Temporal Interception and Present Reconstruction: A Cognitive-Signal Model for Human and AI Decision Making

TL;DR

This paper introduces a new cognitive-signal model for understanding how humans and AI can achieve real-time awareness by minimizing perceptual delays through physical, neurological, and computational approaches.

Contribution

It presents a novel theoretical framework combining physical and cognitive models to explain and enhance real-time perception in humans and AI systems.

Findings

Proposes a physical and cognitive model for perceiving the present as an interference zone.

Suggests experimental methods using neural observation to test the model.

Introduces a mathematical framework for developing temporally efficient AI systems.

Abstract

This paper proposes a novel theoretical model to explain how the human mind and artificial intelligence can approach real-time awareness by reducing perceptual delays. By investigating cosmic signal delay, neurological reaction times, and the ancient cognitive state of stillness, we explore how one may shift from reactive perception to a conscious interface with the near future. This paper introduces both a physical and cognitive model for perceiving the present not as a linear timestamp, but as an interference zone where early-arriving cosmic signals and reactive human delays intersect. We propose experimental approaches to test these ideas using human neural observation and neuro-receptive extensions. Finally, we propose a mathematical framework to guide the evolution of AI systems toward temporally efficient, ethically sound, and internally conscious decision-making processes