Staring reality in the face: A comparison of social attention across laboratory and real world measures suggests little common ground.
Bibliographic record
Abstract
The ability to attend to someone else's gaze is thought to represent one of the essential building blocks of the human sociocognitive system. This behavior, termed social attention, has traditionally been assessed using laboratory procedures in which participants' response time and/or accuracy performance indexes attentional function. Recently, a parallel body of emerging research has started to examine social attention during real life social interactions using naturalistic and observational methodologies. The main goal of the present work was to begin connecting these two lines of inquiry. To do so, here we operationalized, indexed, and measured the engagement and shifting components of social attention using covert and overt measures. These measures were obtained during an unconstrained real-world social interaction and during a typical laboratory social cuing task. Our results indicated reliable and overall similar indices of social attention engagement and shifting within each task. However, these measures did not relate across the two tasks. We discuss these results as potentially reflecting the differences in social attention mechanisms, the specificity of the cuing task's measurement, as well as possible general dissimilarities with respect to context, task goals, and/or social presence. (PsycINFO Database Record
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".