Bibliographic record
Abstract
Humans are extremely sensitive to symmetry when it is foveated but sensitivity drops as a symmetrical region of a fixed size is moved into the periphery. A psychophysical study was undertaken to determine if eccentricity dependent sensitivity loss could be overcome by magnifying stimuli at each eccentricity (E) by a factor F = 1 + E/E2, where E2 indicates the eccentricity at which the size of a stimulus must be doubled, relative to a foveal standard, to achieve equivalent performance. The psychophysical task required subjects to decide on each trial in which of two intervals a symmetrical stimulus had been presented. Stimuli were presented at a range of sizes and eccentricities (0 to 8 degrees) and the probability of a correct discrimination was computed for each condition. In Experiment 1, thresholds were measured with stimuli set to maximum available contrast and, in Experiment 2, stimuli were presented at a constant multiple of contrast detection threshold. In both experiments, a single scaling function removed most of the eccentricity dependent variation from the data. However, the E2 value recovered for one subject tested in both experiments was larger by about 65% when stimuli were not equated for visibility. We conclude that symmetry detection can be equated across a range of eccentricities by scaling stimuli with an E2 in the range of 0.88 to 1.38 degrees. Failure to equate for visibility across all viewing conditions may result in an inflated estimate of E2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".