Binocular cues to 3D face structure increase activation in depth-selective visual cortex with negligible effects in face-selective areas
Bibliographic record
Abstract
Studies of visual face processing often use flat images as proxies for real faces due to their ease of manipulation and experimental control. Although flat images capture many features of a face, they lack the rich three-dimensional (3D) structural information available when binocularly viewing real faces (e.g., binocular cues to a long nose). We used functional magnetic resonance imaging to investigate the contribution of naturalistic binocular depth information to univariate activation levels and multivariate activation patterns in depth- and face-selective human brain regions. We used two cameras to capture images of real people from the viewpoints of the two eyes. These images were presented with natural viewing geometry (such that the size, distance, and binocular disparities were comparable to a real face at a typical viewing distance). Participants viewed stereopairs under four conditions: accurate binocular disparity (3D), zero binocular disparity (two-dimensional [2D]), reversed binocular disparity (pseudoscopic 3D), and no binocular disparity (monocular 2D). Although 3D faces (both 3D and pseudoscopic 3D) elicited higher activation levels than 2D faces, as well as distinct activation patterns, in depth-selective occipitoparietal regions (V3A, V3B, IPS0, IPS1, hMT+), face-selective occipitotemporal regions (OFA, FFA, pSTS) showed limited sensitivity to internal facial disparities. These results suggest that 2D images are a reasonable proxy for studying the neural basis of face recognition in face-selective regions, although contributions from 3D structural processing within the dorsal visual stream warrant further consideration.
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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 | 0.000 |
| 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) | 0.003 | 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".