Cognitive Foundations

The science of how minds work, and what that means for design.

When to Use This Skill

• Explaining why a design works or fails (grounded in research, not opinion)

• Evaluating cognitive load or working memory demands

• Predicting user performance (Fitts, Hick-Hyman)

• Diagnosing mental model misalignment

• Justifying design decisions to stakeholders with evidence

• Understanding attention, perception, or memory failures

Output Contracts

For Single-Principle Analysis

Cognitive Principle: [Name]

Principle: [1-sentence explanation]

Evidence in Design: [Where/how this applies]

Implication: [Specific, actionable recommendation]

Confidence: [High/Medium/Low] — [rationale]

For Cognitive Audit (Comprehensive)

Cognitive Audit: [Screen/Flow Name]

Working Memory Load

• Items requiring recall: [count]
• Cross-screen memory demands: [Y/N]
• Verdict: [Acceptable / High / Overloaded]

Attention Demands

• Preattentive features for critical info: [Y/N]
• Competing attention demands: [list]
• Change blindness risk: [areas where changes may go unnoticed]

Mental Model Alignment

• Expected user model: [what users likely think]
• System behavior: [what actually happens]
• Gap: [mismatch, if any]

Predictive Laws

• Fitts's Law concerns: [target size/distance issues]
• Hick's Law concerns: [choice overload areas]

Gulf Analysis

• Gulf of Execution: [unclear how to act?]
• Gulf of Evaluation: [unclear what happened?]

Violations of Nielsen's Heuristics

Recommendations

1. [Highest priority fix]
2. [Second priority]
3. [Third priority]

For Explaining a Failure

Failure Analysis: [What Went Wrong]

Observed Behavior: [What users did]

Cognitive Explanation: [Which principle explains this]

Root Cause: [Design element that caused it]

Fix: [Specific change]

Quick Reference: Predictive Laws

Law Formula Rule of Thumb

Fitts's Law MT = a + b × log₂(2D/W) Bigger + closer = faster. Screen edges are infinite.

Hick-Hyman RT = a + b × log₂(n+1) More choices = slower. Reduce or organize options.

Steering Law T = a + b × (A/W) Narrow paths are slow. Cascading menus are hard.

Power Law T = a × N^(-b) Practice helps. Design for learnability.

Quick Reference: Nielsen's 10 Heuristics

Heuristic Quick Test

1 Visibility of system status Can user always tell what's happening?

2 Match system ↔ real world Language familiar? Metaphors sensible?

3 User control and freedom Easy undo? Clear exits?

4 Consistency and standards Same words/actions mean same things?

5 Error prevention Constraints prevent errors before they occur?

6 Recognition over recall Options visible? No memory required?

7 Flexibility and efficiency Shortcuts for experts?

8 Aesthetic and minimalist Only relevant info? No clutter?

9 Error recovery Errors explained in plain language with fix?

10 Help and documentation Searchable, task-focused, concise?

Quick Reference: Working Memory

• Capacity: ~4 chunks (not 7)

• Duration: ~20 seconds without rehearsal

• Test: Count items user must hold in mind across screens/steps

Red flags:

• "Remember this code and enter it on the next page"

• Multi-step forms without visible progress/state

• Complex comparisons requiring mental tracking

Quick Reference: Preattentive Features

Detected in <200ms, no focused attention required:

• Color (hue, saturation)

• Size (length, area)

• Orientation (angle)

• Motion (flicker, direction)

• Shape (curvature, enclosure)

Use for: Critical info, errors, changes, status Don't use for: Everything (loses signal value)

Cognitive Load Checklist

Quick assessment for any interface:

Factor Low Load High Load

Choices visible 2-4 options 10+ options

Memory demands Recognition Recall

Steps to goal 1-3 clicks 5+ clicks

Interruptions None Frequent modals

Novel elements Familiar patterns New conventions

Error recovery Clear undo Destructive actions

Visual complexity Clean, grouped Dense, undifferentiated

Scoring: Each "High Load" = +1. Score >3 = redesign needed.

Common Violations → Principle

Symptom Likely Violation Fix

Users don't notice changes Change blindness Animate, highlight transitions

Users can't find the button Poor Fitts's Law Increase size, reduce distance

Users freeze at options Hick's Law overload Reduce choices, progressive disclosure

Users forget mid-task Working memory exceeded Show state, don't require recall

Users misunderstand state Gulf of Evaluation Better feedback, visibility

Users click wrong thing Poor affordance/signifier Clearer visual treatment

Users make same error repeatedly Mode error Visible mode indicators

Users abandon complex forms Cognitive load Chunk, scaffold, save progress

Process

• Identify cognitive demands — What is the interface asking the user to perceive, remember, decide, or do?

• Match to principles — Which cognitive constraints or laws apply?

• Evaluate alignment — Does the design respect or violate these?

• Recommend changes — Specific modifications grounded in the principle

Deep Reference Files

For comprehensive principles and research:

• PSYCHOLOGY.md — Perception, memory, attention, biases, emotion, motivation

• HCI-THEORY.md — Norman's model, predictive laws, error theory, research methods, heuristics

Primary Sources

• A Feature-Integration Theory of Attention.md — Treisman & Gelade on preattentive processing (informs: Quick Reference: Preattentive Features)

• Judgment under Uncertainty- Heuristics and Biases.md — Kahneman & Tversky on cognitive biases (informs: PSYCHOLOGY.md § Decision Making)

Key Researchers

• Don Norman: Affordances, gulfs, emotional design

• Daniel Kahneman: Dual process theory, heuristics and biases

• Stuart Card: GOMS, information foraging, Fitts's Law

• Anne Treisman: Feature integration, preattentive processing

• Jakob Nielsen: Usability heuristics, discount usability

• Ben Shneiderman: Direct manipulation, golden rules

Remember

• Cognitive science explains why design principles work

• Individual differences exist—design for variability, not averages

• Lab findings may not generalize (ecological validity matters)

• Theory informs but doesn't replace observing real users

• When in doubt, reduce cognitive load—users have less capacity than you think