Bibliographic record
Abstract
When we move, our self-induced motion provides a constant “flow” of visual information (e.g. objects, people, etc.) across the retina; these regular changes in the overall image on the retina are known as optic flow. Optic flow allows one to calculate distance to objects and judge time to collision - aiding in locomotion and balance. Results from both lesion and cerebellar trans-magnetic stimulation (TMS) studies indicate the cerebellum plays an important role in ability to perceive optic flow. The aim of this study was to investigate the role of cerebellar activity on the perception of optic flow. If the cerebellum generates inhibitory output towards other optic flow-processing areas, then increased activation of cerebellum should improve optic flow perception. Research indicates transcranial direct-current stimulation can modulate excitability in cortical cells; thereby a useful tool for modulating cerebellar activity. Task performance was measured under three stimulation conditions: anodal, cathodal, sham. A threshold for optic flow was measured through a motion discrimination task using random-dot-stimuli to simulate optic flow. Results showed that anodal stimulation resulted in significant increases in performance during and post stimulation for right/left motion. No effect of anodal stimulation was found for up/down motion. In addition, no effect of cathodal stimulation for either up/down, or right/left motion. These results demonstrate that cerebellar activity can be modulated with tDCS to enhance motion perception, but the direction of motion appears to be a critical factor. The results will be discussed in relation to cerebellar organization and connections with cortical motion areas. Discipline: Psychology Faculty Mentor: Dr. Jean-Francois Nankoo
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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) | 0.003 | 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".