Can we predict vividness from the characteristics of imagined images? A novel database featuring vividness judgments of the Natural Scene Dataset
Bibliographic record
Abstract
What makes an image easy to imagine? Previous research on mental imagery mostly used very limited samples, which prevented establishing a direct association between image characteristics and vividness ratings (i.e., the clarity and detail level of imagined images). Here, we present a large-scale database of vividness judgments associated with the natural scenes from the Natural Scenes Dataset (NSD; Allen et al., 2021), which consists of 73,000 annotated natural images. During each trial, participants sequentially view two NSD images and are then randomly asked to imagine one or the other (i.e., retro-cued target image) for 4 s. Participants then rate the vividness of their mental image (on a continuous scale from 0 to 100), followed by a test to ensure that they imagined the correct target. Participants (n = 1825), recruited from Prolific, are directed to the Meadows platform for online experiments. Each complete 120 trials of our vividness task, for a total of 219,000 vividness ratings across participants. They also complete the Vividness of Visual Imagery Questionnaire (Marks, 1973) to measure their visual imagery ability. Overall, preliminary data reveals excellent performance on the task (average accuracy of 95.83% correct target identification), as well as substantial interimage (M = 0.65 ± 0.25) and interindividual (M = 0.66 ± 0.17) variability in average vividness scores. This large-scale dataset of vividness ratings will offer invaluable insights into visual imagery by allowing to train predictive models of subjective imagery experiences. It will enable a deeper understanding of the visual and cognitive factors influencing mental image vividness, and serve as a guiding resource for future experiments. Furthermore, integrating these vividness judgments with neural data from the NSD will allow for an exploration of the relationship between subjective experience and objective brain responses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".