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Yayın Imagined contact in high conflict settings: The role of ethnic group identification and the perspective of minority group members(Wiley, 2018-01) Bağcı Hemşinlioğlu, Sabahat Çiğdem; Piyale, Zeynep Ecem; Ebcim, EfsaneRecent contact literature has shown that imagining a positive intergroup encounter improves intergroup attitudes and behaviors, yet less is known about the effects of imagined contact in high conflict settings. We conducted three studies to understand the potential effects of imagined intergroup contact among ethnic Turks ( majority status) and ethnic Kurds ( minority status) in the Turkish-Kurdish interethnic conflict setting. Study 1 (N = 47, Turkish) tested standard imagined contact effects ( neutral vs. standard imagined contact condition) among majority Turks and showed that imagined contact was effective on outgroup attitudes, perceived threat, intergroup anxiety, and support for multiculturalism only among participants with higher ethnic identification. Study 2 (N = 107, Turkish) examined how ethnic identification of the contact partner would influence the effectiveness of the standard imagined contact scenario ( neutral vs. standard vs. ethnic identification condition) and demonstrated that imagined contact effects were more negative when the contact partner identified with his/her ethnic group during imagined contact. Study 3 (N = 55, Kurdish) investigated imagined contact effects ( neutral vs. standard imagined contact condition) among an ethnic minority group and showed that imagined contact did not improve minority group members' outgroup attitudes, but did decrease intergroup anxiety and perceived discrimination (marginally significantly) and increased perceived positive attitudes from the majority group. Practical implications of the use of imagined intergroup contact strategy in conflict-ridden settings were discussed.Yayın Theta and Beta1 frequency band values predict dyslexia classification(John Wiley and Sons Ltd, 2025-12-29) Eroğlu, Günet; Harb, Mhd Raja AbouDyslexia, 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.
