Distinguishing Subregions of the Human MT+ Complex Using Visual Fields and Pursuit Eye Movements
Bibliographic record
Abstract
In humans, functional imaging studies have demonstrated a homologue of the macaque motion complex, MT+ [suggested to contain both middle temporal (MT) and medial superior temporal (MST)], in the ascending limb of the inferior temporal sulcus. In the macaque monkey, motion-sensitive areas MT and MST are adjacent in the superior temporal sulcus. Electrophysiological research has demonstrated that while MT receptive fields primarily encode the contralateral visual field, MST dorsal (MSTd) receptive fields extend well into the ipsilateral visual field. Additionally, macaque MST has been shown to receive extraretinal smooth-pursuit eye-movement signals, whereas MT does not. We used functional magnetic resonance imaging (fMRI) and the neural properties that had been observed in monkeys to distinguish putative human areas MT from MST. Optic flow stimuli placed in the full field, or contralateral field only, produced a large cluster of functional activation in our subjects consistent with previous reports of human area MT+. Ipsilateral optic flow stimuli limited to the peripheral retina produced activation only in an anterior subsection of the MT+ complex, likely corresponding to putative MSTd. During visual pursuit of a single target, a large portion of the MT+ complex was activated. However, during nonvisual pursuit, only the anterolateral portion of the MT+ complex was activated. This subsection of the MT+ cluster could correspond to putative MSTl (lateral). In summary, we observed three distinct subregions of the human MT+ complex that were arranged in a manner similar to that seen in the monkey.
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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 | 0.000 |
| 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".