Visual regions V2, V3, and MT can discriminate between visual motion trajectories even when you can't.
Bibliographic record
Abstract
Radial frequency (RF) motion trajectories are a class of visual stimuli that consist of a target moving along a closed trajectory defined by a sinusoidal variation of the radius relative to a circular path. The results of a previous fMRI multivoxel pattern analysis study demonstrated that trajectory shape can be distinguished in regions V2 and V3 for radial frequency patterns ranging from RF2 to RF5 (Gorbet, Wilkinson, and Wilson, 2012). These low frequency trajectories formed recognizable shapes that were oval-, triangle-, diamond-, and star-like. These results indicate that V2 and V3 have a role in processing closed-circuit visual motion but do not reveal whether discrimination involves encoding global categories of shape or more local differences in trajectory curvatures. If a region is involved in recognizing the overall shape of a trajectory, multivoxel pattern discrimination should disappear for high frequency trajectories that form non-discriminable shapes. In the current study, we used a multivoxel pattern analysis fMRI approach to test this prediction. In particular, we tested whether patterns of voxel activity in independently localized visual regions could distinguish both between recognizable RF4 and RF5 trajectories and between unrecognizable RF9 and RF10 trajectories. As expected, RF4 and RF5 trajectories could be reliably distinguished in regions V2, V3, and additionally, in region MT. However, the data revealed that these same regions can also discriminate between RF9 and RF10 trajectories even though separate psychophysical testing indicates that observers cannot tell these motion trajectories apart any better than chance. These results suggest that distinguishing between different RF motion trajectories in regions V2, V3, and MT relies on local properties of trajectory curvature. Preliminary further examination of the data using a whole-brain recursive feature elimination approach suggests that perception of global trajectory shape may occur in higher level parietal and frontal cortical regions. Meeting abstract presented at VSS 2013
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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) | 0.002 | 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".