Visually evoked potentials (VEPs) across the visual field in hearing and deaf cats
Bibliographic record
Abstract
Introduction Congenitally deaf cats perform better on visual localization tasks than hearing cats, and this advantage has been attributed to the posterior auditory field. Successful visual localization requires both visual processing of the target and timely generation of an action to approach the target. Activation of auditory cortex in deaf subjects during visual localization in the peripheral visual field can occur either via bottom-up stimulus-driven and/or top-down goal-directed pathways. Methods In this study, we recorded visually evoked potentials (VEPs) in response to a reversing checkerboard stimulus presented in the hemifield contralateral to the recorded hemisphere in both hearing and deaf cats under light anesthesia. Results Although VEP amplitudes and latencies were systematically modulated by stimulus eccentricity, we found little evidence of changes in VEP in deaf cats that can explain their behavioral advantage. A statistical trend was observed, showing larger peak amplitudes and shorter peak latencies in deaf subjects for stimuli in the near- and mid-peripheral field. Additionally, latency of the P1 wave component had a larger inter-sweep variation in deaf subjects. Discussion Our results suggested that cross-modal plasticity following deafness does not play a major part in cortical processing of the peripheral visual field when the “vision for action” system is not recruited.
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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 | 0.001 | 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".