Consciousness As Entropy Reduction (Short Version)
Yifeng Chen, J. W. Sanders
TL;DR
Consciousness is framed as the outcome of entropy reduction over distributions of subconscious content across multiple channels, formalized in the Consciousness as Entropy Reduction (CER) model. The approach defines a S2C (subconsciousness-to-consciousness) interface and employs weighted general entropy and refusal entropy to drive gradient-descent dynamics toward a 0-entropic conscious scenario, connecting ideas from GWT, IIT, and Hopfield networks. Primitive and higher-order CER extend this core mechanism with feedback loops (C2S) that enable imagination, planning, and the emergence of self-referential content, effectively simulating thought experiments and dream-like cognition. The framework is falsifiable through its mathematical structure and offers a computational playground to test psychological hypotheses about consciousness, attention, planning, and dream phenomena, while acknowledging boundaries imposed by biological definitions and energy costs.
Abstract
A model of consciousness is proposed which, having a logical basis, lends itself to simulation using a simple mathematical model called Consciousness as Entropy Reduction (CER). The approach has been inspired by previous models such as GWT, IIT and an earlier less mainstream model called "Feature Map" in Psychology. CER considers the contents of consciousness and subconsciousness as \textit{scenarios}: a vector of patterns (or features) on various "channels" (or feature locations). In CER, a feature map itself is not consciousness but only the input \textit{scenario} into a world of possible subconscious \textit{scenarios} from which the conscious \textit{scenario} (i.e., conscious experience) is chosen. Essentially, it creates an internal simulation of the outside world. Solving problems in simulation internally as a "thought experiment" is obviously more economical than doing experiments in a real environment and lends itself to adaptability and hence is a major evolutionary advantage. CER also has connections with the Hopfield model in artificial neural networks.