Predictive Formats and Informational Attenuation: A Unified Account of Autism Across Cognition, Phenomenology, and Social Interaction

Persistent differences between autistic and neurotypical cognition are widely documented across perception, reasoning, and social interaction, yet existing explanations remain fragmented across disciplinary levels. This paper proposes a unified account grounded in predictive processing, information theory, representational format, and phenomenology. It explains these differences as systematic consequences of how cognitive systems selectively attenuate information to explicit inference. We argue that the critical distinction does not concern the capacity for prediction as such, but the manner in which predictive systems attenuate low-probability, high-information signals. Informational attenuation is introduced as a common organizing mechanism linking multiple explanatory levels. At the computational level, it defines a principled trade-off between informational fidelity and computational efficiency, grounded in information-theoretic relations between probability and surprisal. At the algorithmic level, this trade-off is implemented through precision weighting and thresholding processes that determine which signals are propagated or suppressed. At the representational level, differential attenuation shapes predictive format itself, biasing cognition toward either compressed content or formal structure. At the phenomenological level, the same mechanisms account for systematic differences in experiential organization, explaining why some forms of cognition are characterized by transparency and immediacy, while others involve heightened explicitness and structural salience. At the social and interactional level, differential attenuation explains how inductively reinforcing norms support epistemic stability—and why structurally oriented agents may experience such normativity as opaque, underdetermined, or coercive. By linking these levels through a single mechanistic principle, the proposed framework avoids reductionism, deficit-based framing and explanatory fragmentation.