Mind the curve: What saccadic curvature can tell us about face processing
Bibliographic record
Abstract
There is a bias to look at other people’s faces. What drives this bias is unclear, however, and there is debate in the literature regarding whether upright faces capture attention more strongly than do inverted faces, or whether faces are processed more effectively by one hemisphere over the other. To investigate how faces attract attention, we adapted a traditional saccadic trajectory task. The examination of saccadic trajectories is well suited to exploring how attention is deployed to different face stimuli, as saccades aimed to target objects will show characteristic curvature in response to nearby distractor stimuli. When saccades are executed rapidly, a distractor will cause the saccades’ trajectory to curve towards the distractor’s location, indicating that it has captured attention. In contrast, when a saccade is executed more slowly, its trajectory will curve away from a distractor, suggesting that the object has been inhibited in order to better accommodate target selection. Further, the magnitude of a saccade’s curvature is determined in part by the saliency of the distractor: the more salient the distractor, the greater the curvature. With this in mind, we asked participants to make saccades to vertical targets in the presence or absence of task-irrelevant distractor faces (upright or inverted, presented in the left or right visual hemifield). Somewhat unexpectedly, face orientation did not strongly influence saccadic trajectory, suggesting that in this context, inverted faces capture attention as much as upright faces. Interestingly, presentation location did matter: faces presented in the left hemifield produced greater deviation away from the distractor than did faces presented in the right hemifield. Future studies are aimed at better understanding the implications of this effect and whether it is specific to faces. Meeting abstract presented at VSS 2012
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".