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Record W3044692559 · doi:10.31234/osf.io/9umav_v1

Holographic Declarative Memory and the Fan Effect: A Test Case for A New Memory Module for ACT-R

2025· preprint· en· W3044692559 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsCarleton University
Fundersnot available
KeywordsDeclarative memoryMemory testComputer scienceTest (biology)Cognitive psychologyLong-term memoryCognitive scienceProgramming languagePsychologyArtificial intelligenceGeologyCognitionNeurosciencePaleontology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.277
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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