A human cortical specialization for the processing of velocity gradients in moving stimuli
Bibliographic record
Abstract
It is known that the primate visual system possesses neurons specialized in motion processing. Many of these neurons are selective for the direction of translational motion (i.e. in areas V1, MT) or for the specific arrangement of different motion directions (i.e., in areas MST and 7a) in moving random dot patterns (RDP). Some studies have reported that a subset of neurons in monkey area MT is selective for the orientation of velocity gradients (VG) in such patterns. Currently it is unclear whether humans possess a similar specialization. Here, we use event-related fMRI to investigate this issue. In experimental trials, two RDPs moving in the same direction were presented on a computer screen at both sides of a fixation cross during 500ms. Human observers (n=5) indicated which pattern moved faster. In 1/3th of the trials the RDPs contained a velocity gradient (VG-trials, i.e., dots accelerating), in other 1/3th of the trials dots moved at the same speed (without-VG-trials). In the last 1/3th, the dots were stationary (ST-trials) and subjects indicated which RDP contained more dots. The dots' density and average speed in VG- and without-VG-trials was matched. We measured BOLD responses (neuro-optimized GE Signa LX 1.5 T scanner) while subjects performed the task. In agreement with previous studies, BOLD-related activation was stronger, in VG- and without-SG-trials relative to ST trials, in area V1 and the MT/V5+ complex. We found an increase in activation within the human complex MT/V5+ in VG-trials relative to without-VG-trials. Additionally, we compared the activation evoked by VG-stimuli against the one evoked by expanding patterns containing a similar type of VG but a different arrangement of motion directions. We found that the former patterns evoked a significantly stronger activation within the human MT/V5+ complex relative to the latter. Our results suggest that the human motion processing system possess specialized regions for the processing of VG information in moving stimuli.
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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) | 0.005 | 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".