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Record W2035642456 · doi:10.1037/a0029523

The longer we have to forget the more we remember: The ironic effect of postcue duration in item-based directed forgetting.

2012· article· en· W2035642456 on OpenAlexaff
Tyler D. Bancroft, William E. Hockley, Riley Farquhar

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMotivated forgettingForgettingDuration (music)PsychologyRetrieval-induced forgettingCognitive psychology

Abstract

fetched live from OpenAlex

The effects of the duration of remember and forget cues were examined to test the differential rehearsal account of item-based directed forgetting. In Experiments 1 and 2, cues were shown for 300, 600, or 900 ms, and a directed forgetting effect (better recognition of remember than forget items) was found at each duration. In addition, recognition of both remember and forget items increased with cue duration. These 2 effects did not interact. The results of Experiment 2 further showed that memory for the cue associated with the study items increased with cue duration as well. The results of Experiment 1 were replicated in Experiment 3 for cue durations of 1, 2, and 3 s. Finally, a similar pattern of results was found for cue durations of 2, 4, and 6 s for associative recognition of random word pairs. If subjects cannot immediately terminate the processing of forget items, the lingering processing of these items is as beneficial as the continued processing of remember items. Alternatively, subjects may use inefficient or counterproductive strategies that ironically improve memory for the information they wish to forget.

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)0.001
metaresearch head score (Gemma)0.005
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.029
GPT teacher head0.349
Teacher spread0.321 · 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

Citations48
Published2012
Admission routes1
Has abstractyes

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