Resolving the projection of a moving stimulus on the human cortical surface
Bibliographic record
Abstract
Introduction. Optical imaging techniques have demonstrated that the cortical response to a moving visual stimulus appears to have an anticipatory leading edge component to the representation (Jancke et al., 2004). We sought to temporally and spatially resolve a moving stimulus on the cortical surface in humans using functional magnetic resonance imaging (fMRI). While the hemodynamic signal is sluggish, its response characteristics are highly reliable, and the ultimate resolving power is an issue of signal and noise. Our experimental goal was to determine the limits of the fMRI technique to resolve the path of a moving stimulus in the retinotopic human visual cortex. Methods. Subjects' brains were scanned with a 3 T MRI scanner and a 32-channel head coil. Standard retinotopic mapping and cortical flattening procedures were performed. We experimented with EPI sequences with different k-space trajectories as well as reconstruction techniques to optimize the spatial and temporal resolution limits. The stimulus was a high contrast flickering checkerboard with a circular aperture that moved through the visual field with a constant velocity in polar angle at a fixed eccentricity. Results. For each stimulus velocity, we were able to determine the amount of data required to achieve the same precision in the estimation of spatiotemporal position. We noted asymmetries between the leading and trailing edges, as a function of velocity. Conclusions. We have demonstrated the limits of resolving moving stimuli along the human cortical surface. Being able to image the complete cortical representation of an object's trajectory allows us to test a number of hypotheses in areas of visual perception, including attentional object tracking and the properties of objects as the disappear behind occluders.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".