The Mind, Experience, Observation, and Reality: A Framework and Technique for Fundamental Experience of Rūparealm1

This paper introduces a novel framework for understanding the fundamental experience of rūparealm1 through the individual mind2 governing causation. We present a sensor-based model that captures the inseparable connection between the mind and rūparealm. The framework analyses fundamental qualities o...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 3027; no. 1; pp. 012012 - 12032
Main Author: Samarawickrama, Mahendra
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.06.2025
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ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:This paper introduces a novel framework for understanding the fundamental experience of rūparealm1 through the individual mind2 governing causation. We present a sensor-based model that captures the inseparable connection between the mind and rūparealm. The framework analyses fundamental qualities of rūparealm detectable through the sensory organs and mind, such as stiffness, viscosity, compressibility, temperature, colour, smell, taste, reactivity, space, sound, and a few more, totalling 28. Specific eight of these qualities collectively form the Suddhāṭṭhaka3 which represents the common fundamental experience of all forms of rūparealm. We emphasise that all experiences are mind-made, and secondary constructs, including mathematics and physics, arise from the analytical processing of these fundamental experiences. Furthermore, we propose Buddhist meditation as a tool to explore and train the mind to be aware of fundamental experiences before undergoing cognitive processes. The paper discusses contemporary research unifying relativity, quantum mechanics, and consciousness, positing that consciousness is the governance process of universal laws. The discussion emphasises the role of the mind in constructing the perceived reality and advocates the importance of incorporating the mind into fundamental physics when modelling causation.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/3027/1/012012