Irreversibility of recursive Heaviside memory functions: a distributional perspective on structural cognition
1Seoul National University, Seoul, Republic of Korea.
Cognitive Neurodynamics
|December 1, 2025
Summary
This study introduces a novel framework using smoothed step functions to model recursive mental activity. It explains how cognitive layers develop over time, influenced by emotional salience, for AI consciousness.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Computational Neuroscience
Background:
- Current AI lacks the temporal, self-referential depth of human cognition.
- Existing models struggle to capture the dynamic evolution of thought processes.
Purpose of the Study:
- To present a mathematically grounded framework for modeling recursive mental activity.
- To simulate layered cognition, temporal attention, and adaptive memory in AI.
Main Methods:
- Utilized smoothed step functions (sigmoid approximations of Heaviside functions).
- Introduced an 'impressiveness parameter' to modulate cognitive layer activation.
- Formulated recursive dynamics for cognitive layers.
Main Results:
- Demonstrated the emergence of layered cognition and time-based attention.
- Showcased adaptive memory reinforcement through a recursive, impressiveness-sensitive pathway.
- Developed context-dependent memory traces.
Conclusions:
- The framework offers a new perspective on the temporal evolution of awareness and memory.
- Provides a foundation for AI systems simulating temporally grounded consciousness.
- Highlights the role of emotional salience in cognitive processing.
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