Information, representation, and cognition
Bibliographic record
Abstract
This article discusses the concept of information in the context of its application in theories of cognition mainly those based on the idea of consciousness as information processing. The concept of Natural Information is presented as a complement to the classic Shannon’s information model by shifting the focus of the information concept from the message to the entire communication system. This model proposes that: (1) Information is not something itself but it is always about something; (2) Information is not an object but a relationship; (3) Information is an emergent property of interfaces; (4) Information is the subset of elements of a given instance connected, related, caused, or paired with elements of another instance; (5) Information is present in the universe at all organizational levels including mental states; (6) Information is physically made of the same substance as the instance that acquires the information. We introduce the concepts of codable and non-codable elements of information to account for the emergence of meaning and qualia. The contribution of these concepts to the discussions about the emergence of meaning and the structure of self are discussed. We propose this model as a road map to describe information processing in mental processes locating classic ideas and old problems in the context of new concepts. Our model is still a work in progress aiming to contribute to the understanding of the role of information in computational, psychological, and social contexts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".