Consciousness Mapping: A Dynamic Computational Architecture

Abstract

This paper proposes a novel computational architecture for mapping and simulating consciousness, termed the Dynamic Consciousness Mapping (DCM) architecture. The core claim rests on the ability to dynamically analyze and map subjective states of consciousness, enabling a system to both understand and potentially control these states. The proposed mechanism utilizes real-time physiological data, primarily electroencephalography (EEG) and eye-tracking, to construct a dynamic mapping model of consciousness. This model is then integrated into a dynamic computational architecture, allowing for simulation and, ultimately, a more nuanced understanding of the underlying processes. The architecture represents a departure from traditional computational models of cognition, which often treat consciousness as an emergent property rather than a directly addressable phenomenon. The research aims to translate subjective experience into quantifiable signals, offering a potential pathway for bridging the gap between neuroscience and artificial intelligence. The architecture's key innovation lies in its dynamic, adaptive nature, allowing it to respond to and reflect changes in the user's conscious state. This paper outlines the key components of the DCM architecture, focusing on the mapping model, the dynamic computational engine, and potential applications.