Viewpoint Aftereffects: Adapting to full faces, head outlines, and features
Bibliographic record
Abstract
Previous research has shown that adapting to a face horizontally rotated about a vertical axis produces a perceptual shift, where the test face appears rotated slightly away from the direction of the adapting face (Fang & He, Neuron, 2005). We have recently confirmed this finding in our lab using synthetic face stimuli. In the current study, we sought to explore how the geometric elements of our stimuli independently contribute to this effect. In a two alternative forced choice task, subjects were presented with an adapting face oriented 20 degrees to the left or right for four seconds, followed by a briefly presented test face, which was randomly chosen in each trial from a set of seven faces spanning +/− 6° around a frontal view. Subjects were instructed to choose whether each test face appeared left or right of center. By assessing the orientation of the test face at which subjects were equally likely to choose left or right (point of subjective equality), we were able to assess the strength of adaptation. We tested subjects in three conditions: Adapting to full faces (Intact), head outlines only (Outline), and features only (Features). In all conditions, the test faces were full faces. We found that Intact adapted more strongly than Features (pOutline adapted stronger than Features (pIntact vs. Outline (p[[lt]]0.123). These results suggest a non-linear combination of outline and features, with a privileged role for the head outline in encoding the direction of gaze.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".