Bibliographic record
Abstract
It has been argued that the process by which the brain accomplishes face perception is mostly holistic; that faces are perceived as a whole, rather than as a collection of separate components. The evidence for this comes from geometric differences in facial features being more accurately perceived when the whole face is presented as opposed to only some parts being shown. Our research aims to clarify the nature of holistic perception of interocular distance by using synthetic faces where we have complete control over the presence and configuration of face components. We measured subjects' thresholds for discriminating changes in the interocular distance across conditions where either the whole face was present with the eyes represented by circles, or the two circles were presented alone. We found a significantly lower(P<0.05) threshold in the case of the whole face being presented(3.45±0.56min' versus 4.76±0.81min'). Next, we measured subjects' thresholds across conditions where the external features of the face (the head outline) were either present or absent. We found no significant differences(P>0.5) in discrimination thresholds between the full face and the condition lacking the head outline(3.38±0.63min' versus 3.20±0.58min'). Further experiments determined the contribution of other facial features to interocular discrimination. We also used pairs of pictures with the same geometrical reference points, one of which is an abstract square-shaped face and the other a non-face abstract image. We found that when viewing the face image subjects show significantly lower(P<0.05) discrimination thresholds than with the abstract image(3.75±0.75min' versus 5.50±1.49min') despite possessing an identical set of reference points which could be used for the task. Together these results suggest that stimuli being recognized as a face provide a benefit for the purposes of discriminating face-specific features such as the interocular distance that may be distinct from the potential benefits of additional reference points. Meeting abstract presented at VSS 2012
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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 | 0.001 |
| 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) | 0.002 | 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".