الملخص
Automated neurodevelopmental assessment increasingly combines computer vision, speech, eye tracking, physiology, and machine learning, yet multimodality and discrimination do not establish construct validity, clinical usefulness, or appropriate interpretation of behavioral variation. We propose a testable architecture for neurodiversity-aware multimodal affective computing in which population-relative deviation is not treated as sufficient evidence of adverse functioning. This conceptual article reports neither a new participant-level study nor a trained predictive system, and no predictive or clinical superiority is claimed. The framework keeps observable signals, modality quality and availability, context, population-relative information, pooled within-person references and, when sufficiently supported, context-conditioned within person references, and predictive uncertainty distinguishable throughout inference. It defines structured decision-support outputs and auditable requirements for target validity, temporal integrity, contextual leakage, missing-modality robustness, calibration, subgroup evaluation, interpretability, privacy, and human decision boundaries. The contribution is architectural rather than algorithmic: it specifies empirically testable constraints that can be instantiated with different multimodal learning methods. Autism provides the principal motivating evidence base, without assuming unchanged transfer to other neurodevelopmental conditions.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Pomianek, M., & Łężniak-Seruga, A. (2026). Neurodiversity-Aware Multimodal Affective Computing for Neurodevelopmental Assessment: From Norm-Referenced Classification to Context-Sensitive Decision Support. https://omanscience.com/ar/articles/neurodiversity-aware-multimodal-affective-computing-for-neurodevelopmental-assessment-from-norm-referenced-classification-to-context-sensitive-decisio
MLA 9
Pomianek, Mateusz, and Anna Łężniak-Seruga. "Neurodiversity-Aware Multimodal Affective Computing for Neurodevelopmental Assessment: From Norm-Referenced Classification to Context-Sensitive Decision Support." https://omanscience.com/ar/articles/neurodiversity-aware-multimodal-affective-computing-for-neurodevelopmental-assessment-from-norm-referenced-classification-to-context-sensitive-decisio.
شيكاغو (المؤلف–التاريخ)
Pomianek, Mateusz, and Anna Łężniak-Seruga. 2026. "Neurodiversity-Aware Multimodal Affective Computing for Neurodevelopmental Assessment: From Norm-Referenced Classification to Context-Sensitive Decision Support." https://omanscience.com/ar/articles/neurodiversity-aware-multimodal-affective-computing-for-neurodevelopmental-assessment-from-norm-referenced-classification-to-context-sensitive-decisio.
هارفارد
Pomianek, M. and Łężniak-Seruga, A. (2026) 'Neurodiversity-Aware Multimodal Affective Computing for Neurodevelopmental Assessment: From Norm-Referenced Classification to Context-Sensitive Decision Support', Available at: https://omanscience.com/ar/articles/neurodiversity-aware-multimodal-affective-computing-for-neurodevelopmental-assessment-from-norm-referenced-classification-to-context-sensitive-decisio.
فانكوفر
Pomianek M, Łężniak-Seruga A. Neurodiversity-Aware Multimodal Affective Computing for Neurodevelopmental Assessment: From Norm-Referenced Classification to Context-Sensitive Decision Support. https://omanscience.com/ar/articles/neurodiversity-aware-multimodal-affective-computing-for-neurodevelopmental-assessment-from-norm-referenced-classification-to-context-sensitive-decisio
IEEE
M. Pomianek, and A. Łężniak-Seruga, "Neurodiversity-Aware Multimodal Affective Computing for Neurodevelopmental Assessment: From Norm-Referenced Classification to Context-Sensitive Decision Support," https://omanscience.com/ar/articles/neurodiversity-aware-multimodal-affective-computing-for-neurodevelopmental-assessment-from-norm-referenced-classification-to-context-sensitive-decisio.