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Bölüm: Işık University, School of Graduate Studies, Master’s Program in Computer Engineering × search.filters.applied.f.publicationcategory: Ön Baskı – Uluslararası – Öğrenci ×

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    Electrophysiological signatures of developmental dyslexia: towards EEG-based biomarker identification and neurogenetic correlates
    (MDPI, 2025-06-30) Eroğlu, Günet; Harb, Mhd Raja Abou
    Dyslexia is a neurodevelopmental disorder characterized by altered hemispheric specialization and disrupted phonological processing. In this study, we applied Principal Component Analysis (PCA) to high-dimensional electroencephalographic (EEG) recordings from 200 children (100 dyslexic, 100 controls) to extract latent neurophysiological features associated with reading impairment. Our findings revealed significant right-hemisphere dominance in dyslexic individuals, particularly in the P8 electrode within the alpha band, consistent with compensatory neural strategies. Despite the absence of clinical comorbidities or medication use, distinct clustering emerged, supporting the utility of PCA for early screening. Future directions include correlating EEG-derived features with known dyslexia-related gene expression profiles (e.g., DCDC2, KIAA0319), neurotransmitter imbalances, and neuroinflammatory markers. These integrative analyses may establish EEG signals as reliable, non-invasive biomarkers for molecular-level screening in developmental learning disorders.
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    Geopolitical parallax: beyond Walter Lippmann just after large language models
    (Cornell Univ, 2025-08-27) Yavuz, Mehmet Can; Kabir, Humza Gohar; Özkan, Aylin
    Objectivity in journalism has long been contested, oscillating between ideals of neutral, fact-based reporting and the inevitability of subjective framing. With the advent of large language models (LLMs), these tensions are now mediated by algorithmic systems whose training data and design choices may themselves embed cultural or ideological biases. This study investigates geopolitical parallax—systematic divergence in news quality and subjectivity assessments—by comparing articlelevel embeddings from Chinese-origin (Qwen, BGE, Jina) and Western-origin (Snowflake, Granite) model families. We evaluate both on a human-annotated news quality benchmark spanning fifteen stylistic, informational, and affective dimensions, and on parallel corpora covering politically sensitive topics, including Palestine and reciprocal China–United States coverage. Using logistic regression probes and matched-topic evaluation, we quantify per-metric differences in predicted positive-class probabilities between model families. Our findings reveal consistent, nonrandom divergences aligned with model origin. In Palestinerelated coverage, Western models assign higher subjectivity and positive emotion scores, while Chinese models emphasize novelty and descriptiveness. Cross-topic analysis shows asymmetries in structural quality metrics—Chinese-on-US scoring notably lower in fluency, conciseness, technicality, and overall quality—contrasted by higher negative emotion scores. These patterns align with media bias theory and our distinction between semantic, emotional, and relational subjectivity, and extend LLM bias literature by showing that geopolitical framing effects persist in downstream quality assessment tasks. We conclude that LLMbased media evaluation pipelines require cultural calibration to avoid conflating content differences with model-induced bias.