To what extent do cognitive abilities, conscientiousness, and physical activity predict academic performance?

We tested a novel framework for explaining individual differences in academic performance with a large sample of undergraduates (n = 263). Using structural equation modeling, we evaluated how cognitive abilities, disposition, and healthy lifestyle separately influence grade point average. Employing a large cognitive battery assessing eight cognitive constructs and a bifactor model, we parsed variance due to general cognitive ability and residual specific cognitive abilities. Participants wore a Fitbit watch for three weeks to track activity levels, and completed the Big Five Inventory. We found that general cognitive ability did not predict academic performance after accounting for specific abilities, conscientiousness, and physical activity, but the specific abilities secondary memory and fluid intelligence each uniquely predicted about 4% of the variance in grade point average. Neither conscientiousness nor physical activity contributed unique variance in academic performance after accounting for other factors.

The relative value of specific versus general cognitive abilities for the prediction of practical outcomes has been debated since the inception of modern intelligence theorizing and testing. This editorial introduces a special issue dedicated to exploring this ongoing “great debate”. It provides an overview of the debate, explains the motivation for the special issue and two types of submissions solicited, and briefly illustrates how differing conceptualizations of cognitive abilities demand different analytic strategies for predicting criteria, and that these different strategies can yield conflicting findings about the real-world importance of general versus specific abilities.

Many studies have found that variation in music training is associated with intellectual abilities, but research disagrees over whether music education should primarily correlate with general intelligence (g) or with specific lower-level cognitive abilities (e.g., fluid reasoning, verbal ability, or spatial reasoning). Past research, however, has not modeled the data in ways that can separate general abilities like g from specific abilities. To examine if the associations between music training and intelligence are general, specific, or both, a bifactor modeling approach was applied to data from a sample of 237 young adults who varied substantially in musical expertise. People completed a range of tasks that measured several lower-order abilities: fluid intelligence, crystallized intelligence (vocabulary knowledge), verbal fluency, and auditory discrimination ability. Simple correlations showed that music training correlated with all 4 lower-order abilities. A bifactor model, however, found that music training had both general (a strong association with g: s = .74 [.50, .98]) and specific (a moderate association with auditory ability: s = .37 [.08, .67]) relationships. The findings reconcile past research on the breadth of music training’s relationships and illustrate a fruitful method for identifying its links with cognitive abilities.

Individuals scoring higher in tests of general cognitive abilities tend to perform better on novel and familiar mathematical tasks. It has been scarcely investigated how this superior mathematical performance relates to the amount of cognitive resources that is invested to solve a given task. In this study we propose that, on novel tasks, individuals with high cognitive abilities outperform less able individuals, because they allocate a higher amount of resources. On familiar tasks, however, individuals with higher abilities profit from more efficient processes compared to individuals of lower cognitive abilities. We tested this hypothesis by administering to 11th graders a geometric analogy task not practiced at school and an algebraic transformation task comprising operations that are routinely required during mathematical courses. General cognitive abilities were measured with Ravens Advanced Progressive matrices (fluid intelligence), the d2 (focused attention) and KAI-N (working memory capacity). Resource allocation was measured by assessing pupil diameter during the problem-solving process. Performance on both the analogy and the algebra task was correlated with general cognitive abilities, especially fluid intelligence. In line with our assumptions, a positive correlation between fluid intelligence and resource allocation was observed in the novel geometric analogy task, whereas the correlation was not significant in the more familiar algebra task.

Introduction

Face cognition (FC) is a specific ability that cannot be fully explained by general cognitive functions. Cortical thickness (CT) is a neural correlate of performance and learning. In this registered report, we used data from the Human Connectome Project (HCP) to investigate the relationship between CT in the core brain network of FC and performance on a psychometric task battery, including tasks with facial content. Using structural equation modelling (SEM), we tested the existence of face-specific interindividual differences at behavioural and neural levels. The measurement models include general and face-specific factors of performance and CT. There was no face-specificity in CT in functionally localized areas. In post hoc analyses, we compared the preregistered, small regions of interest (ROIs) to larger, non-individualized ROIs and identified a face-specific CT factor when large ROIs were considered. We show that this was probably due to low reliability of CT in the functional localization (intra-class correlation coefficients (ICC) between 0.72 and 0.85). Furthermore, general cognitive ability, but not face-specific performance, could be predicted by latent factors of CT with a small effect size. In conclusion, for the core brain network of FC, we provide exploratory evidence (in need of cross-validation) that areas of the cortex sharing a functional purpose did also share morphological properties as measured by CT.

Periventricular and deep abnormal white matter differ in associations with cognitive performance at midlife.

What counts in preschool number knowledge? A Bayes factor analytic approach toward theoretical model development

Many older adults have inadequate health-related literacy, which is associated with poor health outcomes. Thus, it is important to identify determinants of health literacy. We investigated relationships between health literacy and general cognitive and sensory abilities, as well as education, health, and demographic variables, in a community sample of middle-aged and older adults. Participants were 314 community-dwelling adults (67% female, 48% African American) diagnosed with chronic heart failure recruited for a pharmacist-based intervention study to improve adherence to chronic heart failure medications. We adminstered demographic, health, education, cognitive (e.g., processing speed, working memory), and sensory measures, and the Short Test of Functional Health Literacy in Adults (STOFHLA), as part of the baseline condition of this study. STOFHLA scores were lower for participants who were older, less educated, male, African American, had more comorbidities, or scored lower on all cognitive ability measures. Hierarchical linear regression analyses showed that education and cognitive ability were independently associated with the STOFHLA measure and explained age differences in health literacy. The association of cognitive abilities and literacy has important implications for health literacy models and for interventions to reduce the impact of low health literacy on health outcomes. For example, medication instructions should be designed to reduce comprehension demands on general cognitive abilities as well as literacy skills.

Infant motor and cognitive abilities and subsequent executive function

The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example.

Bilingual speakers have shown better performance than their monolingual peers on tasks that examine a range of cognitive abilities (e.g. attention, phonological awareness); however, other studies report no group differences. Two reasons may explain the inconsistencies across studies: 1) many studies did not examine and control, when needed, group differences in cognitive abilities that could influence performance on the ability in question; 2) variability in the language proficiency and linguistic similarity between first (L1) and second (L2) language. The current project examined language-related cognitive abilities (i.e. phonological awareness) and general cognitive abilities (i.e. attention and inhibition) in two studies of 8-10 year-old children in Hong Kong. Study 1 investigated whether these abilities, as well as reading, were affected by bilingualism and L1-L2 linguistic distance. There were three groups: English monolingual, Cantonese- English bilingual, and European language-English bilingual. All children had similar educational backgrounds, as they attended English speaking International schools. Particular effort was made to control for group differences in cognitive abilities that could act as confounding variables, which included: intelligence, English proficiency, working memory, and short-term memory. There were no group differences on the tasks of attention and inhibition. However, there were for phonological awareness and reading. L1-L2 linguistic distance provided an advantage over the monolinguals on phonological awareness as only the European bilinguals group performed better than the monolinguals. Whereas, bilingual children of linguistically distant L1- L2 (Cantonese-English) demonstrated difficulty with phonological awareness and reading tasks using nonwords but not real words compared with the other groups. The results suggest that children who speak two linguistically distant languages may have difficulty transferring L2 skills from familiar words to novel words, which is a skill needed in literacy development.\n\n\n\nStudy 2 explored the influence of L2 proficiency on the two sets of abilities. The data from the English monolingual and Cantonese bilingual group in Study 1 was analyzed with data from a new group of children, those who spoke Cantonese (L1) and were learning English as a L2 (ESL). Once again, particular effort was made to statistically control for cognitive abilities that could act as confounding factors. The results of the general cognitive abilities show no group differences. The results of the phonological awareness task show that the ESL group performed worse than the English monolingual and Cantonese bilingual group. This was explained by their low L2 proficiency, as well as difference in the instructional method of English reading.\n\n\n\nThe results of this project advance our understanding of bilingualism. Bilingual advantages are not observed in all children learning a L2 but are influenced by factors embedded in bilingualism (i.e. L2 proficiency, L1-L2 linguistic distance). This highlights that the effects of bilingualism need to be qualified. Additionally, bilingualism does not produce wide-spread advantages; rather, the influence of speaking two languages can affect one cognitive domain, such as language-related abilities, but not the general cognitive abilities within the same groups of children. The results are discussed in relation to the larger body of work and direction of future work is suggested.

It has long been recognized that generalized deficits in cognitive ability represent a core component of schizophrenia, evident prior to full illness onset and independent of medication. The possibility of genetic overlap between risk for schizophrenia and cognitive phenotypes has been suggested by the presence of cognitive deficits in first-degree relatives of patients with schizophrenia; however, until recently, molecular genetic approaches to test this overlap have been lacking. Within the last few years, large-scale genome-wide association studies (GWAS) of schizophrenia have demonstrated that a substantial proportion of the heritability of the disorder is explained by a polygenic component consisting of many common SNPs of extremely small effect. Similar results have been reported in GWAS of general cognitive ability. The primary aim of the present study is to provide the first molecular genetic test of the classic endophenotype hypothesis, which states that alleles associated with reduced cognitive ability should also serve to increase risk for schizophrenia. We tested the endophenotype hypothesis by applying polygenic SNP scores derived from a large-scale cognitive GWAS meta-analysis (~5000 individuals from 9 non-clinical cohorts comprising the COGENT consortium) to four schizophrenia case-control cohorts. As predicted, cases had significantly lower cognitive polygenic scores compared to controls. In parallel, polygenic risk scores for schizophrenia were associated with lower general cognitive ability. Additionally, using our large cognitive meta-analytic dataset, we identified nominally significant cognitive associations for several SNPs that have previously been robustly associated with schizophrenia susceptibility. Results provide molecular confirmation of the genetic overlap between schizophrenia and general cognitive ability, and may provide additional insight into pathophysiology of the disorder.

Placing inspection time, reaction time, and perceptual speed in the broader context of cognitive ability: The VPR model in the Lothian Birth Cohort 1936

School grades are still used by universities and employers for selection purposes. Thus, identifying determinants of school grades is important. Broadly, two predictor categories can be differentiated from an individual difference perspective: cognitive abilities and personality traits. Over time, evidence accumulated supporting the notion of the g-factor as the best single predictor of school grades. Specific abilities were shown to add little incremental validity. The current paper aims at reviving research on which cognitive abilities predict performance. Based on ideas of criterion contamination and deficiency as well as Spearman’s ability differentiation hypothesis, two mechanisms are suggested which both would lead to curvilinear relations between specific abilities and grades. While the data set provided for this special issue does not allow testing these mechanisms directly, we tested the idea of curvilinear relations. In particular, polynomial regressions were used. Machine learning was applied to identify the best fitting models in each of the subjects math, German, and English. In particular, we fitted polynomial models with varying degrees and evaluated their accuracy with a leave-one-out validation approach. The results show that tests of specific abilities slightly outperform the g-factor when curvilinearity is assumed. Possible theoretical explanations are discussed.