Trial-by-Trial Vividness Self-Reports Versus VVIQ
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".