Constructing Causal Knowledge Representation with the Common Model of Cognition
Bibliographic record
Abstract
The field of causal learning was focused on associative learning models (Anderson, 1990; Anderson & Sheu, 1995) until the turn of the century when structural knowledge representation in cognitive architectures like ACT-R (Anderson & Lebiere, 1998; Glymour, 1999; Shoppek, 2001) allowed for a new approach to represent causal reasoning. By implementing relation types analogous to a do-operator (Pearl, 2010, 2013, 2021) and structuring symbolic representation of knowledge in the Python ACT-R declarative memory module according to the Ladder of Causation (Pearl & Mackenzie, 2018), I constructed five cognitive models that demonstrate counterfactual causal reasoning on a bomb diffusion task. SGOMS (West & Nagy, 2007) and K-HDM (Arora, West, Brook, & Kelly, 2018) were used to facilitate declarative memory structure in each model. By implementing causal reasoning in this way, I demonstrate how proper declarative representation of intervention can be used to perform counterfactual reasoning on causal outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".