Peripheral sensitivity to biological motion is unaffected by dividing attention
Bibliographic record
Abstract
Previous research has shown that stimulus magnification is sufficient to equate sensitivity to biological motion across the visual field. However, this research used point-light walkers with fixed direction differences that make it impossible to judge whether the limits of walker direction discrimination change with eccentricity. We addressed this question by measuring walker direction-discrimination thresholds at a range of sizes from 0° to 16°. We found asymptotic thresholds, at all eccentricities, to be ±1.14 degrees from straight ahead. The psychometric functions at each eccentricity were shifted versions of each other on a log size axis. Therefore, when we divided stimulus size at each eccentricity (E) by an appropriate F = 1 + E/E2 (where E2 is the eccentricity at which stimulus size must double to achieve equivalent-to-foveal performance) all thresholds collapsed onto a single psychometric function. Therefore, stimulus magnification was sufficient to equate sensitivity to walker direction across the visual field. The average E2 value required to achieve this was 1.02. We also examined the role of attention in eccentricity-dependent sensitivity loss using a dual-task procedure in which participants were asked to judge first the colour (red or green) then the direction of the point-light walker. The difficulty of the colour judgment, and hence the level of attentional-engagement, was controlled by maintaining colour contrast at threshold levels. The dual-task returned a single E2 value of 1.20 for walker direction discrimination, suggesting that there is no effect of splitting attention between colour and direction at either fixation or 16°. Although there were no costs of splitting attention in the present study, it may be that such costs would be seen when subjects have to divide attention either between (i) two different spatial aspects of a stimulus or (ii) two different locations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".