7 resultados para aptitude

em University of Queensland eSpace - Australia


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The phenotypic and genetic factor structure of performance on five Multidimensional Aptitude Battery (MAB) subtests and one Wechsler Adult Intelligence Scale-Revised (WAIS-R) subtest was explored in 390 adolescent twin pairs (184 monozygotic [MZ]; 206 dizygotic (DZ)). The temporal stability of these measures was derived from a subsample of 49 twin pairs, with test-retest correlations ranging from .67 to .85. A phenotypic factor model, in which performance and verbal factors were correlated, provided a good fit to the data. Genetic modeling was based on the phenotypic factor structure, but also took into account the additive genetic (A), common environmental (C), and unique environmental (E) parameters derived from a fully saturated ACE model. The best fitting model was characterized by a genetic correlated two-factor structure with specific effects, a general common environmental factor, and overlapping unique environmental effects. Results are compared to multivariate genetic models reported in children and adults, with the most notable difference being the growing importance of common genes influencing diverse abilities in adolescence. (C) 2003 Elsevier Inc. All rights reserved.

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In this study, we examined genetic and environmental influences on covariation among two reading tests used in neuropsychological assessment (Cambridge Contextual Reading Test [CCRT], [Beardsall, L., and Huppert, F. A. ( 1994). J. Clin. Exp. Neuropsychol. 16: 232 - 242], Schonell Graded Word Reading Test [SGWRT], [ Schonell, F. J., and Schonell, P. E. ( 1960). Diagnostic and attainment testing. Edinburgh: Oliver and Boyd.]) and among a selection of IQ subtests from the Multidimensional Aptitude Battery (MAB), [Jackson, D. N. (1984). Multidimensional aptitude battery, Ontario: Research Psychologists Press.] and the Wechsler Adult Intelligence Scale-Revised (WAIS-R) [Wechsler, D. (1981). Manual for the Wechsler Adult Intelligence Scale-Revised (WAIS-R). San Antonio: The Psychological Corporation]. Participants were 225 monozygotic and 275 dizygotic twin pairs aged from 15 years to 18 years ( mean, 16 years). For Verbal IQ subtests, phenotypic correlations with the reading tests ranged from 0.44 to 0.65. For Performance IQ subtests, phenotypic correlations with the reading tests ranged from 0.23 to 0.34. Results of Structural Equation Modeling (SEM) supported a model with one genetic General factor and three genetic group factors ( Verbal, Performance, Reading). Reading performance was influenced by the genetic General factor ( accounting for 13% and 20% of the variance for the CCRT and SGWRT, respectively), the genetic Verbal factor ( explaining 17% and 19% of variance for the CCRT and SGWRT), and the genetic Reading factor ( explaining 21% of the variance for both the CCRT and SGWRT). A common environment factor accounted for 25% and 14% of the CCRT and SGWRT variance, respectively. Genetic influences accounted for more than half of the phenotypic covariance between the reading tests and each of the IQ subtests. The heritabilities of the CCRT and SGWRT were 0.54 and 0.65, respectively. Observable covariance between reading assessments used by neuropsychologists to estimate IQ and IQ subtests appears to be largely due to genetic effects.

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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.

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To determine whether the visuospatial n-back working memory task is a reliable and valid measure of cognitive processes believed to underlie intelligence, this study compared the reaction times and accuracy of perforniance of 70 participants, with performance on the Multidimensional Aptitude Battery (MAB). Testing was conducted over two sessions separated by 1 week. Participants completed the MAB during the second test session. Moderate testretest reliability for percentage accuracy scores was found across the four levels of the n-back task, whilst reaction times were highly reliable. Furthermore, participants' performance on the MAB was negatively correlated with accuracy of performance at the easier levels of the n-back task and positively correlated with accuracy of performance at the harder task levels. These findings confirm previous research examining the cognitive basis of intelligence, and suggest that intelligence is the product of faster speed of information processing, as well as superior working memory capacity. (C) 2004 Elsevier Inc. All rights reserved.

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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.

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A genome-wide linkage scan of 795 microsatellite markers (761 autosomal, 34 X chromosome) was performed on Multidimensional Aptitude Battery subtests and verbal, performance and full scale scores, the WAIS-R Digit Symbol subtest, and two word-recognition tests (Schonell Graded Word Reading Test, Cambridge Contextual Reading Test) highly predictive of IQ. The sample included 361 families comprising 2-5 siblings who ranged in age from 15.7 to 22.2 years; genotype, but not phenotype, data were available for 81% of parents. A variance components analysis which controlled for age and sex effects showed significant linkage for the Cambridge reading test and performance IQ to the same region on chromosome 2, with respective LOD scores of 4.15 and 3.68. Suggestive linkage (LOD score > 2.2) for various measures was further supported on chromosomes 6, 7, 11, 14, 21 and 22. Where location of linkage peaks converged for IQ subtests within the same scale, the overall scale score provided increased evidence for linkage to that region over any individual subtest. Association studies of candidate genes, particularly those involved in neural transmission and development, will be directed to genes located under the linkage peaks identified in this study.