Zenodo (CERN European Organization for Nuclear Research)
September 7, 2026
Jincheng Zhang
preprint
This paper proposes a novel approach to understanding consciousness by constructing an ontological framework – a first-person "ontology" – derived from simulating the information processing mechanisms within biological neural systems. The core claim is that consciousness, fundamentally, is an emergent property of complex information processing. We advocate for a bottom-up approach, utilizing...
Zenodo (CERN European Organization for Nuclear Research)
September 7, 2026
Jincheng Zhang
preprint
This paper presents a formal model of consciousness, aiming to bridge the gap between theoretical neuroscience and rigorous mathematical analysis. The model leverages the principles of Integrated Information Theory (IIT) to quantify the degree of integrated information (Φ) within a system, and employs Bayesian inference to dynamically update beliefs based on sensory input. The core of the model...
Zenodo (CERN European Organization for Nuclear Research)
September 6, 2026
Jincheng Zhang
preprint
This paper explores the potential of simulating embodied cognition through the construction of simulated sensorimotor loops. The core claim is that creating more realistic artificial intelligence agents requires modeling the embodied experience of the agent, specifically by replicating the feedback mechanisms inherent in human sensorimotor processes. The proposed mechanism involves an agent...
Zenodo (CERN European Organization for Nuclear Research)
September 6, 2026
Jincheng Zhang
preprint
This paper proposes a novel formal model of consciousness grounded in the principles of Integrated Information Theory (IIT) and graph representation. The core idea is to represent the brain as a complex graph, where nodes correspond to neurons and edges denote synaptic connections. Utilizing IIT, we quantitatively assess the integrated information within this graph, aiming to correlate this...
Zenodo (CERN European Organization for Nuclear Research)
September 5, 2026
Jincheng Zhang
preprint
This paper proposes a computational model designed to simulate the emergent phenomenon of consciousness. The core of the model lies in the construction of a complex network, governed by nonlinear dynamics and exhibiting self-organization, feedback mechanisms, and information integration. The model utilizes a massively parallel computing architecture to explore the potential for subjective...
Zenodo (CERN European Organization for Nuclear Research)
September 5, 2026
Jincheng Zhang
preprint
This paper presents a novel formal model of consciousness, integrating the principles of Integrated Information Theory (IIT) with Bayesian Networks. The core challenge in understanding consciousness lies in its lack of a rigorous, quantifiable mathematical representation. This model addresses this deficiency by utilizing IIT's concept of integrated information (Φ) – representing the amount of...
Zenodo (CERN European Organization for Nuclear Research)
September 1, 2026
Jincheng Zhang
preprint
This paper proposes a novel framework for assessing the value of Artificial Intelligence (AI) systems, grounded in theories of consciousness. Traditional AI value assessment methods often rely on utilitarian or deontological approaches, which can be insufficient in capturing the nuanced ethical considerations surrounding advanced AI. This framework, termed the "AI Value Assessment Framework...
Zenodo (CERN European Organization for Nuclear Research)
August 31, 2026
Jincheng Zhang
preprint
This paper proposes a formal mathematical model of consciousness based on Giulio Tononi's Integrated Information Theory (IIT) and graph theory. The core idea is that consciousness can be understood through a quantifiable measure derived from the structure and complexity of an information network. We represent consciousness as a graph where nodes represent individual cognitive elements and edges...
Zenodo (CERN European Organization for Nuclear Research)
August 31, 2026
Jincheng Zhang
preprint
This paper investigates the potential for mapping human consciousness states through multi-scale time series prediction. The core idea is to analyze the inherent structure within time series data, specifically focusing on the relationships between different temporal scales, and establish a predictive model that correlates with subjective reports or physiological measures of consciousness. We...
Zenodo (CERN European Organization for Nuclear Research)
August 28, 2026
Jincheng Zhang
preprint
This paper investigates the potential of nonlinear time-delay networks (NTDNs) in simulating aspects of human consciousness. The core claim is that by constructing networks with intricate nonlinear time-delay dependencies, it may be possible to model fundamental features of consciousness, such as subjective experience and contextual awareness. The proposed mechanism involves designing an NTDN...