Bibliographic record
Abstract
Previous research has shown the existence of the face viewpoint aftereffect (Fang & He, 2005), where adapting to a left or right oriented face causes a perceptual shift in the orientation of a subsequently presented frontal face. Thus far, this aftereffect has only been explored in the central region of the visual field. In the current study we used a novel adaptation technique which differs from previous studies in that in each trial there was one adapting stimulus followed by two simultaneously presented test stimuli. Here, the adapting stimulus was displayed in either half of the visual field, and the two test stimuli were displayed in both halves of the visual field, separated by ±3.3 degrees of visual angle. Instead of judging whether a single test stimulus was oriented to the left or to the right (relative to straight ahead), subjects judged whether one test stimulus was oriented to the left or to the right of the other stimulus. Since only one of the test stimuli is presented in the adapted region, this allows us to assess the strength of the aftereffect by measuring the perceived differences between the two stimuli. This technique has the advantage of allowing aftereffects to be probed relative to arbitrary orientations, rather than to those that are exclusively centered around 0 degrees. Using this technique, we discovered that a viewpoint aftereffect occurs in the periphery. An additional finding was a bias in the left visual field to perceive faces in the periphery as facing slightly more towards the observers (p <0.006). The effects of adapting and testing with faces in the upper and lower halves of the visual field were also tested.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".