Galton, Francis, 26
Gamm, R., 174–175
gender differences brain activity, 76–78
white matter correlations with IQ, 106
gene expression, 40regulation of, 59
role of methylation, 59
types of, 38
genes coding for proteins, 40
definition of a gene, 40, 59
forms of (alleles), 60
generalist genes, 41–42
locus on a chromosome, 60
molecular genetics research, 56–59
pleiotropy, 41–42
polygenicity of intelligence, 41–42
protein formation, 59
repeat copies at a locus, 60
structure of (base pairs), 60
genetic code, 59
genetic engineering CRISPR/Cas9 method of genome editing, 164
Doogie strain of mice, 56–57
genetics basic concepts, 59–60
genetics and intelligence, 40anti-genetic feeling, 40–41
behavioral genetics, 41–42
common genes for brain structure and intelligence, 126–132
debate over, 43–45
heritability of intelligence, 41–42
three-component model, 51–53
twin studies, 46–50
genius, 14, 189
genome, 59
genome-wide association studies (GWAS), 60, 61–62
genomic informatics, 60
genomics, 59
genotype, 41
gifted children, 25–30, 32–33
glucose metabolic rate (GMR), 71
Google, 28, 180
Gottfredson, Linda, 22
Graduate Record Exam (GRE), 20
graph analysis of neuroimaging data, 101–103
Gray, Jeremy, 92
guanine (G), 59
guanylate kinase (MAGUK), 62
Halstead, Ward C., 98
Hawkins, Jeff, 179–180
Head Start education program (USA), 42–45
heritability of intelligence, 41–42Continuity Hypothesis, 53–54
Discontinuity Hypothesis, 53–54
effect of age at testing, 50–51
three laws of complex traits, 53
Herrnstein, Richard, 24–25, 197
Holden, Constance, 81
homotopic analysis of neuroimaging data, 103–104
Human Brain Project, 181
Human Connectome Project, 181–182
human genome, 59
Human Genome Project, 40, 166
Hunt, Earl, 22
IMAGEN consortium, 134
increasing intelligence active reading for children, 154
assessing claims for, 137–139, 153
brain-altering technologies, 158–162
childhood nutrition studies, 154
claims for classical music, 139–143
compensatory education programs, 42–45
computer games, 150–153
Database of Raising Intelligence (NYU) meta-analyses, 153–155
deep brain stimulation (DBS), 161–162
drugs to boost intelligence, 155–158
early failures, 42–45
effect of early education, 154
effect of preschool attendance, 154–155
ethical issues, 157–158
fundamental problem of measurement, 138–139
future possibilities, 163–164
Head Start education program (USA), 42–45
IQ pill, 155–158
light from low-power “cold” lasers, 162
memory training, 143–150
missing weight of evidence for, 138–139, 162–164
Mozart Effect, 139–143
need for independent replication of studies, 153
role of neurotransmitters, 155
transcranial alternating current stimulation (tACS), 160–161
transcranial direct current stimulation (tDCS), 159–160
transcranial magnetic stimulation (TMS), 158–159
independent component analysis, 104
independent replication of studies, 153
intelligence as a general mental ability, 4–5
confounding with social–economic status (SES), 55–56, 192–200
defining, 2–5, 123–124
g-factor, 10–11
g-factor relationships, 5–9
influence on longevity, 30–32
involvement of multiple areas of the brain, 76–79
Parieto-Frontal Integration Theory (PFIT), 92–95
relationship to reasoning, 124–126
savant abilities, 2–4
intelligence genes and genes for brain structure, 126–132
DNA analysis techniques, 57–58
evidence from neuroimaging and molecular genetics, 132–135
molecular genetics research, 56–59
problems with early candidate gene studies, 58–59
intelligence measurement development of IQ testing, 11–15
key problem for, 18–19
reasons for myths about, 33–35
relative score problem, 18–19
intelligence research defining intelligence, 4–5
negative connotations, 43–45
three laws governing, 168 See also future of intelligence research
intelligence testing chronometric testing, 168
disadvantages of interval scales, 168–169
influence of age at testing, 50–51
limitations of psychometric tests, 168–169
mental age concept, 12–13
need for a ratio scale of measurement, 168–169
original purpose of, 11–12
predictive validity for everyday life functioning, 22–25
predictive validity for job performance, 21–22
predictive validity for learning ability, 19–21
predictive validity in longitudinal studies, 25–33
reasons for myths about, 33–35
intelligence tests achievement tests, 16–17
alternatives to IQ tests, 15–17
analogy tests, 16
aptitude tests, 16–17
Binet–Simon intelligence test, 12–13
correlations between, 5–9
fairness of, 17–18
meaningfulness of, 17–18
Moray House Test, 31
myths about, 17–18
predictive value of, 18
question of bias, 17–18
Raven’s Advanced Progressive Matrices (RAPM) test, 15–16
SAT (Scholastic Assessment Test), 16–17
Stanford–Binet test, 13
time-limited tests, 15–16
Wechsler Adult Intelligence Scale (WAIS), 13–15
Wechsler Intelligence Scale for Children (WISC), 15
International Society for Intelligence Research (ISIR), 81, 92
IQ (intelligence quotient) development of IQ testing, 11–15
distinction from g-factor, 10–11
IQ (intelligence quotient) score as a relative measure, 13
average test score differences between groups, 34–35
calculation of deviation scores, 13–15
differences in group average scores, 43–45
Flynn Effect, 49
generation of norms, 14–15
genius range, 14
normal distribution of IQ scores, 13–15
original calculation for children, 12
relative scores issue, 18–19
stability over time, 30–32
IQ in The Meritocracy (Herrnstein), 25, 44, 192, 197
Jaeggi, Suzanne M., 143–150
Jensen, Arthur, 1, 41, 197, 198–199chronometric testing, 169–171
genetic basis of intelligence, 43–45
report on Burt’s twin studies, 48
review of compensatory education programs, 43–45
job performance predictive ability of intelligence tests, 21–22
requirements for expertise, 22
Jung, Rex, 92, 119, 184, 189–190
Kennedy, John F., 166
Lady Gaga, 28
laser light low-power “cold” laser brain stimulation, 162
Lashley, Karl, 176
Law School Admission Test (LSAT), 20
learning ability predictive validity of intelligence tests, 19–21
learning and brain efficiency, 73–75
Lerner, Barbara, 1
life events relative risk related to IQ, 23
longevity influence of intelligence, 30–32
longitudinal studies predictive validity of intelligence testing, 25–33
Lubinski, David, 30, 193
machine intelligence based on human intelligence, 179–183
mathematical reasoning gender differences in brain activity, 76–78
meaningfulness of intelligence tests, 17–18
Medical College Admission Test (MCAT), 20
MEG (magneto-encephalogram), 70brain efficiency studies, 112–117
memory cognitive neuroscience of memory and super-memory, 171–175
memory training claims to increase intelligence, 143–150
mnemonic methods, 172–175
super-memory cases, 172–175
mental abilities structure of, 5–9
mental ability tests correlations between, 5–9
factor analysis, 5–9
positive manifold, 7, 8–9
mental age concept, 12–13
mental calculators, 174–175
methylation role in gene expression regulation, 59
mice Doogie strain, 56–57
Microsoft, 180
Miller, Zell, 141
Minnesota Multiphasic Personality Inventory (MMPI), 122
mnemonic methods of memory training, 172–174
molecular genetic studies, 61–66benefits of a consortium approach, 61
brain proteins and IQ, 63
combined with neuroimaging studies, 132–135
construction of IQ-related neural pathways, 64–65
costs involved, 65
DUF1220 brain protein subtypes and IQ, 63
factors in recovery after traumatic brain injury, 62–63
GWAS search for intelligence genes, 61–62
neurobiology of intelligence, 61–62
research commitment in China, 64–65
SNPs and intelligence genes, 61
SNPs associated with general cognitive ability, 64
SNPs associated with variation in intelligence in children, 63–64
molecular genetics, 41–42basic genetics concepts, 59–60
hunt for intelligence genes, 56–59
Montreal Neurological Institute (MNI) coordinates, 86
Moody, David E., 148–149
Moray House Test, 31
Mozart Effect, 139–143
MR spectroscopy (MRS), 90–91
MRI (magnetic resonance imaging) basic structural MRI findings, 84–85
diffusion tensor imaging (DTI), 90
functional MRI (fMRI), 91–92
imaging white matter tracts, 90–91
improved MRI analyses, 85–89
MR spectroscopy (MRS), 90–91
principles and techniques, 81–84
size of brain regions and intelligence, 85
voxel-based morphometry (VBM), 85–89
voxels, 82–83
whole brain size/volume and intelligence, 84–85
multiple demand theory, 110
multiple regression equations, 120–123
Murray, Charles, 24–25, 197
n-back test, 145–147
NAA (N-acetylaspartate) correlation with IQ, 90
nature–nurture debate all (or mostly) environment scenario, 39
all (or mostly) gene scenario, 38–39
Behaviorist view, 39
Blank Slate view, 39
epigenetic view, 39–40
middle position, 39–40
types of gene expression, 38
Neubauer, Aljoscha, 92
neural pruning, 76
neuro-g, 108, 124
neuroimaging brain networks and intelligence, 100–110
CAT scans, 69
combined with molecular genetics, 132–135
common genes for brain structure and intelligence, 126–132
defining intelligence, 123–124
early applications in intelligence research, 68–69
findings from recent studies, 98–100
functional brain efficiency, 110
graph analysis of neuroimaging data, 101–103
homotopic analysis of neuroimaging data, 103–104
Parieto-Frontal Integration Theory (PFIT) of intelligence, 92–95
predicting IQ from brain images, 118–124
relationship between intelligence and reasoning, 124–126
use of templates in brain image analysis, 122
X-ray imaging, 69, 73 See also MRI; PET
neuromorphic chip technology, 180
neuro-poverty, 192–200public policy approach, 196–200
neuro-social–economic status, 192–200
neurotransmitters, 155
Newton, Isaac, 4, 9
NMDA (N-methyl d-aspartate) receptor, 56, 61–62
normal distribution of IQ scores, 13–15implications for social policy, 24–25
NR2B gene, 56
nucleotides (base pairs), 59, 60
nutrition in children influence on intelligence, 154
Obama, Barack, 166
obesity research, 42
optogenetic techniques, 177–178
Parieto-Frontal Integration Theory (PFIT) of intelligence, 92–95evidence from brain network studies, 100–110
evidence from MEG studies, 114–117
recent neuroimaging evidence, 99–100
Parkinson’s disease, 161
Pavacinni, Derek, 3–4, 11
Peek, Kim, 3, 11
PET (positron emission tomography) brain activity during mathematical reasoning, 76–78
brain activity in a non-problem solving situation, 78–79
brain activity in low-IQ groups, 75–76
brain efficiency and intelligence, 71–76
early PET studies of brain activity, 69–73
FDG (fluorodeoxyglucose) tracer, 70–71
gender differences in brain activity, 76–78
individual differences in brain activity, 76–79
learning and brain efficiency, 73–75
multiple areas involved in intelligence, 76–79
radioactive oxygen tracer, 70
radioactive tracers, 69–71
search for a center of intelligence in the brain, 80–81
what early studies revealed, 79–81
phenotype, 41
Pinker, Steven, 37, 45
pleiotropy, 41–42
Plomin, Robert, 37, 58, 65
polygenicity of intelligence, 41–42
positive manifold of mental ability tests, 7, 8–9
Posthuma, Danielle, 37
Prabhakaran, Vivek, 92
predicting IQ from brain images, 118–124
predictive value of intelligence tests, 18longitudinal studies, 25–33
preschool attendance effect on IQ, 154–155
profile analysis, 122–123
proteins formation by genes, 59
genes coding for, 40
proteomics, 60
psychometric tests limitations of, 168–169
public policy neuro-poverty and the achievement gap, 196–200
relevance of intelligence research, 24–25
quantitative genetics, 41, 50–56
quantitative trait locus (QTL), 60
radioactive tracers, 69–71
Rauscher, Francis, 139–143
Raven’s Advanced Progressive Matrices (RAPM) test, 144
Raven’s Advanced Progressive Matrices (RAPM) test, 15–16
Reaga
n, Ronald, 166
reasoning relationship to intelligence, 124–126
regression to the mean, 151
relative risk of life events relationship to IQ, 23
RNA, 59
Rosenthal, David, 46–47
SAT (Scholastic Assessment Test), 16–17
SAT-Math, 27–30, 76–78
savant abilities, 2–4, 9, 11
schizophrenia research, 41, 42, 46–47
Scottish Mental Survey, 30–32, 121
Shaw, Gordon, 143
Shwachman–Diamond syndrome, 106
Simon, Theodore, 12–13
single-nucleotide polymorphisms (SNPs), 60, 61, 63–64
Skinner, B.F., 68
social–economic status (SES) confounding with intelligence, 55–56, 192–200
heritability, 55–56
neuro-social–economic status, 192–200
social policy relevance of intelligence research, 24–25
sociobiology, 25
Spearman, Charles, 7, 8–9
SSGAC (Social Science Genetic Association Consortium), 61
Stanford–Binet test, 13–15
Stanley, Julian, 27–28, 77
statistical issues effects of restricted range of scores, 33–34
statistical parametric mapping (SPM), 85, 86
Steele, Kenneth, 142
Stern, William, 12
structural equation modeling, 121
structure of mental abilities, 5–9
Study of Mathematically & Scientifically Precocious Youth, 27–30
SyNAPSE Program, 181
synesthesia, 3
Tammet, Daniel, 2–3
Terman, Lewis, 13, 25–27
Tetris, 74–75
The Bell Curve (Herrnstein and Murray), 24–25, 197
The Intelligent Brain (Haier, Great Courses), 10, 24, 68, 92, 115, 145, 170
Thompson, Paul, 92, 126, 128
Thurstone, Louis, 33–34
thymine (T), 59
time-limited intelligence tests, 15–16
traffic accident risk and IQ, 23
The Neuroscience of Intelligence Page 34