Bibliographic record
Abstract
Many previous studies have used noise tolerance to quantify sensitivity to point-light walkers heading +/-90 degrees from straight-ahead. Here we measured the smallest deviations from straight-ahead that observers could detect (azimuth thresholds) in the absence of noise. Thresholds were measured at a range of stimulus sizes and eccentricities for (1) upright and (2) inverted walkers, (3) intact walkers, those without feet and those with only feet, and (4) in the presence and absence of a second, attention-absorbing task. At large stimulus sizes azimuth thresholds were very small (between 1 and 2 degrees ) except in the case of inverted walkers. Size scaling generally compensated for eccentricity dependent sensitivity loss, however in the case of inverted walkers the data were quite noisy. At large sizes walkers without feet elicited higher thresholds than those with only feet, suggesting a special role for the feet even when walkers are not viewed side-on. Unlike others, we found no evidence that competing tasks affected performance. We argue that the value of our modified direction-discrimination task lies in its focus on the limits of discrimination within the domain of interest, rather than the amount of noise needed to impair discrimination of widely separated stimulus values.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".