The Universal & Atomic Elements of Organization
A falsifiable scientific theory proposing that the same irreducible structures organize both thought and reality — from the quantum to the social scale.
O-Theory proposes that four irreducible structures — Distinctions, Systems, Relationships and Perspectives (DSRP) — form the universal grammar of organization. Rather than being merely useful ways of thinking, these structures are hypothesized to be the atomic elements from which every organized phenomenon emerges, whether in cognition, biology, physics, mathematics, society, or the cosmos.
| D | S | R | P |
| Distinctions | Systems | Relationships | Perspectives |
| Identity ↔ Other | Part ↔ Whole | Action ↔ Reaction | Point ↔ View |
| D := (i ↔ o) | S := (p ↔ w) | R := (a ↔ r) | P := (ṗ ↔ v) |
This is a living scientific evidence compendium: an open, continually evolving collection of independent empirical research, formal theory, mathematical proofs, cross-disciplinary analyses, applications, critiques, and proposed falsifications. Every entry is included because it supports, refines, challenges, or attempts to falsify the theory.
Scientific theories are strengthened not only by evidence that confirms their predictions, but also by surviving attempts to falsify them. This compendium brings both together: independent evidence from researchers who were not testing DSRP and proposed counterexamples evaluated against the formal theory.
One counterexample is enough to falsify O-Theory. Until then, the question remains: do the same four structures organize everything from quantum systems to human thought?
Independent convergence is one of the strongest forms of scientific evidence because researchers arrive at the same conclusion while investigating different questions for different reasons.
The number is not the point. Researchers in different fields, studying different questions with different methods, repeatedly arrived at the same structural predictions—almost always without testing DSRP or using its language.
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This compendium began as the peer-reviewed literature review “A Literature Review of the Universal and Atomic Elements of Complex Cognition,” published in the Journal of Systems Thinking with 109 studies. That paper is the peer-reviewed foundation. What you see here is its living, continuously updated version . New studies are checked before they are added, and the collection now holds and keeps growing. Open any card to see what the researchers found, why it bears on DSRP, and where the original review discusses it, the fuller account.
The collection is updated continuously, so the citation carries the date you consulted it rather than a study count — the count changes weekly, and putting it in the reference would make the same collection look like a different work to everyone who cites it. To cite a single claim or study, use its own address: every one has a permanent link.
This is the adversarial half of the compendium. Where the evidence track asks what converges on DSRP, this one asks what would end it: a single organized phenomenon whose structure needs a fifth pattern, a ninth element, or a fifth structural dynamic. It holds written up from candidates across territories, and resolutions — the general answers those cases settle against. Every case is published whether it held or failed, including the ones still open.
Cite this rather than the evidence collection when the point is what survived attack. The two are separate works with separate addresses: one asks what converges on the theory, the other asks what would end it, and a reference to the first does not support a claim about the second. Case and resolution numbers change when the record is revised, so cite a case by its own permanent link rather than by number.
This collection is meant to keep growing. Send us a study, paper, book or critique that bears on DSRP, whether it supports the theory or cuts against it, and we will read it and decide whether it belongs.
Authors Jacopo de Berardinis, Michalis Vamvakaris, Angelo Cangelosi, Eduardo Coutinho
Year 2020
Publisher Transactions of the International Society for Music Information Retrieval
Kind of work article
Discipline Computer Science & AI
Secondary disciplines Music Theory
Applied false
Read it at the publisher 10.5334/tismir.41
Human perception of musical structure is supposed to depend on the generation of hierarchies, which is inherently related to the actual organisation of sounds in music. Musical structures are indeed best retained by listeners when they form hierarchical patterns, with consequent implications on the appreciation of music and its performance. The automatic detection of musical structure in audio recordings is one of the most challenging problems in the field of music information retrieval, since even human experts tend to disagree on the structural decomposition of a piece of music. However, most of the current music segmentation algorithms in literature can only produce flat segmentations, meaning that they cannot segment music at different levels in order to reveal its hierarchical structure. We propose a novel methodology for the hierarchical analysis of music structure that is based on graph theory and multi-resolution community detection. This unsupervised method can perform both the tasks of boundary detection and structural grouping, without the need of particular constraints that would limit the resulting segmentation. To evaluate our approach, we designed an experiment that allowed us to compare its segmentation performance with that of the current state of the art algorithms for hierarchical segmentation. Our results indicate that the proposed methodology can achieve state of the art performance on a well-known benchmark dataset, thus providing a deeper analysis of musical structure.
To develop and validate an unsupervised method that reveals music's nested, multi-level hierarchical structure from audio, rather than producing only a single flat segmentation.
A well-known benchmark dataset for music structure analysis, used for computational evaluation rather than a human-subjects study.
A graph-theoretic, multi-resolution community-detection algorithm performing simultaneous boundary detection and structural grouping, benchmarked against existing hierarchical and flat segmentation algorithms.
An unsupervised graph-based multi-resolution community-detection algorithm recovered music's nested multi-level structure and achieved state-of-the-art performance against existing hierarchical segmentation algorithms on a standard benchmark, whereas most prior algorithms produced only flat, single-level segmentations.
Music is organized as a nested part-whole hierarchy across multiple levels, and that hierarchy can only be recovered by first identifying the boundaries between structural segments.
Patterns it shows D, S
Added 2026-09-22
How to cite this Jacopo de Berardinis, Michalis Vamvakaris, Angelo Cangelosi, Eduardo Coutinho (2020). Unveiling the Hierarchical Structure of Music by Multi-Resolution Community Detection. Transactions of the International Society for Music Information Retrieval.