Cognitive Compensation and Predictive Processing A Theoretical Integration from an Ontological Perspective

Abstract

As the dominant paradigm in contemporary cognitive science, predictive processing theory has achieved great success in explaining neural computational mechanisms. However, its underlying ‘Free Energy Principle’ faces the dilemma of an ‘ontological explanatory deficit’ and the ‘Dark Room Problem’ due to its excessive universality. Meanwhile, the debate between cognitivist predictive processing and free-energy enactivism also highlights the theoretical rift when integrating the embodied dimension. This paper introduces the ‘cognitive compensation’ theory and the RID model (Regularization R, Information/Integration I, Distress/Demand D) from Knowing and Saying, attempting to provide a solid ontological foundation for predictive processing. By anchoring prediction error to distress/demand, hierarchical generative models to structure generation, and precision weighting to regularization, this paper argues that predictive processing is essentially a higher-order compensatory activity of finite beings coping with the pressure of decreasing degree of existence. Within this framework, the brain’s internal representations are no longer mirrors of the objective world, but ‘Compensatory Schemas’ actively constructed by finite beings to suppress distress/demand–this concept is the core critique and transcendence of traditional cognitivist representationalism. Furthermore, this paper takes schizophrenia (abnormal precision weighting) and autism (sensory hypersensitivity) as test fields of ‘compensatory failure’ to verify the theoretical efficacy of this integrative framework. The introduction of this ontological perspective not only provides a fundamental answer to the ‘Dark Room Problem’, but also offers a theoretical way out for the long-standing debate between cognitivism and embodied cognition.