Does familiarity play a role in producing viewpoint aftereffects with faces?
Bibliographic record
Abstract
Viewpoint aftereffects refer to the finding that after a prolonged exposure to the image of an object oriented to either the left or the right side (adaptor image), the perception of the image presented near the frontal view is biased in a direction opposite to the adaptor image (Fang & He, 2005). The aftereffects suggest that representations of visual stimuli are organized in a viewpoint-specific manner. The present study investigated whether qualitative or quantitative differences between representations of familiar and unfamiliar faces suggested in the literature on face processing can be reflected in differences in the viewpoint aftereffects. Familiarization of faces was achieved by presenting semantic information about each face, along with multiple images of faces presented at different viewpoints. Magnitude of the viewpoint aftereffects elicited by familiar faces was significantly greater than that induced by unfamiliar faces. In addition, when the adaptor image was orientated to the left side, greater aftereffects were obtained, regardless of the degree of familiarity associated with the faces. The results indicate that familiarity exert influences on perceptual processes mediating the viewpoint aftereffects.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".