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search.filters.applied.f.publicationcategory: Makale - Uluslararası Hakemli Dergi - Öğrenci × Konu: Cognition ×

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    Associations among adolescents' mindfulness, sympathy, cognitive empathy, and sibling relationships
    (Sage Publication, 2024-02) Barata, Özge; Acar, İbrahim Hakkı; Bostancı, Selen
    In the current study, we examined the direct and indirect paths from mindfulness to adolescents’ sibling relationships through their cognitive empathy and sympathy. The sample consisted of 220 adolescents (50.9 % female) between age of 13 and 17 years (M = 15.86, SD = 0.91). Participants reported their mindfulness (acceptance and awareness), cognitive empathy and sympathy, and sibling relationships. The parallel mediation model revealed that mindful awareness and acceptance predicted kindness, involvement, and empathy within sibling relationships through sympathy. In addition, there was a significant indirect effect of mindful awareness to empathy in sibling relationships through cognitive empathy. Findings provided information regarding the importance of indirect contributions of mindfulness to sibling relationships through cognitive empathy and sympathy.
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    Theta and Beta1 frequency band values predict dyslexia classification
    (John Wiley and Sons Ltd, 2025-12-29) Eroğlu, Günet; Harb, Mhd Raja Abou
    Dyslexia, impacting children's reading skills, prompts families to seek cost-effective neurofeedback therapy solutions. Utilising machine learning, we identified predictive factors for dyslexia classification. Employing advanced techniques, we gathered 14-channel Quantitative Electroencephalography (QEEG) data from 200 participants, achieving 99.6% dyslexic classification accuracy through cross-validation. During validation, 48% of dyslexic children's sessions were consistently classified as normal, with a 95% confidence interval of 47.31 to 48.68. Focusing on individuals consistently diagnosed with dyslexia during therapy, we found that dyslexic individuals exhibited higher theta values and lower beta1 values compared to typically developing children. This study pioneers machine learning in predicting dyslexia classification factors, offering valuable insights for families considering neurofeedback therapy investment.