The cognitive reality monitoring network and theories of consciousness

• Published in: Neuroscience research 2024-04, Vol.201, p.31-38
• Main Authors: Cortese, Aurelio, Kawato, Mitsuo
• Format: Article
• Language: English
• Subjects: Cognitive reality monitoring network; Computational theory of consciousness; Marr’s levels; Metacognition; Representations
• ISSN: 0168-0102; 1872-8111
• DOI: 10.1016/j.neures.2024.01.007

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. •Marr’s three levels of understanding can help the study of consciousness.•We define the prerequisites of consciousness within the CRMN framework.•The CRMN seeks to explain consciousness and metacognition through computational theory.•We discuss the neural architecture and computations of the CRMN.•We propose new experiments to test assumptions and predictions of the CRMN.