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cognitive-foundations skill

/skills/cognitive-foundations

This skill explains the scientific rationale behind usability choices, analyzes cognitive load, and grounds interface decisions in research.

npx playbooks add skill petekp/claude-code-setup --skill cognitive-foundations

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SKILL.md
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---
name: cognitive-foundations
description: Apply cognitive science and HCI research to design decisions. Use when you need the scientific 'why' behind usability, explaining user behavior, understanding perception/memory/attention limits, evaluating cognitive load, assessing mental model alignment, predicting performance with Fitts's/Hick's Law, or grounding interface decisions in research rather than opinion.
---

# 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

```markdown
## 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)

```markdown
## 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
| Heuristic | Violation | Severity |
|-----------|-----------|----------|
| ... | ... | 1-4 |

### Recommendations
1. [Highest priority fix]
2. [Second priority]
3. [Third priority]
```

### For Explaining a Failure

```markdown
## 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

1. **Identify cognitive demands** — What is the interface asking the user to perceive, remember, decide, or do?
2. **Match to principles** — Which cognitive constraints or laws apply?
3. **Evaluate alignment** — Does the design respect or violate these?
4. **Recommend changes** — Specific modifications grounded in the principle

---

## Deep Reference Files

For comprehensive principles and research:

- [PSYCHOLOGY.md](PSYCHOLOGY.md) — Perception, memory, attention, biases, emotion, motivation
- [HCI-THEORY.md](HCI-THEORY.md) — Norman's model, predictive laws, error theory, research methods, heuristics

### Primary Sources

- [A Feature-Integration Theory of Attention.md](A%20Feature-Integration%20Theory%20of%20Attention.md) — Treisman & Gelade on preattentive processing (informs: Quick Reference: Preattentive Features)
- [Judgment under Uncertainty- Heuristics and Biases.md](Judgment%20under%20Uncertainty-%20Heuristics%20and%20Biases.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

Overview

This skill applies cognitive science and HCI research to product and interface design decisions. It explains the scientific ‘why’ behind usability problems, predicts user performance with established laws, and produces evidence-based recommendations to reduce cognitive load and align mental models.

How this skill works

I inspect an interface, flow, or observed user behavior and map findings to cognitive principles (working memory, attention, perception, mental models). Outputs include single-principle analyses, full cognitive audits, or failure analyses that cite predictive laws (Fitts, Hick, Steering) and Nielsen-style heuristic violations. Recommendations are specific, prioritized, and tied to measurable design changes.

When to use it

  • When you need to justify a design choice with research rather than opinion
  • To diagnose why users are confused, slow, or error-prone in a flow
  • Before launching a feature to estimate cognitive load and likely performance
  • When stakeholder requests evidence-based usability recommendations
  • While redesigning complex forms, menus, or multi-step tasks

Best practices

  • Start by identifying cognitive demands: what users must perceive, remember, decide, or do
  • Measure working memory items across screens; keep active chunks ≈4 or fewer
  • Use preattentive features sparingly for critical info and avoid feature overload
  • Apply Fitts/Hick/Steering laws to predict targets and choice performance; test edge cases
  • Prioritize fixes: reduce recall, simplify choices, improve feedback, then refine aesthetics

Example use cases

  • Cognitive audit of checkout flow to reduce abandonment and memory errors
  • Failure analysis after usability tests where users misinterpreted system state
  • Single-principle writeup explaining why a status indicator is missed and how to fix it
  • Estimating impact of adding more menu options using Hick-Hyman predictions
  • Design checklist to score cognitive load and decide whether a redesign is required

FAQ

How confident are recommendations from this skill?

Recommendations are grounded in published HCI and cognitive research; confidence is labeled High/Medium/Low based on evidence and context applicability.

Can this replace user testing?

No. Theory narrows hypotheses and prioritizes fixes, but real users and ecological testing remain essential for validation.