Bibliographic record
Abstract
Cognitive Architectures are used to test theoretical and conceptual frameworks identifying and explaining the underlying components of thought, namely the essential structures, mechanisms, and processes realized through models of human-like information processing.They define and prescribe those boundaries deemed both necessary and sufficient for intelligent agents based on our current understanding of human cognition.The Common Model of Cognition (CMC) attempts to establish a community consensus on theoretical commitments and assumptions built into commonly used architectures, and group the assumptions according to structure and processing, memory, learning, and perceptual interfaces.The CMC remains a verbal-conceptual consensus across broad theories essential for general phenomenon (i.e., a Meta-Model of Cognition), however, lacks a formal specification to support domain-general task model comparisons for evaluation and validation of new theories implemented in new or existing architectures, or specific micro-theoretic implementations as cognitive architecture models and task models.Thus, a lack of a formal model supporting the CMC inhibits exploration of philosophical enquiries iii about core theoretical assumptions, and the adoption of refined peripheral theories across architectures.This thesis presents a formal meta-model specific to the constraints represented at Newell's Cognitive level using the principles of Model-Driven Design (MDD) to encapsulate these entities and relationships across architectures.This formal model can be used as a framework generator, and to specify the abstract interfaces common across Common Model agents, allowing modelers to explore verbal-conceptual theories through experimentation with virtual environments, further supporting a common ground.Frameworks generated through MDD support an empirical evaluation and comparison of variations on the Common Model for the purpose of application to Artificial Intelligence problem domains, lending additional credibility to computational cognitive modeling as a formal discipline, and the Cognitive Science research enterprise as a whole.iv To Lenora, who never stopped believing in me.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 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".