Contextually-Based Social Attention Diverges across Covert and Overt Measures
Bibliographic record
Abstract
Humans spontaneously attend to social cues like faces and eyes. However, recent data show that this behavior is significantly weakened when visual content, such as luminance and configuration of internal features, as well as visual context, such as background and facial expression, are controlled. Here, we investigated attentional biasing elicited in response to information presented within appropriate background contexts. Using a dot-probe task, participants were presented with a face-house cue pair, with a person sitting in a room and a house positioned within a picture hanging on a wall. A response target occurred at the previous location of the eyes, mouth, top of the house, or bottom of the house. Experiment 1 measured covert attention by assessing manual responses while participants maintained central fixation. Experiment 2 measured overt attention by assessing eye movements using an eye tracker. The data from both experiments indicated no evidence of spontaneous attentional biasing towards faces or facial features in manual responses; however, an infrequent, though reliable, overt bias towards the eyes of faces emerged. Together, these findings suggest that contextually-based social information does not determine spontaneous social attentional biasing in manual measures, although it may act to facilitate oculomotor behavior.
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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 | 0.003 |
| 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) | 0.002 | 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".