3D Faces Evoke Stronger fMRI Activation than 2D Faces
Bibliographic record
Abstract
Despite behavioural evidence that three-dimensional (3D) faces are processed more accurately and quickly than two-dimensional (2D) faces, functional magnetic resonance imaging (fMRI) studies of face processing typically rely on 2D images of faces. Moreover, fMRI studies of 3D vision typically use random dot stereograms, a highly unnatural stimulus. Given the importance of stereopsis in 3D form perception, we expected that neural activation would differ for 3D faces and 2D faces not only in dorsal-stream areas previously implicated in processing depth for simple visual stimuli, but also within face-selective areas in the ventral stream. We used fMRI to investigate brain activation for images of real people presented orthostereoscopically (at the geometrically correct distance and size) with high-quality displays (using a PROPixx MRI 3D projector, viewed through polarized glasses and first-surface mirrors). In the 2D condition, the same image was presented to both eyes, producing zero disparity, as when viewing a 2D picture. In the 3D condition, stereopairs were presented separately to each eye. Stimuli were presented in a block design with a one-back task to maintain attention. Localizers were used to identify face- and depth-preferring regions of interest. Higher activation for 3D than 2D faces was observed not only in depth-selective occipitoparietal cortex (in the caudal intraparietal sulcus) but also in fusiform and occipital face areas, demonstrating that depth information affects processing in both visual streams. Our results suggest that while pictures are a reasonable proxy for studying faces in the real world, models of face processing should consider the impact of 3D form in tasks like face recognition. Moreover, this approach opens new avenues for investigating the contribution of 3D information to category-specific responses in high-level vision.
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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) | 0.007 | 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".