Perception of shape-from-texture in the periphery using a simulated central scotoma
Bibliographic record
Abstract
Purpose. Studies of eccentricity dependent sensitivity loss typically require participants to maintain fixation while making judgments about stimuli presented at various eccentricities in the peripheral visual field. However, training participants to fixate can prove difficult. Therefore, we have developed a novel alternative in which eccentricity of stimulus presentation is controlled using a simulated central scotoma of variable size. Method. Participants were asked to identify 3D surfaces comprising hills, valleys and plains in three possible locations. Therefore there were 27 different surfaces, yielding a 27 alternative forced choice task. Surface shape was conveyed by texture. Participants performed the task for simulated scotomas of 0, 1, 2, 4, 8 and 16° diameter over an eight-fold range of stimulus sizes. Position of scotoma was based on current fixation location captured with an eye tracker (SR Research Eyelink 1000, binocular tracking). Results. The psychometric functions for each simulated scotoma were left shifted versions of each other on a log size axis. Therefore, when we divided stimulus size at each eccentricity (E) by an appropriate F = 1 + E/E2 (where E2 is the eccentricity at which stimulus size must double to achieve equivalent-to-foveal performance) all thresholds collapsed onto a single psychometric function. Therefore, stimulus magnification was sufficient to equate sensitivity to shape-from-texture for all scotoma sizes. The average E2 value required to achieve this was 1.67° (N = 4, SEM = 0.304, 95% CI - 0.62° to 2.73°). Conclusions. In all cases, scaling with F = 1 + E/E2 eliminated most scotoma-dependent variation from the data. The data show clear evidence that size scaling is sufficient to equate the perception of shape-from-texture across in the presence of scotomas.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".