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Record W2084986946 · doi:10.1037/cep0000009

The offline production effect.

2013· article· en· W2084986946 on OpenAlexafffund
Randall K. Jamieson, Jackie Spear

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyProduction (economics)Cognitive psychologySketchRead aloudTest (biology)CommunicationLinguisticsComputer scienceReading (process)

Abstract

fetched live from OpenAlex

People remember words they say aloud better than ones they do not, a result called the production effect. The standing explanation for the production effect is that producing a word renders it distinctive in memory and thus memorable at test. Whereas it is now clear that motoric production benefits remembering over nonproduction, and that more intense motoric production benefits remembering to a greater extent than less intense motoric production, there has been no comparison of the memorial benefit conferred by motoric versus imagined production. One reason for the gap is that the standard production-by-vocalization procedure confounds the analysis. To make the comparison, we used a production-by-typing procedure and tested memory for words that people typed, imagined typing, and did not type. Whereas participants remembered the words that they typed and imagined typing better than words that they did not, they remembered the words they typed better than the ones they imagined typing; an advantage that was consistent over tests of recognition memory and source discrimination. We conclude that motoric production is a sufficient and facilitative (but not a necessary) condition to observe the production effect. We explain our results by a sensory feedback account of the production effect and sketch a computational framework to implement that approach.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.042
GPT teacher head0.314
Teacher spread0.272 · 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

Citations18
Published2013
Admission routes2
Has abstractyes

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