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Record W3160795260 · doi:10.31234/osf.io/nfp93

Repetition Blindness and Retrieval-Time Effects of Full- vs. Partial-Report Following the Rapid Serial Visual Presentation (RSVP) of Letters in Words

2018· preprint· en· W3160795260 on OpenAlexaff
John Richard Vokey, Scott W. Allen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsRapid serial visual presentationRepetition (rhetorical device)Encoding (memory)PerceptionPsychologyCognitive psychologyPresentation (obstetrics)Visual perceptionAudiologyComputer scienceNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Although commonly accepted as an encoding/representational/perceptual phenomenon, repeti- tion deficits (“repetition blindness”) in Rapid Serial Visual Presentation (RSVP) can be shown to be markedly influenced by retrieval-time tasks independently of item encoding. We demonstrate such influences in a series of within-participant experiments where retrieval conditions are un- predictably varied after items have been experienced. Repetition deficits are demonstrated when full report of the presented item is required and in partial-report conditions where the repeated letter is included in the retrieval cue but not in partial-report conditions where the repeated letter is not included in the retrieval cue. Such effects are not expected if repetition deficits in RSVP are thought to be principally a function of the encoding/representation/perception of the trial experience.

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)0.036
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.342
Teacher spread0.326 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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