umx: Twin and Path-based Structural Equation Modeling in R

Bates, Timothy C.; Maes, Hermine H.; Neale, Michael C.

doi:10.7287/peerj.preprints.3354v1

Cited by 30 publications

(38 citation statements)

References 12 publications

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“…This single factor accounted for 39.8% of variance. Testing fit of a 1-factor model using structural equation modeling using OpenMx (Neale et al, 2015) and umx (Bates, 2014;Bates, Neale, & Maes, 2017) packages in R (R Core Team, 2016). This indicated that a model with one modification (a covariance between brain storming and group checkers) fit well (χ²(113) = 4.2, p = 0.380; CFI = 0.993; TLI = 0.984; RMSEA = 0.043).…”

Section: Resultsmentioning

confidence: 99%

“…Empathizing has no effect on the latent group-IQ factor, but is allowed to affect one or more single test scores. Each of these models was built and compared using OpenMx (Neale et al, 2015) and umx (Bates, 2014;Bates et al, 2017) packages in R (R Core Team, 2016). Empathizing is also part-dependent on IQ, but has zero effect on group-IQ.…”

Section: Four Alternative Models Of the Origins Of Group-iq Performancementioning

confidence: 99%

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Smart groups of smart people: Evidence for IQ as the origin of collective intelligence in the performance of human groups

Bates

Gupta

2017

Intelligence

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What allows groups to behave intelligently? One suggestion is that groups exhibit a collective intelligence accounted for by number of women in the group, turn-taking and emotional empathizing, with group-IQ being only weakly-linked to individual IQ (Woolley, Chabris, Pentland, Hashmi, & Malone, 2010). Here we report tests of this model across three studies with 312 people. Contrary to prediction, individual IQ accounted for around 80% of group-IQ differences. Hypotheses that group-IQ increases with number of women in the group and with turn-taking were not supported. Reading the mind in the eyes (RME) performance was associated with individual IQ, and, in one study, with group-IQ factor scores. However, a well-fitting structural model combining data from studies 2 and 3 indicated that RME exerted no influence on the group-IQ latent factor (instead having a modest impact on a single group test). The experiments instead showed that higher individual IQ enhances group performance such that individual IQ determined 100% of latent group-IQ. Implications for future work on group-based achievement are examined. * We examined group-IQ in three independent studies. * Gender balance and turn-taking were unrelated to group performance. * Social sensitivity had no impact on latent group-IQ. * Individual IQ emerged as the cause of group-IQ. * Group-IQ almost exclusively reflects individual cognition.

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Section: Resultsmentioning

confidence: 99%

Section: Four Alternative Models Of the Origins Of Group-iq Performancementioning

confidence: 99%

Smart groups of smart people: Evidence for IQ as the origin of collective intelligence in the performance of human groups

Bates

Gupta

2017

Intelligence

Self Cite

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“…All analyses were completed using R (R Core Team, 2016) and umx (Bates, 2014;Bates, Neale, & Maes, 2017). Standardized effect sizes are reported to aid interpretability and incorporation into subsequent meta-analyses.…”

Section: Resultsmentioning

confidence: 99%

Does growth mindset improve children’s IQ, educational attainment or response to setbacks? Active-control interventions and data on children’s own mindsets

Li¹,

Bates²

2017

Preprint

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Mindset theory predicts that children's IQ and school grades are positively linked to their belief that basic ability is malleable rather than fixed. We test this prediction in three experimental studies (total n = 624 individually-tested 10-12-year-olds). Two studies included active-control conditions to test effects of fixed-ability beliefs independent of motivation. In addition, we tested whether children's own mindsets relate to real-life IQ, educational attainment in longitudinal analyses of school grades. Praise for intelligence had no significant effect on cognitive performance. Nor were any effects of mindset were found for challenging material.Active-control data showed that occasional apparent effects of praise for hard work on easy outcome measures reflected motivational confounds rather than effects of implicit beliefs about the malleability of intelligence (study 3, active control condition). Children's own mindsets showed no relationship to IQ, school grades, or change in grades across the school year, with the only significant result being in the reverse direction to prediction (better performance in children holding a fixed mindset). Fixed beliefs about basic ability appear to be unrelated to ability, and we found no support for mindset-effects on cognitive ability, response to challenge, or educational progress.

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“…The first step involved the vectorization of functional connectivity matrices' lower triangle ("Shen" -35,778 edges; "Gordon" -55,278 edges). The heritability analyses were performed using the umx package 43 , after regressing out the effect of age and sex using 'umx_residualize'.…”

Section: Heritability Analysesmentioning

confidence: 99%

“…Multivariate ACE models were fitted with bootstrapping. The Cholesky's ACE model 43 used in this framework allocates observed phenotypic variability into three factors: A (heritabilityh 2 ), C (shared environmentc 2 ) and E (measurement error or external sources of variabilitye 2 ) in a multivariate fashion. In our iterative multivariate approach, 10 edges were randomly selected and used as covariates to fit an ACE model (umxACEv).…”

Section: Heritability Analysesmentioning

confidence: 99%

Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach

Ribeiro

Santos

Sato

et al. 2018

Preprint

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Functional connectome fingerprints are partially heritable. Specifically, a higher heritability of high-order associative networks, which are greatly variable between individuals, has been proposed. However, such an interpretation relies on linear assumptions for the heritability inference and does not take into account the influence of the number of nodes on connectome fingerprinting analyses, since associative networks tend to include more brain regions. To overcome these limitations, we considered stable individual factors to estimate the relative genetic influence on whole-brain and eigenvector centrality-based networks of fixed size using the Human Connectome Project twin dataset. Individual identification accuracies were found to be higher in networks including more central nodes (94%) than the ones comprising peripheral nodes (22%). Nonetheless, the inferred additive genetic influence was more prominent in networks comprising peripheral nodes (50%) than hubs' networks (28%). Our results suggest that a brain hub's backbone is individually unique and less heritable than the connectivity profile of a subset of peripheral regions.

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umx: Twin and Path-based Structural Equation Modeling in R

Cited by 30 publications

References 12 publications

Smart groups of smart people: Evidence for IQ as the origin of collective intelligence in the performance of human groups

Smart groups of smart people: Evidence for IQ as the origin of collective intelligence in the performance of human groups

Does growth mindset improve children’s IQ, educational attainment or response to setbacks? Active-control interventions and data on children’s own mindsets

Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach

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