Cognitive Architectures and Adaptive Specialization: A Network-Level Framework for Understanding Neurodivergent Cognition, Memory, and Resilience

This paper introduces a paradigm-shifting framework for understanding neurodivergent cognition through the lens of adaptive specialization. Drawing from the Information Bottleneck Principle and Narrative Resilience Theory, we reconceptualize conditions such as ADHD, autism, dyslexia, and aphantasia as distinct cognitive architectures—each reflecting trade-offs that prioritize certain processing modes over others. Rather than viewing these traits as pathological deficits, we argue they represent evolved strategies of cognitive optimization within complex environments. This integrated, network-level model highlights how structural constraints (e.g., reduced visual imagery in aphantasia) may give rise to compensatory enhancements in verbal-symbolic reasoning, memory systems, and emotional resilience. By synthesizing insights from neuroscience, clinical psychology, and information theory, the paper offers testable predictions, practical applications for therapy and education, and a strong case for abandoning deficit-based frameworks in favor of cognitive equity. The model also introduces the “Narrative Resilience Variant” (NRV), a subtype marked by strong autobiographical coherence and resistance to trauma collapse—despite atypical working memory profiles. This approach not only challenges prevailing diagnostic models but offers a robust template for future research on cognitive diversity.