Visual Perception of Motion and 3-D Structure from Motion: an fMRI Study
Bibliographic record
Abstract
Functional magnetic resonance imaging was used to study the cortical bases of 3-D structure perception from visual motion in human. Nine subjects underwent three experiments designed to locate the areas involved in (i) motion processing (random motion versus static dots), (ii) coherent motion processing (expansion/ contraction versus random motion) and (iii) 3-D shape from motion reconstruction (3-D surface oscillating in depth versus random motion). Two control experiments tested the specific influence of speed distribution and surface curvature on the activation results. All stimuli consisted of random dots so that motion parallax was the only cue available for 3-D shape perception. As expected, random motion compared with static dots induced strong activity in areas V1/V2, V5+ and the superior occipital gyrus (SOG; presumptive V3/V3A). V1/V2 and V5+ showed no activity increase when comparing coherent motion (expansion or 3-D surface) with random motion. Conversely, V3/V3A and the dorsal parieto-occipital junction were highlighted in both comparisons and showed gradually increased activity for random motion, coherent motion and a curved surface rotating in depth, which suggests their involvement in the coding of 3-D shape from motion. Also, the ventral aspect of the left occipito-temporal junction was found to be equally responsive to random and coherent motion stimuli, but showed a specific sensitivity to curved 3-D surfaces compared with plane surfaces. As this region is already known to be involved in the coding of static object shape, our results suggest that it might integrate various cues for the perception of 3-D shape.
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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) | 0.001 | 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".