There is something eerie in rapidly perceiving faces
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
As artificial faces and deepfakes become more prevalent, distinguishing between humans and artificial beings is crucial. Previous studies show that when a face transitions from clearly artificial to clearly human, ratings of affinity increase until the face becomes almost human. Here, the ratings will suddenly drop and quickly recover, revealing the uncanny valley. The faces in the uncanny valley elicit aversive reactions due to their subjective eeriness, and we investigated whether eeriness results in any special treatment in visual processing in humansby conducting a series of cueless temporal order judgment (TOJ) experiments. In Experiment 1, we confirmed the stimulus set. In the following experiments, participants identified which of the two faces appeared first, with faces having equal levels of eeriness (Experiments 2a and 2b) or different levels of eeriness (Experiments 3 and 4). We conclude that eeriness may not be the sole driver of attentional prioritization with uncanny valley faces.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 it