An Analysis of Internal Representations for Two Artificial Neural Networks that Classify Musical Chords
Bibliographic record
Abstract
Cognitive informatics is a field of research that is primarily concerned with the information processing of intelligent agents; it can be characterised in terms of an evolving notion of information (Wang, 2007). When it originated six decades ago, conventional accounts of information were concerned about using probability theory and statistics to measure the amount of information carried by an external signal. This, in turn, developed into the notion of modern informatics which studied information as “properties or attributes of the natural world that can be generally abstracted, quantitatively represented, and mentally processed” (Wang, 2007, p. iii). The current incarnation of cognitive informatics recognised that both information theory and modern informatics defined information in terms of factors that were external to brains, and has replaced this with an emphasis on exploring information as an internal property. This emphasis on the internal processing of information raises fundamental questions about how such information can be represented. One approach to answering such questions — and for proposing new representational accounts — would be to train a brain-like system to perform an intelligent task, and then to analyse its internal structure to determine the types of representations that the system had developed to perform this intelligent behaviour. The logic behind this approach is that when artificial neural networks Cognitive informatics is a field of research that is primarily concerned with the information processing of intelligent agents; it can be characterised in terms of an evolving notion of information (Wang, 2007). When it originated six decades ago, conventional accounts of information were concerned about using probability theory and statistics to measure the amount of information carried by an external signal. This, in turn, developed into the notion of modern informatics which studied information as “properties or attributes of the natural world that can be generally abstracted, quantitatively represented, and mentally processed” (Wang, 2007, p. iii). The current incarnation of cognitive informatics recognised that both information theory and modern informatics defined information in terms of factors that were external to brains, and has replaced this with an emphasis on exploring information as an internal property. This emphasis on the internal processing of information raises fundamental questions about how such information can be represented. One approach to answering such questions — and for proposing new representational accounts — would be to train a brain-like system to perform an intelligent task, and then to analyse its internal structure to determine the types of representations that the system had developed to perform this intelligent behaviour. The logic behind this approach is that when artificial neural networks
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".