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Record W2079853971 · doi:10.1037/h0087369

Encoding specificity revisited: The role of semantics.

2001· article· en· W2079853971 on OpenAlexaff
Brenda Hannon, Fergus I. M. Craik

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2001
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEncoding (memory)Encoding specificity principleRecallContext-dependent memoryPsychologySemantics (computer science)Congruence (geometry)Cue-dependent forgettingWord (group theory)Cognitive psychologyNatural language processingCommunicationFree recallComputer scienceLinguisticsSocial psychology

Abstract

fetched live from OpenAlex

Three experiments examined the effects of semantic characteristics of word pairs on memory using the encoding specificity paradigm. The paradigm involved four phases: (a) an encoding phase to relate cues and targets, (b) a phase in which words were generated to new cues, (c) a phase for recognition of generated targets, and (d) a cued-recall phase using the original encoding cues. Encoding pairs were classified a priori as either semantically similar (e.g., alluring-PRETTY), semantically contrasting (e.g., drab-PRETTY), or semantically unrelated (e.g., sore-PRETTY). Generation pairs were classified a priori as either semantically similar (e.g., beautiful-PRETTY) or semantically contrasting (e.g., ugly-PRETTY). For recall, the results showed that both the semantic relations between the encoding cue and target and the reprovision of the encoding cue at retrieval were important factors. In the case of recognition, however, both the semantic congruence between the encoding and generation contexts and the amount of semantic elaboration provided by the encoding context were important factors.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.320
Teacher spread0.259 · 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 teacher head, not a consensus.

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

Citations24
Published2001
Admission routes1
Has abstractyes

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