Population Receptive Field Estimation Reveals New Retinotopic Maps in Human Subcortex
Bibliographic record
Abstract
The human subcortex contains multiple nuclei that govern the transmission of information to and among cortical areas. In the visual domain, these nuclei are organized into retinotopic maps. Because of their small size, these maps have been difficult to precisely measure using phase-encoded functional magnetic resonance imaging, particularly in the eccentricity dimension. Using instead the population receptive field model to estimate the response properties of individual voxels, we were able to resolve two previously unreported retinotopic maps in the thalamic reticular nucleus and the substantia nigra. We measured both the polar angle and eccentricity components, receptive field size and hemodynamic response function delay, in the these nuclei and in the lateral geniculate nucleus, the superior colliculus, and the lateral and intergeniculate pulvinars. The anatomical boundaries of these nuclei were delineated using multiple averaged proton density-weighted images and were used to constrain and confirm the functional activations. Deriving the retinotopic organization of these small, subcortical nuclei is the first step in exploring their response properties and their roles in neural dynamics.
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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 | 0.002 |
| 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".