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Record W2047417538 · doi:10.1037/a0023923

The influence of levels of processing on recall from working memory and delayed recall tasks.

2011· article· en· W2047417538 on OpenAlexaff
Vanessa M. Loaiza, David P. McCabe, Jessie L. Youngblood, Nathan S. Rose, Joel Myerson

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2011
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBaycrest Hospital
FundersNational Institute on Aging
KeywordsRecallTask (project management)Cognitive psychologyMemory spanWorking memorySpan (engineering)PsychologyEpisodic memoryReading (process)Computer scienceCognitionNeuroscience

Abstract

fetched live from OpenAlex

Recent research in working memory has highlighted the similarities involved in retrieval from complex span tasks and episodic memory tasks, suggesting that these tasks are influenced by similar memory processes. In the present article, the authors manipulated the level of processing engaged when studying to-be-remembered words during a reading span task (Experiment 1) and an operation span task (Experiment 2) in order to assess the role of retrieval from secondary memory during complex span tasks. Immediate recall from both span tasks was greater for items studied under deep processing instructions compared with items studied under shallow processing instructions regardless of trial length. Recall was better for deep than for shallow levels of processing on delayed recall tests as well. These data are consistent with the primary-secondary memory framework, which suggests that to-be-remembered items are displaced from primary memory (i.e., the focus of attention) during the processing phases of complex span tasks and therefore must be retrieved from secondary memory.

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)0.007
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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 science0.0000.001
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 head0.094
GPT teacher head0.339
Teacher spread0.245 · 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

Citations67
Published2011
Admission routes1
Has abstractyes

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