Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Biological Influences on Intelligence01:30

Biological Influences on Intelligence

106
Intelligence is often thought to be linked to brain size, but the relationship is more complex than that. While brain size does correlate modestly with some abilities, like verbal skills, the connection is weaker for others, such as spatial reasoning. Other factors, like brain structure, also play crucial roles. For instance, despite Einstein's smaller-than-average brain, his parietal cortex, which is involved in spatial reasoning, was 15% wider, suggesting that neural density might matter...
106
Environmental Influences on Intelligence01:29

Environmental Influences on Intelligence

295
Despite the strong genetic influence on traits like intelligence, environmental factors significantly shape outcomes. For example, while over 90% of height variation is due to genetic differences, environmental factors such as nutrition also have a notable impact. Similarly, for intelligence, changes in a child's surroundings can significantly alter their IQ. Research shows that enriched environments boost children's academic success and help them develop key cognitive skills. Children...
295
Cattell's Theory of Intelligence01:25

Cattell's Theory of Intelligence

6.6K
Raymond Cattell, along with John Horn, made significant contributions to our understanding of intelligence by distinguishing between two types: fluid intelligence and crystallized intelligence.
Fluid intelligence involves the capacity to solve new problems and adapt to unfamiliar situations. It's the type of intelligence individuals use when they encounter a novel problem or puzzle that requires innovative thinking. For instance, figuring out how to operate a new gadget relies heavily on...
6.6K
Binet's Contribution to Measures of Intelligence01:23

Binet's Contribution to Measures of Intelligence

1.3K
Alfred Binet, along with his student Théophile Simon, was tasked by the French Ministry of Education in 1904 to create a method for identifying students who struggled to learn through conventional classroom instruction. This initiative aimed to address overcrowding by placing such students in specialized schools. Binet and Simon developed an intelligence test comprising 30 tasks, ranging from simple commands, like touching one's nose or ear, to more complex tasks, such as drawing...
1.3K
Measures of Intelligence01:29

Measures of Intelligence

7.3K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
7.3K
Wechsler's Contribution to Measures of Intelligence01:23

Wechsler's Contribution to Measures of Intelligence

1.5K
David Wechsler, a psychologist who worked with World War I veterans, developed a significant IQ test in 1939 called the Wechsler-Bellevue Intelligence Scale. This test was innovative because it combined several subtests that measured both verbal and nonverbal skills, reflecting Wechsler's belief that intelligence is a global capacity involving purposeful action, rational thinking, and effective interaction with the environment. This test later evolved into the Wechsler Adult Intelligence...
1.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Blueprint for adapting the cardiac rehabilitation model for oncology patients with cardiovascular-kidney-metabolic syndrome.

American journal of preventive cardiology·2026
Same author

The physiological foundation of extinction improvement by tDCS over the ventromedial prefrontal cortex (vmPFC) in healthy humans: an fMRI study.

Translational psychiatry·2026
Same author

Neural correlates of appetitive extinction learning: an fMRI study with actively participating pigeons.

Scientific reports·2026
Same author

Life Narratives and the Ten Aspects of the Big Five Across Open-Ended and Targeted Prompts.

Journal of personality·2026
Same author

An individual participant data meta-analysis of how physical activity relates to affective well-being in daily life.

Nature human behaviour·2026
Same author

Predicting individual differences of fear and cognitive learning and extinction.

Nature communications·2026

Related Experiment Video

Updated: Jul 5, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

Investigating robust associations between functional connectivity based on graph theory and general intelligence.

Dorothea Metzen1,2, Christina Stammen3, Christoph Fraenz3

  • 1Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801, Bochum, Germany. dorothea.metzen@rub.de.

Scientific Reports
|January 16, 2024
PubMed
Summary
This summary is machine-generated.

This study found no consistent links between general intelligence (g factor) and brain network properties using graph theory. Replicable associations between functional connectivity and intelligence remain elusive with current resting-state imaging methods.

More Related Videos

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K
Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

14.9K

Related Experiment Videos

Last Updated: Jul 5, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K
Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

14.9K

Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Network Science

Background:

  • Previous studies on the relationship between general intelligence (g factor) and brain network graph theory have produced conflicting results.
  • Multi-center analysis is a valuable approach for resolving inconsistencies in research findings.
  • Understanding the neural basis of general intelligence is a key goal in cognitive neuroscience.

Purpose of the Study:

  • To identify robust associations between general intelligence (g factor) scores and brain network graph-theoretical properties.
  • To investigate global and node-specific graph metrics using a large, multi-center dataset.
  • To assess the replicability of these associations across independent samples.

Main Methods:

  • Analysis of resting-state functional connectivity data from four independent datasets (N > 2000).
  • Calculation of global graph metrics (global efficiency, small-world propensity, global clustering coefficient).
  • Application of elastic-net regression for node-specific efficiency and local clustering, with cross-dataset prediction attempts.

Main Results:

  • No significant global associations were found between the g factor and global efficiency or small-world propensity across all samples.
  • Significant positive associations between the g factor and global clustering coefficient were observed in two of the four samples.
  • No brain regions showed consistent node-specific associations for nodal efficiency or local clustering across datasets; cross-dataset predictions were largely unsuccessful.

Conclusions:

  • Conventional graph theoretical measures derived from resting-state fMRI do not yield replicable associations with general intelligence.
  • The relationship between functional brain network topology and general intelligence may be more complex than captured by current graph metrics.
  • Further methodological advancements are needed to reliably link brain network properties to cognitive abilities like general intelligence.