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The Neuroscience of Intelligence

Page 34

by Richard J Haier


  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

 

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