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Record W4317481564 · doi:10.1027/1618-3169/a000563

The Impact of Depth of Encoding on the Transfer of Test Enhanced Learning

2022· article· en· W4317481564 on OpenAlexaff
Donnelle DiMarco, Harvey H. C. Marmurek

Bibliographic record

VenueExperimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · 2022
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPsychologyRecallEncoding (memory)Cognitive psychologyTest (biology)Task (project management)Recall testLevels-of-processing effectEncoding specificity principleFree recallDevelopmental psychologyCommunicationCognitionNeuroscience

Abstract

fetched live from OpenAlex

Abstract. The mediator effectiveness hypothesis states the benefit of retrieval practice is a consequence of the activation of mediators linking cue and target items during review. Evidence has found that mediators are more effective at prompting recall of target words than words not associated with the original cue, a pattern that is larger following testing than restudy. The benefit of testing for unstudied cues at the final test is referred to as transfer of test-enhanced learning. The current study examined whether the activation of mediators is moderated by the depth of processing completed at encoding. During an initial study of weakly related word pairs (e.g., Mother–CHILD), participants completed an encoding task that directed deep, shallow, or no specific depth of processing. During review, participants restudied the pairs or attempted to recall the target given the original cue (e.g., Mother). On the final test, participants were presented with unstudied cues that were related to either the original cue (mediators, e.g., Father) or the target (target-related cues, e.g., Baby). The results found mediator generation during review to be greatly impaired by shallow processing tasks completed during encoding. In contrast, the effectiveness of target-related cues was not affected by depth of processing tasks.

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 score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.069
GPT teacher head0.407
Teacher spread0.338 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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