Development of a computerized 2D rating scale for continuous and simultaneous evaluation of two dimensions of a sensory stimulus
Bibliographic record
Abstract
Introduction: One-dimensional rating scales are widely used in research and in the clinic to assess individuals' perceptions of sensory stimuli. Although these scales provide essential knowledge of stimulus perception, their limitation to one dimension hinders our understanding of complex stimuli. Methods: To allow improved investigation of complex stimuli, a two-dimensional scale based on the one-dimensional Gracely Box Scale was developed and tested in healthy participants on a visual and an auditory task (rating changes in brightness and size of circles and rating changes in frequency and sound pressure of sounds, which was compared to ratings on one-dimensional scales). Before performing these tasks, participants were familiarized with the intensity descriptors of the two-dimensional scale by completing two tasks. First, participants sorted the descriptors based on their judgment of the intensity of the descriptors. Second, participants evaluated the intensity of the descriptors by pressing a button for the duration they considered matching the intensity of the descriptors or squeezing a hand grip dynamometer as strong as they considered matching the intensity of the descriptors. Results: Results from these tasks confirmed the order of the descriptors as displayed on the original rating scale. Results from the visual and auditory tasks showed that participants were able to rate changes in the physical attributes of visual or auditory stimuli on the two-dimensional scale as accurately as on one-dimensional scales. Discussion: These results support the use of a two-dimensional scale to simultaneously report multiple dimensions of complex stimuli.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.007 | 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".