Mental attribution is not sufficient or necessary to trigger attentional orienting to gaze
Bibliographic record
Abstract
Attention can be shifted in the direction that another person is looking, but the role played by an observer's mental attribution to the looker is controversial. And whether mental attribution to the looker is sufficient to trigger an attention shift is unknown. The current study introduces a novel paradigm to investigate this latter issue. An actor is presented on video turning his head to the left or right before a target appears, randomly, at the gazed-at or non-gazed at location. Time to detect the target is measured. The standard finding is that target detection is more efficient at the gazed-at than the nongazed-at location, indicating that attention is shifted to the gazed-at location. Critically, in the current study, an actor is wearing two identical masks -- one covering his face and the other the back of his head. Thus, after the head turn, participants are presented with the profile of two faces, one looking left and one looking right. For a gaze cuing effect to emerge, participants must attribute a mental state to the actor -- as looking through one mask and not the other. Over the course of four experiments we report that when mental attribution is necessary, a shift in social attention does not occur (i.e., mental attribution is not sufficient to produce a social attention effect); and when mental attribution is not necessary, a shift in social attention does occur. Thus, mental attribution is neither sufficient nor necessary for the occurrence of an involuntary shift in social attention. The present findings constrain future models of social attention that wish to link gaze cuing to mental attribution.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".