Haphazard neural connections underlie the visual deficits of cats with strabismic or deprivation amblyopia
Bibliographic record
Abstract
Identification of the neural basis of the visual deficits experienced by humans with amblyopia, particularly when associated with strabismus (strabismic amblyopia), has proved to be difficult in part because of the inability to observe directly the neural changes at various levels of the human visual pathway. Much of our knowledge has necessarily been obtained on the basis of sophisticated psychophysical studies as well as from electrophysiological explorations on the visual pathways in animal models of amblyopia. This study combines these two approaches to the problem by employing similar psychophysical probes of performance on animal models of two forms of amblyopia (deprivation and strabismic) to those employed earlier on human amblyopes (Hess & Field, 1994, Vis. Res., 34, 13397-13406). The tests explore two competing explanations for the visual deficits, namely an evenly distributed loss of neural connections (undersampling) with the amblyopic eye as opposed to disordered connections with this eye (neural disarray). Unexpectedly, the results in animal models of deprivation amblyopia were not in accord with expectations based upon an even distribution of lost connections with the amblyopic eye. However, the results were similar to those observed in a strabismic amblyopic animal and to strabismic amblyopic humans. We suggest that deprivation amblyopia may be accompanied by an uneven loss of connections that results in effective neural disarray. By contrast, amblyopia associated with strabismus might arise from neural disarray of a different origin such as an alteration of intrinsic cortical connections.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".