Psychological Reports, 2011, 109, 2, 353-366. © Psychological Reports 2011 Secular IQ Increases by Epigenesis? The Hypothesis of Cognitive Genotype Optimization1, 2 Manfred F. Greiffenstein Psychological Systems, Inc. Summary.—The short timescale of massive secular IQ gains (“Flynn Effect”) is inconsistent with positive selection of a recent gene mutation, but other genetic mechanisms are possible. Principles of evolutionary psychology, combined with secular trends, suggest an epigenetic explanation: the Cognitive Genome Optimization Hypothesis. Per life-history theory, favorable secular trends may change the phenotypic expression of the genotype which controls the neurophysiology of problem solving. The hypothesis posits two intermediate steps between reliable nutrition (the starting point) and higher IQs (ending point): (1) Earlier cognitive maturation and (2) further calibration of cognitive function by reliable social resources (cultural complexity, mandatory education). Unlike earlier generations, more resources can be deployed to cognitive maturation than to physical survival, and more time is available to calibrate cognitive processing into the upper end of the trait value range for intelligence. The secular trend of earlier puberty timing is critical: data show an association between puberty and higher IQ. A large secular increase in measured intelligence scores (hereinafter IQ) since the 1930s is a generally accepted phenomena (Neisser, 1998). Persons who take old IQ tests today score much higher than persons of the same age when that IQ test was first published. The starting point for rising IQs remains unknown, but there is some evidence for a massive increase beginning as early as 1917 (Tuddenham, 1948). The eponym “Flynn Effect” was applied to the phenomenon because of political scientist James Flynn’s prolific writing on the topic, but published data predate Flynn’s first paper (see Tuddenham, 1948; Lynn, 1977, 1982). The trend line across studies involves IQ scores rising at a rate of 0.3 points per year. This works out to 3 IQ points per decade, if the baseline is set in the 1930s. The parameters of the rise include: the observation of increases in many different industrialized countries; greatest rise in measures of “fluid intelligence” (hereinafter g); and more recent leveling off (or pause) in IQ gains (Sundet, Barlaug, & Torjussen, 2004). The best evidence comes from national military records, because these samples are especially representative of the population in countries with conscription Address correspondence to Manfred F. Greiffenstein, Ph.D., 32121 Woodward Avenue, Suite 201, Royal Oak, MI or e-mail (mfgreiff@comcast.net). The author thanks Professor Stephen Hinshaw (Berkley) and Ida Sue Baron (private practice) for providing helpful literature, and Professor James Flynn, who waived anonymity as a peer reviewer, for critical comments. (Their assistance does not imply complete agreement with conclusions!) 1 2 DOI 10.2466/03.04.10.19.PR0.109.5.353-366 ISSN 0033-2941 354 M. F. Greiffenstein (Flynn, 1987a, 1987b). Similar increases were reported for other complex cognitive tasks, such as Piaget’s “combinatory thought” (Flieller, 1999) and “top-level” operational thinking (Shayer & Ginsburg, 2009). Oddly, some report a secular decline on simpler Piagetian conservation tasks from the mid-1960s to early 2000s (Shayer, Ginsburg, & Coe, 2007), of unknown significance. To date, however, there is no widely accepted explanation of intergenerational IQ trends. There is not even agreement that the increase represents actual changes in underlying g, versus ordinary facultative changes (learning and rote memorization) or methodological artifact (e.g., differences in recruitment bias between standardization cohorts). Single-process explanations of rising IQ include improved nutrition (Lynn, 1998; Martorell, 1998; Sigman & Whaley, 1998), learning to solve abstract problems without increased g (Flynn, 2007), compulsory education (Schaie, Willis, & Pennak, 2005), increasing cultural complexity (Greenfield, 1998), and heterosis (“hybrid vigor”; Mingroni, 2007). But the intermediate steps between the proposed single causes and rising IQs have not been specified in any concrete way. If nutrition is a prime mover, a position the author favors, what is the path taken to higher intelligence? What steps mediate between the broad influences of better nutrition at the start and rising IQs at the end? The author proposes an epigenetic mechanism: improvements in secular nutrition initiated a cascade of events which accelerated cognitive maturation, and modern social resources (cultural complexity, mandatory education) further calibrated higher cognitive function to its genomic optimum. The result is higher measured IQs. Background: Evolutionary Perspectives The sources of secular IQ increase can be understood from an evolutionary perspective. Positive selection for a cognition-enhancing gene mutation could not possibly explain IQ increases on the short time scale of three or four generations. That is because it would take hundreds of generations for a cognitive mutation to spread in a population widely enough to strongly influence the standardization group for a test. But there are other genetic mechanisms to consider. Several evolutionary premises, middle theories, and concepts are available to formulate genetic explanations other than positive selection. One major premise is that the genotype carries within it a much broader range of potential phenotypes than are obviously expressed in the population. A minor premise is that part of our genotype codes for the neurophysiology of problem solving. Therefore, the cognitive phenotype (like our physical phenotype) does not necessarily match the genotype. The genotype provides plasticity, meaning a range of trait values are Secular IQ Gains By Epigenesis 355 possible, particularly with regard to intelligence. It is not seriously disputed that environmental factors facilitate or inhibit trait expression. In areas where food is chronically scarce, it is difficult to tell which humans have tallness genes, but chronic food abundance creates wide variance in height. In areas where literacy is low, it is difficult to say who has the myopia (near sightedness) gene, but in areas with chronically high literacy, it is easy to find myopia (Storfer, 1999). The prediction follows that reliable nutrition and social resources should be facilitating factors for higher intelligence. Further, earlier cognitive maturation implies lengthier opportunity to fine-tune higher cognitive skills, during mandatory education for example. Epigenesis A recognized mode of inheritance is epigenesis. Epigenetic inheritance means trait transmission without changes in the DNA sequence (genome), and it operates over much shorter timescales than positive selection. Epigenetic causation often involves nutrition and the prenatal environment. Nutrition is the major intrauterine environmental factor that alters the expression, not the sequence, of the fetal genome. Examples include inheritance of efficient calorie use by offspring of starving mothers after the “Dutch Hunger Winter” of WWII (see McClellan, Susser, & King, 2006). Epigenetic effects are not just manifested at birth; they can change the timing of developmental programs when taking a life-history approach to ontogeny (Ellis, 2004). Secular improvements in fetal and infant nutrition should result in developmental adaptations that permanently change the timing of cognitive development and puberty. This paper requires accepting that intergenerational improvement in nutrition is the major factor triggering rise in IQs (Lynn, 1990). The complete role of nutrition in the evolution of intelligence is beyond the scope of this paper, and the reader is referred to Kaplan, Hill, Lancaster, and Hurtado (2000) for an exposition on the topic. Life-history Theory A key conceptual framework is life-history theory (Charnov, 1993). This is the view that humans evolved capacities to adjust the timing of developmental steps to match changing circumstances, during ontogeny. Human bodies “know” when optimizing or delaying developmental steps is the best strategy for ensuring fitness; resources are allocated accordingly. Sexual maturation is delayed when resources are reliably scarce, and advanced when resources are reliably rich (Worthman, 1999; Ellis, 2004). It will be argued here that cognitive maturation is advanced, particularly in Western cultures, with the chronic abundance of nutritional and complex social resources. Essentially, chronically favorable environmental conditions affect the epigenetic expression of IQ-related genes and play an im- 356 M. F. Greiffenstein portant role in facilitating rising intelligence. Advanced pubertal timing, and its implications for cognitive development, is another outcome associated with this process, and this will be discussed in depth. The Hypothesis The author proposes the Cognitive Genome Optimization Hypothesis, an epigenetic framework in which genes relevant to the physiology of problem solving became more fully expressed and optimized, because of secular facilitating factors. IQ is not a fixed property in genomic terms. The hypothesis states that the intermediate steps between improved nutrition and rising IQs rest on two crucial fulcrums, accelerated cognitive maturation, and next, complex social resources further calibrate higher cognitive function to an optimum. Humans are a neotinic species: a distinguishing feature of Homo sapiens is prolonged juvenile dependence on parental and social caregivers (Kaplan, et al., 2000). Social resources now available during juvenile dependence include longer mandatory education and greater cultural complexity, compared to earlier generations. Complex social resources are the human equivalent of the biological term “habitat complexity.” This interactive process results in the observed intergenerational increase in IQ scores. The main body of evidence for the hypothesis will rest on puberty, particularly female puberty, for reasons stated later. In terms of evolutionary psychology, this is a life-history “energetics theory” (Ellis, 2004), meaning resources (typically calories) are steadily shifted to brain reorganization and sexual maturation at a younger age when a chronically favorable environment prevails. The proposed sequence of events is as follows: better nutrition à earlier brain reorganization (over-production and pruning of grey matter) à early mental preparedness for complexity à longer exposure to complex habitat (e.g., more education, cultural complexity) à calibration of cognitive function to an optimum à rising IQs. Evaluating the Hypothesis: Fundamental Facts Brain re-organization and ontogeny.—There is a fairly well-developed body of knowledge regarding the neurobiological basis of cognitive maturation. Cognitive development depends on two well-established periods of brain reorganization which are followed by increased computing power and learning capacity. Early childhood (2–3 years) and puberty (10–15 years) are developmental stages associated with brain growth spurts, characterized by a two-step process: overproduction of brain grey-matter and synaptic connections, followed by pruning of synapses (Feinberg, 1982; Chechik, Meilijson, & Ruppin, 1998). The clearest concrete evidence for massive brain reorganization comes from studies of neurological change during early puberty. Puberty is associated with a cascade of neuroendo- Secular IQ Gains By Epigenesis 357 crine events, the myriad details of which are not of concern here and are described elsewhere (Cameron, 2004). The longitudinally obtained brain MRIs of preadolescents show rapid increases in cortical depth, termed grey-matter “over-production,” just prior to puberty (Giedd, Blumenthal, Jeffries, Castellanos, Liu, Zijdenbos, et al., 1999; Lenroot & Giedd, 2006). In girls, the cortex reaches maximal thickness in the parietal lobe at age 10.1 yr., frontal lobe at 11 yr., and temporal lobes at 16.7 yr. (Giedd, 2004). In boys, the respective peak thicknesses are at 12.1, 11.8, and 16.2 years. This is followed by steady grey-matter loss and synaptic pruning. Synaptic elimination is theorized to improve the efficiency of complex mental computing (Feinberg, 1982; Hinshaw, 2009). Advanced pubertal timing.—Cognitive maturation is not synonymous with brain maturation, but depends upon it. If improved nutrition advances the timing of grey-matter production and pruning during development, particularly during puberty, it follows that prospects for earlier cognitive maturation are accelerated to begin at younger ages than during previous generations, leading to improved problem solving and learning, if accompanied by facilitating social resources (e.g., complex technosocial society). In terms of epigenesis, the cognitive genome is optimized by increasing overlap between a state of mental preparedness and time spent in education. In earlier generations, there was less overlap of education with the postpubertal critical learning period, which likely attenuated the value range of intellectual traits for those cohorts. Secular trends parallel to the Flynn Effect provide potential clues; the most critical to this hypothesis being the steady advance of pubertal timing. Age at pubertal onset has advanced steadily over 150 years, a clear trend for girls (Euling, Herman-Giddens, Lee, Selevan, Juul, Sorensen, et al., 2008) and less clear for boys. Age at menarche decreased to an average of 3 yr. younger than a century ago, a rate of 3 to 4 months per decade (Parent, Teilmann, Juul, Skakkebaek, Toppari, & Bourguignon, 2003) with recent leveling (or pause) at a median of 13 yr. (Cole, 2000). The trend is less clear with spemarche because it is less salient and memorable than menarche, but there is some evidence to support advanced timing in boys (Euling, et al., 2008). Average height has also undisputedly increased over two centuries at a rate of 10 to 30 mm/decade (Cole, 2000), with more recent leveling off in a Norwegian military sample, per Sundet, et al. (2004). The author makes no claims about height and IQ. Pubertal timing is the more critical individual differences variable (Ellis, 2004) for the present hypothesis. For example, maternal tempo plays a role, i.e., maternal onset of menarche is correlated with the daughter’s onset (Moisan, Meyer, & Gingras, 1990), a good example of epigenetic transmission. These secular 358 M. F. Greiffenstein trends are probably not independent of each other, i.e., improved nutrition increasing height and strength. Cultural (“habitat”) complexity as calibrator.—The hypothesis of interest assumes that two crucial complex habitats co-exist: a chronically favorable environment with respect to nutrition and chronically favorable social resources (a technosocial complex environment). These two supportive environments are necessary for calibration of the traits within the inherited trait range. An optimizing environment is one that fine-tunes traits to their fullest expression, up to a biological limit (Smith, 1978; Duchaine, Cosmides, & Tooby, 2001). Behavior geneticists have shown that optimization of cognitive traits is demonstrable even during ontogeny. A powerful example is the finding that IQ heritability fractions3 increase with age, meaning persons drift toward the environments that best support their cognitive genotype (McGue, Bouchard, Iacono, & Lykken, 1993). Social policy changes include steady increases in compulsory education; it is beyond dispute that persons have spent successively more years in school since the early 1900s (Snow, 1982). There is also little dispute that IQ is correlated, albeit imperfectly, with cultural complexity (Lynn & Vanhanen, 2002). Genes related to cognition require an environment to support their expression during ontogeny. The genes for nearsightedness would be expressed weakly, if at all, in the absence of reading materials. But secular increases in literacy are nicely matched with increases in nearsightedness (Feldkamper & Schaeffel, 2003; Dirani, Shekar, & Baird, 2008; van de Berg, Dirani, Chen, Haslam, & Baird, 2008). So, a low-stimulating environment should not support fuller expression of IQ, but a complex technosocial environment likely would. It is proposed here that modern cultural complexity likely calibrated advanced cognitive maturation to an optimum. The author appeals to a parallel concept from neurobiology: habitat complexity. High variance environments optimize brain maturation, a common finding in evolutionary neurobiology. There is a large body of literature on habitat complexity and brain development in many animals, a topic beyond the scope of the present paper. Briefly, the study by Pollen, Dobberfuhl, Scace, Igulu, Renn, Shumway, et al. (2007) is representative of this literature. Pollen and colleagues found the largest brain sizes in those species of cichlid fish occupying the most complex habitats, defined in terms of physical (e.g., variance of rock size) and social diversity (e.g., number of other species). In humans, environmental complexity and cognition has been explored by Schooler (1998). Schooler’s empirical work showed how complexity of The heritability fraction is the percent of a phenotypic variable (e.g., IQ or height) attributed to genetic variation. Falconer’s formula is h2 = 2[r(MZ) – r(DZ)], where r(MZ) is the correlation in a trait between monozygotic twins and r(DZ) the correlation in fraternal twins. The contribution of shared environment is c2 = DZ − 1/2 * h2. 3 Secular IQ Gains By Epigenesis 359 work duties optimized cognitive flexibility, and how simpler work attenuated it (Kohn & Schooler, 1978; Caplan & Schooler, 2006). Evaluating the Hypothesis: Empirical Data The present hypothesis, with its emphasis on accelerated cognitive maturation with each generation, is supported by many types of evidence. Rising IQs among preschoolers.—First, the hypothesis of accelerated cognitive maturation requires evidence that rises in IQ should show two intercohort growth spurts associated with both critical periods of brain reorganization, when successive cohorts are compared. The first period ends at 2 or 3 years of age, so there should be evidence for rising computing power among preschoolers and lower gains for primary school students (prepuberty). Terman and Merrill (1973) calculated Stanford-Binet performance of a modern age-stratified cohort against 1937 norms, and found 2- to 5-year-olds had average IQs of 110. The 1937 referenced IQs declined steadily to a mean IQ of 101 between ages 5 to 10 years. The age differences between the young children in the 1972 sample are too small for a birth cohort explanation, but accelerated cognitive development in the two cohorts separated by 35 years is a better-fitting explanation. The Wechsler Intelligence Scale for Children (Wechsler, 1949) and WISC–Revised (Wechsler, 1974) comparison of scores is more difficult to make because the number of items changed for most subtests between 1949 and 1974. The Digit Span subtest, although a weaker measure of g, remained unchanged in item-number or content. The mean “test age” (formerly termed “mental age”) for Digit Span differed little for children of primary school age: for the 1974 cohort, the increase in working memory span was 83% between 6 and 10 years, versus 80% for the 1949 cohort. But from 10 to 15 years of age, a range associated with puberty, the 1974 cohort increased from a raw score of 11 to 15, a gain in working memory span of 36%, while the same-age 1949 cohort improved less: raw scores of 9 to 11, a gain of 22%. Put differently, the average 12-year-old child in 1974 earned a Digit Span raw score of 13, but 1949 children of the same age achieved a score of 10. Puberty and IQ.—The most critical evidence for the hypothesis rests on a compelling body of literature exploring puberty and intelligence. When age is controlled, puberty is reliably associated with higher IQ scores than prepuberty in almost every study in which the issue was explored. Stone and Barker (1937) gave the Otis IQ test to 350 Caucasian girls divided into equal groups by menarcheal status. Despite matching by age and socioeconomic status, the postmenarche group mean IQ was significantly higher than the premenarche group mean. The effect size was calculated as d = 0.26, implying 13% of intellectual variance was associated with sexual maturation. 360 M. F. Greiffenstein The issue of sexual maturity and IQ has repeatedly been addressed in the British literature. Psychologist James Shields studied intelligence in twins by combining the Dominos and Mill-Hill Vocabulary measures. Shields (1962) examined female monozygotic and dyzygotic twins and reported the index cases (achieving menarche first) earned higher IQs than probands in a ratio of 27:7 [χ2 = 10.62 (N = 34) represents a large effect size of d = 1.35]. Earlier, Shields (1954) reported 31 female twin pairs showed an index/proband ratio of 21:10. Applying the arcsine method for proportions for this finding (Lipsey & Wilson, 2001) results in a medium effect size of 0.73 (95%CI = 0.54, 0.90). Douglas and Ross (1964) coded IQs, achievement, and puberty markers longitudinally for 3,300 UK children at ages 8, 11, and 15 years. “Early and very early maturing” girls, benchmarked to the menarcheal median of 13.5 yr. in a mid-1950s birth cohort, showed 2% higher IQs than “average girls” but 7% higher than “late” at age 11 years and 7.9% higher at 15 years. “Mature” boys, based on qualitative Tanner-staging, also earned IQs 7% higher than “infantile” boys at 11 years and 8.3% higher at 15 years. More recently, Shangguan and Shi (2009) gave boys ages 8–12 years Cattell’s Culture Fair Intelligence Test and correlated IQs with saliva testosterone levels. There was a significant positive correlation in 10-year-old boys, suggesting that puberty timing, when treated as an individual differences variable, is significantly associated with fluid intelligence. Little is known about puberty onset and national IQ, but in cross-cultural research there are a few data points. Sources of data include Lynn and Vanhanen’s “national IQ” data tables (2002), and Carol Worthman’s anthropological investigations (1999). The average age to menarche is 12 to 12.5 years in postindustrial societies such as Japan and Korea, where the average obtained IQs are 105 to 106. In rural Kenya, median menarche is 15.9 yr. (Worthman, 1999) and the average obtained IQ in Kenya is 72. New Guinea appears associated with both the longest median menarche at 18 to 18.5 years, and the lowest “national IQ” of 59. There are many confounding variables which are difficult to tease apart, but these preliminary data tentatively support the hypothesis being assessed. In reviewing literature on ethnicity and IQ, it is important to avoid the temptation of treating intelligence as a fixed property. Social Resources More years of education is a part of technosocial complexity. Education is clearly longer now than at the turn of the last century. Tuddenham (1948) compared military cohorts from World War I (inducted 1917) and World War II (inducted 1943) on an IQ test. He found a stunning 0.8 per year IQ gain on the Armed Forces General Classification Test. The median education was 8 years in 1917 but 10 years in 1943. After adjusting for ed- Secular IQ Gains By Epigenesis 361 ucation, WWII soldiers had a 0.3-point increase in mean IQ per year from 1917 to 1943, 35 years before Flynn’s first publication. Clearly, education plays a role in the Flynn Effect, but only a partial one. Discussion This hypothesis is testable. Depending on which maturation data are coded, a re-analysis of American twin studies can be used to examine index-proband differences in puberty onset. There are currently many large twin registries (e.g., Klump & Burt, 2006; Lemery-Chalfant, Goldsmith, Schmidt, Arneson, & Van Hulle, 2006). If the hypothesis is viable, the monozygotic and dizygotic probands with earlier onsets of puberty would have slightly higher IQs. The hypothesis must include a ceiling effect, because the cognition-related portion of the genome is not infinitely plastic, just as height is not infinitely plastic, i.e., we have not become a race of giants. The more recent scholarship shows IQ gains leveled off sometime in the 1990s (Sundet, et al., 2004). Use of postpubertal retrospective examination of IQs obtained before puberty to inform the question of whether higher IQs precede sexual maturity would change the causal arrow the author has proposed: IQs during childhood influence puberty timing. Longitudinal studies of brain MRI should show earlier cortical thickness and quicker synaptic pruning in persons with earlier puberty, measured by menarcheal age or Tanner stage. Also, the onset of decline in EEG delta power in marking the start of adolescent brain reorganization should occur earlier than decades ago, if secular data are available (Feinberg & Campbell, 2010). Finally, “cognitive dips” during early adolescence in domain-specific functions such as facial emotion processing should occur at earlier ages than previously reported by Carey, Diamond, and Woods (1980). Explanations adverse to the present hypothesis require consideration. Alternate hypotheses include “absence of impairment effect,” meaning the Flynn Effect could be explained by progressively fewer children and adults with cognitive impairments in the population, because of improved healthcare. For example, since the 1950s children born with phenylketonuria are intellectually normalized with a phenylalanine-free diet but markedly impaired without. Recruitment bias is a variation on this theme. Sundet, et al. (2004) found secular IQ gains associated with decreasing prevalence of low IQ scores, not a proportional shift up in all score ranges. The standardization sampling for the WISC and WISC–Revised was different, as the WISC included 2% persons who were “feeble minded” (now termed “intellectual disability”), but the WISC–Revised sample did not. Recruitment bias as explanation may be weakened by the fact that medical science today saves many low-IQ premature infants who would have died during earlier generations (Flynn, 2009). Additionally, 362 M. F. Greiffenstein the standardization policies for the Wechsler scales do not necessarily reflect the fluctuating base rate for intellectual disability. The potential role of epigenesis is questioned by Flynn (2009). He contends Dutch males exposed prenatally to the late-war famine showed IQ gains in line with earlier and later cohorts, an assertion contrary to the current hypothesis. But Flynn overestimates the extent of malnutrition. Stein, Susser, Saenger, and Marolla (1975) studied IQ in this population, found no deficits, and pointed out that their cohort was exposed to famine prenatally for only two months and nutrition normalized from birth forward. Otherwise, nobody disputes that IQ is affected by prolonged malnutrition prenatally and after birth. Advancing puberty and associated IQ gains, if definitively supported, should not be viewed as totally positive. Deployment of resources favoring one adaptive strategy over another comes with costs, and natural selection only favors adaptations that on balance provide net benefits over costs for the individual, not perfect adaptations (Confer, Easton, Fleischman, Goetz, Lewis, Perilloux, et al., 2010). For example, the larger human brain size (relative to body mass) provides homo sapiens with complex computing power but has a metabolic cost of higher calorie consumption, with all the diet-related diseases that calorie-seeking entails (Nesse, 2001). Mendle, Turkheimer, and Emery (2007) and Ellis (2004) listed the negative side effects of earlier puberty. Earlier puberty in females increases risk for reproductive system cancers, teenage pregnancy, sexual abuse, medical problems, lower resource investment in offspring, and a cascade of future burdens such as truncated education. Greater cognitive complexity and flexibility at younger ages also increase risks for experimentation with drugs (Chechik, et al., 1998). References Cameron, J. L. (2004) Interrelationships between hormones, behavior, and affect during adolescence: understanding hormonal, physical, and brain changes occurring in association with pubertal activation of the reproductive axis. In R. Dahl & L. P. Spear (Eds.), Introduction to Part III: adolescent brain development: vulnerabilities and opportunities. New York: New York Academy of Sciences. Pp. 110-123. Caplan, L. J., & Schooler, C. (2006) Household work complexity, intellectual functioning, and self-esteem in men and women. Journal of Marriage and Family, 68, 883-900. Carey, S., Diamond, R., & Woods, B. (1980) Development of face recognition: a maturational component? Developmental Psychology, 16, 257-269. Charnov, E. L. (1993) Life history invariants. Oxford, UK: Oxford Univer. Press. Chechik, G., Meilijson, I., & Ruppin, E. (1998) Synaptic pruning in development: a computational account. Neural Computation, 10, 1759-1777. Cole, T. J. (2000) Secular trends in growth. Proceedings of the Nutrition Society, 59, 317324. Secular IQ Gains By Epigenesis 363 Confer, J. C., Easton, J. A., Fleischman, D. S., Goetz, C. D., Lewis, D. M. G., Perilloux, C., & Buss, D. M. (2010) Evolutionary psychology: controversies, questions, prospects, and limitations. American Psychologist, 65, 110-126. Dirani, M., Shekar, S. N., & Baird, P. N. (2008) The role of educational attainment in refraction: the genes in myopia (GEM) twin study. Investigative Ophthalmology & Visual Science, 49, 534-538. Douglas, J. W. B., & Ross, J. M. (1964) Age of puberty related to educational ability, attainment, and school leaving age. Journal of Child Psychology and Psychiatry, 5, 185-196. Duchaine, B., Cosmides, L., & Tooby, J. (2001) Evolutionary psychology and the brain. Current Opinion in Neurobiology, 11, 225-230. Ellis, B. J. (2004) Timing of pubertal maturation in girls: an integrated life history approach. Psychological Bulletin, 130, 920-958. Euling, S. Y., Herman-Giddens, M. E., Lee, P. A., Selevan, S. G., Juul, A., Sorensen, T. I., Dunkel, L., Himes, J. H., Teilmann, G., & Swan, S. H. (2008) Examination of U.S. puberty-timing data from 1940 to 1994 for secular trends: panel findings. Pediatrics, 121, 172-191. Feinberg, I. (1982) Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence? Journal of Psychiatric Research, 17, 319-334. Feinberg, I., & Campbell, I. G. (2010) Sleep EEG changes during adolescence: an index of a fundamental brain reorganization. Brain and Cognition, 72, 56-65. Feldkamper, M., & Schaeffel, F. (2003) Interactions of genes and environment in myopia. Developmental Ophthalmology, 37, 34-49. Flieller, A. (1999) Comparison of the development of formal thought in adolescent cohorts aged 10 to 15 years (1967–1996 and 1972–1993). Developmental Psychology, 35, 1048-1058. Flynn, J. R. (1987a) Massive IQ gains in 14 nations: what IQ tests really measure. Psychological Bulletin, 101, 171-191. Flynn, J. R. (1987b) Massive IQ gains in 14 nations: what IQ tests really measure: correction to Flynn. Psychological Bulletin, 101, 427. Flynn, J. R. (2007) What is intelligence? Cambridge, UK: Cambridge Univer. Press. Flynn, J. R. (2009) What is intelligence? Beyond the Flynn Effect. Cambridge, UK: Cambridge Univer. Press. Giedd, J. N. (2004) Structural magnetic resonance imaging of the adolescent brain. Annals of the New York Academy of Sciences, 1021, 77-85. Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., Paus, T., Evans, A. C., & Rapoport, J. L. (1999) Brain development during childhood and adolescence: a longitudinal MRI study. Nature Neurosciences, 2, 861-863. Greenfield, P. M. (1998) The cultural evolution of IQ. In U. Neisser (Ed.), The rising curve: long-term gains in IQ and related measures. Washington, DC: American Psychological Association. Pp. 81-95. Hinshaw, S. P. (2009) Origins of the human mind. Chantilly, VA: The Teaching Company. Kaplan, H. S., Hill, K. R., Lancaster, J. B., & Hurtado, A. M. (2000) A theory of human life history evolution: diet, intelligence, and longevity. Evolutionary Anthropology, 9, 156-185. Klump, K. L., & Burt, S. A. (2006) The Michigan State University Twin Registry (MSUTR): genetic, environmental, and neurobiological influences on behavior across development. Twin Research and Human Genetics, 9, 971-977. 364 M. F. Greiffenstein Kohn, M. L., & Schooler, C. (1978) The reciprocal effects of the substantive complexity of work and intellectual flexibility: a longitudinal assessment. American Journal of Sociology, 84, 24-52. Lemery-Chalfant, K., Goldsmith, H. H., Schmidt, N. L., Arneson, C. L., & Van Hulle, C. A. (2006) Wisconsin Twin Panel: current directions and findings. Twin Research and Human Genetics, 9, 1030-1037. Lenroot, R. K., & Giedd, J. N. (2006) Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neuroscience & Biobehavior al Reviews, 30, 718-729. Lipsey, M. W., & Wilson, D. B. (2001) Practical meta-analysis. Thousand Oaks, CA: Sage. Lynn, R. (1977) The intelligence of the Japanese. Bulletin of the British Psychological Society, 30, 69-72. Lynn, R. (1982) IQ in Japan and the United States shows a growing disparity. Nature, 297, 222-223. Lynn, R. (1990) The role of nutrition in secular increases in intelligence. Personality and Individual Differences, 11, 273-285. Lynn, R. (1998) In support of the nutrition theory. In U. Neisser (Ed.), The rising curve: long-term gains in IQ and related measures. Washington, DC: American Psychological Association. Pp. 207-218. Lynn, R., & Vanhanen, T. (2002) IQ and the wealth of nations. Westport, CT: Praeger. Martorell, R. (1998) Nutrition and the worldwide rise in IQ scores. In U. Neisser (Ed.), The rising curve: long-term gains in IQ and related measures. Washington, DC: American Psychological Association. Pp. 183-195. McClellan, J. M., Susser, E., & King, M-C. (2006) Maternal famine, de novo mutations, and schizophrenia. Journal of the American Medical Association, 296, 582-584. McGue, M., Bouchard, T. J., Jr., Iacono, W. G., & Lykken, D. T. (1993) Behavioral genetics of cognitive ability: a life-span perspective. In R. Plomin & G. E. McClearn (Eds.), Nature, nurture, and psychology. Washington, DC: American Psychological Association. Pp. 59-76. Mendle, J., Turkheimer, E., & Emery, R. E. (2007) Detrimental psychological outcomes associated with early pubertal timing in adolescent girls. Developmental Review, 27, 151-171. Mingroni, M. A. (2007) Resolving the IQ paradox: heterosis as a cause of the Flynn Effect and other trends. Psychological Review, 114, 806. Moisan, J., Meyer, F., & Gingras, S. (1990) A nested case-control study of the correlates of early menarche. American Journal of Epidemiology, 132, 953-961. Neisser, U. (1998) Introduction: rising test scores and what they mean. U. Neisser (Ed.), The rising curve: long-term gains in IQ and related measures. Washington, DC: American Psychological Association. Pp. 3-22. Nesse, R. M. (2001) How is Darwinian medicine useful? Western Journal of Medicine, 174, 358-360. Parent, A. S., Teilmann, G., Juul, A., Skakkebaek, N. E., Toppari, J., & Bourguignon, J-P. (2003) The timing of normal puberty and the age limits of sexual precocity: variations around the world, secular trends, and changes after migration. Endocrine, 24, 668-686. Secular IQ Gains By Epigenesis 365 Pollen, A. A., Dobberfuhl, A. P., Scace, J., Igulu, M. M., Renn, S. C. P., Shumway, C. A., & Hofmann, H. A. (2007) Environmental complexity and social organization sculpt the brain in the Lake Tanganyikan cichlid fish. Brain, Behavior, and Evolution, 70, 21-39. Schaie, K. W., Willis, S. L., & Pennak, S. (2005) An historical framework for cohort differences in intelligence. Research Human Development, 2, 43-67. Schooler, C. (1998) Environmental complexity and the Flynn Effect. In U. Neisser (Ed.), The rising curve: long-term gains in IQ and related measures. Washington, DC: American Psychological Association. Pp. 67-79. Shangguan, F., & Shi, J. (2009) Puberty timing and fluid intelligence: a study of correlations between testosterone and intelligence in 8- to 12-year-old Chinese boys. Psychoneuroendocrinology, 34, 983-988. Shayer, M., & Ginsburg, D. (2009) Thirty years on—a large anti-Flynn Effect? (II): 13and 14-year-olds. Piagetian tests of formal operations norms 1976–2006/7. British Journal of Educational Psychology, 79, 409-418. Shayer, M., Ginsburg, D., & Coe, R. (2007) Thirty years on—a large anti-Flynn effect? The Piagetian test volume & heaviness norms 1975–2003. British Journal of Educational Psychology, 77, 25. Shields, J. (1954) Personality differences and neurotic traits in normal twin school children. Eugenics Review, 45, 213-245. Shields, J. (1962 ) Monozygotic and dizygotic twins raised together and separately. London: Oxford Univer. Press. Sigman, M., & Whaley, S. E. (1998) The role of nutrition in the development of intelligence. In U. Neisser (Ed.), The rising curve: long-term gains in IQ and related measures. Washington, DC: American Psychological Association. Pp. 155-182. Smith, J. M. (1978) Optimization theory in evolution. Annual Review of Ecology and Systematics, 9, 31-56. Snow, R. E. (1982) Education and intelligence. In R. J. Sternberg (Ed.), Handbook of human intelligence. New York: Cambridge Univer. Press. Pp. 493-585. Stein, Z., Susser, M., Saenger, G., & Marolla, F. (1975) Famine and human development: the Dutch hunger winter of 1944–45. New York: Oxford Univer. Press. Stone, C. P., & Barker, R. S. (1937) Aspects of personality and intelligence in postmenarcheal and premenarcheal girls of the same chronological age. Journal of Comparative Psychology, 23, 439-455. Storfer, M. (1999) Brain size, intelligence, and myopia. International Journal of Neuroscience, 98, 153-276. Sundet, J. M., Barlaug, D. G., & Torjussen, T. M. (2004) The end of the Flynn Effect? A study of secular trends in mean intelligence test scores of Norwegian conscripts during half a century. Intelligence, 32, 349-362. Terman, L. A., & Merrill, M. A. (1973) Stanford-Binet Intelligence Scale: manual for the Third Revision Form L-M (1972 Norms Edition). New York: Houghton Mifflin Company. Tuddenham, R. D. (1948) Soldier intelligence in World Wars I and II. American Psychologist, 3, 54-56. van de Berg, R., Dirani, M., Chen, C. Y., Haslam, N., & Baird, P. N. (2008) Myopia and personality: the genes in myopia (GEM) personality study. Investigative Ophthalmology & Vision Science, 49, 882-886. 366 M. F. Greiffenstein Wechsler, D. (1949) Wechsler Intelligence Scale for Children manual. New York: The Psychological Corporation. Wechsler, D. (1974) Wechsler Inteligence Scale for Children–Revised manual. New York: The Psychological Corporation. Worthman, C. (1999) Evolutionary perspectives on the onset of puberty. In W. R. Trevathan (Ed.), Evolutionary medicine. New York: Oxford. Pp. 135-163. Accepted September 1, 2011.
Purchase answer to see full attachment