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Record W2287330329 · doi:10.1177/0276236615587490

Trial-by-Trial Vividness Self-Reports Versus VVIQ

2015· article· en· W2287330329 on OpenAlex
Matthew Runge, Valery Bakhilau, Faisa Omer, Amedeo D’Angiulli

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

VenueImagination Cognition and Personality · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyMental imageTest (biology)Predictive validityPredictive valueSample size determinationSample (material)CognitionClinical psychologyStatisticsMathematicsMedicinePsychiatry

Abstract

fetched live from OpenAlex

The Vividness of Visual Imagery Questionnaire (VVIQ) globally defines an individual according to their propensity to form visual mental imagery. A less frequently used approach to the study of mental imagery is based on self-reports on a trial-by-trial basis. The current meta-analysis consisted of three tests designed to compare the VVIQ and trial-by-trial vividness ratings against more objective criteria to address the predictive validity of these different measure instruments. Test 1 was based on a convenient sample and the calculation of effect sizes using exact p values. Tests 2 and 3 were based on a systematic sample, but while Test 2 used exact p values, Test 3 used effect sizes directly. Trial-by-trial vividness reports demonstrated significantly larger effect sizes than the VVIQ across all three experimental methodologies, with neural measures yielding significantly greater effect sizes than behavioral and cognitive ones. Therefore, we conclude that trial-by-trial self-reports have higher predictive value than VVIQ.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

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

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 head0.280
GPT teacher head0.434
Teacher spread0.154 · 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