Detecting curvature in first and second-order periodic line stimuli
Bibliographic record
Abstract
It has been suggested that second-order processes may have coarser orientation tuning functions than first-order mechanisms. The purpose of the present study was to determine if differences between these two classes of stimuli are evident when processing periodic line stimuli of different frequencies and to further determine whether periodic line discriminations solicit oriented receptors. The stimuli were composed of D4 luminance or contrast defined lines that were distorted with sinusoidal curvature modulations. The curvature modulations varied in frequencies between 1/8 and 1 cycle per deg and the total image size was 8x8 deg. The lowest spatial frequency was such that a minimum of one full cycle was visible. Five young healthy observers participated in the study. Individual contrast thresholds were obtained for the first and second-order stimuli to adjust for stimulus visibility. The thresholds were obtained with a temporal forced choice paradigm where the subject had to indicate whether the stimulus was present in the first or second presentation for contrast detection, or whether curvature was present in the first or second stimulus for the curvature amplitude measurements. The results show that, when the visibility is individually adjusted, there is no difference between first and second-order class stimuli for this type of task. This suggests that the mechanisms involved in detecting curvature in periodic line stimuli are common for both first and second-order processing mechanisms and probably minimally solicit oriented receptive fields which would make this processing analogous to a Vernier alignment hyperacuity type task.
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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) | 0.002 | 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".