Bibliographic record
Abstract
It has been argued that curvature discrimination requires receptive fields sensitive to different orientations. Furthermore, aging monkeys are less efficient for orientation discrimination when compared to their younger counterparts. One purpose of the present study was to determine if curvature shape influences curvature perception. Another was to assess whether normal aging affects curvature perception given that curvature is believed to involve the integration of different oriented filters. Stimuli consisted of curvatures with three different shapes. The three shapes differed in that one was bell shaped, the second looked like a regular arc and the third appeared as a compressed arc. Ten young and ten older healthy observers participated in this study. Individual contrast thresholds were obtained to adjust for stimuli visibility. A 2AFC paradigm where the observers had to indicate in which interval the curvature was presented versus a straight line was used. The dependent variable observed was curvature amplitude. Results show that different amplitudes are necessary to detect curvatures of different shapes. Both aging and young observers obtained differences in sensitivity for the different shapes. The older observers showed higher thresholds for the arc and compressed arc shapes, while they were similar to the younger observers for the bell-shaped function. Those data suggest that alternate processes are required for different shapes. Possible explanations presently considered are that different orientation receptor pooling is necessary for the individual shapes or that they solicit different energy levels, both of which could be affected by normal aging.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".