Social Attention and Real-World Scenes: The Roles of Action, Competition and Social Content
Bibliographic record
Abstract
The present study examined how social attention is influenced by social content and the presence of items that are available for attention. We monitored observers' eye movements while they freely viewed real-world social scenes containing either 1 or 3 people situated among a variety of objects. Building from the work of Yarbus (1965/1967) we hypothesized that observers would demonstrate a preferential bias to fixate the eyes of the people in the scene, although other items would also receive attention. In addition, we hypothesized that fixations to the eyes would increase as the social content (i.e., number of people) increased. Both hypotheses were supported by the data, and we also found that the level of activity in the scene influenced attention to eyes when social content was high. The present results provide support for the notion that the eyes are selected by others in order to extract social information. Our study also suggests a simple and surreptitious methodology for studying social attention to real-world stimuli in a range of populations, such as those with autism spectrum disorders.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".