Investigating overt and covert shifts of attention within social naturalistic scenes
Bibliographic record
Abstract
Eye-tracking studies on social attention have consistently shown that humans prefer to attend to other human beings. Much less is known about whether a similar preference is also evident in covert attentional processes. To enable a direct comparison, this study examined covert and overt attentional guidance within two different experimental setups using complex naturalistic scenes instead of isolated single features. In the first experiment, a modified version of the dot-probe paradigm served as a measure of covert reflexive attention toward briefly presented scenes containing a social feature in one half of the visual field compared to nonsocial elements in the other while controlling for low-level visual saliency. Participants showed a stable congruency effect with faster reaction times and fewer errors for probes presented on the social side of the scene. In a second experiment, we tracked eye movements for the same set of stimuli while manipulating the presentation time to allow for differentiating reflexive and more sustained aspects of overt attention. Supportive of the first results, analyses revealed a robust preference for social features concerning initial saccade direction as well as fixation allocation. Collectively, these experiments imply preferential processing of social features over visually salient aspects for automatic allocation of covert as well as overt attention.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".