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Multi-level cognitive state classification of learners using complex brain networks and interpretable machine learning.

Cogn Neurodyn

December 2025

National Engineering Research Center of Educational Big Data, Central China Normal University, Luoyu Road, Wuhan, 430079 Hubei China.

Identifying the cognitive state can help educators understand the evolving thought processes of learners, and it is important in promoting the development of higher-order thinking skills (HOTS). Cognitive neuroscience research identifies cognitive states by designing experimental tasks and recording electroencephalography (EEG) signals during task performance. However, most of the previous studies primarily concentrated on extracting features from individual channels in single-type tasks, ignoring the interconnection across channels.

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Functional magnetic resonance imaging (fMRI) studies with adults provide evidence that functional brain networks, including the default mode network and frontoparietal network, underlie executive functioning (EF). However, given the challenges of using fMRI with infants and young children, little work has assessed the developmental trajectories of these networks or their associations with EF at key developmental stages. More recently, functional near-infrared spectroscopy (fNIRS) has emerged as a promising neuroimaging tool which can provide information on cortical functional networks and can be more easily implemented with young children.

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Mate availability and social information can influence mating behaviour in both males and females. Social information obtained from conspecifics can influence mate choice, particularly shown by studies on mate choice copying. However, the role of directly observing conspecific mating on mating behaviour has been less explored.

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Introduction: The evidence of association between physical activity (PA), sedentary behavior (SB) and gestational diabetes mellitus (GDM) remains controversial in observational studies, this study aimed to generate new hypotheses between PA, SB and GDM.

Methods: Our study performed Mendelian randomization (MR) analysis to explore the effects of three types of PA (moderate physical activity (MPA), moderate to vigorous physical activity (MVPA), accelerometer-based physical activity (ABPA)), three types of SB (television watching (TV), leisure computer use (PC), driving (DR)) on GDM and the mediating effect of body mass index (BMI). The inverse variance weighted method was used for the major analysis.

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Objectives: Explore digital technology use among recreational runners in Wales and assess attitudes toward a proposed digital intervention for running-related injury (RRI) prevention and self-management.

Design: Exploratory survey.

Setting: Online questionnaire, closed questions distributed to runners over a 7 week period.

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