The g Factor: General Intelligence
The g factor (general intelligence) is considered by many psychometricians to be one of the most important discoveries in the field. It represents the shared variance among all cognitive abilities—the statistical finding that people who perform well on one type of cognitive task tend to perform well on others. However, others remain skeptical of most reported g-loadings and question the commercial and methodological practices behind many g-factor claims. For a comprehensive discussion of these critical perspectives, see Views on g-loading in our adaptive test documentation.
Discovery of g
The g factor was discovered by Charles Spearman in 1904 through factor analysis of children's academic performance. Spearman noticed something remarkable: students' grades in seemingly unrelated subjects showed positive correlations.
Charles Spearman publishes "General Intelligence, Objectively Determined and Measured," introducing factor analysis and discovering g.
Spearman refines his two-factor theory, proposing g (general) and s (specific) factors.
Thurstone challenges g with his theory of primary mental abilities, but later acknowledges a higher-order g factor.
Carroll publishes his three-stratum theory, placing g at the apex of cognitive abilities.
What Exactly is g?
Understanding g requires grasping what it is and isn't:
What g IS:
- A statistical abstraction derived from the positive correlations among cognitive tasks
- The first principal component in factor analysis of cognitive abilities
- The best single predictor of performance across diverse cognitive tasks
- A latent variable that influences all cognitive abilities to varying degrees
What g IS NOT:
- A physical thing or location in the brain
- The only aspect of cognitive ability that matters
- A complete description of intelligence
- Fixed or entirely genetic
The Positive Manifold
The foundation of g is the "positive manifold"—the finding that all cognitive abilities correlate positively with each other. This means:
- People good at verbal tasks tend to be good at spatial tasks
- Those strong in memory tend to be strong in reasoning
- Mathematical ability correlates with verbal ability
- No cognitive abilities show negative correlations
Simplified representation:
Performance on any cognitive task = g × loading + specific ability + error
Evidence for g
1. Statistical Evidence
- Factor analysis consistency: g emerges as the first factor regardless of test battery
- Cross-cultural validity: The g factor appears in all studied populations
- Temporal stability: g shows high test-retest reliability over decades
- Age invariance: The structure of g remains similar across the lifespan
2. Predictive Validity
g predicts a remarkable range of life outcomes:
- Academic achievement (r ≈ 0.5-0.7)
- Job performance (r ≈ 0.3-0.5, higher for complex jobs)
- Income (r ≈ 0.2-0.4)
- Health and longevity (r ≈ 0.2-0.3)
- Social outcomes (lower crime, better decision-making)
3. Biological Correlates
- Brain volume: Modest correlation with total brain volume (r ≈ 0.3)
- Neural efficiency: Higher g associated with more efficient brain activity
- Processing speed: Faster neural conduction and reaction times
- Working memory: Strong correlation with working memory capacity
Biological Basis of g
Research has identified several biological factors associated with g:
Neurological Findings
- Parieto-frontal integration theory: g linked to integration between frontal and parietal regions
- White matter integrity: Better connectivity associated with higher g
- Neural efficiency: Less brain activation needed for cognitive tasks
- Brain network topology: More efficient network organization
Heritability
Twin and adoption studies consistently show that g is substantially heritable:
- Heritability increases with age: ~20% in infancy to ~80% in adulthood
- Shared environment effects decrease with age
- Molecular genetics has identified numerous small-effect variants
Measuring g
g-Loading
Different cognitive tasks vary in their g-loading (correlation with g):
- High g-loading tasks: Matrix reasoning, verbal analogies, working memory
- Medium g-loading: Vocabulary, spatial rotation, arithmetic
- Low g-loading: Simple reaction time, digit span forward
See Views on g-loading for a critical perspective on g-loading claims in psychometrics.
Extraction Methods
- Principal Component Analysis: First unrotated principal component
- Factor Analysis: Common factor underlying cognitive tasks
- Bifactor Models: Separate g from group factors
- Item Response Theory: Modern psychometric approaches
Theoretical and Practical Implications
Theoretical Implications
- Cognitive architecture: Suggests a hierarchical organization of abilities
- Evolution: Points to general-purpose cognitive mechanisms
- Development: Explains why abilities develop together
- Individual differences: Provides framework for understanding variation
Practical Applications
- Education: Informs teaching methods and academic placement
- Clinical assessment: Helps identify cognitive strengths and weaknesses
- Personnel selection: Predicts job performance, especially in complex roles
- Research: Provides common metric across studies
Important Caveats
While g is a powerful construct, it's important to remember:
- g doesn't capture all important aspects of human capability
- Specific abilities matter, especially for specialized tasks
- Non-cognitive factors (personality, motivation) also predict success
- g should never be used to judge human worth or limit opportunities
Future Directions
Current research on g focuses on:
- Neuroscience: Understanding the neural basis of g
- Genetics: Identifying genetic variants and pathways
- Development: How g emerges and changes across the lifespan
- Enhancement: Whether g can be improved through intervention
- Artificial intelligence: Creating AI systems with g-like general ability
The g factor remains one of the most robust findings in psychology, providing a scientific foundation for understanding human cognitive abilities while acknowledging the full complexity of human intelligence.