Perception of biological motion across the visual field
Bibliographic record
Abstract
There is conflicting evidence about whether stimulus magnification is sufficient to equate the discriminability of point-light walkers across the visual field. Ikeda, Watanabe and Blake (2005, Vision Research) found that peak noise tolerance was always highest at fixation and concluded that biological motion was unscalably poor in the periphery. By contrast, Gibson et al. (VSS, 2005) found that in the absence of spatiotemporal noise, stimulus magnification was sufficient to equate point-light walker direction discrimination (left vs right, i.e., ±90° from the line of sight) across the visual field. We measured the accuracy with which observers could report the directions of point-light-walkers moving ±4° from the line of sight. Accuracy was measured over a seven-fold range of sizes at eccentricities from 0 to 16°. All observers achieved 100% accuracy at the largest stimulus sizes (20° height) at all eccentricities. The psychometric functions at each eccentricity were shifted versions of each other on a log size axis. Therefore, by dividing stimulus size at each eccentricity (E) by an appropriate F = 1 + E/E2 (where E2 represents the eccentricity at which stimulus size must double to achieve equivalent-to-foveal performance) all data could be fit with a single function. The average E2 value was .97 (SEM = .19, N = 3). This value is close to that found by Gurnsey et al. (2006, Journal of Vision) in a structure-from-motion task (E2 = .61) but contrasts with the average E2 value of 3.5 found by Gibson et al. (VSS, 2005). In the absence of spatiotemporal noise, size scaling is sufficient to equate discrimination of biological motion across the visual field. The average E2 in this task is smaller than that found by Gibson et al. (2005), showing that task difficulty has an effect on the magnification needed to compensate for eccentricity-dependent sensitivity loss.
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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 | 0.008 |
| 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 | 0.001 | 0.002 |
| 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".