Effects of social stimuli on covert attentional orienting and saccaddic eye-movements during visual search
Bibliographic record
Abstract
Although visual attention and saccadic eye movements are tightly linked, our attention can move to objects in visual space without a saccade to the object, a phenomenon called covert attentional orienting. Socially significant targets like faces and human bodies attract attention. Using a visual search task we examined reaction to social targets by comparing the relationship between performance measures such as reaction time and error rate and saccadic eye movement measures. Participants briefly viewed a word representing 1 of 6 categories. One image from each category then appeared in a circular array on the screen. Participants identified the image in a target frame (the green frame) as either matching or not matching the presented word. On half of the trials, a distracter frame (the red frame) was also present. Consistent with previous results, participants responded faster when seeking a social target (face or body) compared to non-social targets and this effect was not diminished by inversion. They were slower and more error prone on trials containing a distracter frame. Participants saccadeed first and more often to social targets than to non-social targets but spent less time focused on social targets. When images were inverted, participants did not saccade more often to social stimuli than non-social distracters. Participants varied widely on the proportion of trials in which they saccaded to any object, between 2% and 97%,suggesting that some participants are capable of performing this task peripherally. Indeed, a lower proportion of trials with saccades to targets was associated with faster RT. The evidence supported an attentional effect of social stimuli that is independent of saccadic eye movement in addition to modulation of looking behavior. Meeting abstract presented at VSS 2015
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".