A novel adaptative method for measuring point of subjective equality
Bibliographic record
Abstract
Points of Subjective Equality (PSEs) are usually measured with constant stimuli (CS) methods. However, it’s found time-consuming and inefficient in clinic settings such as in our recent studies investigating the Pulfrich phenomenon (Reynaud, A. & Hess, R. F. (2019), An unexpected spontaneous motion-in-depth Pulfrich phenomenon in amblyopia, Vision 3(4). 54). Thus, we wanted to develop a more efficient method to estimate the PSE and its variability. We have developed an adaptive (AD) method in which levels are chosen on a pre-defined scale such as for CS. However, instead of testing each level with the same number of repetitions, each stimulus will be chosen depending on the previous response of the participant. If the participant responded “up”, one random level in the lower range would be picked for the next trial. And if the participant responded “down”, one random level in the upper range would be picked for the next trial. This procedure would result in a bell-shaped distribution of the tested levels around the estimated PSE. We compared this method with traditional CS procedure on a task based on the Pulfrich phenomenon with a fixed number of total trials (75，150 and 300), while the PSEs of participants could be varied using different ND filters (0ND,±1ND). We observed a significant correlation among the PSEs obtained with the two methods. And in most cases, our adaptative method yielded a smaller variability of the estimates of both the PSE and the slope of the psychometric function. Therefore, we can conclude that the adaptive method is an efficient way of measuring PSEs with our Pulfrich paradigm. It could potentially be used for other psychophysical PSE estimation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".