Optokinetic set-point adaptation functions as an internal dynamic calibration mechanism for oculomotor disequilibrium
Bibliographic record
Abstract
Experience-dependent brain circuit plasticity underlies various sensorimotor learning and memory processes. Recently, a novel set-point adaptation mechanism was identified that accounts for the pronounced negative optokinetic afternystagmus (OKAN) following a sustained period of unidirectional optokinetic nystagmus (OKN) in larval zebrafish. To investigate the physiological significance of optokinetic set-point adaptation, animals in the current study were exposed to a direction-alternating optokinetic stimulation paradigm that better resembles their visual experience in nature. Our results reveal that not only was asymmetric alternating stimulation sufficient to induce the set-point adaptation and the resulting negative OKAN, but most strikingly, under symmetric alternating stimulation some animals displayed an inherent bias of the OKN gain in one direction, and that was compensated by the similar set-point adaptation. This finding, supported by mathematical modeling, suggests that set-point adaptation allows animals to cope with asymmetric optokinetic behaviors evoked by either external stimuli or innate oculomotor biases.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".