Why the Common Model of the mind needs holographic a-priori categories
Bibliographic record
Abstract
The enterprise of developing a common model of the mind aims to create a foundational architecture for rational behavior in humans. Philosopher Immanuel Kant attempted something similar in 1781. The principles laid out by Kant for pursuing this goal can shed important light on the common model project. Unfortunately, Kant’s program has become hopelessly mired in philosophical hair-splitting. In this paper, we first use Kant’s approach to isolate the founding conditions of rationality in humans. His philosophy lends support to Newell’s knowledge level hypothesis, and together with it directs the common model enterprise to take knowledge, and not just memory, seriously as a component of the common model of the mind. We then map Kant’s cognitive mechanics to the operations which are used in the current models of cognitive architecture. Finally, we argue that this mapping can pave the way to develop the ontology of the knowledge level for general intelligence. We further show how they can be actualized in a memory system using high dimensional vectors to achieve specific cognitive abilities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".