Temporal Model On Quantum Logic

TL;DR

This work addresses how to model temporal memory dynamics by unifying temporal logic with memory decay and hierarchical contexts. It develops a framework that handles linear and branching time, explicit realization and decay states, reactivation via contextual triggers, and Bayesian updates, all within nested memory contexts. The approach introduces propagation of recall across hierarchies, entropy-based organization, and recursive feedback to capture interference, entanglement, and resilience of memories. The resulting model provides a principled foundation for understanding memory processes across cognitive and computational domains and offers a formal basis for analyzing recall dynamics in complex temporal and contextual settings.

Abstract

This paper introduces a unified theoretical framework for modeling temporal memory dynamics, combining concepts from temporal logic, memory decay models, and hierarchical contexts. The framework formalizes the evolution of propositions over time using linear and branching temporal models, incorporating exponential decay (Ebbinghaus forgetting curve) and reactivation mechanisms via Bayesian updating. The hierarchical organization of memory is represented using directed acyclic graphs to model recall dependencies and interference. Novel insights include feedback dynamics, recursive influences in memory chains, and the integration of entropy-based recall efficiency. This approach provides a foundation for understanding memory processes across cognitive and computational domains.