The aim of the current study was to explore contributions of different types
of reasoning to math word problem solving and whether these contributions have added value compared with each other and general cognitive ability.

Chinese schoolchildren in Grades 3 to 6 (N = 746) completed 8 tasks tapping into spatial, mechanical, verbal, mathematic, hypothetical and analogical reasoning.

Our data showed that when 6 general cognitive factors load onto General cognitive ability factor in a Structural Equation Model (SEM), only spatial visualization has additional contribution to Word problem solving factor. Gaussian Graphical models (GGMs) showed that 2 verbal tasks and spatial visualization showed stable (present in at least 3 out of 4 grades) contributions to both word problem solving tasks.





Intelligence

There is some evidence that SA might be improved via learning character-based language, e.g. Chinese as a second language (CSL).

The study aimed to investigate an effect of CSL learning on SA in schoolchildren from Year 2 to Year 7. Current study employs a sample of Russian schoolchildren (N = 283), who learnt: English only, English and Spanish; or English and Chinese. Participants completed Raven’s progressive matrices and Mental rotation task at the age of 8
and again at the age of 14.

Our data showed negligible group differences in the initial SA level at Year 2 (before learning second language). Similar negligible differences were found at Year 7. Regression analysis showed that SA was predicted by intelligence (Raven’s) and gender but not language learnt at both ages.






Scientific Reports

The current study aimed to investigate whether mutiple personality traits could be predicted from eye-movement in natural settings.

Adolescent participants (N = 35) completed personality questionnaires (Big Five Inventory and Short Dark Triad Questionnaire) and visited an interactive museum while their eye movements were recorded with head-mounted eye tracking. Eight machine-learning methods were used: Random Forest, Adaboost, Naive Bayes, Support Vector Machine, Logistic Regression, k Nearest Neighbours, Decision Tree and a three-layer Perceptron.

Extracted eye movement features predicted personality traits with above 33% chance accuracy (34%–48%). Better prediction was achieved for Machiavellianism and Extraversion compared to other traits (10 and 9 predictions above the chance level by different algorithms from different parts of the recording).


PLOS ONE

We live in an age of rapidly advancing genetic research. This research is generating new knowledge that has implications for personal health and well-being.

The present study assessed the level of genetic knowledge and personal engagement with genetics in a large sample (N = 5404) of participants; and showed that the overal genetic knowledge was poor, with only around 1 percent of participants answering all knoweldge questions correctly.










Journal of Community Genetics