Review of a Heaviside step sequence function and the recursive Heaviside step sequence function for modeling human mental state

TL;DR

This paper introduces a recursive Heaviside step sequence function to model human mental states, capturing cognitive dynamics and evolution over time, with implications for understanding human cognition.

Contribution

It extends the traditional Heaviside function into a recursive sequence framework, providing a novel mathematical model for mental state dynamics and cognitive processes.

Findings

The recursive Heaviside sequence models thought processes and memory dynamics.

It approximates solutions to a multidimensional advection equation.

The model reflects individual variability in mental processing.

Abstract

In this paper, we define a novel recursive Heaviside step sequence function and demonstrate its applicability to modeling human mental states such as thought processes, memory recall, and forgetfulness. By extending the traditional Heaviside step function, which typically represents binary transitions, into a recursive sequence framework, we introduce a dynamic model that better captures the complexities of cognitive states. Furthermore, the recursive Heaviside step sequence function approximates solutions to a multidimensional inviscid advection equation, offering a unique mathematical perspective on the evolution of mental states over time. This continuous model, combined with the recursive delta sequence function, provides a comprehensive approach to exploring how memories and thoughts emerge, evolve, and fade. Through this approach, we propose that mental states can be expressed as time series functions, and the selection of the parameter N reflects individual variability in mental processing, influenced by external environments and internal experiences. We also discuss the implications of this framework for understanding human cognition and potential limitations due to modern technological constraints in replicating such processes.