Holographic Declarative Memory and the Fan Effect: A Test Case for A New Memory Module for ACT-R
Bibliographic record
Abstract
We present Holographic Declarative Memory (HDM), a newmemory module for ACT-R and alternative to ACT-R’sDeclarative Memory (DM). ACT-R is a widely used cognitivearchitecture that models many different aspects of cognition,but is limited by its use of words as symbols to representideas or stimuli. HDM replaces the symbols with holographicvectors. Holographic vectors retain the expressive power ofsymbols but have a similarity metric, allowing for shades ofmeaning, fault tolerance, and lossy compression. The purposeof HDM is to enhance ACT-R’s ability to learn associations,learn over the long-term and store large quantities of data, anduse partial or fuzzy matching. To demonstrate HDM, we fitperformance of an ACT-R model that uses HDM to abenchmark memory task, the fan effect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".