Where is your attention?: Estimating the frequency of gaze following in the cuing task using a trial-by trial analysis.
Bibliographic record
Abstract
Both laboratory and real world studies show that humans spontaneously follow the gaze of others. However, gaze following in the real world occurs surprisingly infrequently, only in about a third of available instances. Here we assessed whether the results from a typical laboratory based measure reflected consistent orienting of attention on most trials or an effect of averaging large performance differences on a handful of trials. To do so, we collected data from 25 participants who performed a gaze cuing procedure. Left or right gazing faces and a neutral face with closed eyes served as attentional cues. Participants detected peripheral targets, which occurred equally often at a left or right location. Data were analyzed in two ways. First, a standard group-based ANOVA replicated a wealth of past research showing overall reliable facilitation for gazed-at targets. Second, to address our questions, we calculated the proportion of gazed-at and not gazed-at trials that were significantly faster than baseline (i.e., closed eyes). We found that a greater proportion of gazed-at trials, i.e., 20% were faster than the baseline relative to 16% of not gazed-at trials. Taken together, these results suggest that performance in the gaze cuing task reflects neither consistent orienting throughout, nor the effect of averaging over several trials. Meeting abstract presented at VSS 2017
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".