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Record W2418726402 · doi:10.1037/cep0000086

The production effect in long-list recall: In no particular order?

2016· article· en· W2418726402 on OpenAlexaff
Angela Lambert, Glen E. Bodner, Alexander Taikh

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2016
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsWestern UniversityUniversity of Calgary
Fundersnot available
KeywordsRecallOptimal distinctiveness theoryPsychologyRecall testFree recallSerial position effectTask (project management)Cognitive psychologyWord listPsycINFOProduction (economics)Test (biology)Encoding (memory)Order (exchange)Reading (process)Social psychologyComputer scienceLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

The production effect reflects a memory advantage for words read aloud versus silently. We investigated how production influences free recall of a single long list of words. In each of 4 experiments, a production effect occurred in a mixed-list group but not across pure-list groups. When compared to the pure-list groups, the mixed-list effects typically reflected a cost to silent words rather than a benefit to aloud words. This cost persisted when participants had to perform a generation or imagery task for the silent items, ruling out a lazy reading explanation. This recall pattern challenges both distinctiveness and strength accounts, but is consistent with an item-order account. By this account, the aloud words in a mixed list disrupt the encoding of item-order information for the silent words, thus impairing silent word recall. However, item-order measures and a forced-choice order test did not provide much evidence that recall was guided by retrieval of item-order information. We discuss our pattern of results in light of another recent study of the effects of production on long-list recall. (PsycINFO Database Record

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.075
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0010.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.320
Teacher spread0.282 · 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.

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

Citations18
Published2016
Admission routes1
Has abstractyes

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