Background. Recent research has suggested a unifactorial structure of spatial ability (SA). However, further studies are needed to replicate this finding in different populations. Objective. This study aims to explore the factorial structure of SA in samples of 921 Russian and 229 Chinese university students. Design. A gamified spatial abilities battery was administered to all participants. The battery consists of 10 different domains of SA, including 2D and 3D visualization, mental rotation, spatial pattern assembly, spatial relations, spatial planning, mechanical reasoning, spatial orientation, and spatial decision-making speed and flexibility. Results. The results of the factor analysis showed a somewhat different pattern for different samples. In the Russian sample, the unifactorial structure, shown previously in a large UK sample (Rimfeld et al., 2017), was replicated. A single factor explained 40% of the variance. In the Chinese sample two factors emerged: the first factor explained 26% of the variance and the second factor, including only mechanical reasoning and cross-sections tests, explained 14%. The results also showed that the Chinese sample significantly outperformed the Russian sample in five out of the 10 tests. Russian students showed better performance in only two of the tests. The effects of all group comparisons were small. The Factorial Structure of Spatial Abilities in Russian and Chinese Students 97 Conclusion. Overall, a similar amount of variance in the 10 tests was explained in the two samples, replicating results from the UK sample. Future research is needed to explain the observed differences in the structure of SA.

Background. Spatial ability (SA) has long been the focus of research in psychology, because it is associated with performance in science, technologies, engineering, and mathematics (STEM). Research has shown that males consistently outperform females in most aspects of SA, which may partially explain the observed overrepresentation of male students seeking STEM degrees.Objective. This study examines sex and field of study (degree) differences in different aspects of spatial ability and its structure.Design. We assessed SA by using an on-line gamified battery, which included 10 spatial tests capturing 10 dimensions of spatial ability, among which were mental rotation, spatial visualization, spatial scanning, spatial reasoning, perspective-taking, and mechanical reasoning. The sample consisted of 882 STEM (55% males) and Humanities (20% males) university students in Russia.Results. Males outperformed females on all assessed components of SA with a small effect size (1-11%). We also found that students from STEM fields outperformed Humanities students on all SA subtests (effect size ranged from 0.2 to 7%). These differences by study choice were not fully explained by the observed over-representation of males in the STEM group. The results of the study suggested no interaction between sex and degree. In other words, on average, males outperformed females, irrespective of whether they were STEM or humanities students; and the STEM advantage was observed for both 38 E. A. Esipenko et al.males and females. The same unifactorial structure of SA was observed in the STEM and Humanities groups. Conclusion.Our results are consistent with previous research, suggesting sex and study field differences in SA. Longitudinal research is needed to explore the causal mechanisms underscoring these differences.

The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven’s total scores and E series), and the ability to operate with verbal information (the “Conclusions” verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven’s total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.

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Background: The late treatment outcomes of pediatric brain tumors and of hematopoietic and lymphoid tissue tumors are an important focus of both rehabilitation and research. Neurocognitive and motor disorders induce further learning problems impeding social-emotional adaptation throughout a whole lifespan. Core deficits in short-term and working memory, visuospatial constructional ability, verbal fluency, and fine motor skills underlie distorted intellectual and academic achievement. This study aimed to assess the individual differences in cognitive ability and fine motor skills of pediatric tumor survivors and the age-matched healthy controls. Methods: A total of 504 tumor survivors after treatment and 646 age-matched healthy controls underwent neurocognitive and fine motor assessments. Findings: The group of tumor survivors scored significantly worse in both neurocognitive and fine motor skill in compared with the healthy control group. The pediatric brain tumor survivors (PBT group) performed worse in cognitive (p < 0.001 for verbal fluency and p < 0.001 for visuospatial constructional ability) and motor tests (p < 0.001) compared to the healthy controls. Hematopoietic and Lymphoid Tissues tumors survivors (THL group) performed worse in verbal fluency (p < 0.01) and visuospatial constructional test (p < 0.001) compared to the control group. Furthermore, the PBT group had worse results in visuospatial constructional ability (p < 0.05) and fine motor (p < 0.001) ability than the THL group. Significant differences between females and males were found in fine motor test performance in the PBT group (p < 0.05), as well as in verbal fluency (p < 0.01) and visuospatial constructional ability (p < 0.01) in the control group. Neurocognitive and fine motor skill characteristics in the THL group did not correlate with age.

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This article is devoted to the development of a model of an artificial neural network for predicting the level of nonverbal intelligence according to the EEG of the brain. Cognitive functioning relies on the synchronization between different brain structures. However, it is still unclear how individual differences in intelligence are related to the global characteristics of information transmission in brain networks. Resting-state functional connectivity studies show the association of patterns of interactions between brain regions from people and different levels of nonverbal intelligence. In this study, we present a process of development of a neural network model used to predict the level of nonverbal intelligence based on EEG data of the brain. We have developed a fully-connected neural network to predict the level of nonverbal intelligence.