+1). More choices → slower decisions (logarithmically). fileciteturn0file0

Design implications

  • Group/categorize to prune choices quickly; prefer one well‑organized menu over many small ones. fileciteturn0file9
  • Combine with Fitts’: size important buttons proportionally to expected frequency. fileciteturn0file9

Limits / caveats

  • Familiarity & modality (text vs. icons vs. audio) affect speed. fileciteturn0file14
  • Dumping all options at once may violate working‑memory limits (see Miller). fileciteturn0file14

3) Miller’s Law Working memory

Short‑term memory capacity is limited; chunk information for recall. Classic “7 ± 2”, with modern estimates often ≈ 4 chunks depending on content & familiarity. fileciteturn0file1 fileciteturn0file11

Design implications

  • Chunk related items (e.g., 514 848 2424 3000, credit-card groups). fileciteturn0file16
  • Split complex visuals into multiple simpler views. fileciteturn0file16

Limits / notes

  • Avoid dogmatic 7±2 usage; focus on reducing cognitive load. fileciteturn0file1
  • Capacity varies: digits < letters < words; lower for longer words and for children/older adults. fileciteturn0file11

4) Occam’s Razor (Law of Parsimony) Simplicity

Prefer the simplest adequate explanation/design; avoid accidental redundancy. Apply strategies: remove, organize, hide, displace. fileciteturn0file10

Design implications

  • Streamline UIs; cut redundant controls & tests in regression suites. fileciteturn0file10

Limits

  • “Simpler” isn’t automatically “better”; balance against inherent task complexity. fileciteturn0file10

5) Tesler’s Law (Conservation of Complexity) Shift the burden

Every system contains irreducible complexity; you can’t remove it, only redistribute it—ideally away from the user and into the system. fileciteturn0file12

Design implications

  • Automate tedium (auto‑fill, suggestions); let the machine do heavy‑lifting. fileciteturn0file12
  • Examples: email address auto‑completion; single sign‑on; smart IDEs; cloud offloading on mobile. fileciteturn0file17 fileciteturn0file19

Limits / notes

  • Cannot simplify beyond the essential complexity; manage & localize it instead. fileciteturn0file12
  • No formal mathematical framework; trade‑offs are contextual. fileciteturn0file15

6) Von Restorff (Isolation) Effect Attention

In a set of similar items, the one that’s different is remembered best—use contrast to highlight key UI elements and alerts. fileciteturn0file15

Design implications

  • Use size, color, whitespace, or placement to make primary actions and warnings pop. fileciteturn0file3

Limits

  • Effect can diminish with age; overuse reduces distinctiveness. fileciteturn0file7

7) Yerkes–Dodson Law Arousal vs. performance

Performance improves with arousal up to an optimal point; too little/too much hurts performance. Difficulty moderates the curve. fileciteturn0file7

Design implications

  • For difficult tasks: minimize distractions; provide context‑sensitive help. fileciteturn0file18
  • For easy but important tasks: increase salience with subtle feedback (focus states, progress). fileciteturn0file18

Limits

  • “Arousal” and “difficulty” are subjective and can shift over time. fileciteturn0file7

Exam‑style prompts to practice

Q1. Given D and W, compute relative pointing time using Fitts’ Law and justify two UI layout changes. (Edges/corners, target sizing.)
Q2. Redesign a 12‑item toolbar using Hick + Miller: how would you group, label, and size items?
Q3. Show two ways to shift complexity from users to software in a signup flow (Tesler).
Q4. Mark up a dialog using the Von Restorff effect to reduce destructive‑action errors.
Q5. Explain when Occam’s Razor harms usability (over‑simplification) and how to balance it.
Q6. For a stressful one‑shot submission form, list UI changes aligned with Yerkes–Dodson.

Scope & framing: Document also notes related laws (Murphy, Metcalfe, Moore, Postel, Weber‑Fechner, Wirth) and that “HCI laws” are scientific/engineering heuristics, not literal legal rules. fileciteturn0file4