Temporal Interception and Present Reconstruction: A Cognitive-Signal Model for Human and AI Decision Making
- URL: http://arxiv.org/abs/2505.09646v1
- Date: Sun, 11 May 2025 15:38:27 GMT
- Title: Temporal Interception and Present Reconstruction: A Cognitive-Signal Model for Human and AI Decision Making
- Authors: Carmel Mary Esther A,
- Abstract summary: This paper proposes a novel theoretical model to explain how the human mind and artificial intelligence can approach real-time awareness.<n>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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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
Related papers
- Active Inference AI Systems for Scientific Discovery [1.450405446885067]
This perspective contends that progress turns on closing three mutually reinforcing gaps in abstraction, reasoning and empirical grounding.<n>Design principles are proposed for systems that reason in imaginary spaces and learn from the world.
arXiv Detail & Related papers (2025-06-26T14:43:04Z) - Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [58.58177409853298]
Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
arXiv Detail & Related papers (2025-05-12T15:05:34Z) - The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI [4.508868068781058]
Drawing on neuroscience and cognitive psychology, this paper examines how heavy reliance on AI systems may impair the consolidation of declarative and procedural memory.<n>We highlight striking parallels between deep learning phenomena such as "grokking" and the neuroscience of overlearning and intuition.<n>The paper concludes with policy implications for education and workforce training in the age of large language models.
arXiv Detail & Related papers (2025-05-03T03:41:33Z) - System 0/1/2/3: Quad-process theory for multi-timescale embodied collective cognitive systems [12.195073658696618]
This paper introduces the System 0/1/2/3 framework as an extension of dual-process theory, employing a quad-process model of cognition.<n>We contextualize this model within Bergson's philosophy by adopting multi-scale time theory to unify the diverse temporal dynamics of cognition.
arXiv Detail & Related papers (2025-03-08T09:31:53Z) - A theory of neural emulators [0.0]
A central goal in neuroscience is to provide explanations for how animal nervous systems can generate actions and cognitive states such as consciousness.
We propose emulator theory (ET) and neural emulators as circuit- and scale-independent predictive models of biological brain activity.
arXiv Detail & Related papers (2024-05-22T07:12:03Z) - Neuromorphic Correlates of Artificial Consciousness [1.4957306171002251]
The concept of neural correlates of consciousness (NCC) suggests that specific neural activities are linked to conscious experiences.
This paper explores the potential for artificial consciousness by merging neuromorphic design and architecture with brain simulations.
arXiv Detail & Related papers (2024-05-03T09:27:51Z) - Hebbian Learning based Orthogonal Projection for Continual Learning of
Spiking Neural Networks [74.3099028063756]
We develop a new method with neuronal operations based on lateral connections and Hebbian learning.
We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities.
Our method consistently solves for spiking neural networks with nearly zero forgetting.
arXiv Detail & Related papers (2024-02-19T09:29:37Z) - Brain-Inspired Machine Intelligence: A Survey of
Neurobiologically-Plausible Credit Assignment [65.268245109828]
We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology.
We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors.
The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
arXiv Detail & Related papers (2023-12-01T05:20:57Z) - A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian
Learning and Free Energy Minimization [55.11642177631929]
Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images.
We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
arXiv Detail & Related papers (2023-10-14T23:28:48Z) - A Neural Active Inference Model of Perceptual-Motor Learning [62.39667564455059]
The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience.
In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans.
We present a novel formulation of the prior function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy.
arXiv Detail & Related papers (2022-11-16T20:00:38Z) - A-ACT: Action Anticipation through Cycle Transformations [89.83027919085289]
We take a step back to analyze how the human capability to anticipate the future can be transferred to machine learning algorithms.
A recent study on human psychology explains that, in anticipating an occurrence, the human brain counts on both systems.
In this work, we study the impact of each system for the task of action anticipation and introduce a paradigm to integrate them in a learning framework.
arXiv Detail & Related papers (2022-04-02T21:50:45Z) - Time Perception: A Review on Psychological, Computational and Robotic
Models [2.223733768286313]
We introduce a brief background from the psychology and neuroscience literature, covering the characteristics and models of time perception.
We summarize the emergent computational and robotic models of time perception.
Most models of timing are developed for either sensory timing (i.e. ability to assess an interval) or motor timing (i.e. ability to reproduce an interval)
arXiv Detail & Related papers (2020-07-23T08:16:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.