Visual Analogy Creation: Cognitive Protocol

Core Principle

Part 1: The Diagnostic Phase

Step 1.1: Extract the Core Relational Structure

1. Write down the concept/system you need to visualize
2. Ask: "What relates to what here?" List all relationships
3. Ask: "What affects what?" List all causal chains
4. Ask: "What changes together?" List all correlations
5. Identify the single most important relationship - this is your core structure

• Relationships: Customer ↔ Company, Purchase ↔ Points, Points ↔ Rewards
• Causal chains: Purchase → Points accumulate → Rewards unlock → More purchases
• Changes together: Time invested ↔ Switching cost increases
• Core structure: Incremental investment creates increasing exit barriers

Step 1.2: Classify the Question Type (6W Framework)

IF asking "what kind of thing is this?" or "how do these categories relate?"
  → WHO/WHAT (Qualitative/Portrait)
  → Use: Venn diagrams, attribute lists, categorical groupings

ELSE IF asking "how much/many?" or "compare quantities"
  → HOW MUCH (Quantitative/Charts)
  → Use: Bar charts, line graphs, proportional areas

ELSE IF asking "where is this in relation to other things?"
  → WHERE (Positional/Maps)
  → Use: 2x2 matrices, positioning maps, spatial layouts

ELSE IF asking "in what order?" or "when does this happen?"
  → WHEN (Temporal/Timelines)
  → Use: Sequential flows, timelines, stages

ELSE IF asking "how does this work?" or "what's the mechanism?"
  → HOW (Process/Flowcharts)
  → Use: Flow diagrams, cycle diagrams, system maps

ELSE IF asking "why does this happen?" or "what causes what?"
  → WHY (Causation/Multi-variable)
  → Use: Causal diagrams, feedback loops, correlation plots

Step 1.3: Identify the Image Schema

• Geometric: Circles, boxes, nested shapes
• Relations: Inside/outside, member/non-member, bounded/unbounded
• Example: "Users in our ecosystem" → Nested circles

• Geometric: Lines with direction, connected segments
• Relations: Start/end, before/after, progress/regress
• Example: "Customer journey" → Path with waypoints

• Geometric: Arrows, vectors, pushing/pulling
• Relations: Cause/effect, strong/weak, resist/yield
• Example: "Market forces" → Opposing arrows

• Geometric: Scales, see-saws, balanced arrangements
• Relations: Equal/unequal, stable/unstable, compensate
• Example: "Work-life balance" → Scale with weights

• Geometric: Lines connecting nodes
• Relations: Connected/disconnected, strong/weak ties, central/peripheral
• Example: "Social network" → Graph with nodes and edges

• Geometric: Loops, circles, spirals
• Relations: Reinforce/balance, growth/decay, iterate
• Example: "Feedback loop" → Circular arrows

• Geometric: Vertical position, size differences, nested levels
• Relations: More/less, superior/subordinate, macro/micro
• Example: "Organizational hierarchy" → Pyramid or tree

Part 2: The Generation Phase

Step 2.1: Source Domain Selection via Structural Matching

1. List your target domain's relational properties:
   • From Step 1.1: "X relates to Y by [relationship type]"
   • From Step 1.3: "The geometric essence is [image schema]"
2. Generate source domain candidates:
   • Query: "What familiar things have this same pattern of relationships?"
   • Consider: Physical objects, natural phenomena, games, human relationships, machines, economics, biology
   • List 5-10 candidates
3. Test structural alignment: Create explicit mapping table:

   Target Element → Source Element
   Relation 1     → Equivalent Relation
   Relation 2     → Equivalent Relation

4. Evaluate mapping quality:
   • ✓ Do ALL key relations have equivalents?
   • ✓ Are the relations the SAME TYPE (causal→causal, hierarchical→hierarchical)?
   • ✓ Does the source domain add insight, not just restate?
   • ✓ Is the source domain more familiar than the target?

Step 2.2: Refine Through Conceptual Blending

1. Create the mapping table:

   Target Domain | Source Domain | Blended Concept | Emergent Insight

2. Identify emergent structure:
   • What does the blend reveal that neither domain alone shows?
   • What new inferences does the mapping enable?
3. Test inferential productivity:
   • Can you make predictions in the target domain using source domain logic?
   • Do these predictions reveal non-obvious insights?

Step 2.3: Determine SQVID Settings

IF concept's insight requires seeing internal mechanisms/interactions
  → ELABORATE (show subsystems, intermediate steps, feedback loops)
ELSE IF concept's insight is in overall pattern/relationship
  → SIMPLE (show high-level structure only)

Test: Does showing internal detail change the conclusion?
  YES → Elaborate reveals necessary structure
  NO → Simple reveals essential pattern

IF insight is about characteristics/attributes/types/categories
  → QUALITY (labels, distinctions, feature descriptions)
ELSE IF insight is about amounts/measurements/comparisons/magnitudes
  → QUANTITY (numbers, scales, proportions, data encodings)

IF showing ideal state OR future goal OR possibility space OR aspirational
  → VISION (how it should/could be, potential)
ELSE IF showing current state OR actual data OR constraints OR reality
  → EXECUTION (how it is, actuality)

IF showing one entity's internal structure/behavior/properties
  → INDIVIDUAL (deep dive into single case)
ELSE IF showing relationships between entities OR contrasting alternatives
  → COMPARISON (multiple entities, relative positioning)

IF emphasizing change/transformation/trend/evolution
  → DELTA (before/after, trajectories, rates of change)
ELSE IF emphasizing current configuration/snapshot/state
  → STATUS QUO (present moment, what exists now)

Part 3: The Construction Phase

Step 3.1: Translate Relational Structure to Spatial Structure

1. Causation → Arrows
   • A causes B: Arrow from A to B
   • Mutual causation: Arrows both directions (cycle)
   • Strong/weak: Thick/thin arrows
2. Magnitude → Size or Position
   • More/greater: Larger size OR higher vertical position
   • Less/smaller: Smaller size OR lower vertical position
3. Time → Horizontal or Vertical Progression
   • Earlier → Left OR bottom
   • Later → Right OR top
4. Similarity → Proximity
   • Related concepts: Close together
   • Different concepts: Far apart
5. Hierarchy → Vertical Position or Nesting
   • Superordinate: Above OR containing
   • Subordinate: Below OR contained
6. Process Flow → Connected Path
   • Sequential: Linked chain
   • Parallel: Side-by-side paths
   • Branching: Path splits
7. Intensity/Degree → Visual Variable
   • More intense: Darker, thicker, larger
   • Less intense: Lighter, thinner, smaller

Step 3.2: Apply Progressive Structure Revelation

1. Identify minimum elements needed to show the relationship
2. Draw the relationship using appropriate spatial encoding
3. STOP. Test: Does this make the stated relationship visible?
   • If YES → Proceed to Layer 2
   • If NO → Revise core relationship or element selection

1. Identify what modulates/constrains/amplifies the core relationship
2. Add these elements and their relationships
3. STOP. Test: Does this reveal how the system behaves under different conditions?
   • If YES → Context adds genuine insight
   • If NO → Remove context, it's not structural

1. Identify whether understanding requires seeing HOW the relationships work internally
2. Add mechanism details ONLY if they change conclusions
3. STOP. Test: Does this detail reveal non-obvious behavior?
   • If YES → Keep, you're showing necessary complexity
   • If NO → Remove, it's decorative

1. Add ONLY if interesting behavior happens at extremes or boundaries
2. Show what breaks, saturates, or reverses
3. Test: Does this explain real-world observations that normal case doesn't?

Step 3.3: Validate Through Mental Simulation

1. Trace test: Can you follow a path through the visual?
   • Start at beginning, follow connections, reach conclusion or cycle
2. Prediction test: Can you predict what happens next?
   • Given initial state, following the relationships, infer next state
3. Manipulation test: Can you mentally change one variable and see effects?
   • If X increases, what happens to Y?
4. Explanation test: Can someone unfamiliar explain it back?
   • If explanation ≠ intended insight, visual fails

Part 4: Refinement Protocols

Anti-Pattern Detection

• If NO → REMOVE IT
• Note: Styling that encodes information (color for category, depth for hierarchy) is NOT decoration

• Test: Push to extremes. Does it still work?
• Test: Apply to relevant edge cases. Does it break?

• Create explicit mapping table from Step 2.1
• Verify RELATIONSHIP TYPE matches (causal→causal, part-whole→part-whole)

• If NO → Create multiple visuals, each with single clear insight
• Note: Complex visuals showing ONE multilayered structure are acceptable

• Test: Is the data/relationship actually that precise?
• If NO → Use approximation that honestly reflects uncertainty

• Test: How many distinct relationships must be held simultaneously?
• If >7 → Consider breaking into multiple linked visuals or hierarchical organization

Structural Validity Test

Quick Reference

Image Schema Quick Match

YOUR CONCEPT INVOLVES...     → USE THIS SCHEMA → VISUAL FORM

Categories/membership        → CONTAINER       → Circles, Venn diagrams
Sequence/journey             → PATH            → Linear flow, timeline
Influence/causation          → FORCE           → Arrows, vectors
Trade-offs/reciprocity       → BALANCE         → Scales, see-saw
Relationships/links          → LINK            → Network graph
Repetition/feedback          → CYCLE           → Loops, circular arrows
Magnitude/hierarchy          → SCALE           → Vertical position, size

6W Quick Decision

QUESTION TYPE → VISUAL FORM     → SHOWS

Who/What?     → Portrait        → Categories, attributes
How much?     → Chart           → Quantities, comparisons
Where?        → Map             → Position, relationships
When?         → Timeline        → Sequence, duration
How?          → Flowchart       → Process, mechanism
Why?          → Causal diagram  → Causation, correlation

Spatial Encoding Rules

RELATIONSHIP → ENCODE AS

Causation    → Arrow
Magnitude    → Size or vertical position
Time         → Horizontal axis or sequence
Similarity   → Proximity
Hierarchy    → Nesting or vertical stack
Flow         → Connected path
Intensity    → Darkness, thickness, size

Summary: The Complete Protocol

1. Extract core relational structure
2. Classify question type (6W)
3. Identify image schema

• Gentner: Match relational structure, not surface features
• Lakoff & Johnson: Ground abstract concepts in spatial experience
• Tversky: Spatial relationships carry conceptual relationships
• Tufte: Maximize information, minimize ink
• Hofstadter: Analogy creates insight through novel mapping

Part 5: Animated Visual Storytelling

5.1: Narrative Arc for Technical Explainers

1. Hero moment (0-15% of duration): The subject fills the frame. Close camera. The viewer understands WHAT they're looking at before anything happens. Never start zoomed out — earn the wide shot.
2. The question (15-25%): Pose the problem or choice. Text overlay: "How do you render it?" This creates tension that the rest of the animation resolves.
3. The mechanism (25-55%): Show HOW the system works. This is where the structural analogy plays out spatially. Side-by-side comparisons are powerful here — the viewer's eye races between the two approaches.
4. The punchline (55-70%): The payoff. The parallel side finishes while the sequential side is still going. The "4× faster" moment. This should arrive when the viewer is primed but not bored.
5. The hold (70-100%): Let the conclusion breathe. Camera orbits toward the winner. But don't hold too long — 3-4 seconds max, not 30% of the video.

5.2: Camera as Attention Director

• Start close, earn the wide shot: Begin with the subject filling the frame. Pull back only when you need to reveal context (new elements, comparison). The pull-back itself is a narrative beat.
• Pull back BEFORE new elements appear: If you're going to add worker nodes below the timeline, pull the camera back first so there's room. Never let elements appear off-screen.
• Orbit toward the winner: In a comparison, the camera should drift toward the winning side in the final act. The loser recedes. The viewer's perspective literally shifts to favor the answer.
• Snap zoom for emphasis: When the key metric appears ("4× faster"), a quick 200-300ms zoom snap toward it, then ease back. This is the visual equivalent of bold text.
• Subtle shake during activity: Very light camera vibration (0.01-0.02 units) during processing phases makes the scene feel alive. Too much is nauseating.

5.3: Comparison Visualization (The Race Pattern)

1. Start unified: Show one object (the composition, the data, the process)
2. Dramatic split: A laser cut, a flash, a duplication event. This is a story beat, not a quiet transition.
3. Label immediately: "Traditional" (dim) vs "Editframe" (bright). Use brand names, not abstract CS terms.
4. Race simultaneously: Both sides process at the same time. The viewer's eye bounces between them. Progress indicators make the speed difference visible.
5. Winner finishes first: The fast side completes while the slow side is visibly behind. Hold this moment — it's the proof.

• Slow/old/bad: warm tones (orange, amber), dimmer, lower opacity
• Fast/new/good: cool tones (blue, teal), brighter, higher emissive

5.4: Concrete > Abstract

5.5: Sizing for the Medium

• If an element is smaller than 5% of the frame width, it's invisible in the rendered output
• Progress bars need to be at least 3-4% of frame height to be visible
• Text overlays need 50% larger font than you'd use in a web page (the video has lower effective resolution)
• Particle effects need to be 2-3× larger than looks good in live preview (they compress poorly)
• Test by rendering and watching the output, not by looking at the live preview

5.6: 3D as a Structural Tool

Additional Resources