Minimal effects of stereopsis on processing realistic faces
Bibliographic record
Abstract
There is some evidence of an upper visual field advantage for face processing that has been taken as support for evolutionary pressures for face detection. However, these effects tend to be small and the outcomes variable; this may be due to the use of 2D images, which lack the volumetric 3D information present in the real world. Here, we evaluate the impact of naturalistic 3D face stimuli (relative to 2D), and their location in the visual field, on face detection and recognition. Stereopairs of photorealistic face stimuli were presented using a mirror stereoscope in 3D and 2D. In all experiments, the target was present in 50% of the trials, and proportion correct was used to compute sensitivity (d’). In Experiment 1 (N=28), we used a visual search paradigm; stimuli were presented in a semi-circular array in either the upper or lower visual field. The distractor faces were tilted 15 deg to the left (or right), and observers indicated if the target face (tilted in the opposite direction) was present. We varied the number of distractors, location and modality (2D vs. 3D). This low-level task showed no effect of modality and a weak effect of location. In subsequent experiments, we used the same stimuli but in high-level recognition-based tasks. We varied task difficulty, modality and also tested both upright and inverted faces (Experiment 2, N=22; Experiment 3, N=26). We found no effect of 3D viewing or location in either of the experiments, nor was there an interaction. The presence of a strong face inversion effect confirmed that observers were processing the faces holistically. Our results suggest that visual field asymmetries may only occur for tasks that rely on low-level properties. Further, the lack of effect of stereopsis implies that 2D images can be reasonable proxies for natural 3D faces.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".