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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".