Project Description

The goal of neuroeducation is to bring together expertise and research findings from developmental psychology, neuroscience, education and artificial intelligence to answer questions and develop methods that make educational practice more effective. Neuroeducation is an exciting, new scientific field of study with many opportunities for discovery and contribution to science and society.

One of the most important novel directions of the field is the study of individual differences in core neurocognitive competences that have strong association with academic achievement and professional success. Specifically, we know that in typically developing children core cognitive competences improve gradually as a function of age [1].

Neuroimaging research shows that advances in cognitive abilities co-occur with changes in brain maturation [2]. Critically, many educators know that despite having the same age, some children significantly over-perform or under-perform compared to their same age peers. Variation in neurocognitive performance within age groups corresponds to a type of individual difference that is particularly understudied. Moreover, brain functions of higher-order association areas (e.g., insula and cingulate cortex) and the interaction of established brain networks (e.g., executive attention and default-mode network) are still unclear in terms of development [3]. Specifically, preliminary research shows that brain areas and networks in children are not interacting similarly as adults, suggesting that children process information differently.

To create research-based methods and neurointerfaces that advance education we first need to understand developmental changes and individual differences in brain-behavior relations. This project is performed with educational partner - National Research University Higher School of Economics and NeuroPsyLab


  • Build predictive models based on behavioral (cognitive tests, school performance etc.) and physiological data (ultrasound data, age and other)
  • Explore task-solving strategies and build individual models based on eye-tracking and behavioral data
  • Build predictive models for mathematical cognition based on behavioral and fMRI data


  1. Arsalidou M., Pascual-Leone J., Johnson J. Misleading cues improve developmental assessment of working memory capacity: The color matching tasks // Cogn. Dev. 2010.
  2. Arsalidou M., Pascual-Leone J. Constructivist developmental theory is needed in developmental neuroscience // npj Sci. Learn. The Author(s), 2016. Vol. 1, № October. P. 16016.
  3. Arsalidou M. et al. Commentary: Selective development of anticorrelated networks in the intrinsic functional organization of the human brain // Front. Hum. Neurosci. 2017. Vol. 11.