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Record W1996606535 · doi:10.1037/0278-7393.32.6.1244

Linking associative and serial list memory: Pairs versus triples.

2006· article· en· W1996606535 on OpenAlexafffund
Jeremy B. Caplan, Mackenzie G. Glaholt, Anthony R. McIntosh

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2006
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Text Analysis Techniques
Canadian institutionsBaycrest Hospital
FundersCanadian Institutes of Health Research
KeywordsDissociation (chemistry)Computer scienceAssociative propertyContent-addressable memoryIsolation (microbiology)ArithmeticArtificial intelligenceMathematicsBiologyPure mathematicsChemistryArtificial neural network

Abstract

fetched live from OpenAlex

Paired associates and serial list memory are typically investigated separately. An "isolation principle" (J. B. Caplan, 2005) was proposed to explain behavior in both paradigms by using a single model, in which serial list and paired associates memory differ only in how isolated pairs of items are from interference from other studied items. In the present study, 2 experiments identify a critical dissociation between the 2 paradigms, challenging this unified account. Specifically, forward and backward probes were highly correlated for pairs and less so for short lists (triples). The authors asked whether the isolation principle could quantitatively accommodate this type of dissociation. A simulation confirmed that a single model incorporating the isolation principle can adequately explain this and other dissociations, supporting the common processes view.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.007
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.022
GPT teacher head0.326
Teacher spread0.304 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2006
Admission routes2
Has abstractyes

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