Neuromorphic Psychological Robotics: An Expanded Foundational Theory of Artificial Neurons, Embodied Behavior, and Human–Machine Psychology
- 1Scretching Quantum Press LLC
Abstract
This conceptual research article advances Neuromorphic Psychological Robotics (NPR) as a proposed interdisciplinary field integrating artificial neural systems, neuromorphic computing, robotics, and observable psychological interaction models. It introduces a falsifiable theoretical architecture for adaptive learning, behavioral regulation, decision-making, and human–robot interaction. The study examines the potential evolution from artificial general intelligence (AGI) toward hypothetical artificial superintelligence (ASI), identifies emerging research applications, and proposes experimental protocols for evaluating NPR systems. Established peer-reviewed findings are distinguished from original theoretical contributions, with explicit mathematical formulations and testable predictions. The article establishes a conceptual foundation for future NPR research without claiming experimental validation, demonstrated AGI or ASI capabilities, or original human-participant findings.
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BibTeX
@misc{scretching2026neuromorphic,
title = {{Neuromorphic Psychological Robotics: An Expanded Foundational Theory of Artificial Neurons, Embodied Behavior, and Human–Machine Psychology}},
author = {Daniel Scretching},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23477501},
url = {https://zenodo.org/records/23477501},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Neuromorphic Psychological Robotics: An Expanded Foundational Theory of Artificial Neurons, Embodied Behavior, and Human–Machine Psychology
AU - Daniel Scretching
PY - 2026
DA - 2026/10/19
PB - Zenodo
DO - 10.5281/zenodo.23477501
UR - https://zenodo.org/records/23477501
AB - This conceptual research article advances Neuromorphic Psychological Robotics (NPR) as a proposed interdisciplinary field integrating artificial neural systems, neuromorphic computing, robotics, and observable psychological interaction models. It introduces a falsifiable theoretical architecture for adaptive learning, behavioral regulation, decision-making, and human–robot interaction. The study examines the potential evolution from artificial general intelligence (AGI) toward hypothetical artificial superintelligence (ASI), identifies emerging research applications, and proposes experimental protocols for evaluating NPR systems. Established peer-reviewed findings are distinguished from original theoretical contributions, with explicit mathematical formulations and testable predictions. The article establishes a conceptual foundation for future NPR research without claiming experimental validation, demonstrated AGI or ASI capabilities, or original human-participant findings.
ER -