Motion Opponency at MT: Preserved Motion Information and the Effect of Perceptual Learning
Bibliographic record
Abstract
Motion opponency, first observed within the primate middle temporal cortex (MT), refers to the suppressing effect of opposite motion directions on neuronal activity. Namely, when opposing motion directional signals stimulate an MT neuron’s receptive field, this neuron’s response is comparable to that induced by flicker noise. Under such suppression, it is unknown whether any directional information is still represented at MT. In this study, we applied Support Vector Machine learning (SVM) to human functional Magnetic Resonance Imaging data to investigate if any motion defined orientation information was still available from suppressed MT. We found that, at least at the level of ±45° discrimination, such orientation information was still available. Interestingly, after behavioral perceptual learning that improved human discrimination of fine orientation discrimination (e.g., 42° vs. 48°) using the MT-suppressive motion stimuli, the SVM discrimination of ±45° did not improve when fMRI signals at post-learning MT were used. This result is consistent with findings in Thompson et al. (2013) that, post perceptual learning, MT suppression was not released, suggesting that motion opponency was perhaps functionally too important for perceptual learning to overcome.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".