The cognitive reality monitoring network and theories of consciousness.

The cognitive reality monitoring network (CRMN) model offers a framework for consciousness that addresses all three of David Marr's levels of understanding—computational theory, algorithms, and hardware—unlike most major theories, which target only one or two levels. The CRMN integrates mixture-of-experts architecture, hierarchical reinforcement learning, and generative/inference computing modules. A key feature is mapping a gating network onto the prefrontal cortex, which monitors the accuracy of mental states and distinguishes them from external reality. By building on the cerebral cortex's hierarchical structure, the model may connect research across species and generate testable hypotheses about consciousness.

Study at a glance

Abstract

Theories of consciousness abound. However, it is difficult to arbitrate reliably among competing theories because they target different levels of neural and cognitive processing or anatomical loci, and only some were developed with computational models in mind. In particular, theories of consciousness need to fully address the three levels of understanding of the brain proposed by David Marr: computational theory, algorithms and hardware. Most major theories refer to only one or two levels, often indirectly. The cognitive reality monitoring network (CRMN) model is derived from computational theories of mixture-of-experts architecture, hierarchical reinforcement learning and generative/inference computing modules, addressing all three levels of understanding. A central feature of the CRMN is the mapping of a gating network onto the prefrontal cortex, making it a prime coding circuit involved in monitoring the accuracy of one's mental states and distinguishing them from external reality. Because the CRMN builds on the hierarchical and layer structure of the cerebral cortex, it may connect research and findings across species, further enabling concrete computational models of consciousness with new, explicitly testable hypotheses. In sum, we discuss how the CRMN model can help further our understanding of the nature and function of consciousness.