The Degree of Neural Awareness: A Generalized Mathematical Framework and Synthetic Computational Proof of Concept for Cross-Species Comparison of Consciousness-Related Neurobiological Parameters

This paper provides an exploratory analysis of the Degree of Neural Awareness (DoNA) framework and extends its multiplicative formulation into a weighted-geometric aggregation method with power-law weighting. The DoNA framework is a generalized mathematical approach for integrating multiple quantifiable neurobiological parameters in cross-species comparisons. It is not intended to be a direct measurement of subjective experience or consciousness. The framework uses human-normalized, dimensionless parameter ratios and a weighted-geometric formulation to generate comparative model outputs relative to the Homo sapiens reference profile and its constituent parameter conditions. The framework is presented as a preliminary model for examining cross-species variation in neurobiological characteristics and similarities in the neurobiological structuring between a target species and the reference species of Homo sapiens. This exploratory comparison of neurobiological systems and architectures may be relevant to consciousness-related processing. The resulting DoNA value represents the comparative relationship produced by the selected neurobiological parameters and the mathematical aggregation method utilized. The mathematical operation and internal coherence of the equation’s formulation and configuration were examined through a proof-of-concept utilizing a preliminary three-parameter input condition applied to two synthetic profiles. The computational proof of concept evaluated 1,048,576 scenarios generated by using a scrambled low-discrepancy Sobol sequences, providing coverage across dominance, single-parameter-advantage, two-against-one trade-off, and one-against-two trade-off comparison families. The framework’s sensitivity and robustness were also analysed through parameter ablation, alternative mathematical aggregation formulations, input-domain width, sampling measure, and numerical convergence. The observed ordering was mathematically expected and preserved throughout the dominance and single-parameter-advantage conditions, whereas the corresponding ordering within the trade-off families varied according to the input magnitudes and assigned weights. The results demonstrate the framework’s mathematical operation and internal coherence under the explicitly defined computational conditions. However, this does not establish its biological, empirical, or construct validity. This paper analyses the framework, the utilized weighted-geometric aggregation method, results of the mathematical proof of concept, and the sensitivity of the framework in further detail, alongside its possible limitations.