Hemispheric disparity theory: a dual-system framework for consciousness.

Conscious experience arises from the dynamic interplay between two structurally distinct cerebral hemispheres. The left hemisphere specializes in order and abstraction through dynamic temporal modeling, while the right hemisphere handles contextual integration, coherence, and model updating via attentional networks. The article presents a three-part model: how unified conscious experience emerges from these hemispheres, how information processing becomes asymmetric through structural arrangements in cortical hierarchies, and how this disparate system generates cumulative concept development unique to the conscious mind. The model draws on neuropsychological and neuroimaging evidence and has implications for clinical, philosophical, and artificial learning systems.

Study at a glance

Abstract

The Hemispheric Disparity Theory conceptualizes one facet of consciousness, defined as the "conscious-mind" experience. Rather than a unified agent, conscious experience arises from the dynamic interplay between a left hemisphere specialized in order and abstraction through dynamic temporal modeling, and a right hemisphere specialized in contextual integration, coherence, and model updating through attentional networks. Drawing on evidence from diverse classical neuropsychological and modern neuroimaging studies, the hemispheric disparity model proposes, in Part I, how the unified conscious experience emerges from two structurally distinct cerebral hemispheres. In Part II, a demonstration of how information processing progresses to a remarkable form of asymmetry through a special structural arrangement that begins at the lower levels of cortical hierarchies. Finally, in Part III, the article proposes how the disparate hemispheric system operates to generate cumulative concept development unique to the conscious-mind. Simple mathematical concepts are used to illustrate this. This model offers potential for further development on multiple levels, including clinical, philosophical, and modern artificial learning systems.