Bibliographic record
Abstract
Background: There is a consensus that crowding is a property of peripheral (but not foveal) vision and that crowding zones are elliptical and oriented towards the fovea. However, Latham and Whitaker (1996) found evidence of crowding at fixation and suggested that multiple linear magnifications were required to explain the changes in crowding from fixation to the periphery. Past studies of crowding have involved gratings (e.g., Latham & Whitaker, 1996, OPO) or alphanumeric characters (e.g., Cavanagh, 2002, VR; Pelli et al. 2007, JoV). Here we ask whether the basic characteristics of crowding apply to biologically relevant stimuli; specifically, symmetry. Furthermore, we ask whether multiple linear magnifications explain the changes in crowding from fixation to the periphery. Method: We measured size thresholds for target/crowder separations of 1.25 to 8.00 times target size, as well as a no-crowder condition, in a symmetry discrimination task. Thresholds were measured with the target at fixation and 8° below or to the right of fixation. In one condition the crowders flanked the target vertically and in another they flanked the target horizontally. In all cases we plotted target size at threshold as a function of separation at threshold. Results: At fixation, size thresholds were independent of target/crowder separation. At all other eccentricities threshold size decreased as separation increased until asymptote was reached, at which point size thresholds were independent of separation. As well, crowding was stronger when flankers were presented parallel to the fixation-to-target axis, consistent with the suggested structure of crowding zones. Conclusions: Consistent with previous literature it appears that there is a qualitative difference in crowding across the visual field; symmetry appears to behave like previously studied stimuli. Therefore, contrary to our expectations, and previous data (Latham & Whitaker, 1996), multiple linear magnifications seem inadequate to characterize the data.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".