In the Blink of an Eye: Reading Mental States From Briefly Presented Eye Regions
Bibliographic record
Abstract
Faces provide not only cues to an individual's identity, age, gender, and ethnicity but also insight into their mental states. The aim was to investigate the temporal aspects of processing of facial expressions of complex mental states for very short presentation times ranging from 12.5 to 100 ms in a four-alternative forced choice paradigm based on Reading the Mind in the Eyes test. Results show that participants are able to recognise very subtle differences between facial expressions; performance is better than chance, even for the shortest presentation time. Importantly, we show for the first time that observers can recognise these expressions based on information contained in the eye region only. These results support the hypothesis that the eye region plays a particularly important role in social interactions and that the expressions in the eyes are a rich source of information about other peoples' mental states. When asked to what extent the observers guessed during the task, they significantly underestimated their ability to make correct decisions, yet perform better than chance, even for very brief presentation times. These results are particularly relevant in the light of the current COVID-19 pandemic and the associated wearing of face coverings.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".