Gaze selection in complex social scenes
Bibliographic record
Abstract
A great deal of recent research has sought to understand the factors and neural systems that mediate the orienting of spatial attention to a gazed-at location. What have rarely been examined, however, are the factors that are critical to the initial selection of gaze information from complex visual scenes. For instance, is gaze prioritized relative to other possible body parts and objects within a scene? The present study springboards from the seminal work of Yarbus (1965/1967), who had originally examined participants’ scan paths while they viewed visual scenes containing one or more people. His work suggested to us that the selection of gaze information may depend on the task that is assigned to participants, the social content of the scene, and/or the activity level depicted within the scene. Our results show clearly that all of these factors can significantly modulate the selection of gaze information. Specifically, the selection of gaze was enhanced when the task was to describe the social attention within a scene, and when the social content and activity level in a scene were high. Nevertheless, it is also the case that participants always selected gaze information more than any other stimulus. Our study has broad implications for future investigations of social attention as well as resolving a number of longstanding issues that had undermined the classic original work of Yarbus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".