Alpha Band Resting-State EEG Connectivity Is Associated With Non-verbal Intelligence

The aim of the present study was to investigate whether EEG resting state connectivity correlates with intelligence. One-hundred and sixty five participants took part in the study. Six minutes of eyes closed EEG resting state was recorded for each participant. Graph theoretical connectivity metrics were calculated separately for two well-established synchronization measures [weighted Phase Lag Index (wPLI) and Imaginary Coherence (iMCOH)] and for sensor- and source EEG space. Non-verbal intelligence was measured with Raven’s Progressive Matrices. In line with the Neural Efficiency Hypothesis, path lengths characteristics of the brain networks (Average and Characteristic Path lengths, Diameter and Closeness Centrality) within alpha band range were significantly correlated with non-verbal intelligence for sensor space but no for source space. According to our results, variance in non-verbal intelligence measure can be mainly explained by the graph metrics built from the networks that include both weak and strong connections between the nodes

The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews

The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus of consumer reviews in Russian about pharmaceutical products for the detection of health-related named entities and the effectiveness of pharmaceutical products. The corpus itself consists of two parts, the raw one and the labelled one. The raw part includes 1.4 million health-related user-generated texts collected from various Internet sources, including social media. The labelled part contains 500 consumer reviews about drug therapy with drug- and disease-related information. Labels for sentences include health-related issues or their absence. The sentences with one are additionally labelled at the expression level for identification of fine-grained subtypes such as drug classes and drug forms, drug indications, and drug reactions. Further, we present a baseline model for named entity recognition (NER) and multi-label sentence classification tasks on this corpus. The macro F1 score of 74.85% in the NER task was achieved by our RuDR-BERT model. For the sentence classification task, our model achieves the macro F1 score of 68.82% gaining 7.47% over the score of BERT model trained on Russian data. We make the RuDReC corpus and pretrained weights of domain-specific BERT models freely available at https://github.com/cimm-kzn/RuDReCComment: 9 pages, 9 tables, 4 figure

BFQ-RB (Russian Brief): Short Big-Five Questionnaire to Measure Facets and Factors of Personality

Several questionnaires exist for brief estimation of the Big Five personality factors. The majority of the short forms of the Big Five instruments aim to estimate the Big Five factors but not the facets within each factor. Assessing facets can be beneficial because facets may explain external behavior better than factors do. This paper presents a short form of the Big Five Questionnaire (BFQ) designed to assess both factors and facets, validated on a sample of Russian adolescents (14-18 years old). We created a short version (BFQ – Russian Brief; BFQ-RB), using data from a sample of 1128 adolescents (14-18 years) and then confirmed the factor structure on another subsample of 1087 adolescents. The psychometric properties of the newly created instrument – the BFQ-RB (Russian Brief) – were evaluated via item-level confirmatory factor analysis. We estimated three main models. In the first model, the selected items represented the Big Five factors. In the second model, the selected items represented ten correlated latent factors (facets). The third model was the second-order factor model fitted the data well, suggesting that the BFQ-RB enabled the estimation of both facets and factors. Our final instrument consists of 43 items, with each facet represented by 3-4 items and each second-order factor consisting of two facets, including the Lie scale

Why do spatial abilities predict mathematical performance?

Spatial ability predicts performance in mathematics and eventual expertise in science, technology and engineering. Spatial skills have also been shown to rely on neuronal networks partially shared with mathematics. Understanding the nature of this association can inform educational practices and intervention for mathematical underperformance. Using data on two aspects of spatial ability and three domains of mathematical ability from 4174 pairs of 12-year-old twins, we examined the relative genetic and environmental contributions to variation in spatial ability and to its relationship with different aspects of mathematics. Environmental effects explained most of the variation in spatial ability (~70%) and in mathematical ability (~60%) at this age, and the effects were the same for boys and girls. Genetic factors explained about 60% of the observed relationship between spatial ability and mathematics, with a substantial portion of the relationship explained by common environmental influences (26% and 14% by shared and non-shared environments respectively). These findings call for further research aimed at identifying specific environmental mediators of the spatial–mathematics relationship

Number Sense and Mathematics: Which, When and How?

Individual differences in number sense correlate with mathematical ability and performance, although the presence and strength of this relationship differs across studies. Inconsistencies in the literature may stem from heterogeneity of number sense and mathematical ability constructs. Sample characteristics may also play a role as changes in the relationship between number sense and mathematics may differ across development and cultural contexts. In this study, 4,984 16-year-old students were assessed on estimation ability, one aspect of number sense. Estimation was measured using two different tasks: number line and dot-comparison. Using cognitive and achievement data previously collected from these students at ages 7, 9, 10, 12, and 14 years of age, the study explored for which of the measures and when in development these links are observed; how strong these links are and how much these links are moderated by other cognitive abilities. The two number sensemeasures correlated modestly with each other (r = .22), but moderately with mathematics at age 16. Both measures were also associated with earlier mathematics; but this association was uneven across development and was moderated by other cognitive abilities