The effects of orientation on detection and identification of facial expressions of emotion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Signal detection procedures were used to examine the ability of participants to detect and label facial expressions of emotion in an upright or inverted orientation when the faces were rapid videotaped presentations. The detection and identification of facial expressions were remarkably accurate. In the upright orientation, the A' measure of sensitivity was above.9 for detection and identification of all six facial expressions of emotion. Sensitivity to inverted expressions was diminished for all emotions; however, the extent of the decline in sensitivity depended upon the specific facial expression. If the expression was difficult to detect or label in the upright orientation, the sensitivity score was lower in the inverted orientation. An assessment of the errors made in the detection and labelling process allowed a demonstration of the specific facial expressions that were confused in either the upright or inverted orientation. The assessment of sensitivity and analysis of the errors suggests that the nature of perceptual processing of some, but not all, facial expressions is changed by inversion.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it