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Record W2597717299 · doi:10.1037/cep0000126

Are lexical factors immune to response modality in backward recall? The effects of imageability and word frequency.

2017· article· en· W2597717299 on OpenAlexfundno aff
Olivia Beaudry, Jean Saint‐Aubin, Katherine Guérard, Myriam Pâquet

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2017
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRecallFree recallRecall testModality effectSerial position effectWord lists by frequencyModality (human–computer interaction)PsychologyCognitive psychologyShort-term memoryCognitionComputer scienceNatural language processingWorking memoryArtificial intelligence

Abstract

fetched live from OpenAlex

In immediate serial recall, it is well established that performance is influenced by lexical factors such as imageability and word frequency. However, when participants are asked to recall the to-be-remembered items in their reverse order, known as backward recall, lexical factors produced contradictory findings. In 4 experiments, we tested the role of response modality in modulating the effects of imageability and word frequency in backward recall. The magnitude of the 2 tested lexical factors was only slightly reduced in backward compared with forward recall when participants responded manually. Critically, when participants recalled words orally, the magnitude of the lexical factors was severely reduced in backward recall. It is concluded that response modality has a strong influence on the memory processes involved in backward 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 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)0.002
metaresearch head score (Gemma)0.014
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.072
GPT teacher head0.354
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 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

Citations12
Published2017
Admission routes1
Has abstractyes

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