Rule Detection - Advanced Level: transformation rules ADVANCED

MULTI-DIMENSIONAL PATTERN ANALYSIS:

Transformation 1 - Rotation Analysis:
Step 1: Measure rotation angles
- Figure 1: 0°
- Figure 2: 45°
- Figure 3: 90°
- Figure 4: 135°

Rotation increment: +45° per step ✓

Transformation 2 - Size Analysis:
Step 2: Measure square dimensions
- Figure 1: 15 units
- Figure 2: 18 units
- Figure 3: 21 units
- Figure 4: 24 units

Size increment: +3 units per step ✓

COMBINED RULE HYPOTHESIS:
TWO simultaneous transformations:
1. Rotation: +45° clockwise per step
2. Scaling: +3 units per step

VERIFICATION:
Both patterns verified independently ✓
Check for correlation: None (independent transformations) ✓

APPLICATION:
Figure 5 predictions:
- Rotation: 135° + 45° = 180°
- Size: 24 + 3 = 27 units

ADVANCED MULTI-RULE DETECTION:
- Decompose complex transformations
- Analyze each dimension independently
- Verify pattern consistency for each rule
- Check for rule interactions or dependencies
- Combine predictions from all rules

COMMON MISTAKES TO AVOID:
- Focusing on only one transformation
- Missing the scaling while tracking rotation
- Not verifying both patterns independently
- Assuming transformations must be related
- Incomplete pattern analysis

CONDITIONAL RULE ANALYSIS:

Step 1: Classify each figure
- Figure 1: Closed shape (circle) → has DOT inside
- Figure 2: Open shape (arc) → has LINE segment
- Figure 3: Closed shape (square) → has DOT inside
- Figure 4: Open shape (curve) → has LINE segment

Step 2: Identify the conditional pattern
Check correlation between shape type and marking:
- ALL closed shapes → contain dots ✓
- ALL open shapes → contain lines ✓

CONDITIONAL RULE HYPOTHESIS:
IF shape is CLOSED → THEN add dot inside
IF shape is OPEN → THEN add line segment

VERIFICATION:
Test hypothesis against all figures:
- Figure 1: Closed + Dot ✓
- Figure 2: Open + Line ✓
- Figure 3: Closed + Dot ✓
- Figure 4: Open + Line ✓

Rule verified across all cases ✓

APPLICATION:
Given: Next shape is a CLOSED triangle
Apply rule: IF closed → THEN add dot
Result: Triangle with dot inside

BOOLEAN LOGIC FRAMEWORK:
- Condition: IsClosed(shape)
- True branch: AddDot()
- False branch: AddLine()

CONDITIONAL RULE DETECTION STRATEGY:
1. Identify potential condition variables
2. Classify all examples by condition
3. Check for consistent outcomes per condition
4. Formulate IF-THEN rule
5. Verify rule on all examples
6. Apply to new case based on its condition

COMMON MISTAKES TO AVOID:
- Not recognizing the conditional nature
- Treating as simple alternating pattern
- Ignoring the shape property (open/closed)
- Applying wrong transformation for given condition
- Missing the IF-THEN logical structure

PATTERN ANALYSIS:
Step 1: Examine the fill/shading in each figure
Step 2: Look for systematic changes in shading

RULE HYPOTHESIS:
The shading pattern adds one more filled segment each time

VERIFICATION:
Check pattern consistency across all four figures ✓

APPLICATION:
Based on the identified rule, the next figure should continue the pattern

SHADING ANALYSIS TECHNIQUES:
- Check for binary patterns (filled/unfilled)
- Look for cyclic color patterns
- Count filled vs unfilled elements
- Observe progressive filling patterns
- Check for symmetry in shading

COMMON MISTAKES TO AVOID:
- Missing subtle shading differences
- Not recognizing cyclic patterns
- Assuming random shading changes
- Overlooking progressive patterns

PATTERN ANALYSIS:
Step 1: Track the position of the dot in each figure
- Figure 1: Position at (85, 60)
- Figure 2: Position at (60, 85)
- Figure 3: Position at (35, 60)
- Figure 4: Position at (59, 35)

Step 2: Analyze movement vectors
- Fig 1→2: Δx = -25, Δy = 25
- Fig 2→3: Δx = -25, Δy = -25
- Fig 3→4: Δx = 24, Δy = -25

Step 3: Detect movement pattern
The dot follows a circular path (90° steps clockwise)

RULE HYPOTHESIS:
Systematic positional movement following circular path (90° steps clockwise)

VERIFICATION:
All movements conform to the identified pattern ✓

APPLICATION:
Next position: (85, 59)
Following the established circular path (90° steps clockwise)

ADVANCED TECHNIQUES:
- Plot positions on coordinate system
- Check for symmetry and periodicity
- Analyze velocity and acceleration vectors
- Consider boundary conditions

COMMON MISTAKES TO AVOID:
- Assuming simple linear movement
- Not considering boundary reflections
- Missing rotational or circular patterns
- Ignoring spatial constraints

PATTERN ANALYSIS:
Step 1: Count elements in each figure
- Figure 1: 6 circles
- Figure 2: 5 circles
- Figure 3: 4 circles
- Figure 4: 3 circles

Step 2: Calculate differences between consecutive figures
- Fig 2 - Fig 1 = -1
- Fig 3 - Fig 2 = -1
- Fig 4 - Fig 3 = -1

RULE HYPOTHESIS:
The number of circles is decreasing by 1 in each step

VERIFICATION:
All consecutive differences are consistent: -1 ✓

APPLICATION:
Figure 5 should have 3 -1 = 2 circles

COMMON MISTAKES TO AVOID:
- Miscounting elements in figures
- Not checking all consecutive differences
- Assuming non-linear progressions too early

PATTERN ANALYSIS:
Step 1: Compare consecutive figures - observe the orientation changes
Step 2: Measure rotation angle between Figure 1 and Figure 2 = 45°
Step 3: Verify this pattern: Figure 2→3 also shows 45° rotation
Step 4: Verify Figure 3→4 also follows the same 45° rotation

RULE HYPOTHESIS:
The figure rotates clockwise by 45° in each step

VERIFICATION:
- Figure 1 to 2: 45° clockwise ✓
- Figure 2 to 3: 45° clockwise ✓
- Figure 3 to 4: 45° clockwise ✓

APPLICATION:
Figure 5 should be Figure 4 rotated clockwise by 45°
Total rotation from start = 180°

COMMON MISTAKES TO AVOID:
- Confusing clockwise with counterclockwise rotation
- Measuring rotation incorrectly
- Assuming different rotation angles

PATTERN ANALYSIS:
Step 1: Measure the radius of circles in each figure
- Figure 1: radius = 10 units
- Figure 2: radius = 20 units
- Figure 3: radius = 40 units
- Figure 4: radius = 80 units

Step 2: Calculate size changes between consecutive figures
- Fig 1 → 2: 20 ÷ 10 = 2×
- Fig 2 → 3: 40 ÷ 20 = 2×
- Fig 3 → 4: 80 ÷ 40 = 2×

RULE HYPOTHESIS:
The circle radius doubles each time (geometric progression)

VERIFICATION:
All consecutive ratios are consistent: 2× ✓

APPLICATION:
Figure 5 radius = 80 × 2 = 160 units

SCALING PATTERN TYPES:
- Geometric progression: constant r = 2

COMMON MISTAKES TO AVOID:
- Confusing diameter with radius
- Assuming linear when pattern is geometric (or vice versa)
- Miscounting the number of steps
- Not checking both differences AND ratios to identify pattern type

HIERARCHICAL RULE DETECTION:

LEVEL 1 ANALYSIS - Outer Shape Pattern:
Step 1: Identify shapes by counting sides
- Figure 1: Triangle (3 sides)
- Figure 2: Square (4 sides)
- Figure 3: Pentagon (5 sides)
- Figure 4: Hexagon (6 sides)

Rule 1 (Outer): Shape gains +1 side per step

LEVEL 2 ANALYSIS - Inner Element Pattern:
Step 2: Count dots inside each shape
- Figure 1: 1 dot
- Figure 2: 2 dots
- Figure 3: 3 dots
- Figure 4: 4 dots

Rule 2 (Inner): Number of dots increases by +1 per step

NESTED RULE HYPOTHESIS:
TWO independent hierarchical rules:
1. Outer rule: Polygon sides = 3, 4, 5, 6, ...
2. Inner rule: Dot count = 1, 2, 3, 4, ...

CORRELATION CHECK:
Both patterns follow same arithmetic progression (n+1)
Correlation: dot count = shape sides - 2 ✓

VERIFICATION:
- Figure 1: 3 sides, 1 dot (3-2=1) ✓
- Figure 2: 4 sides, 2 dots (4-2=2) ✓
- Figure 3: 5 sides, 3 dots (5-2=3) ✓
- Figure 4: 6 sides, 4 dots (6-2=4) ✓

APPLICATION:
Figure 5 should have:
- Outer: 6 + 1 = 7 sides (heptagon)
- Inner: 4 + 1 = 5 dots
- Verification: 7 - 2 = 5 ✓

ADVANCED NESTED RULE TECHNIQUES:
- Analyze each level independently
- Check for correlations between levels
- Verify consistency across all levels
- Apply all rules to predict next state
- Cross-verify using relationships

COMMON MISTAKES TO AVOID:
- Analyzing only one level
- Missing the relationship between levels
- Not verifying correlation formulas
- Applying only one rule to prediction
- Incomplete hierarchical decomposition

MULTI-DIMENSIONAL PATTERN ANALYSIS:

Transformation 1 - Rotation Analysis:
Step 1: Measure rotation angles
- Figure 1: 0°
- Figure 2: 45°
- Figure 3: 90°
- Figure 4: 135°

Rotation increment: +45° per step ✓

Transformation 2 - Size Analysis:
Step 2: Measure square dimensions
- Figure 1: 15 units
- Figure 2: 18 units
- Figure 3: 21 units
- Figure 4: 24 units

Size increment: +3 units per step ✓

COMBINED RULE HYPOTHESIS:
TWO simultaneous transformations:
1. Rotation: +45° clockwise per step
2. Scaling: +3 units per step

VERIFICATION:
Both patterns verified independently ✓
Check for correlation: None (independent transformations) ✓

APPLICATION:
Figure 5 predictions:
- Rotation: 135° + 45° = 180°
- Size: 24 + 3 = 27 units

ADVANCED MULTI-RULE DETECTION:
- Decompose complex transformations
- Analyze each dimension independently
- Verify pattern consistency for each rule
- Check for rule interactions or dependencies
- Combine predictions from all rules

COMMON MISTAKES TO AVOID:
- Focusing on only one transformation
- Missing the scaling while tracking rotation
- Not verifying both patterns independently
- Assuming transformations must be related
- Incomplete pattern analysis

CONDITIONAL RULE ANALYSIS:

Step 1: Classify each figure
- Figure 1: Closed shape (circle) → has DOT inside
- Figure 2: Open shape (arc) → has LINE segment
- Figure 3: Closed shape (square) → has DOT inside
- Figure 4: Open shape (curve) → has LINE segment

Step 2: Identify the conditional pattern
Check correlation between shape type and marking:
- ALL closed shapes → contain dots ✓
- ALL open shapes → contain lines ✓

CONDITIONAL RULE HYPOTHESIS:
IF shape is CLOSED → THEN add dot inside
IF shape is OPEN → THEN add line segment

VERIFICATION:
Test hypothesis against all figures:
- Figure 1: Closed + Dot ✓
- Figure 2: Open + Line ✓
- Figure 3: Closed + Dot ✓
- Figure 4: Open + Line ✓

Rule verified across all cases ✓

APPLICATION:
Given: Next shape is a CLOSED triangle
Apply rule: IF closed → THEN add dot
Result: Triangle with dot inside

BOOLEAN LOGIC FRAMEWORK:
- Condition: IsClosed(shape)
- True branch: AddDot()
- False branch: AddLine()

CONDITIONAL RULE DETECTION STRATEGY:
1. Identify potential condition variables
2. Classify all examples by condition
3. Check for consistent outcomes per condition
4. Formulate IF-THEN rule
5. Verify rule on all examples
6. Apply to new case based on its condition

COMMON MISTAKES TO AVOID:
- Not recognizing the conditional nature
- Treating as simple alternating pattern
- Ignoring the shape property (open/closed)
- Applying wrong transformation for given condition
- Missing the IF-THEN logical structure

MULTI-DIMENSIONAL PATTERN ANALYSIS:

DIMENSION 1 - Row Pattern Analysis:
Step 1: Analyze Row 1 (keeping row constant, varying column)
- Col 1: Triangle at 0°
- Col 2: Triangle at 90°
- Col 3: Triangle at 180°

Row Rule: Each column adds +90° rotation

DIMENSION 2 - Column Pattern Analysis:
Step 2: Analyze Column 1 (keeping column constant, varying row)
- Row 1: Triangle (3 sides)
- Row 2: Square (4 sides)

Column Rule: Each row adds +1 side to the polygon

RULE INTEGRATION:
For any cell[row, col]:
- Base shape determined by row (row rule)
- Rotation determined by column (column rule)

VERIFICATION:
Test on known cells:
- Cell[1,1]: Triangle + 0° ✓
- Cell[1,2]: Triangle + 90° ✓
- Cell[1,3]: Triangle + 180° ✓
- Cell[2,1]: Square + 0° ✓
- Cell[2,2]: Square + 90° ✓

APPLICATION TO MISSING CELL:
Cell[2,3] should have:
- Shape from Row 2: Square (4 sides)
- Rotation from Col 3: 180°
- Result: Square rotated 180°

MATRIX PATTERN PRINCIPLES:
- Rows often control one property
- Columns often control another property
- Cell value = f(row_property, col_property)
- Both rules apply independently

SYSTEMATIC APPROACH:
1. Identify row-wise pattern (vary column)
2. Identify column-wise pattern (vary row)
3. Verify both patterns independently
4. Combine both rules for prediction
5. Cross-verify using diagonal patterns if present

COMMON MISTAKES TO AVOID:
- Analyzing only rows or only columns
- Not recognizing independent property control
- Mixing up row and column rules
- Failing to apply both rules to prediction
- Not verifying patterns across multiple rows/columns

HIERARCHICAL RULE DETECTION:

LEVEL 1 ANALYSIS - Outer Shape Pattern:
Step 1: Identify shapes by counting sides
- Figure 1: Triangle (3 sides)
- Figure 2: Square (4 sides)
- Figure 3: Pentagon (5 sides)
- Figure 4: Hexagon (6 sides)

Rule 1 (Outer): Shape gains +1 side per step

LEVEL 2 ANALYSIS - Inner Element Pattern:
Step 2: Count dots inside each shape
- Figure 1: 1 dot
- Figure 2: 2 dots
- Figure 3: 3 dots
- Figure 4: 4 dots

Rule 2 (Inner): Number of dots increases by +1 per step

NESTED RULE HYPOTHESIS:
TWO independent hierarchical rules:
1. Outer rule: Polygon sides = 3, 4, 5, 6, ...
2. Inner rule: Dot count = 1, 2, 3, 4, ...

CORRELATION CHECK:
Both patterns follow same arithmetic progression (n+1)
Correlation: dot count = shape sides - 2 ✓

VERIFICATION:
- Figure 1: 3 sides, 1 dot (3-2=1) ✓
- Figure 2: 4 sides, 2 dots (4-2=2) ✓
- Figure 3: 5 sides, 3 dots (5-2=3) ✓
- Figure 4: 6 sides, 4 dots (6-2=4) ✓

APPLICATION:
Figure 5 should have:
- Outer: 6 + 1 = 7 sides (heptagon)
- Inner: 4 + 1 = 5 dots
- Verification: 7 - 2 = 5 ✓

ADVANCED NESTED RULE TECHNIQUES:
- Analyze each level independently
- Check for correlations between levels
- Verify consistency across all levels
- Apply all rules to predict next state
- Cross-verify using relationships

COMMON MISTAKES TO AVOID:
- Analyzing only one level
- Missing the relationship between levels
- Not verifying correlation formulas
- Applying only one rule to prediction
- Incomplete hierarchical decomposition

MULTI-DIMENSIONAL PATTERN ANALYSIS:

DIMENSION 1 - Row Pattern Analysis:
Step 1: Analyze Row 1 (keeping row constant, varying column)
- Col 1: Triangle at 0°
- Col 2: Triangle at 90°
- Col 3: Triangle at 180°

Row Rule: Each column adds +90° rotation

DIMENSION 2 - Column Pattern Analysis:
Step 2: Analyze Column 1 (keeping column constant, varying row)
- Row 1: Triangle (3 sides)
- Row 2: Square (4 sides)

Column Rule: Each row adds +1 side to the polygon

RULE INTEGRATION:
For any cell[row, col]:
- Base shape determined by row (row rule)
- Rotation determined by column (column rule)

VERIFICATION:
Test on known cells:
- Cell[1,1]: Triangle + 0° ✓
- Cell[1,2]: Triangle + 90° ✓
- Cell[1,3]: Triangle + 180° ✓
- Cell[2,1]: Square + 0° ✓
- Cell[2,2]: Square + 90° ✓

APPLICATION TO MISSING CELL:
Cell[2,3] should have:
- Shape from Row 2: Square (4 sides)
- Rotation from Col 3: 180°
- Result: Square rotated 180°

MATRIX PATTERN PRINCIPLES:
- Rows often control one property
- Columns often control another property
- Cell value = f(row_property, col_property)
- Both rules apply independently

SYSTEMATIC APPROACH:
1. Identify row-wise pattern (vary column)
2. Identify column-wise pattern (vary row)
3. Verify both patterns independently
4. Combine both rules for prediction
5. Cross-verify using diagonal patterns if present

COMMON MISTAKES TO AVOID:
- Analyzing only rows or only columns
- Not recognizing independent property control
- Mixing up row and column rules
- Failing to apply both rules to prediction
- Not verifying patterns across multiple rows/columns

PATTERN ANALYSIS:
Step 1: Examine the fill/shading in each figure
Step 2: Look for systematic changes in shading

RULE HYPOTHESIS:
The shading pattern adds one more filled segment each time

VERIFICATION:
Check pattern consistency across all four figures ✓

APPLICATION:
Based on the identified rule, the next figure should continue the pattern

SHADING ANALYSIS TECHNIQUES:
- Check for binary patterns (filled/unfilled)
- Look for cyclic color patterns
- Count filled vs unfilled elements
- Observe progressive filling patterns
- Check for symmetry in shading

COMMON MISTAKES TO AVOID:
- Missing subtle shading differences
- Not recognizing cyclic patterns
- Assuming random shading changes
- Overlooking progressive patterns

PATTERN ANALYSIS:
Step 1: Track the position of the dot in each figure
- Figure 1: Position at (40, 60)
- Figure 2: Position at (60, 40)
- Figure 3: Position at (80, 60)
- Figure 4: Position at (60, 80)

Step 2: Analyze movement vectors
- Fig 1→2: Δx = 20, Δy = -20
- Fig 2→3: Δx = 20, Δy = 20
- Fig 3→4: Δx = -20, Δy = 20

Step 3: Detect movement pattern
The dot follows a spiral inward pattern

RULE HYPOTHESIS:
Systematic positional movement following spiral inward pattern

VERIFICATION:
All movements conform to the identified pattern ✓

APPLICATION:
Next position: (50, 60)
Following the established spiral inward pattern

ADVANCED TECHNIQUES:
- Plot positions on coordinate system
- Check for symmetry and periodicity
- Analyze velocity and acceleration vectors
- Consider boundary conditions

COMMON MISTAKES TO AVOID:
- Assuming simple linear movement
- Not considering boundary reflections
- Missing rotational or circular patterns
- Ignoring spatial constraints

PATTERN ANALYSIS:
Step 1: Compare consecutive figures - observe the orientation changes
Step 2: Measure rotation angle between Figure 1 and Figure 2 = 45°
Step 3: Verify this pattern: Figure 2→3 also shows 45° rotation
Step 4: Verify Figure 3→4 also follows the same 45° rotation

RULE HYPOTHESIS:
The figure rotates clockwise by 45° in each step

VERIFICATION:
- Figure 1 to 2: 45° clockwise ✓
- Figure 2 to 3: 45° clockwise ✓
- Figure 3 to 4: 45° clockwise ✓

APPLICATION:
Figure 5 should be Figure 4 rotated clockwise by 45°
Total rotation from start = 180°

COMMON MISTAKES TO AVOID:
- Confusing clockwise with counterclockwise rotation
- Measuring rotation incorrectly
- Assuming different rotation angles

PATTERN ANALYSIS:
Step 1: Examine Figure 1 and Figure 2 - note the orientation change
Step 2: Check if rotation: No consistent rotation angle found
Step 3: Check for reflection: Yes! Figure 2 is a mirror image of Figure 1
Step 4: Identify reflection axis: vertical axis
Step 5: Verify pattern: Figure 3 is reflection of Figure 2 (back to original orientation)

RULE HYPOTHESIS:
The figures alternate between original and reflected across vertical axis

VERIFICATION:
- Figure 1: Original position
- Figure 2: Reflected across vertical axis ✓
- Figure 3: Reflected back to original ✓

APPLICATION:
Figure 4 should be reflected version (same as Figure 2)
Pattern: Original → Reflected → Original → Reflected

COMMON MISTAKES TO AVOID:
- Confusing reflection with rotation
- Identifying wrong axis of reflection
- Not recognizing alternating pattern

MULTI-DIMENSIONAL PATTERN ANALYSIS:

DIMENSION 1 - Row Pattern Analysis:
Step 1: Analyze Row 1 (keeping row constant, varying column)
- Col 1: Triangle at 0°
- Col 2: Triangle at 90°
- Col 3: Triangle at 180°

Row Rule: Each column adds +90° rotation

DIMENSION 2 - Column Pattern Analysis:
Step 2: Analyze Column 1 (keeping column constant, varying row)
- Row 1: Triangle (3 sides)
- Row 2: Square (4 sides)

Column Rule: Each row adds +1 side to the polygon

RULE INTEGRATION:
For any cell[row, col]:
- Base shape determined by row (row rule)
- Rotation determined by column (column rule)

VERIFICATION:
Test on known cells:
- Cell[1,1]: Triangle + 0° ✓
- Cell[1,2]: Triangle + 90° ✓
- Cell[1,3]: Triangle + 180° ✓
- Cell[2,1]: Square + 0° ✓
- Cell[2,2]: Square + 90° ✓

APPLICATION TO MISSING CELL:
Cell[2,3] should have:
- Shape from Row 2: Square (4 sides)
- Rotation from Col 3: 180°
- Result: Square rotated 180°

MATRIX PATTERN PRINCIPLES:
- Rows often control one property
- Columns often control another property
- Cell value = f(row_property, col_property)
- Both rules apply independently

SYSTEMATIC APPROACH:
1. Identify row-wise pattern (vary column)
2. Identify column-wise pattern (vary row)
3. Verify both patterns independently
4. Combine both rules for prediction
5. Cross-verify using diagonal patterns if present

COMMON MISTAKES TO AVOID:
- Analyzing only rows or only columns
- Not recognizing independent property control
- Mixing up row and column rules
- Failing to apply both rules to prediction
- Not verifying patterns across multiple rows/columns

MULTI-DIMENSIONAL PATTERN ANALYSIS:

Transformation 1 - Rotation Analysis:
Step 1: Measure rotation angles
- Figure 1: 0°
- Figure 2: 45°
- Figure 3: 90°
- Figure 4: 135°

Rotation increment: +45° per step ✓

Transformation 2 - Size Analysis:
Step 2: Measure square dimensions
- Figure 1: 15 units
- Figure 2: 18 units
- Figure 3: 21 units
- Figure 4: 24 units

Size increment: +3 units per step ✓

COMBINED RULE HYPOTHESIS:
TWO simultaneous transformations:
1. Rotation: +45° clockwise per step
2. Scaling: +3 units per step

VERIFICATION:
Both patterns verified independently ✓
Check for correlation: None (independent transformations) ✓

APPLICATION:
Figure 5 predictions:
- Rotation: 135° + 45° = 180°
- Size: 24 + 3 = 27 units

ADVANCED MULTI-RULE DETECTION:
- Decompose complex transformations
- Analyze each dimension independently
- Verify pattern consistency for each rule
- Check for rule interactions or dependencies
- Combine predictions from all rules

COMMON MISTAKES TO AVOID:
- Focusing on only one transformation
- Missing the scaling while tracking rotation
- Not verifying both patterns independently
- Assuming transformations must be related
- Incomplete pattern analysis

ABSTRACT RELATIONSHIP ANALYSIS:

Step 1: Analyze the relationship between Figure 1 and Figure 2
- Figure 1: Triangle
- Figure 2: 3 circles

Step 2: Identify properties of Figure 1
- Shape: Triangle
- Number of corners/vertices: 3
- Number of sides: 3

Step 3: Identify properties of Figure 2
- Shape: Circles
- Count: 3

RELATIONSHIP HYPOTHESIS:
The count of shapes in Figure 2 equals the number of corners in Figure 1

Step 4: Formulate the rule
Rule: "The second figure in each pair contains as many shapes as the first figure has corners"

VERIFICATION:
- Figure 1: Triangle has 3 corners
- Figure 2: Shows 3 circles ✓
- Relationship confirmed!

APPLICATION TO NEW PAIR:
- Figure 3: Square has 4 corners
- Figure 4 should: Show 4 shapes (circles)

ABSTRACT REASONING PRINCIPLES:
- Look beyond geometric transformations
- Consider numerical relationships
- Examine property mappings (corners → count)
- Test hypothesis on known pairs
- Apply verified rule to new cases

RELATIONSHIP DETECTION STRATEGIES:
1. Identify all properties of source figure
2. Identify all properties of target figure
3. Look for numerical correspondences
4. Check property-to-property mappings
5. Formulate relationship rule
6. Verify on all given pairs
7. Apply to predict unknown

COMMON MISTAKES TO AVOID:
- Looking only for visual transformations
- Missing abstract numerical relationships
- Not considering shape properties (corners, sides, etc.)
- Assuming relationship must be geometric
- Not verifying hypothesis on all pairs