The Observer Problem and Artificial Neural Networks: Towards a Transdisciplinary Observer Concept
Bibliographic record
Abstract
This article offers a radical reconceptualization of the observer problem within the context of natural science, philosophy, and artificial intelligence. The analysis traces the concept’s evolution from its origins in quantum mechanics (Heisenberg, Pauli) through the ontological approach of Mamardashvili, the phenomenology of Merleau-Ponty, and Luhmann’s systems theory, which defines observation as the fundamental operation of making distinctions. Building on this foundation, the article transitions to a process ontology. Prigogine’s concept of “active matter” shows that under non-equilibrium conditions, matter itself acquires the capacity for self-measurement and distinction, while Rovelli’s relational quantum mechanics defines reality as a network of relations and interactions, rather than a collection of entities. The article’s central argument is a shift from understanding the observer as an isolated entity to its conceptualization as a dynamic, relational, and constitutive process embedded within a “network of observers”. It is argued that observation is a fundamental process of distinction and becoming, inherent in complex systems at various levels: from the physical and biological to the technical and social. Artificial neural networks are examined in a dual role: as a new type of observer that performs operations of distinction without consciousness, and as a conceptual tool for deepening the understanding of the phenomenon of observation itself. This reconceptualization leads to the concept of “transversal cognition” – a transindividual, techno-semiotic intelligence arising from the interaction of heterogeneous observers. Finally, it substantiates the need for a methodological shift towards “thinking with complexity”, which paves the way for the formation of a new, transdisciplinary concept of the observer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".