Review of a Heaviside step sequence function and the recursive Heaviside step sequence function for modeling human mental state
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915012072701952 |
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| author | Shin, Changsoo |
| author_facet | Shin, Changsoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Review of a Heaviside step sequence function and the recursive Heaviside step sequence function for modeling human mental state Shin, Changsoo Neurons and Cognition math archive only A.m 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. |
| title | Review of a Heaviside step sequence function and the recursive Heaviside step sequence function for modeling human mental state |
| topic | Neurons and Cognition math archive only A.m |
| url | https://arxiv.org/abs/2411.05800 |