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Record W3096171606 · doi:10.1027/1618-3169/a000494

Testing a Strategy-Disruption Account of the List-Strength Effect

2020· article· en· W3096171606 on OpenAlex
Tyler M. Ensor, Tyler D. Bancroft, Dominic Guitard, Tamra J. Bireta, William E. Hockley, Aimée M. Surprenant

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExperimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversité de MonctonWilfrid Laurier UniversitySt. Thomas UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsRecallFree recallPsychologyInterference theorySerial position effectCognitive psychologyNull (SQL)StatisticsSocial psychologyComputer scienceCognitionMathematicsData miningWorking memory

Abstract

fetched live from OpenAlex

Presenting items multiple times on a study list increases their memorability, a process known as item strengthening. The list-strength effect (LSE) refers to the finding that, compared to unstrengthened (pure) lists, lists for which a subset of the items have been strengthened produce enhanced memory for the strengthened items and depressed memory for the unstrengthened items. Although the LSE is found in free recall (Tulving & Hastie, 1972), it does not occur in recognition (Ratcliff et al., 1990). In free recall, the LSE in mixed lists is attributed to a sampling bias promoting priority recall of strong items and consequent output interference affecting weak items. We suggest that, in recognition, the disruption of this pattern through the randomization of test probes is responsible for the null LSE. We present several pilot experiments consistent with this account; however, the registered experiment, which had more statistical power, did not support this account.

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.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.098
GPT teacher head0.399
Teacher spread0.301 · 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