Validating Phenomenological Aspects of the Mental Imagery Experience through Meta-Analysis: Beyond Global Assessment of Imagery Ability
Bibliographic record
Abstract
The phenomenological experience of mental imagery vividness is of increasing interest within the field of mental health science, yet commentators refute its empirical validity as an independent scientific construct.Two assessments of mental imagery vividness were compared through meta-analysis, the Vividness of Visual Imagery Questionnaire (VVIQ), and trial-by-trial ratings of vividness.Each vividness assessment was further divided into behavioural/cognitive or neuroscientific measures.A corpus of 965 peer-reviewed journal articles were retrieved from four major databases and relevant statistical outcomes from each paper were recorded.Effect size estimates were computed for 3579 statistical outcomes, which were categorized as into one of four comparison groups (Vividness and Behavioural/Cognitive, VVIQ and Behavioural/Cognitive, Vividness and Neuroscientific, and VVIQ and Neuroscientific).It was found that the average effect size magnitude for trial-by-trial vividness exceeded that of the VVIQ for behavioural/cognitive, but not neuroscientific measures.However, the average effect sizes magnitude for neuroscientific measures was generally greater than behavioural/cognitive ones.Additionally, the average effect sizes magnitude for trial-by-trial vividness ratings was generally greater than the VVIQ.It is suggested that trial-by-trial ratings, in conjunction with neuroscientific measurement, may provide a more precise and reliable measure of mental imagery vividness.Despite face validity, unique observations correlating trial-by-trial vividness ratings with the VVIQ were weak to moderate on average.Theoretical considerations on the empirical validity of the construct of vividness are discussed.
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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 | 0.127 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".