Binocular depth information modulates object-selective activation in high-level visual cortex
Bibliographic record
Abstract
Despite the fact that we know a great deal about the processing of binocular disparity for synthetic stimuli (such as random dot stereograms) in early stages of visual processing (e.g., V1, V2), little is understood about the contribution of stereopsis to the processing of more naturalistic stimuli in later stages of the visual hierarchy. Here we used functional magnetic resonance imaging (fMRI) to investigate how binocular vision contributes to high-level neural processing of visual objects. Participants viewed objects and scrambled objects (along with faces, scenes and bodies, not discussed here) as 2D or stereoscopic 3D images with a high-quality 3D MRI projector and polarized eyewear. High-resolution 3D objects models were used and displayed using the average human interpupillary distance. The objects were re-scaled and rotated to attain a similar disparity range across the set. Blender software was used to segment the objects into similar parts; importantly each fragment preserved its original disparity profile. The parts were then redistributed to create a cloud of fragments. In the 2D condition one of the images of the stereopair was presented to both eyes. Additionally, we ensured that the viewing geometry was consistent with natural viewing of real objects. Our results show stronger activation for 3D stimuli versus 2D stimuli in both the dorsal and ventral visual streams. More surprisingly, 3D viewing decreased object-selectivity (objects – scrambled objects) in shape-selective regions such as LOC and V3A. This modulation in selectivity was due to a greater increase in activation with the addition of stereopsis (3D – 2D) for scrambled than intact objects. Our results suggest the high-level regions that process different visual categories might be differentially sensitive to availability of binocular disparity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".