Covariation between intelligence and speed of cognitive processing: Genetic and environmental influences

This study is the first analysis of the etiology of the relationship between general intelligence and speed of cognitive processing. The genetic and environmental sources of this covariation were examined using data from 60 pairs of twins (30 monozygotic and 30 same-sexed dizygotic), ages 8–18. Full-Scale IQ scores on the Wechsler Intelligence Scale for Children-Revised (WISC-R) served as an index of general intelligence. Measures of speed of processing employed were the Rapid Automatic Naming tests and Colorado Perceptual Speed tests. Results of multivariate biometrical genetic analyses revealed the importance of genetic influences underlying the IQ/speed association. The relative importance of correlated genetic effects, however, appeared to be dependent upon the specific speed-of-processing measure. Our results indicate that the phenotypic relationship between the measures of general intelligence and the measures of speed of processing employed are due largely to correlated genetic effects. While correlated specific environmental effects were less important, correlated common environmental effects were negligible. In general, the findings support the notion of some common biological mechanism(s) underlying both general intelligence and speed-of-processing measures.     

A Genetic Investigation of the Covariation Among Inspection Time, Choice Reaction Time, and IQ Subtest Scores

Information processing speed, as measured by elementary cognitive tasks, is correlated with higher order cognitive ability so that increased speed relates to improved cognitive performance. The question of whether the genetic variation in Inspection Time (IT) and Choice Reaction Time (CRT) is associated with IQ through a unitary factor was addressed in this multivariate genetic study of IT, CRT, and IQ subtest scores. The sample included 184 MZ and 206 DZ twin pairs with a mean age of 16.2 years (range 15-18 years). They were administered a visual (pi-figure) IT task, a two-choice RT task, five computerized subtests of the Multidimensional Aptitude Battery, and the digit symbol substitution subtest from the WAIS-R. The data supported a factor model comprising a general, three group (verbal ability, visuospatial ability, broad speediness), and specific genetic factor structure, a shared environmental factor influencing all tests but IT, plus unique environmental factors that were largely specific to individual measures. The general genetic factor displayed factor loadings ranging between 0.35 and 0.66 for the IQ subtests, with IT and CRT loadings of -0.47 and -0.24, respectively. Results indicate that a unitary factor is insufficient to describe the entire relationship between cognitive speed measures and all IQ subtests, with independent genetic effects explaining further covariation between processing speed (especially CRT) and Digit Symbol.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

儿童青少年智力的双生子研究

目的 应用双生子法探讨遗传和环境因素对儿童青少年智力影响.方法 采用中国修订版-韦氏儿童智力量表评定6～16岁333对双生子的智力特点.应用结构方程模型分析遗传和环境因素对智力的影响.结果 6～16岁儿童青少年的总智商(intelligence quotient,IQ)遗传度为0.43,其中言语智商(verbal intelligence quotient,VIQ)遗传度为0.37,而遗传因素对操作智商(performance intelligence quotient,PIQ)影响甚小.10～16岁的青少年智力遗传度大于6～9岁的儿童(IQ:0.82 vs 0.00,VIQ:0.80 vs 0.00,PIQ:0.51 vs 0.00).男性的言语智商(0.47)受遗传影响大于女性(0.05);而在男性和女性中,操作智商均主要受共享环境的影响.结论 在儿童青少年时期,总智商和言语智商有中度的遗传度,而共享环境因素对操作智商的影响更大;但智商总分及言语智商和操作智商分测试分在年龄较大的青少年的遗传度都明显高于年龄较小的儿童.     

Perceptual Speed and IQ Are Associated Through Common Genetic Factors

Individual differences in inspection time explain about 20% of IQ test variance. To determine whether the association between inspection time and IQ is mediated by common genes or by a common environmental factor, inspection time and IQ were assessed in an extended twin design. Data from 688 participants from 271 families were collected as part of a large ongoing project on the genetics of adult brain function and cognition. The sample consisted of a young adult cohort (mean age 26.2 years) and an older adult cohort (mean age 50.4 years). IQ was assessed with the Dutch version of the WAIS-3R. Inspection time was measured in the so-called -paradigm, in which a subject is asked to decide which leg of the -figure is longest at varying display times of the -figure. The number of correct inspections per second (i.e., the reciprocal of inspection time) was used to index perceptual speed. For Verbal IQ and Performance IQ, heritabilities were 85% and 69%, respectively. For perceptual speed, 46% of the total variance was explained by genetic variance. No differences in heritability estimates across age cohorts or sexes were found. Across the whole sample, a significant phenotypic correlation was found between perceptual speed and Verbal IQ (0.19) and between perceptual speed and Performance IQ (0.27). These correlations were entirely due to a common genetic factor that accounted for 10% of the genetic variance in verbal IQ and for 22% of the genetic variance in performance IQ. This factor is hypothesized to reflect the influence of genetic factors that determine axonal myelination in the central nervous system.     

Genetic Mediation of the Correlation Between Peripheral Nerve Conduction Velocity and IQ

Variation in peripheral nerve conduction velocity (PNCV) and intelligence was studied in 18-year-old Dutch twins. It has been suggested that both brain nerve conduction velocity and PNCV are positively correlated with intelligence (Reed, 1984) and that heritable differences in nerve conduction velocity may explain part of the well-established heritability of intelligence. The relationship among IQ, obtained with the Wechsler Adult Intelligence Scale, and median nerve PNCV was examined in 159 twin pairs. Genetic analyses showed a heritability of 81% for IQ and 66% for onset PNCV. The small but significant phenotypic correlation between IQ and onset PNCV (.15) was entirely mediated by common genetic factors. Analyses of difference scores for PNCV of this study and PNCV from the same subjects collected at age 16 suggest that there might still be development in PNCV in this age interval. This maturation is highly controlled by genetic factors. It is suggested that variation in IQ that is associated with nerve conduction velocity becomes apparent only after the developmental processes in peripheral nerves are completed. This is in line with the suggestion of increasing heritability of IQ in adulthood.     

Reconsidering the Heritability of Intelligence in Adulthood: Taking Assortative Mating and Cultural Transmission into Account

Heritability estimates of general intelligence in adulthood generally range from 75 to 85%, with all heritability due to additive genetic influences, while genetic dominance and shared environmental factors are absent, or too small to be detected. These estimates are derived from studies based on the classical twin design and are based on the assumption of random mating. Yet, considerable positive assortative mating has been reported for general intelligence. Unmodeled assortative mating may lead to biased estimates of the relative magnitude of genetic and environmental factors. To investigate the effects of assortative mating on the estimates of the variance components of intelligence, we employed an extended twin-family design. Psychometric IQ data were available for adult monozygotic and dizygotic twins, their siblings, the partners of the twins and siblings, and either the parents or the adult offspring of the twins and siblings (N = 1314). Two underlying processes of assortment were considered: phenotypic assortment and social homogamy. The phenotypic assortment model was slightly preferred over the social homogamy model, suggesting that assortment for intelligence is mostly due to a selection of mates on similarity in intelligence. Under the preferred phenotypic assortment model, the variance of intelligence in adulthood was not only due to non-shared environmental (18%) and additive genetic factors (44%) but also to non-additive genetic factors (27%) and phenotypic assortment (11%).This non-additive nature of genetic influences on intelligence needs to be accommodated in future GWAS studies for intelligence.     

The Longitudinal Relationship between Processing Speed and Cognitive Ability: Genetic and Environmental Influences

Goals of the present study were to investigate the relationship between age changes in speed and cognition and the genetic and environmental influences on that relationship. Latent growth models and quantitative genetic methods were applied to data from the Swedish Adoption/Twin Study of Aging. The sample included 778 individuals from both complete and incomplete twin pairs who participated in at least 1 of 4 testing occasions over a 13-year-period. Four factors were constructed from 11 cognitive measures: verbal, spatial, memory, and processing speed. Results indicate that for measures of fluid abilities, the explanatory value of processing speed is paramount for both mean cognitive performance and acceleration with age. A significant proportion of the genetic influences on cognitive ability arose from genetic factors affecting processing speed. For measures of fluid abilities, it is not the linear age changes but the accelerating age changes in cognition that share genetic variance with processing speed.     

The Genetic Basis of Academic Achievement on the Queensland Core Skills Test and its Shared Genetic Variance with IQ

First, this study examined genetic and environmental sources of variation in performance on a standardised test of academic achievement, the Queensland Core Skills Test (QCST) (Queensland Studies Authority, 2003a). Second, it assessed the genetic correlation among the QCST score and Verbal and Performance IQ measures using the Multidimensional Aptitude Battery (MAB), [Jackson, D. N. (1984) Multidimensional Aptitude Battery manual. Port Huron, MI:Research Psychologist Press, Inc.]. Participants were 256 monozygotic twin pairs and 326 dizygotic twin pairs aged from 15 to 18 years (mean 17 years ± 0.4 [SD]) when achievement tested, and from 15 to 22 years (mean 16 years ± 0.4 [SD]) when IQ tested. Univariate analysis indicated a heritability for the QCST of 0.72. Adjustment to this estimate due to truncate selection (downward adjustment) and positive phenotypic assortative mating (upward adjustment) suggested a heritability of 0.76 The phenotypic (0.81) and genetic (0.91) correlations between the QCST and Verbal IQ (VIQ) were significantly stronger than the phenotypic (0.57) and genetic (0.64) correlations between the QCST and Performance IQ (PIQ). The findings suggest that individual variation in QCST performance is largely due to genetic factors and that common environmental effects may be substantially accounted for by phenotypic assortative mating. Covariance between academic achievement on the QCST and psychometric IQ (particularly VIQ) is to a large extent due to common genetic influences.     

Genetic Factor Analyses of Specific Cognitive Abilities in 5-Year-Old Dutch Children

The genetic and environmental factor structures of intellectual abilities in 5-year-old Dutch twins were examined. Six subtests of the RAKIT, a Dutch intelligence test, were administered to 209 twin pairs. The subtests were categorized as either verbal or nonverbal. The genetic covariance structure displayed a two-common factor structure including specific factors to account for subtest residual variance. The correlation between the genetic Verbal and genetic Nonverbal factors did not differ significantly from zero. The shared environmental influence displayed a single-common factor structure. Unique environmental influences did not contribute to the covariance between subtests and were specific in origin. Estimates of heritability of the subtests ranged from 15% to 56%. Shared environmental influences were significantly present, but were modest in magnitude. The phenotypic data was best described by an oblique two-factor model. This model was not mirrored in the factor structures found for either the genetic or environmental covariances.     

The genetic and environmental variance underlying elementary cognitive tasks

Although previous studies have examined the genetic and environmental influences upon general intelligence and specific cognitive abilities in school-age children, few studies have examined elementary cognitive tasks in this population. The current study included 149 MZ and 138 same-sex DZ twin pairs who participated in the Western Reserve Twin Project. Thirty measures from the Cognitive Abilities Test (CAT; Detterman, 1986) were studied. Results indicate that (1) these measures are reliable indicators of general intelligence in children and (2) the structure of genetic and environmental influences varies across measures. These results not only indicate that elementary cognitive tasks display heterogeneous genetic and environmental effects, but also may demonstrate that individual differences in biologically based processes are not necessarily due to genetic variance.