Don't look! Orienting to the eyes is not (entirely) under volitional control
Bibliographic record
Abstract
People look at eyes more than other facial features. What is unknown is if this bias is automatically or volitionally driven. We used a unique “Don't Look” paradigm to discriminate between these two alternatives. Participants were asked to freely view a series of faces or to avoid looking at either the eyes or the mouth of the faces. The free viewing data replicated previous results that people normally tend to fixate the eyes of faces. When asked to avoid looking at the eyes or the mouth of the faces, people were able to reduce fixations to the to-be-avoided feature, but they were less successful when asked to avoid looking at the eyes. These data demonstrate that looking at the eyes is not entirely under volitional control. In a second experiment, participants viewed inverted faces, which is known to disrupt face processing. Results again revealed a bias to look at the eyes during free viewing, but now when asked to avoid the eyes or the mouth, participants were equally successful at avoiding either feature. Thus, when normal face processing is impaired by inversion, attention to the eyes is under greater volitional control. Together, these data indicate that the preferential bias to attend to the eyes of upright faces reflects the combination of automatic and volitional processes. Our research also introduces the “Don't Look” paradigm as a simple and powerful paradigm for teasing apart the automatic and volitional processes that are contributing to a particular cognitive phenomenon.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".