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Record W3028382750 · doi:10.1080/09658211.2020.1762896

The word length effect in backward recall: the role of response modality

2020· article· en· W3028382750 on OpenAlexaff
Jean Saint‐Aubin, Olivia Beaudry, Dominic Guitard, Myriam Pâquet, Katherine Guérard

Bibliographic record

VenueMemory · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsRecallRecall testFree recallPsychologySerial position effectModality (human–computer interaction)Modality effectWord (group theory)Cognitive psychologyShort-term memoryCognitionLinguisticsArtificial intelligenceWorking memoryComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

In immediate serial recall, it is well known that participants are better at recalling short rather than long words. This benchmark memory effect, known as word length effect, has been observed numerous times in forward recall. However, in backward recall, when participants are required to recall items in the reverse order, contradictory findings have been reported. For instance, in some studies, the word length effect was abolished in backward recall, whereas in others it was maintained. In the present study, we investigated the role of response modality in accounting for this discrepancy. Our results showed that in forward recall, the word length effect is unaffected by response modality. In backward recall with a manual response (click or written), the word length effect is as large as in forward recall. Critically, when participants recalled a word orally, the word length effect was severely reduced in backward recall. We concluded that response modality interacts with the processes called upon in backward recall.

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)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.274
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 designObservational
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

Citations9
Published2020
Admission routes1
Has abstractyes

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