Where Is Your Attention? Assessing Individual Instances of Covert Attentional Orienting in Response to Gaze and Arrow Cues
Bibliographic record
Abstract
Humans spontaneously follow where others are looking. However, recent investigations suggest such gaze-following behavior during natural interactions occurs relatively infrequently, only in about a third of available instances. Here we investigated if a similar frequency of orienting is also found in laboratory tasks that measure covert attentional orienting using manual responses. To do so, in two experiments, we analyzed responses from a classic gaze cuing task, with arrow cues serving as control stimuli. We reasoned that the proportions of attentional benefits and costs, defined as responses falling outside of 1 standard deviation of the average performance for the neutral condition, would provide a good approximation of individual instances of attentional shifts. We found that although benefits and costs occurred in less than half of trials, benefits emerged on a greater proportion of validly cued relative to invalidly cued trials. This pattern of data held across two different measures of neutral performance, as assessed by Experiments 1 and 2, as well as across the two cue types. These results suggest that similarly to gaze-following in naturalistic settings, covert orienting within the cuing task also appears to occur relatively infrequently.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".